Whitebox Workflows for Python v1.2 User Manual
Written by John Lindsay, PhD
© 2022-2024 Whitebox Geospatial Inc. All rights reserved.
www.whiteboxgeo.com

Introduction

What is Whitebox Workflows for Python?

Whitebox Workflows (WbW) is a Python library for advanced geoprocessing, including more than 400 functions for GIS and remote sensing analysis operations and for for manipulating common types of raster, vector and LiDAR geospatial data. Learn more about WbW at www.whiteboxgeo.com.

WbW allows you to write Python scripts for automated data processing in areas such as geographical information systems (GIS), remote sensing (image processing and LiDAR point cloud processing), digital elevation model (DEM) analysis, spatial hydrology, stream network analysis, and many related spatial analysis fields. WbW is developed at Whitebox Geospatial Inc, a Canadian-based geomatics company with deep roots in the geospatial industry and academia (Whitebox started at the University of Guelph).

While WbW is free for use, any financial support that that you can provide the project is greatly appreciated. You can also support the project through the purchase of a license for the professional-tier product, Whitebox Workflows for Python Professional (WbW-Pro), which includes many additional geoprocessing functions.

How does WbW compare with related Whitebox products?

The WbW codebase is based on the open-source project WhiteboxTools Open Core (WbOC), also developed at Whitebox Geospatial Inc. Compared with WbOC, WbW has a number of advantages for Python based geoprocessing. The largest difference between the two projects is that WbW is compiled as a Python native extension module, much like NumPy and SciPy, whereas WbOC is a command-line application. Because WbW is a Python extension module, it affords a much deeper level of interaction with geospatial data, allowing users to manipulate raster, vector, and lidar data objects directly. This reduces the need to read/write files considerably, significantly improving the performance of complex workflows with many intermediate steps and reducing the wear on system hardware.

Interacting directly with spatial objects also means that WbW has a more natural scripting style, providing a much richer geoprocessing environment. For example, you can use Python directly for raster map algebra, rather than a raster calculator or individual tools (e.g. add, divide, etc.):
result = (raster1 + raster2) / 2.0

WbW also has an improved memory model for raster data. With WbOC, raster data are always stored in memory as 64-bit floats, which can lead to large memory requirements. WbW, however, stores rasters in computer memory using their native data format; a raster storing 16-bit integers or 32-bit floating point values will require substantially less memory.

Unlike the WbOC, WbW is not open-source, however, it is freeware and does not require you to purchased a license to use. In addition to the standard version of WbW there is also a professional-tier verison called Whitebox Workflows for Python Professional (WbW-Pro). A license for WbW-Pro, which can be purchased from www.whiteboxgeo.com, allows users to access dozens of extra functions for advanced geospatial analysis.

Getting Started

• Installing Whitebox Workflows
• Running your first script
• Scripting and type hints
• WbW versus WbW-Pro
  • Registering a WbW-Pro license
  • Node-locked and floating licenses for WbW-Pro
  • How much time remains on my WbW-Pro license?
  • Transferring and deactivating WbW-Pro licenses
• Setting up the Whitebox Environment

Installing Whitebox Workflows

You'll need to install WbW on your computer or virtual environment to use it. If you have Python installed on your computer, simply type the following line at the command prompt of your terminal application:

pip install whitebox-workflows

If your default Python is v2.X, you may need to use the pip3 command instead. You may wish to use a Python virtual environment (venv, Conda, etc.) to test the whitebox-workflows package but this isn't necessary.

WbW is supported on Windows (64-bit), Mac (Intel and ARM) and Linux (x86_64).

If you have an older version of WbW installed and want to update it to the latest version, simply type the following command:

pip install whitebox-workflows -U

Updating your WbW to a newer version won't impact your license. You should update every time a new version is released.

For a demonstration of the install process, please see the video below:

Running your first script

Now that we have WbW installed, it's time to start using it. Create a new Python file called wbw_test.py and type the following script into the file.

wbw_test.py

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment()

print(wbe.version())

Notice the underscore in the WbW library name whitebox_workflows compared with the hyphen used in the pip package name whitebox-workflows.

This above script does a few things. First, it imports the WbEnvrionment class from the whitebox_workflows module. Next, we set up the Whitebox Environment. WbEnvrionment is the most important class contained within the whitebox_workflows module. It's the class that is used to manipulate environmental settings, e.g. setting the current working directory. Importantly, all of the tool functions for processing spatial data are methods of the WbEnvironment class, as are the functions for reading and writing spatial data. Lastly, the version() method is called, printing information about our installed version of WbW.

When you run the above script, it should print something similar to this:

Whitebox Workflows for Python v1.1.3 by Whitebox Geospatial Inc. 
Developed by Dr. John B. Lindsay, (c) 2022-2024

Description:
Whitebox Workflows for Python is an advanced geospatial data analysis platform 
and Python extension module.

If the script runs without error, we can be assured that WbW has been installed correctly on our system.

Now let's modify the script above to show us all of the tool functions that we have available to us:

wbw_test.py

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment()

wbe.available_functions()

Depending on the version of WbW that you're running, you should see an ouput similar to this:

...
419. user_defined_weights_filter                  420. vector_hex_binning
421. vector_lines_to_raster                       422. vector_points_to_raster
423. vector_polygons_to_raster                    424. vector_stream_network_analysis
425. version                                      426. viewshed
427. visibility_index                             428. voronoi_diagram
429. watershed                                    430. watershed_from_raster_pour_points
431. weighted_overlay                             432. weighted_sum
433. wetness_index                                434. wilcoxon_signed_rank_test
435. write_function_memory_insertion              436. write_lidar
437. write_raster                                 438. write_vector
439. z_scores                                     440. zonal_statistics

The standard free version of WbW contains at least 440 tool functions, while the professional tier (WbW-Pro) contains over 500 tool functions.

The license_type property of the WbEnvironment class can tell you which version of WbW you are currently running.

license_type.py

from whitebox_workflows import license_info, WbEnvironment

wbe = WbEnvironment()
print(wbe.license_type) # prints either LicenseType.WbW or LicenseType.WbWPro

Scripting and type hints

Scripting with WbW is much easier when you use a good Python editor. Python editors, or general purpose programming editors like Visual Studio Code, will provide syntax highlighting, line numbering, and if configured correctly, autocomplete. Autocomplete features in particular are useful when using WbW, and allow you to explore the WbW application programming interface (API).

This feature can also help the programmer to learn about the arguments that are required by a function as they type.

You may also want to take advantage of Python's built-in help function to learn more about individual members of the WbW API, e.g. help(wbw.Raster.con) can be used to provide the help documentation for the con (conditional evaluation) method of the Raster class.

WbW has been designed to provide Python editors with type hints and default values of arguments. These features can greatly enhance the programming experience.

WbW and WbW-Pro

There are two versions, or tiers, of Whitebox Workflows, including the standard WbW tier and the professional tier, Whitebox Worfklows for Python Professional (WbW-Pro). The standard tier of WbW is free and includes over 400 tool functions. While WbW is free for use, any financial support that you can provide is greatly appreciated. WbW-Pro contains all of the same functions as the standard WbW product and adds access to approximately 65 tool additional functions for advanced geoprocessing. WbW is free while use of WbW-Pro requires users to purchase a license from www.whiteboxgeo.com.

To determine whether a particular tool function is a WbW or WbW-Pro use the whitebox_workflows.is_wbw_pro_function function.

wbw_pro_function.py

from whitebox_workflows import is_wbw_pro_function

print(f"filter_lidar is WbW-Pro function: {is_wbw_pro_function('filter_lidar')}")

You can also see which functions require a WbW-Pro license in the API associated with the WbEnvironment class and in the separate WbW-Pro help documentation.

Learn more about WbW-Pro

Here are some topics specifically relevant to the WbW-Pro tier product:

• Registering a WbW-Pro license
• Node-locked and floating licenses for WbW-Pro
• How much time remains on my WbW-Pro license?
• Transferring and deactivating WbW-Pro licenses

Registering a WbW-Pro license

Notice, you do not need to register a license to use the standard version of WbW. This section only pertains to WbW-Pro.

After you purchase a WbW-Pro license, you'll be redirected to a website providng you with an activation key and a customized license registration Python script like the one below. If you've purchased multiple seats, you'll be provided a single activation key that contains your purchased number of seats.

Be sure to copy your activation key, or the embedded Python script, before closing the redirect page. You will need this to activate your license.

register_license.py

import whitebox_workflows

# Be sure to replace the key below with your issued key; this one is just an
# example. Also update with your first and last name and email address. Note,
# by running the script, you are agreeing to the terms of the license, found
# on www.whiteboxgeo.com
whitebox_workflows.activate_license(
key="889d88d3c7ccc6c9d3ccc9cad3c8d3ced3cecbc6cbcfd3cbcacdc6c7d3cec9c9cec8c8cfc", 
firstname="Jane", 
lastname="Doe", 
email="jdoe@gmail.com", 
agree_to_license_terms=True
)

To register your license, copy the Python script from the license purchase redirect page into an empty Python file, update your name and email address, and then run the script. Once you have run your license activation script, you will receive an email that contains important information about your purchase, your license, as well as your floating-license user ID. It is important that you type your email correctly in the registration script.

Notice, it is important that use your correct email when registering your license or you will not be able to access your floating-license user ID and you will only be able to use the node-locked license on the computer system that you registered WbW from.

For a demonstration of the registration process, please see the video below:

Node-locked and floating licenses for WbW-Pro

A purchase of WbW comes with an equal number of node-locked and floating licenses. For example, if you've purchased two seats, you will be able to register two node-locked licenses on two computers and you will also be able to check-out two silmutaneous floating licenses at any time from any computer.

When you register your WbW-Pro license, WbW writes information about your license on your computer, stored in a configuation file. This will allow you to use WbW on your computer at any time, even when you are not connected to the Internet. This is a so-called 'node-locked' license (i.e., it is tied to a specific computer). You can use your node-locked WbW-Pro license in the same way as we saw WbW used previously, except that you should now have access to the full set of tool functions:

sample_wbwpro_script.py

from whitebox_workflows import available_functions, WbEnvironment

# Checks for a valid node-locked WbW license stored locally
wbe = WbEnvironment()

available_functions(wbe) # You should now see more than 500 functions listed

# Do some processing here, including calling some WbW-Pro tool functions...

The code above uses a node-locked WbW license, i.e., the license is tied to the machine that it was registered on. Sometimes, you will want to use WbW in other computing environments, e.g. on different computers within a network, or on cloud-based computing platforms like Google Colab. When this is the case, you need to use a floating-license user ID. You will have been provided a unique floating license ID consisting of a three-word string (e.g. 'white-bolting-camel') in an email that was sent to you after registering your license activation code. This string connects your floating license with the WbW-Pro license server and allows you to use WbW-Pro on any computer or computing environment (e.g., Google Colab or Jupyter Notebooks).

Your floating-license user ID is unique to you and you should keep this string private. Sharing your user ID with other people will result in the early termination of your license.

Using your floating license is easy. Instead of using the usual means of intializing a WbEnvironment, you simply need to specify your floating-license user ID as an optional parameter, being sure to check-in the license when you have completed:

sample_wbwpro_script.py

from whitebox_workflows import available_functions, WbEnvironment

# Checks for a valid floating license stored on remote server
wbe = WbEnvironment('your-license-id') 
try:
    available_functions(wbe) # You should now see more than 500 functions listed

    # Do some processing here including calling some WbW-Pro tool functions...
except Exception as e:
  print("The error raised is: ", e)
finally:
    wbe.check_in_license('your-license-id')

Using the floating-license requires Internet access. If you are unable to access the Internet for a time, you should fall back on your node-locked license instead, assuming you are on the computer used to register the license.

A note on checking-in your license: Because the floating license uses a check-out/check-in model, you must remember to check-in your license after you have completed processing your script. The last thing you do in each script, therefore, should be to call the WbEnvironment.check_in_license method. There is no need to check-in your node-locked license.

When we specify our floating-license user ID in the WbEnvironment initializer, the program will communicate with the Whitebox Geospatial Inc. remote license server web app. It will check to see if there is an available seat associated with the specified user ID. Importantly, while your script is running, and until you check-in your license, it will not be available for use again. This means that it is very important that the last thing you successfully check-in the license at the end of the script, which is why we call check_in_license within the finally block of a try-finally construct in the script above. This way, if the script throws an error, we can still be sure that the license will be checked back in and we'll be able to use it again in the future.

If all of the existing 'seats' of a license are unavailable because they are either in use, or haven't yet been checked back in, you will receive an error indicating that 'All floating licenses are currently in use'. The number of 'users' that you specified when you purchased your WbW license will indicate how many silmutaneous floating license seats you have to work with.

Sometimes a script may panic, which is an unrecoverable type of error. This happens for example, if you provide an unexpected input parameter to a function. When this is the case, our checked-out license may not be checked back in correctly. What do we do in this case? Well, after a certain amount of time (usually two hours), an un-checked-in license will be returned to the pool of available licenses. But, of course, that leaves us in a difficult situation for those hours wihtout being able to run further WbW-Pro scripts. When this occurs, we can call the check_in_license function associated directly with the whitebox_workflows module, i.e. whitebox_workflows.check_in_license('your-license-id'). Note, you should always prefer using WbEnvironment.check_in_license('your-license-id) over whitebox_workflows.check_in_license('your-license-id') because the latter will throw an error if there is no unchecked-in license to check-in. However, in the event that a script panic results in a 'zombie' floating license seat, this method is a usable solution to get you up-and-running with WbW geoprocessing once again.

from whitebox_workflows import check_in_license

print(check_in_license('your-license-id')) # print to confirm the successful check-in

How much time remains on my WbW-Pro license?

To determine how much time is remaining on your license, you may use the license_info method:

wbwpro_license_duration.py

from whitebox_workflows import license_info

# on the host machine with the node-locked license...
print(license_info())

# or for a floating license...
print(license_info('your-license-id')) # specify your floating-license user ID

Transferring and deactivating WbW-Pro licenses

Licenses are transferable, if for example you would like to move your node-locked license to a different system. For this, you need to use the whitebox_workflows.transfer_license() method, which will issue you a new activation key, with the number of days remaining on your license; you may then use the issued key to register WbW-Pro on the other machine. Transferring a license will deactivate the license on the current computer.

wbwpro_transfer_license.py

from whitebox_workflows import transfer_license

transfer_license()
# Note, this function will print out the information that you will need to register the
# license on another computer, including the required activation key. Also note that
# after the license has been transferred, there may be a print out that says something 
# like, "The data in the license file appears to be corrupt..." This is simply because
# after transfering the license, WbW can no longer be used on this current machine,
# without registering another license. In fact, if you would like to re-register it 
# on the current computer using the issued activation code, that will work fine too.

Similarly, the whitebox_workflows.deactivate_license() method is used to deactivate a license, although note that this simply removes the license but does not uninstall WbW from your system. To uninstall the software, use pip uninstall whitebox-workflows. The only reason to deactivate a license is if you no longer want to use WbW-Pro.

Setting up the Whitebox Environment

We saw in an earlier section that the WbEnvironment class is used to set up the Whitebox geoprocessing environment. This important initialization step should be one of the first things that we do in most WbW scripts.

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment() # Create a WbEnvironment object, here named wbe.
# We can use this object to provide settings to tools that are run later in the script.
# If you aren't on the computer that you registered WbW on, you may use your
# floating-license user ID as a parmeter, e.g. wbe = wbw.WbEnvironment('cute-flying-pig').
# This ID will have been emailed to you upon registration.

# `max_procs` determines the number of processors used by functions that are parallelized. 
# If set to -1, the default, all available processors will be used.
wbe.max_procs = -1

# To limit tools to a certain number of processors, set `max_procs` to a positive whole 
# number less than the number of system processors. This can be important in cloud-based
# and distributed computing environments where you may not want WbW to fully utilize all
# available processors.
wbe.max_procs = 4

# `verbose` determines whether tool output sto stdout (`wbe.verbose=True`), or if
# output is suppressed (`wbe.verbose=False`). Tools are often very chatty, outputting
# frequent updates of progress. When you're running long workflows, it can be useful to
# turn this stream of tool output on and off during critical parts of the workflow.
wbe.verbose = True
# Run something critical...
wbe.verbose = False
# Run something less critical...

# Of course, when verbose=False, you will not be able to see any warnings or errors 
# issued by the tool, so this may not be desirable while testing a script.

# You can set and get the working directory as follows:
wbe.working_directory = '/path/to/my/data'

print(wbe.working_directory)

# How much time is remaining on your license?
print(wbw.license_info()) # Also takes the optional floating-license user ID.

# The working directory is the current path used to find data resources. When reading and 
# writing data, the working directory is the default location in which the data are read 
# from and written to.

my_raster = wbe.read_raster('image1.tif') # Will search for file "/path/to/my/data/image1.tif"

# You can also use full path names when specifying data.
my_raster = wbe.read_raster('/other/path/to/my/data/image2.tif')

# You can update the working directory multiple times in a script.
wbe.working_directory = '/other/path/to/my/data'
my_vector = wbe.read_vector('vector1.shp') # Will search for file "/other/path/to/my/data/vector1.shp"

Working with Whitebox Workflows

• Sample datasets
• Reading and writing data
• Using tool functions
• Working with raster data
• Working with vector data
• Working with lidar data

Sample datasets

There are a number of available sample datasets that can be readily used to test Whitebox Workflows for Python. The following is a description of all available datasets:

The data can be downloaded using the download_sample_data function within the whitebox_workflows module. For example,

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment()

wbe.working_directory = wbw.download_sample_data('Kitchener_lidar')
print(f'Data have been stored in: {wbe.working_directory}')

The data will be downloaded to a location within your HOME directory and the download_sample_data function will return the address of the dataset directory. This can be useful for updating the WbEnvironment.working_directory property, as in the script above. The data will be downloaded in a compressed file format (zip) and will be automatically decompressed after the download has completed. The download_sample_data function will automatically terminate after ten minutes. You may encounter this issue if you attempt to download some of the larger sample datasets using a slower Internet connection.

Reading and writing data

WbW supports reading and writing many types of raster, vector, and LiDAR data formats.

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment()

wbe.working_directory = 'path/containing/data/files'

# Reading raster data
my_raster = wbe.read_raster('file_name.tif')

# Or read_rasters (with an 's') for multiple rasters at once...
my_raster1, my_raster2 = wbe.read_rasters('file_name1.tif', 'file_name2.tif')

# Writing raster data
wbe.write_raster(my_raster, 'output.tif')

# You can also ask WbW to compress the written raster
wbe.write_raster(my_raster, 'output.tif', compress=True) # Only GeoTIFF compression is supported.

# Reading vector data
my_vector = wbe.read_vector('file_name.shp')

my_vector1, my_vector2 = wbe.read_vectors('file_name1.shp', 'file_name2.shp')

# Writing vector data
wbe.write_vector(my_vector, 'output.shp')

# Reading LiDAR data
my_lidar = wbe.read_lidar('file_name.laz')

my_lidar1, my_lidar2 = wbe.read_lidars('file_name1.laz', 'file_name2.laz')

# Writing LiDAR data
wbe.write_lidar(my_lidar, 'output.laz')

Supported Data Formats

Raster formats

WbW can currently support reading/writing raster data in several common formats.

Throughout this manual code examples that manipulate raster files all use the GeoTIFF format (.tif) but any of the supported file extensions can be used in its place.

WbW is able to read GeoTIFFs compressed using the PackBits, DEFLATE, and LZW methods. Compressed GeoTIFFs, created using the DEFLATE algorithm, can also be output from any tool that generates raster output files by using compress=True.

Vector Formats

At present, there is limited support in WbW for working with vector geospatial data formats. The only supported vector format is the ESRI Shapefile. Shapefiles geometries (.shp) and attributes (.dbf) can be read and written.

While the Shapefile format is extremely common, it does have certain limitations for vector representation. For example, owing to their 32-bit indexing, Shapefiles are limited in the number of geometries that can be stored in these files. Furthermore, Shapefiles are incapable of storing geometries of more than one type (point, lines, polygons) within the same file. As such, the vector-related tools in WhiteboxTools also carry these same limitations imposed by the Shapefile format.

Point Cloud (LiDAR) Formats

LiDAR data can be read/written in the common LAS and compressed LAZ data formats.

Using tool functions

Most of WbW's tools exist as functions of the WbEnvironment class.

from whitebox_workflows import WbEnvironment
import math

wbe = WbEnvironment()
wbe.verbose = True
wbe.max_procs = -1

# Let's begin by downloading the Whitebox Workflows 'Guelph_landsat' sample data
wbe.working_directory = wbw.download_sample_data('Guelph_landsat')
print(f'Data have been stored in: {wbe.working_directory}')

# Read some of the image bands into memory
band2, band3, band4, band5 = wbe.read_rasters('band2.tif', 'band3.tif', 'band4.tif', 'band5.tif')

# Now let's call the create_colour_composite tool
true_colour_composite = wbe.create_colour_composite(
    red=band4, 
    green=band3, 
    blue=band2,
    enhance=False,
    treat_zeros_as_nodata=False
)
# The result 'true_colour_composite' is an in-memory raster. If we want to
# save it to disc to visualize it, we need to call 'write_raster'.
# wbe.write_raster(true_colour_composite, 'true_cc.tif', compress=True) # Uncomment this line to save to file

# Notice, that we don't need to specify the argument names of positional
# arguments (red, green, and blue above), and that we don't need to specify
# values for optional arguments if we accept their default values.
false_colour_composite = wbe.create_colour_composite(band5, band4, band3, enhance=False)
# wbe.write_raster(false_colour_composite, 'false_cc.tif', compress=True) # Uncomment this line to save to file

# If we don't want to see all of the progress updates output to stdout, 
# turn verbose mode off. But we also won't see errors or warnings.
wbe.verbose = False

# Now let's perform some image enhancements...
bce = wbe.balance_contrast_enhancement(true_colour_composite)

dds = wbe.direct_decorrelation_stretch(bce, achromatic_factor=0.3)
wbe.write_raster(dds, 'final_true_cc.tif', True)

# We can overwrite objects as well.
bce = wbe.balance_contrast_enhancement(false_colour_composite)

dds = wbe.direct_decorrelation_stretch(bce, achromatic_factor=0.3)
wbe.write_raster(dds, 'final_false_cc.tif', True)

There are also a large number of functions associated with the Raster class for manipulating raster data sets. Each of these Raster functions allow for Python-based raster algebra operations. For example:

# Calculate the normalized difference vegetation index...
ndvi = (band5 - band4) / (band5 + band4)
wbe.write_raster(ndvi, 'ndvi.tif', compress=True)

# use a multiplier and offset to adjust the values of a raster
multiplier = 0.012152
offet = -60.76071
sun_elev = math.radians(64.31337609)
reflectance = (band1 * multiplier + offset) * math.sin(sun_elev)

max_val = band1.max(band2)

Working with raster data

from whitebox_workflows import PhotometricInterpretation, RasterDataType, WbEnvironment

wbe = WbEnvironment()
wbe.verbose = True
wbe.max_procs = -1

# Let's begin by downloading the Whitebox Workflows 'Jay_State_Forest' sample data
wbe.working_directory = wbw.download_sample_data('Jay_State_Forest')
print(f'Data have been stored in: {wbe.working_directory}')

# Now read the 'DEM.tif' file...
dem = wbe.read_raster('DEM.tif')

# The RasterConfigs of a Raster object contains useful metadata about the Raster.
print(f'Rows: {dem.configs.rows}')
print(f'Columns: {dem.configs.columns}')
print(f'Resolution (x direction): {dem.configs.resolution_x}')
print(f'Resolution (y direction): {dem.configs.resolution_y}')
print(f'North: {dem.configs.north}')
print(f'South: {dem.configs.south}')
print(f'East: {dem.configs.east}')
print(f'West: {dem.configs.west}')
print(f'Min value: {dem.configs.minimum}')
print(f'Max value: {dem.configs.maximum}')
print(f'EPSG code: {dem.configs.epsg_code}') # 0 if not set
print(f'Nodata value: {dem.configs.nodata}')
# What data type are stored in raster grid cells?
# See the RasterDataType class for more info.
print(f'Data type: {dem.configs.data_type}') 
# What is the photometric interpretation, continuous, categorical, RGB, etc.?
# See the PhotometricInterpretation class for more info.
print(f'Photometric interpretation: {dem.configs.photometric_interp}')

# We create new rasters most frequently by copying the RasterConfigs from another
# existing Raster object. We can also create a new RasterConfigs manually but
# when we want to create a new Raster that has the same rows, columns and extent
# as another Raster, copying the other Raster's RasterConfigs, and modifying it
# as needed, is a good way forward.
out_configs = dem.configs

# Once you create a new Raster, you cannot change certain things about it, such
# as the number of rows and columns and the data type. The RasterDataType must
# be able to hold the data values. In the case below, we are reclassifying the
# raster to a Boolean, with 1's and 0's. So we really only need small, integer
# level data. I16 is used in this case to allow for NoData values, which will
# be set to -32768, the smallest possible 16-bit int.
out_configs.data_type = RasterDataType.I16 
out_configs.nodata = -32768.0
out_configs.photometric_interp = PhotometricInterpretation.Categorical

# Now let's create the new raster, based on our customized RasterConfigs...
high_areas = wbe.new_raster(out_configs)

# When we create a new raster, it is initially filled with NoData values, as
# set in its RasterConfigs.
print(f'Cell(500, 500) = {high_areas[500, 500]}') # = -32768.0

# Let's manipulate the raster data at the individual grid cell level.
print("Finding high elevations")
old_progress = -1
for row in range(dem.configs.rows):
    for col in range(dem.configs.columns):
        elev = dem[row, col] # Read a cell value from a Raster
        if elev > 800.0 and elev != dem.configs.nodata:
            high_areas[row, col] = 1.0 # Write the cell value of a Raster

            # Regardless of the RasterDataType used to store the cell
            # data in memory and in file, data are always passed to and
            # returned from rasters as floats. Note that the cell value
            # is set to 1.0 above and not 1.
        elif elev != dem.configs.nodata:
            # We must do the check for NoData, or else we'll replace
            # NoData values in the input raster with 0's in the output. 
            # NoData in must be NoData out.
            high_areas[row, col] = 0.0
    
    # Update the progress after each completed row scan.
    progress = int(((row + 1.0) / dem.configs.rows) * 100.0)
    if progress != old_progress:
        old_progress = progress
        print(f'Progress: {progress}%')

# Write the new Raster to file.
print('Saving data to file...')
wbe.write_raster(high_areas, 'high_areas.tif', compress=True)

# This allows for very fine-grained raster manipulation for custom data processing.
# But if the same functionality exists within the WbW toolset, you should always 
# prefer the native solution, because it will be faster than the Python alternative. 
# The code above could have been more efficiently processed using the following:
high_areas = dem > 800.0

Working with vector data

from whitebox_workflows import AttributeField, FieldData, FieldDataType, VectorGeometryType, WbEnvironment

wbe = WbEnvironment()

# Let's begin by downloading the Whitebox Workflows 'Southern_Ontario_roads' sample data
wbe.working_directory = wbw.download_sample_data('Southern_Ontario_roads')
print(f'Data have been stored in: {wbe.working_directory}')

# Read in the roads file
roads = wbe.read_vector('roads_utm.shp')

# Let's see some of the properties of this file...
print(f'Vector geometry type: {roads.header.shape_type}') # A VectorGeometryType.PolyLine
print(f'Min X: {roads.header.x_min}')
print(f'Max X: {roads.header.x_max}')
print(f'Min Y: {roads.header.y_min}')
print(f'Max Y: {roads.header.y_max}')
print(f'Number of records: {roads.num_records}')
print(f'Projection: {roads.projection}')

# To retrieve a vector geometry, use the [] syntax.
geom = roads[100]
print(f'Geometry type: {geom.shape_type}')
print(f'Num vertices: {geom.num_points}')
print(f'Num parts: {geom.num_parts}')
print(f'Min X: {geom.x_min}') # There min/max x, y, measure, and z
print(f'First vertex: ({geom.points[0].x}, {geom.points[0].y})')

# What about the file attributes?
print(f'Num records: {roads.attributes.header.num_records}') # Should be same as roads.num_records
print(f'Num fields: {roads.attributes.get_num_fields()}')

# Retrieve and print the attribute fields
att_fields = roads.get_attribute_fields()
for i in range(len(att_fields)):
    print(att_fields[i])

# What's the index of a specific field, by attribute name?
index_of_road_class = roads.get_attribute_field_num("ROAD_CLASS")

# Let's create a new Vector object...

# First, we need to create the attribute fields used for the new vector
out_att_fields = [
    AttributeField("FID", FieldDataType.Int, 6, 0),
    AttributeField("SRC_FID", FieldDataType.Int, 6, 0),
]

# Now create the Vector itself.
out_roads = wbe.new_vector(VectorGeometryType.PolyLine, out_att_fields, proj=roads.projection)

# You can also add a new attribute field after creating the file.
out_roads.add_attribute_field(att_fields[index_of_road_class])

# Now let's filter all the records to find those geometries with a ROAD_CLASS of indicating major highways
old_progress = -1
fid = 1
for i in range(roads.num_records):
    road_class = roads.get_attribute_value(i, 'ROAD_CLASS').get_as_string().lower()
    if 'freeway' in road_class or 'collector' in road_class or 'expressway' in road_class:
        geom = roads[i]
        out_roads.add_record(geom) # Add the record to the output Vector

        # Create the output attribute record...
        att_rec = roads.get_attribute_record(i)
        rec_data = [
            FieldData.new_int(fid), 
            FieldData.new_int(i+1),
            att_rec[index_of_road_class]
        ]
        fid += 1
        # then add it to the table.
        out_roads.add_attribute_record(rec_data, deleted=False)

    # Update the progress after completing each 1% of the records.
    progress = int((i + 1.0) / roads.num_records * 100.0)
    if progress != old_progress:
        old_progress = progress
        print(f'Progress: {progress}%')


# Write the output to file.
print('Writing vector to file...')
wbe.write_vector(out_roads, 'out_roads.shp')

Working with LiDAR data

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment()

# Let's begin by downloading the Whitebox Workflows 'Kitchener_lidar' sample data
wbe.working_directory = wbw.download_sample_data('Kitchener_lidar')
print(f'Data have been stored in: {wbe.working_directory}')

# Read in an existing lidar data set
lidar = wbe.read_lidar('Kitchener_lidar.laz')

# To create a new Lidar object, you need a LidarHeader, which documents metadata
# about a LiDAR file. It is the LiDAR equivalent to the RasterConfigs.
print(f'File creation day: {lidar.header.file_creation_day}')
print(f'File creation year: {lidar.header.file_creation_day}')
print(f'Generating software: {lidar.header.generating_software}')
num_points = lidar.header.get_num_points()
print(f'Number of points: {num_points}')
print(f'Version major: {lidar.header.version_major}')
print(f'Version minor: {lidar.header.version_minor}')
print(f'Point format: {lidar.header.point_format}')
print(f'Min X: {lidar.header.min_x}')
print(f'Max X: {lidar.header.max_x}')
print(f'Min Y: {lidar.header.min_y}')
print(f'Max Y: {lidar.header.max_y}')
print(f'Min Z: {lidar.header.min_z}')
print(f'Max Z: {lidar.header.max_z}')

# Now, create a new Lidar object. In doing so, the new Lidar object will copy 
# over some of the properties in the source LidarHeader, but won't copy any of 
# the things like generating software, creation day/year, point extent values,
# or the source point data. It's a newly initialized file ready to receive its
# own point data.
lidar_out = wbe.new_lidar(lidar.header)

# You likely want to copy over the VariableLengthRecord (VLR) data too. VLRs
# usually contain important information, such as the coordinate reference 
# system.
lidar_out.vlr_data = lidar.vlr_data

print('Filtering point data...')
old_progress = -1
for i in range(num_points):
    # Notice that if the file does not contain time, colour, or waveform data,
    # each of these will simply be None. You can use the has_time_data(),
    # has_colour_data(), and has_waveform_data() methods to determine if these
    # data are stored in the file.
    point_data, time, colour, waveform = lidar.get_point_record(i)

    # The PointData returned by the get_point_record method has the raw
    # untransformed point coordinate information as well as all the info
    # about point intensity, class, return values, etc. If you simply want
    # the transformed x,y,z coordinates, use the get_transformed_xyz method
    # instead.
    
    # Now let's filter the data based on return data...
    if point_data.is_first_return() or point_data.is_intermediate_return():
        # Save the point to lidar_out
        lidar_out.add_point(point_data, time, colour, waveform)

    # Update the progress once we've completed another 1% of the points.
    progress = int((i + 1.0) / num_points * 100.0)
    if progress != old_progress:
        old_progress = progress
        print(f'Progress: {progress}%')


# Write lidar_out to file
wbe.write_lidar(lidar_out, "new_lidar.laz")

WbW Tutorials

We have created a number of WbW tutorials that use Jupyter Notebooks.

• Tutorial 1: Hydrological Analysis
• Tutorial 2: Geomorphometric Analysis
• Tutorial 3: Mapping Building Footprints from LiDAR

Tool function documentation

• Aplication Programming Interface (API)
• WbW documentation
• WbW-Pro documentation
• Raster class documentation

WbW Application Programming Interface (API)

The following is a listing of function signatures, including argument names and default values, for the purpose of serving as a quick reference. The tool function documentation contains this same information as well as associated help docs. Notice that the WbEnvironment class information lists both the WbW and WbW-Pro tier product functions.

• module whitebox_workflows
• class AttributeField
• class AttributeHeader
• class BoundingBox
• class ColourData
• class DateData
• class FieldData
• class FieldDataType
• class GlobalEncodingField
• class LicenseType
• class LidarHeader
• class LidarPointData
• class Lidar
• class PhotometricInterpretation
• class Point2D
• class Point3D
• class RasterDataType
• class RasterConfigs
• class RasterType
• class Raster
• class VariableLengthRecord
• class VectorAttributes
• class VectorGeometry
• class VectorGeometryType
• class VectorHeader
• class Vector
• class WaveformPacket
• class WbEnvironment
• class WbPalette

module whitebox_workflows

def activate_license(key: str, firstname: str, lastname: str, email: str, agree_to_license_terms: bool) -> None: ...

def check_in_license(key: str) -> str: ...

def deactivate_license() -> None: ...

def download_sample_data(data_set: str) -> str: ...

def license_info() -> None: ...

def transfer_license() -> None: ...

class AttributeField

@property
def name(self) -> str: ...

@property
def field_type(self) -> int: ...

@property
def field_length(self) -> int: ...

@property
def decimal_count(self) -> int: ...

@staticmethod
def new(name: str, field_type: FieldDataType, field_length: int, decimal_count: int) -> AttributeField: ...

class AttributeHeader

@property
def version(self) -> int: ...

@property
def year(self) -> int: ...

@property
def month(self) -> int: ...

@property
def day(self) -> int: ...

@property
def num_records(self) -> int: ...

@property
def num_fields(self) -> int: ...

@property
def bytes_in_header(self) -> int: ...

@property
def bytes_in_record(self) -> int: ...

@property
def incomplete_tansaction(self) -> int: ...

@property
def encryption_flag(self) -> int: ...

@property
def mdx_flag(self) -> int: ...

@property
def language_driver_id(self) -> int: ...

class BoundingBox

@property
def min_x(self) -> float: ...

@min_x.setter
def min_x(self, value: float) -> None: ...

@property
def min_y(self) -> float: ...

@min_y.setter
def min_y(self, value: float) -> None: ...

@property
def max_x(self) -> float: ...

@max_x.setter
def max_x(self, value: float) -> None: ...

@property
def max_y(self) -> float: ...

@max_y.setter
def max_y(self, value: float) -> None: ...

@staticmethod
def new(min_x: float, max_x: float, min_y: float, max_y: float) -> Point2D: ...

@staticmethod
def from_two_points(p1: Point2D, p2: Point2D) -> BoundingBox: ...

def initialize_to_inf(self) -> None: ...

def get_height(self) -> float: ...

def get_width(self) -> float: ...

def is_point_in_box(self, x: float, y: float) -> bool: ...

def overlaps(self, other: BoundingBox) -> bool: ...

def nearly_overlaps(self, other: BoundingBox) -> bool: ...

def intersects_edge_of(self, other: BoundingBox) -> bool: ...

def entirely_contained_within(self, other: BoundingBox) -> bool: ...

def within(self, other: BoundingBox) -> bool: ...

def entirely_contains(self, other: BoundingBox) -> bool: ...

def contains(self, other: BoundingBox) -> bool: ...

def intersect(self, other: BoundingBox) -> BoundingBox: ...

def expand_to(self, other: BoundingBox) -> None: ...

def contract_to(self, other: BoundingBox) -> None: ...

def expand_by(self, value: float) -> None: ...

def contract_by(self, value: float) -> None: ...

class ColourData

@property
def red(self) -> int: ...

@red.setter
def red(self, value: int) -> None: ...

@property
def green(self) -> int: ...

@green.setter
def green(self, value: int) -> None: ...

@property
def blue(self) -> int: ...

@blue.setter
def blue(self, value: int) -> None: ...

@property
def nir(self) -> int: ...

@nir.setter
def nir(self, value: int) -> None: ...

class DateData

@property
def year(self) -> int: ...

@property
def month(self) -> int: ...

@property
def day(self) -> int: ...

class FieldData

@staticmethod
def new() -> FieldData: ...

@staticmethod
def new_int(value: int) -> FieldData: ...

@staticmethod
def new_real(value: float) -> FieldData: ...

@staticmethod
def new_text(value: str) -> FieldData: ...

@staticmethod
def new_date(value: DateData) -> FieldData: ...

@staticmethod
def new_bool(value: bool) -> FieldData: ...

@staticmethod
def new_null() -> FieldData: ...

def get_type(self) -> FieldDataType: ...

def get_value_as_f64(self) -> float: ...

def get_as_string(self) -> str: ...

class FieldDataType

Bool: int
Date: int
Int: int
Real: int
Text: int

class GlobalEncodingField

def __init__(self):

class LicenseType

WbW: str
WbWPro: str

class LidarHeader

@property
def file_signature(self) -> str: ...

@property
def file_source_id(self) -> int: ...

@property
def global_encoding(self) -> GlobalEncodingField: ...

@property
def project_id_used(self) -> bool: ...

@property
def project_id1(self) -> int: ...

@property
def project_id2(self) -> int: ...

@property
def project_id3(self) -> int: ...

@property
def project_id4(self) -> Tuple[int]: ...

@property
def version_major(self) -> int: ...

@property
def version_minor(self) -> int: ...

@property
def system_id(self) -> str: ...

@property
def generating_software(self) -> str: ...

@property
def file_creation_day(self) -> int: ...

@property
def file_creation_year(self) -> int: ...

@property
def header_size(self) -> int: ...

@property
def offset_to_points(self) -> int: ...

@property
def number_of_vlrs(self) -> int: ...

@property
def number_of_extended_vlrs(self) -> int: ...

@property
def offset_to_ex_vlrs(self) -> int: ...

@property
def point_record_length(self) -> int: ...

@property
def point_format(self) -> int: ...

@property
def number_of_points_old(self) -> int: ...

@property
def number_of_points(self) -> int: ...

@property
def number_of_points_by_return_old(self) -> Tuple[int, int, int, int, int]: ...

@property
def number_of_points_by_return(self) -> Tuple[int, int, int, int, int, int, int, int, int, int, int, int, int, int, int]: ...

@property
def x_scale_factor(self) -> float: ...

@property
def y_scale_factor(self) -> float: ...

@property
def z_scale_factor(self) -> float: ...

@property
def x_offset(self) -> float: ...

@property
def y_offset(self) -> float: ...

@property
def z_offset(self) -> float: ...

@property
def max_x(self) -> float: ...

@property
def min_x(self) -> float: ...

@property
def max_y(self) -> float: ...

@property
def min_y(self) -> float: ...

@property
def max_z(self) -> float: ...

@property
def min_z(self) -> float: ...

@property
def waveform_data_start(self) -> int: ...

def get_num_points(self) -> int: ...

class LidarPointData

@property
def x(self) -> int: ...

@property
def y(self) -> int: ...

@property
def z(self) -> int: ...

@property
def intensity(self) -> int: ...

@property
def point_bit_field(self) -> int: ...

@property
def class_bit_field(self) -> int: ...

@property
def scan_angle(self) -> int: ...

@property
def user_data(self) -> int: ...

@property
def point_source_id(self) -> int: ...

@property
def is_64bit(self) -> bool: ...

def get_32bit_from_64bit(self) -> Tuple[int, int]: ...

def return_number(self) -> int: ...

def set_return_number(self, value: int) -> None: ...

def number_of_returns(self) -> int: ...

def set_number_of_returns(self, value: int) -> None: ...

def is_only_return(self) -> bool: ...

def is_multiple_return(self) -> bool: ...

def is_early_return(self) -> bool: ...

def is_late_return(self) -> bool: ...

def is_last_return(self) -> bool: ...

def is_first_return(self) -> bool: ...

def is_intermediate_return(self) -> bool: ...

def scan_direction_flag(self) -> bool: ...

def set_scan_direction_flag(self, value: bool) -> None: ...

def edge_of_flightline_flag(self) -> bool: ...

def set_edge_of_flightline_flag(self, value: bool) -> None: ...

def classification(self) -> int: ...

def set_classification(self, value: int) -> None: ...

def classification_string(self) -> str: ...

def is_classified_vegetation(self) -> bool: ...

def synthetic(self) -> bool: ...

def set_synthetic(self, value: bool) -> None: ...

def keypoint(self) -> bool: ...

def set_keypoint(self, value: bool) -> None: ...

def withheld(self) -> bool: ...

def set_withheld(self, value: bool) -> None: ...

def overlap(self) -> bool: ...

def set_overlap(self, value: bool) -> None: ...

def is_classified_noise(self) -> bool: ...

def scanner_channel(self) -> int: ...

def set_scanner_channel(self, value: int) -> None: ...

class Lidar

@property
def file_name(self) -> str: ...

@file_name.setter
def file_name(self, value: str): ...

@property
def header(self) -> LidarHeader: ...

@property
def vlr_data(self) -> List[VariableLengthRecord]: ...

@vlr_data.setter
def vlr_data(self, value: List[VariableLengthRecord]): ...

@property
def wkt(self) -> str: ...

@property
def use_point_intensity(self) -> bool: ...

@property
def use_point_userdata(self) -> bool: ...

def get_point_record(self, index: int) -> Tuple[LidarPointData, Optional[float], Optional[ColourData], Optional[WaveformPacket]]: ...

def get_transformed_xyz(self, index: int) -> Tuple[float, float, float]: ...

def add_point(self, point_data: LidarPointData, time: Optional[float] = None, colour_data: Optional[ColourData] = None, waveform_data: Optional[WaveformPacket] = None) -> None: ...

def has_time_data(self) -> bool: ...

def has_colour_data(self) -> bool: ...

def has_waveform_data(self) -> bool: ...

def get_well_known_text(self) -> str: ...

def print_variable_length_records(self) -> str: ...

class PhotometricInterpretation

Continuous: int
Categorical: int
Boolean: int
RGB: int
Paletted: int
Unknown: int

class Point2D

@property
def x(self) -> float: ...

@x.setter
def x(self, value: float) -> None: ...

@property
def y(self) -> float: ...

@y.setter
def y(self, value: float) -> None: ...

@staticmethod
def new(x: float, y: float) -> Point2D: ...

class Point3D

@property
def x(self) -> float: ...

@x.setter
def x(self, value: float) -> None: ...

@property
def y(self) -> float: ...

@y.setter
def y(self, value: float) -> None: ...

@property
def z(self) -> float: ...

@z.setter
def z(self, value: float) -> None: ...

@staticmethod
def new(x: float, y: float, z: float) -> Point2D: ...

class RasterDataType

F64: int
F32: int
I64: int
U64: int
RGB48: int
I32: int
U32: int
RGB24: int
RGBA32: int
I16: int
U16: int
I8: int
U8: int
Unknown: int
def get_data_size(self) -> int: ...

def is_float(self) -> bool: ...

def is_integer(self) -> bool: ...

def is_unsigned_integer(self) -> bool: ...

def is_signed_integer(self) -> bool: ...

def is_colour_data(self) -> bool: ...

def return_wider(self, other: RasterDataType) -> RasterDataType: ...

class RasterConfigs

@property
def title(self) -> str: ...

@title.setter
def title(self, value): ...

@property
def rows(self) -> int: ...

@rows.setter
def rows(self, value: int) -> None: ...

@property
def columns(self) -> int: ...

@columns.setter
def columns(self, value: int) -> None: ...

@property
def nodata(self) -> float: ...

@nodata.setter
def nodata(self, value: float) -> None: ...

@property
def north(self) -> float: ...

@north.setter
def north(self, value: float) -> None: ...

@property
def south(self) -> float: ...

@south.setter
def south(self, value: float) -> None: ...

@property
def east(self) -> float: ...

@east.setter
def east(self, value: float) -> None: ...

@property
def west(self) -> float: ...

@west.setter
def west(self, value: float) -> None: ...

@property
def resolution_x(self) -> float: ...

@resolution_x.setter
def resolution_x(self, value: float) -> None: ...

@property
def resolution_y(self) -> float: ...

@resolution_y.setter
def resolution_y(self, value: float) -> None: ...

@property
def minimum(self) -> float: ...

@minimum.setter
def minimum(self, value: float) -> None: ...

@property
def maximum(self) -> float: ...

@maximum.setter
def maximum(self, value: float) -> None: ...

@property
def palette(self) -> str: ...

@property
def projection(self) -> str: ...

@property
def photometric_interp(self) -> PhotometricInterpretation: ...

@photometric_interp.setter
def photometric_interp(self, value: PhotometricInterpretation) -> None: ...

@property
def data_type(self) -> RasterDataType: ...

@data_type.setter
def data_type(self, value: RasterDataType) -> None: ...

@property
def z_units(self) -> str: ...

@property
def xy_units(self) -> str: ...

@property
def reflect_at_edges(self) -> bool: ...

@reflect_at_edges.setter
def reflect_at_edges(self, value: bool) -> None: ...

@property
def pixel_is_area(self) -> bool: ...

@pixel_is_area.setter
def pixel_is_area(self, value: bool) -> None: ...

@property
def epsg_code(self) -> int: ...

@epsg_code.setter
def epsg_code(self, value: int) -> None: ...

@property
def coordinate_ref_system_wkt(self) -> str: ...

@epsg_code.setter
def coordinate_ref_system_wkt(self, value: str) -> None: ...

@staticmethod
def new() -> RasterConfigs: ...

class RasterType

Unknown: int
ArcAscii: int
ArcBinary: int
EsriBil: int
GeoTiff: int
GrassAscii: int
IdrisiBinary: int
SagaBinary: int
Surfer7Binary: int
SurferAscii: int
Whitebox: int

class Raster

@property
def file_name(self) -> str: ...

@file_name.setter
def file_name(self, value: str) -> None: ...

@property
def file_mode(self) -> str: ...

@property
def raster_type(self) -> RasterType: ...

@property
def configs(self) -> RasterConfigs: ...

@staticmethod
def new_from_other(other: Raster, data_type: Optional[RasterDataType]) -> Raster: ...

def get_value(self, row: int, column: int) -> float: ...

def set_value(self, row: int, column: int, value: float) -> None: ...

def decrement(self, row: int, column: int, value: float) -> None: ...

def increment(self, row: int, column: int, value: float) -> None: ...

def set_row_data(self, row: int, values: List[float]) -> None: ...

def get_row_data(self, row: int) -> List[float]: ...

def increment_row_data(self, row: int, values: List[float]) -> None: ...

def decrement_row_data(self, row: int, values: List[float]) -> None: ...

def set_data_from_raster(self, other: Raster) -> Optional[str]: ...

def reinitialize_values(self, value: float) -> None: ...

def get_value_as_rgba(self, row: int, column: int) -> Tuple[int, int, int, int]: ...

def get_value_as_hsi(self, row: int, column: int) -> Tuple[float, float, float]: ...

def set_value_from_rgba(self, row: int, column: int, rgba: Tuple[int, int, int, int]) -> None: ...

def get_data_size_in_bytes(self) -> int: ...

def get_x_from_column(self, column: int) -> float: ...

def get_y_from_row(self, row: int) -> float: ...

def get_column_from_x(self, x: float) -> int: ...

def get_row_from_y(self, y: float) -> int: ...

def size_of(self) -> int: ...

def __add__(self, other: Union[Raster, float]) -> Raster: ...

def __sub__(self, other: Union[Raster, float]) -> Raster: ...

def __mul__(self, other: Union[Raster, float]) -> Raster: ...

def __truediv__(self, other: Union[Raster, float]) -> Raster: ...

def __floordiv__(self, other: Union[Raster, float]) -> Raster: ...

def __mod__(self, other: Union[Raster, float]) -> Raster: ...

def __pow__(self, other: Union[Raster, float], modulo: Optional[float] = None) -> Raster: ...

def __neg__(self) -> Raster: ...

def __abs__(self) -> Raster: ...

def __iadd__(self, other: Union[Raster, float]) -> None: ...

def __isub__(self, other: Union[Raster, float]) -> None: ...

def __imul__(self, other: Union[Raster, float]) -> None: ...

def __idiv__(self, other: Union[Raster, float]) -> None: ...

def __getitem__(self, row_column: Tuple[int, int]) -> float: ...

def __setitem__(self, row_column: Tuple[int, int], value: float) -> None: ...

def __gt__(self, other: Union[Raster, float]) -> Raster: ...

def __ge__(self, other: Union[Raster, float]) -> Raster: ...

def __lt__(self, other: Union[Raster, float]) -> Raster: ...

def __le__(self, other: Union[Raster, float]) -> Raster: ...

def __eq__(self, other: Union[Raster, float]) -> Raster: ...

def __ne__(self, other: Union[Raster, float]) -> Raster: ...

def acos(self) -> Raster: ...

def acosh(self) -> Raster: ...

def asin(self) -> Raster: ...

def asinh(self) -> Raster: ...

def atan(self) -> Raster: ...

def atan2(self, other: Union[Raster, float]) -> Raster: ...

def atanh(self) -> Raster: ...

def ceil(self) -> Raster: ...

def con(self, con_statement: str, true_raster_or_float: Union[Raster, float, str], false_raster_or_float: Union[Raster, float, str]) -> Raster: ...

def cos(self) -> Raster: ...

def cosh(self) -> Raster: ...

def exp(self) -> Raster: ...

def exp2(self) -> Raster: ...

def floor(self) -> Raster: ...

def is_nodata(self) -> Raster: ...

def ln(self) -> Raster: ...

def log2(self) -> Raster: ...

def log10(self) -> Raster: ...

def max(self, other: Union[Raster, float]) -> Raster: ...

def min(self, other: Union[Raster, float]) -> Raster: ...

def normalize(self) -> Raster: ...

def signum(self) -> Raster: ...

def sin(self) -> Raster: ...

def sinh(self) -> Raster: ...

def sqrt(self) -> Raster: ...

def square(self) -> Raster: ...

def tan(self) -> Raster: ...

def tanh(self) -> Raster: ...

def to_degrees(self) -> Raster: ...

def to_radians(self) -> Raster: ...

def trunc(self) -> Raster: ...

def num_cells(self) -> int: ...

def num_valid_cells(self) -> int: ...

def calculate_mean(self) -> float: ...

def calculate_mean_and_stdev(self) -> Tuple[float, float]: ...

def calculate_clip_values(self, percent: float) -> Tuple[float, float]: ...

def deep_copy(self) -> Raster: ...

def update_min_max(self) -> None: ...

class VariableLengthRecord

@property
def reserved(self) -> int: ...

@property
def user_id(self) -> str: ...

@property
def record_id(self) -> int: ...

@property
def record_length_after_header(self) -> int: ...

@property
def description(self) -> str: ...

@property
def binary_data(self) -> List[int]: ...

class VectorAttributes

@property
def header(self) -> AttributeHeader: ...

@property
def fields(self) -> List[AttributeField]: ...

@property
def is_deleted(self) -> List[bool]: ...

def get_num_fields(self) -> int: ...

class VectorGeometry

@property
def shape_type(self) -> VectorGeometryType: ...

@property
def x_min(self) -> float: ...

@property
def x_max(self) -> float: ...

@property
def y_min(self) -> float: ...

@property
def y_max(self) -> float: ...

@property
def num_parts(self) -> int: ...

@property
def num_points(self) -> int: ...

@property
def parts(self) -> List[int]: ...

@property
def points(self) -> List[Point2D]: ...

@property
def z_min(self) -> float: ...

@property
def z_max(self) -> float: ...

@property
def z_array(self) -> List[float]: ...

@property
def m_min(self) -> float: ...

@property
def m_max(self) -> float: ...

@property
def m_array(self) -> List[float]: ...

@staticmethod
def new_vector_geometry(shape_type: VectorGeometryType) -> VectorGeometry: ...

def add_point(self, p: Point2D) -> None: ...

def add_pointm(self, p: Point2D, m: float) -> None: ...

def add_pointz(self, p: Point2D, m: float, z: float) -> None: ...

def add_geom_part(self, points: List[Point2D]) -> None: ...

def add_geom_partm(self, points: List[Point2D], measures: List[float]) -> None: ...

def add_geom_partz(self, points: List[Point2D], measures: List[float], z_values: List[float]) -> None: ...

def get_bounding_box(self) -> BoundingBox: ...

def get_length(self) -> int: ...

def has_m_data(self) -> bool: ...

def has_z_data(self) -> bool: ...

def is_hole(self, part_num: int) -> bool: ...

class VectorGeometryType

Null: int
Point: int
PolyLine: int
Polygon: int
MultiPoint: int
PointZ: int
PolyLineZ: int
PolygonZ: int
MultiPointZ: int
PointM: int
PolyLineM: int
PolygonM: int
MultiPointM: int

class VectorHeader

@property
def file_length(self) -> int: ...

@property
def version(self) -> int: ...

@property
def shape_type(self) -> VectorGeometryType: ...

@property
def x_min(self) -> float: ...

@property
def y_min(self) -> float: ...

@property
def x_max(self) -> float: ...

@property
def y_max(self) -> float: ...

@property
def z_min(self) -> float: ...

@property
def z_max(self) -> float: ...

@property
def m_min(self) -> float: ...

@property
def m_max(self) -> float: ...

class Vector

@property
def file_name(self) -> str: ...

@file_name.setter
def file_name(self, value: str) -> None: ...

@property
def file_mode(self) -> str: ...

@property
def header(self) -> VectorHeader: ...

@property
def num_records(self) -> int: ...

@property
def records(self) -> List[VectorGeometry]: ...

@property
def attributes(self) -> VectorAttributes: ...

@property
def projection(self) -> str: ...

def __getitem__(self, index: int) -> VectorGeometry: ...

def add_record(self, geometry: VectorGeometry) -> None: ...

def add_attribute_field(self, field: AttributeField) -> None: ...

def add_attribute_fields(self, fields: List[AttributeField]) -> None: ...

def add_attribute_record(self, rec: List[FieldData], deleted: bool) -> None: ...

def contains_attribute_field(self, field: AttributeField) -> bool: ...

def get_attribute_fields(self) -> List[AttributeField]: ...

def get_attribute_record(self, index: int) -> List[FieldData]: ...

def get_attribute_field_info(self, index: int) -> AttributeField: ...

def get_attribute_field_num(self, name: str) -> Optional[int]: ...

def get_num_attributes_fields(self) -> int: ...

def get_attribute_value(self, record_index: int, field_name: str) -> FieldData: ...

def is_attribute_field_numeric(self, index: int) -> bool: ...

def reinitialize_attributes(self) -> None: ...

def set_attribute_value(self, record_index: int, field_name: str, field_data: FieldData) -> None: ...

class WaveformPacket

@property
def packet_descriptor_index(self) -> int: ...

@packet_descriptor_index.setter
def packet_descriptor_index(self, value: int) -> None: ...

@property
def offset_to_waveform_data(self) -> int: ...

@offset_to_waveform_data.setter
def offset_to_waveform_data(self, value: int) -> None: ...

@property
def waveform_packet_size(self) -> int: ...

@waveform_packet_size.setter
def waveform_packet_size(self, value: int) -> None: ...

@property
def ret_point_waveform_loc(self) -> float: ...

@ret_point_waveform_loc.setter
def ret_point_waveform_loc(self, value: float) -> None: ...

@property
def xt(self) -> float: ...

@xt.setter
def xt(self, value: float) -> None: ...

@property
def yt(self) -> float: ...

@yt.setter
def yt(self, value: float) -> None: ...

@property
def zt(self) -> float: ...

@zt.setter
def zt(self, value: float) -> None: ...

class WbEnvironment

@property
def max_procs(self) -> int: ...

@max_procs.setter
def max_procs(self, value: int) -> None: ...

@property
def verbose(self) -> bool: ...

@verbose.setter
def verbose(self, value: bool) -> None: ...

@property
def working_directory(self) -> str: ...

@working_directory.setter
def working_directory(self, value: str) -> None: ...

def __new__(cls, user_id: str = "") -> WbEnvironment: ...

def version(self) -> str: ...

def read_lidar(self, file_name: str, file_mode: str = "r") -> Lidar: ...

def read_lidars(self, file_names: List[str]) -> List[Lidar]: ...

def new_lidar(self, header: LidarHeader) -> Lidar: ...

def write_lidar(self, lidar: Lidar, file_name: str) -> None: ...

def read_raster(self, file_name: str) -> Raster: ...

def read_rasters(self, file_name: List[str]) -> List[Raster]: ...

def new_raster(self, configs: RasterConfigs) -> Raster: ...

def write_raster(self, raster: Raster, file_name: str, compress: bool = False) -> None: ...

def read_vector(self, file_name: str) -> Vector: ...

def read_vectors(self, file_name: List[str]) -> List[Vector]: ...

def new_vector(self,  shape_type: VectorGeometryType, attributes: Optional[List[AttributeField]] = None, proj: str = "") -> Vector: ...

def write_vector(self, vector: Vector, file_name: str) -> None: ...

def write_text(self, text: str, file_name: str) -> None: ...

# Requires WbW-Pro license
def accumulation_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def adaptive_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, threshold: float = 2.0) -> Raster: ...

def add_point_coordinates_to_table(self, input: Vector) -> Vector: ...

def aggregate_raster(self, raster: Raster, aggregation_factor: int = 2, aggregation_type: str = "mean") -> Raster: ...

def anova(self, input_raster: Raster, features_raster: Raster, output_html_file: str) -> None: ...

def ascii_to_las(self, input_ascii_files: List[str], pattern: str, epsg_code: int) -> None: ...

def aspect(self, dem: Raster, z_factor: float = 1.0) -> Raster: ...

# Requires WbW-Pro license
def assess_route(self, routes: Vector, dem: Raster, segment_length: float = 100.0, search_radius: int = 15) -> Vector: ...

def attribute_correlation(self, input: Vector, output_html_file: str) -> None: ...

def attribute_histogram(self, input: Vector, field_name: str, output_html_file: str) -> None: ...

def attribute_scattergram(self, input: Vector, field_name_x: str, field_name_y: str, output_html_file: str, add_trendline: bool = False) -> None: ...

def average_flowpath_slope(self, dem: Raster) -> Raster: ...

# Requires WbW-Pro license
def average_horizon_distance(self, dem: Raster, az_fraction: float = 5.0, max_dist: float = float('inf'), observer_hgt_offset: float = 0.05) -> Raster: ...

def average_normal_vector_angular_deviation(self, dem: Raster, filter_size: int = 11) -> Raster: ...

def average_overlay(self, input_rasters: List[Raster]) -> Raster: ...

def average_upslope_flowpath_length(self, dem: Raster) -> Raster: ...

def balance_contrast_enhancement(self, image: Raster, band_mean: float = 100.0) -> Raster: ...

def basins(self, d8_pntr: Raster, esri_pntr: bool = False) -> Raster: ...

def bilateral_filter(self, raster: Raster, sigma_dist: float = 0.75, sigma_int: float = 1.0) -> Raster: ...

def block_maximum(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None) -> Raster: ...

def block_minimum(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None) -> Raster: ...

def bool_and(self, input1: Raster, input2: Raster) -> Raster: ...

def bool_not(self, input1: Raster, input2: Raster) -> Raster: ...

def bool_or(self, input1: Raster, input2: Raster) -> Raster: ...

def bool_xor(self, input1: Raster, input2: Raster) -> Raster: ...

def boundary_shape_complexity(self, raster: Raster) -> Raster: ...

def breach_depressions_least_cost(self, dem: Raster, max_cost: float = float('inf'), max_dist: int = 100, flat_increment: float = float('nan'), fill_deps: bool = False, minimize_dist: bool = False) -> Raster: ...

def breach_single_cell_pits(self, dem: Raster) -> Raster: ...

# Requires WbW-Pro license
def breakline_mapping(self, dem: Raster, threshold: float = 0.8, min_length: int = 3) -> Vector: ...

def buffer_raster(self, input: Raster, buffer_size: float, grid_cells_units: bool = False) -> Raster: ...

def burn_streams_at_roads(self, dem: Raster, streams: Vector, roads: Vector, road_width: float) -> Raster: ...

# Requires WbW-Pro license
def canny_edge_detection(self, input: Raster, sigma: float = 0.5, low_threshold: float = 0.05, high_threshold: float = 0.15, add_back_to_image: bool = False) -> Raster: ...

def centroid_raster(self, input: Raster) -> Tuple[Raster, str]: ...

def centroid_vector(self, input: Vector) -> Vector: ...

def change_vector_analysis(self, date1_rasters: List[Raster], date2_rasters: List[Raster]) -> Tuple[Raster, Raster, str]: ...

def check_in_license(self, key: str) -> str: ...

def circular_variance_of_aspect(self, dem: Raster, filter_size: int = 11) -> Raster: ...

def classify_buildings_in_lidar(self, in_lidar: Lidar, building_footprints: Vector) -> Lidar: ...

# Requires WbW-Pro license
def classify_lidar(self, input_lidar: Optional[Lidar], search_radius: float = 2.5, grd_threshold: float = 0.1, oto_threshold: float = 1.0, linearity_threshold: float = 0.5, planarity_threshold: float = 0.85, num_iter: int = 30, facade_threshold: float = 0.5) -> Optional[Lidar]: ...

# Requires WbW-Pro license
def colourize_based_on_class(self, input_lidar: Optional[Lidar], intensity_blending_amount: float = 50.0, clr_str: str = "", use_unique_clrs_for_buildings: bool = False, search_radius: float = 2.0) -> Optional[Lidar]: ...

# Requires WbW-Pro license
def colourize_based_on_point_returns(self, input_lidar: Optional[Lidar], intensity_blending_amount: float = 50.0, only_ret_colour: str = "(230,214,170)", first_ret_colour:str = "(0,140,0)", intermediate_ret_colour: str = "(255,0,255)", last_ret_colour: str = "(0,0,255)") -> Optional[Lidar]: ...

def classify_overlap_points(self, in_lidar: Lidar, resolution: float = 1.0, overlap_criterion: str = "max scan angle", filter: bool = False) -> Lidar: ...

def clean_vector(self, input: Vector) -> Vector: ...

def clip(self, input: Vector, clip_layer: Vector) -> Vector: ...

def clip_lidar_to_polygon(self, input: Lidar, polygons: Vector) -> Lidar: ...

def clip_raster_to_polygon(self, raster: Raster, polygons: Vector, maintain_dimensions: bool = False) -> Raster: ...

def closing(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def clump(self, raster: Raster, diag: bool = False, zero_background: bool = False) -> Raster: ...

def compactness_ratio(self, input: Vector) -> Vector: ...

def conservative_smoothing_filter(self, raster: Raster, filter_size_x: int = 3, filter_size_y: int = 3) -> Raster: ...

def construct_vector_tin(self, input_points: Vector, field_name: str = "FID", use_z: bool = False, max_triangle_edge_length: float = float('inf')) -> Vector: ...

def contours_from_points(self, input: Vector, field_name: str = "", use_z_values: bool = False, max_triangle_edge_length: float = float('inf'), contour_interval: float = 10.0, base_contour: float = 0.0, smoothing_filter_size: int = 9) -> Vector: ...

def contours_from_raster(self, raster_surface: Raster, contour_interval: float = 10.0, base_contour: float = 0.0, smoothing_filter_size: int = 9, deflection_tolerance: float = 10.0) -> Vector: ...

def convert_nodata_to_zero(self, raster: Raster) -> Raster: ...

def corner_detection(self, raster: Raster) -> Raster: ...

def correct_vignetting(self, image: Raster, principal_point: Vector, focal_length: float = 304.8, image_width: float = 228.6, n_param: float = 4.0) -> Raster: ...

def cost_allocation(self, source: Raster, backlink: Raster) -> Raster: ...

def cost_distance(self, source: Raster, cost: Raster) -> Tuple[Raster, Raster]: ...

def cost_pathway(self, destination: Raster, backlink: Raster, zero_background: bool = False) -> Raster: ...

def count_if(self, input_rasters: List[Raster], comparison_value: float) -> Raster: ...

def create_colour_composite(self, red: Raster, green: Raster, blue: Raster, opacity: Optional[Raster] = None, enhance: bool = True, treat_zeros_as_nodata: bool = False) -> Raster: ...

def create_plane(self, base_file: Raster, gradient: float, aspect: float, constant: float) -> Raster: ...

def crispness_index(self, raster: Raster, output_html_file: str) -> None: ...

def cross_tabulation(self, raster1: Raster, raster2: Raster, output_html_file: str) -> None: ...

def csv_points_to_vector(self, input_file: str, x_field_num: int = 0, y_field_num: int = 1, epsg: int = 0) -> Vector: ...

def cumulative_distribution(self, raster: Raster) -> Raster: ...

# Requires WbW-Pro license
def curvedness(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def d8_flow_accum(self, raster: Raster, out_type: str = "SCA", log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False, esri_pntr: bool = False) -> Raster: ...

def d8_mass_flux(self, dem: Raster, loading: Raster, efficiency: Raster, absorption: Raster) -> Raster: ...

def d8_pointer(self, dem: Raster, esri_pointer: bool = False) -> Raster: ...

# Requires WbW-Pro license
def dbscan(self, input_rasters: List[Raster], scaling_method: str = "none", search_distance: float = 1.0, min_points: int = 5) -> Raster: ...

# Requires WbW-Pro license
def dem_void_filling(self, dem: Raster, fill: Raster, mean_plane_dist: int = 20, edge_treatment: str = "use DEM", weight_value: float = 2.0) -> Raster: ...

def depth_in_sink(self, dem: Raster, zero_background: bool = False) -> Raster: ...

# Requires WbW-Pro license
def depth_to_water(self, dem: Raster, streams: Optional[Vector] = None, lakes: Optional[Vector] = None) -> Raster: ...

def deviation_from_mean_elevation(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def deviation_from_regional_direction(self, input: Vector, elongation_threshold: float = 0.75) -> Vector: ...

def diff_of_gaussians_filter(self, raster: Raster, sigma1: float = 2.0, sigma2: float = 4.0) -> Raster: ...

def difference(self, input: Vector, overlay: Vector) -> Vector: ...

# Requires WbW-Pro license
def difference_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def difference_from_mean_elevation(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def dinf_flow_accum(self, dem: Raster, out_type: str = "SCA", convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False) -> Raster: ...

def dinf_mass_flux(self, dem: Raster, loading: Raster, efficiency: Raster, absorption: Raster) -> Raster: ...

def dinf_pointer(self, dem: Raster) -> Raster: ...

def direct_decorrelation_stretch(self, image: Raster, achromatic_factor: float = 0.5, clip_percent: float = 1.0) -> Raster: ...

def directional_relief(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf')) -> Raster: ...

def dissolve(self, input: Vector, dissolve_field: str = "", snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

def distance_to_outlet(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def diversity_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def downslope_distance_to_stream(self, dem: Raster, streams: Raster, use_dinf: bool = False) -> Raster: ...

def downslope_flowpath_length(self, d8_pointer: Raster, watersheds: Raster, weights: Raster, esri_pntr: bool = False) -> Raster: ...

def downslope_index(self, dem: Raster, vertical_drop: float, output_type: str = "tangent") -> Raster: ...

def edge_contamination(self, dem: Raster, flow_type: str = "mfd", z_factor: float = -1.0) -> Raster: ...

def edge_density(self, dem: Raster, filter_size: int = 11, normal_diff_threshold: float = 5.0, z_factor: float = 1.0) -> Raster: ...

def edge_preserving_mean_filter(self, raster: Raster, filter_size: int = 11, threshold: float = 15.0) -> Raster: ...

def edge_proportion(self, raster: Raster) -> Tuple[Raster, str]: ...

def elev_relative_to_min_max(self, dem: Raster) -> Raster: ...

def elev_relative_to_watershed_min_max(self, dem: Raster, watersheds: Raster) -> Raster: ...

def elevation_above_pit(self, dem: Raster) -> Raster: ...

def elevation_above_stream(self, dem: Raster, streams: Raster) -> Raster: ...

def elevation_above_stream_euclidean(self, dem: Raster, streams: Raster) -> Raster: ...

def elevation_percentile(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...

def eliminate_coincident_points(self, input: Vector, tolerance_dist: float) -> Vector: ...

def elongation_ratio(self, input: Vector) -> Vector: ...

def embankment_mapping(self, dem: Raster, roads_vector: Vector, search_dist: float = 2.5, min_road_width: float = 6.0, typical_embankment_width: float = 30.0, typical_embankment_max_height: float = 2.0, embankment_max_width: float = 60.0, max_upwards_increment: float = 0.05, spillout_slope: float = 4.0, remove_embankments: bool = False) -> Tuple[Raster, Optional[Raster]]: ...

def emboss_filter(self, raster: Raster, direction: str = "n", clip_amount: float = 0.0) -> Raster: ...

def erase(self, input: Vector, erase_layer: Vector) -> Vector: ...

def erase_polygon_from_lidar(self, input: Lidar, polygons: Vector) -> Lidar: ...

def erase_polygon_from_raster(self, raster: Raster, polygons: Vector) -> Raster: ...

def euclidean_allocation(self, input: Raster) -> Raster: ...

def euclidean_distance(self, input: Raster) -> Raster: ...

# Requires WbW-Pro license
def evaluate_training_sites(self, input_rasters: List[Raster], training_polygons: Vector, class_field_name: str, output_html_file: str) -> None: ...

def export_table_to_csv(self, input: Vector, output_csv_file: str, headers: bool = True) -> None: ...

def exposure_towards_wind_flux(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf'), z_factor: float = 1.0) -> Raster: ...

def extend_vector_lines(self, input: Vector, distance: float, extend_direction: str = "both ends") -> Vector: ...

def extract_by_attribute(self, input: Vector, statement: str) -> Vector: ...

def extract_nodes(self, input: Vector) -> Vector: ...

def extract_raster_values_at_points(self, rasters: List[Raster], points: Vector) -> Tuple[Vector, str]: ...

def extract_streams(self, flow_accumulation: Raster, threshold: float = 0.0, zero_background: bool = False) -> Raster: ...

def extract_valleys(self, dem: Raster, variant: str = "LQ", line_thin: bool = False, filter_size: int = 5) -> Raster: ...

def farthest_channel_head(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def fast_almost_gaussian_filter(self, raster: Raster, sigma: float = 1.8) -> Raster: ...

def fd8_flow_accum(self, dem: Raster, out_type: str = "SCA", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...

def fd8_pointer(self, dem: Raster) -> Raster: ...

def feature_preserving_smoothing(self, dem: Raster, filter_size: int = 11, normal_diff_threshold: float = 8.0, iterations: int = 3, max_elevation_diff: float = float('inf'), z_factor: float = 1.0) -> Raster: ...

def fetch_analysis(self, dem: Raster, azimuth: float = 0.0, height_increment: float = 0.05) -> Raster: ...

def fill_burn(self, dem: Raster, streams: Vector) -> Raster: ...

def fill_depressions(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan'), max_depth: float = float('inf')) -> Raster: ...

def fill_depressions_planchon_and_darboux(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...

def fill_depressions_wang_and_liu(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...

def fill_missing_data(self, dem: Raster, filter_size: int = 11, weight: float = 2.0, exclude_edge_nodata: bool = False) -> Raster: ...

def fill_pits(self, dem: Raster) -> Raster: ...

# Requires WbW-Pro license
def filter_lidar(self, statement: str, input_lidar: Optional[Lidar]) -> Optional[Lidar]: ...

# Requires WbW-Pro license
def filter_lidar_by_percentile(self, input_lidar: Optional[Lidar],  percentile: float = 0.0, block_size: float = 1.0) -> Optional[Lidar]: ...

# Requires WbW-Pro license
def filter_lidar_by_reference_surface(self, input_lidar: Lidar, ref_surface: Raster, query: str = "within", threshold: float = 0.0) -> Lidar: ...

def filter_lidar_classes(self, input: Lidar, exclusion_classes: List[int]) -> Lidar: ...

def filter_lidar_scan_angles(self, in_lidar: Lidar, threshold: int) -> Lidar: ...

def filter_raster_features_by_area(self, input: Raster, threshold: int, zero_background: bool = False) -> Raster: ...

def find_flightline_edge_points(self, in_lidar: Lidar) -> Lidar: ...

def find_lowest_or_highest_points(self, raster: Raster, output_type: str = "lowest") -> Vector: ...

def find_main_stem(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def find_noflow_cells(self, dem: Raster) -> Raster: ...

def find_parallel_flow(self, d8_pntr: Raster, streams: Raster) -> Raster: ...

def find_patch_edge_cells(self, raster: Raster) -> Raster: ...

def find_ridges(self, dem: Raster, line_thin: bool = True) -> Raster: ...

# Requires WbW-Pro license
def fix_dangling_arcs(self, input: Vector, snap_dist: float) -> Vector: ...

def flatten_lakes(self, dem: Raster, lakes: Vector) -> Raster: ...

def flightline_overlap(self, input_lidar: Lidar, resolution: float = 1.0) -> Raster: ...

def flip_image(self, raster: Raster, direction: str = "vertical") -> Raster: ...

def flood_order(self, dem: Raster) -> Raster: ...

def flow_accum_full_workflow(self, dem: Raster, out_type: str = "SCA", log_transform: bool = False, clip: bool = False, esri_pntr: bool = False) -> Tuple[Raster, Raster, Raster]: ...

def flow_length_diff(self, d8_pointer: Raster, esri_pointer: bool = False, log_transform: bool = False) -> Raster: ...

def gamma_correction(self, raster: Raster, gamma_value: float = 0.5) -> Raster: ...

def gaussian_contrast_stretch(self, raster: Raster, num_tones: int = 256) -> Raster: ...

def gaussian_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def gaussian_filter(self, raster: Raster, sigma: float = 0.75) -> Raster: ...

def geomorphons(self, dem: Raster, search_distance: int = 50, flatness_threshold: float = 0.0, flatness_distance: int = 0, skip_distance: int = 0, output_forms: bool = True, analyze_residuals: bool = False) -> Raster: ...

# Requires WbW-Pro license
def generalize_classified_raster(self, raster: Raster, area_threshold: int = 5, method: str = "longest") -> Raster: ...

# Requires WbW-Pro license
def generalize_with_similarity(self, raster: Raster, similarity_rasters: List[Raster], area_threshold: int = 5) -> Raster: ...

# Requires WbW-Pro license
def generating_function(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def hack_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def heat_map(self, points: Vector, field_name: Optional[str] = None, bandwidth: float = 0.0, cell_size: float = 0.0, base_raster: Optional[Raster] = None, kernel_function: str = "quartic") -> Raster: ...

def height_above_ground(self, input: Lidar) -> Lidar: ...

def hexagonal_grid_from_raster_base(self, base: Raster, width: float, orientation: str = "h") -> Vector: ...

def hexagonal_grid_from_vector_base(self, base: Vector, width: float, orientation: str = "h") -> Vector: ...

def high_pass_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def high_pass_median_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...

def highest_position(self, input_rasters: List[Raster]) -> Raster: ...

def hillshade(self, dem: Raster, azimuth: float = 315.0, altitude: float = 30.0, z_factor: float = 1.0) -> Raster: ...

def hillslopes(self, d8_pntr: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...

def histogram_equalization(self, raster: Raster, num_tones: int = 256) -> Raster: ...

def histogram_matching(self, image: Raster, histogram: List[List[float]], histo_is_cumulative: bool = False) -> Raster: ...

def histogram_matching_two_images(self, image1: Raster, image2: Raster) -> Raster: ...

def hole_proportion(self, input: Vector) -> Vector: ...

def horizon_angle(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf')) -> Raster: ...

# Requires WbW-Pro license
def horizon_area(self, dem: Raster, az_fraction: float = 5.0, max_dist: float = float('inf'), observer_hgt_offset: float = 0.05) -> Raster: ...

# Requires WbW-Pro license
def horizontal_excess_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def horton_ratios(self, dem: Raster, streams_raster: Raster) -> Tuple[float, float, float, float]: ...

def horton_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

# Requires WbW-Pro license
def hydrologic_connectivity(self, dem: Raster, exponent: float = 1.1, convergence_threshold: float = 0.0, z_factor: float = 1.0 ) -> Tuple[Raster, Raster]: ...

def hypsometric_analysis(self, dem_rasters: List[Raster], output_html_file: str, watershed_rasters: Optional[List[Raster]] = None) -> None: ...

def hypsometrically_tinted_hillshade(self, dem: Raster, solar_altitude: float = 45.0, hillshade_weight: float = 0.5, brightness: float = 0.5, atmospheric_effects: float = 0.0, palette: WbPalette = WbPalette.Atlas, reverse_palette: bool = False, full_360_mode: bool = False, z_factor: float = 1.0) -> Raster: ...

def idw_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, weight: float = 2.0, radius: float = 0.0, min_points: int = 0, cell_size: float = 0.0, base_raster: Optional[Raster] = None) -> Raster: ...

def ihs_to_rgb(self, intensity: Raster, hue: Raster, saturation: Raster) -> Tuple[Raster, Raster, Raster]: ...

def image_autocorrelation(self, rasters: List[Raster], output_html_file: str, contiguity_type: str = "bishop") -> None: ...

def image_correlation(self, rasters: List[Raster], output_html_file: str) -> None: ...

def image_correlation_neighbourhood_analysis(self, raster1: Raster, raster2: Raster, filter_size: int = 11, correlation_stat: str = "pearson") -> Tuple[Raster, Raster]: ...

def image_regression(self, independent_variable: Raster, dependent_variable: Raster, output_html_file: str, standardize_residuals: bool = False, output_scattergram: bool = False, num_samples: int = 1000) -> Raster: ...

# Requires WbW-Pro license
def image_segmentation(self, input_rasters: List[Raster], dist_threshold: float = 0.5, num_steps: int = 10, area_threshold: int = 4) -> Raster: ...

# Requires WbW-Pro license
def image_slider(self, left_raster: Raster, right_raster: Raster, output_html_file: str, left_palette: WbPalette = WbPalette.Grey, left_reverse_palette: bool = False, left_label: str = "",  right_palette: WbPalette = WbPalette.Grey, right_reverse_palette: bool = False, right_label: str = "", image_height: int = 600) -> None: ...

def image_stack_profile(self, images: List[Raster], points: Vector, output_html_file: str) -> None: ...

def impoundment_size_index(self, dem: Raster, max_dam_length: float, output_mean: bool = False, output_max: bool = False, output_volume: bool = False, output_area: bool = False, output_height: bool = False) -> Tuple[Optional[Raster], Optional[Raster], Optional[Raster], Optional[Raster], Optional[Raster]]: ...

# Requires WbW-Pro license
def improved_ground_point_filter(self, input: Lidar, block_size = 1.0, max_building_size = 150.0, slope_threshold = 15.0, elev_threshold = 0.15) -> Lidar: ...

def individual_tree_detection(self, input_lidar: Optional[Lidar],  min_search_radius: float = 1.0, min_height: float = 0.0, max_search_radius: Optional[float] = None, max_height: Optional[float] = None, only_use_veg: bool = False) -> Optional[Vector]: ...

def insert_dams(self, dem: Raster, dam_points: Vector, dam_length: float) -> Raster: ...

def isobasins(self, dem: Raster, target_size: float, connections: bool = False, csv_file: str = "" ) -> Raster: ...

def integral_image_transform(self, raster: Raster) -> Raster: ...

def intersect(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

# Requires WbW-Pro license
def inverse_pca(self, rasters: List[Raster], pca_report_file: str) -> List[Raster]: ...

def jenson_snap_pour_points(self, pour_pts: Vector, streams: Raster, snap_dist: float = 0.0) -> Vector: ...

def join_tables(self, primary_vector: Vector, primary_key_field: str, foreign_vector: Vector, foreign_key_field: str, import_field: str = "") -> None: ...

def k_means_clustering(self, input_rasters: List[Raster], output_html_file: str = "", num_clusters: int = 5, max_iterations: int = 10, percent_changed_threshold: float = 2.0, initialization_mode: str = "diagonal", min_class_size: int = 10) -> Raster: ...

def k_nearest_mean_filter(self, raster: Raster, filter_size_x: int = 3, filter_size_y: int = 3, k: int = 5) -> Raster: ...

def kappa_index(self, class_raster: Raster, reference_raster: Raster, output_html_file: str = "") -> None: ...

# Requires WbW-Pro license
def knn_classification(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str, scaling_method: str = "none", k: int = 5, test_proportion: float = 0.2, use_clipping: bool = False, create_output: bool = False) -> Optional[Raster]: ...

# Requires WbW-Pro license
def knn_regression(self, input_rasters: List[Raster], training_data: Vector, field_name: str, scaling_method: str = "none", k: int = 5, distance_weighting: bool = False, test_proportion: float = 0.2, create_output: bool = False) -> Optional[Raster]: ...

def ks_normality_test(self, raster: Raster, output_html_file: str, num_samples: int) -> None: ...

def laplacian_filter(self, raster: Raster, variant: str = "3x3(1)", clip_amount: float = 0.0) -> Raster: ...

def laplacian_of_gaussians_filter(self, raster: Raster, sigma: float = 0.75) -> Raster: ...

def las_to_ascii(self, input_lidar: Optional[Lidar]) -> None: ...

def las_to_shapefile(self, input_lidar: Optional[Lidar], output_multipoint: bool = False) -> Vector: ...

def layer_footprint_raster(self, input: Raster) -> Vector: ...

def layer_footprint_vector(self, input: Vector) -> Vector: ...

def lee_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sigma: float = 10.0, m_value: float = 5.0) -> Raster: ...

def length_of_upstream_channels(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def license_type(self) -> LicenseType: ...

def lidar_block_maximum(self, input_lidar: Optional[Lidar], cell_size: float = 1.0) -> Raster: ...

def lidar_block_minimum(self, input_lidar: Optional[Lidar], cell_size: float = 1.0) -> Raster: ...

def lidar_classify_subset(self, base_lidar: Lidar, subset_lidar: Lidar, subset_class_value: int, nonsubset_class_value: int) -> Lidar: ...

def lidar_colourize(self, in_lidar: Lidar, in_image: Raster) -> Lidar: ...

def lidar_construct_vector_tin(self, input_lidar: Optional[Lidar], returns_included: str = "all", excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Vector: ...

# Requires WbW-Pro license
def lidar_contour(self, input_lidar: Optional[Lidar], contour_interval: float = 10.0, base_contour: float = 0.0, smooth: int = 5, interpolation_parameter: str = "elevation", returns_included: str = "all",  excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), tile_overlap: float = 0.0, max_triangle_edge_length: float = float('inf')) -> Optional[Vector]: ...

def lidar_digital_surface_model(self, input_lidar: Optional[Lidar], cell_size: float = 1.0, search_radius: float = 0.5, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Raster: ...

# Requires WbW-Pro license
def lidar_eigenvalue_features(self, input_lidar: Optional[Lidar], num_neighbours: Optional[int], search_radius: Optional[float]) -> None: ...

def lidar_elevation_slice(self, input: Lidar, minz: float = float('-inf'), maxz: float = float('inf'), classify: bool = False, in_class_value: int = 2, out_class_value: int = 1) -> Lidar: ...

def lidar_ground_point_filter(self, input_lidar: Optional[Lidar], search_radius: float = 2.0, min_neighbours: int = 0, slope_threshold: float = 45.0, height_threshold: float = 1.0, classify: bool = False, slope_norm: bool = True, height_above_ground: bool = False) -> Lidar: ...

def lidar_hex_bin(self, input_lidar: Lidar, width: float, orientation: str = "h") -> Vector: ...

def lidar_hillshade(self, input: Lidar, search_radius: float = -1.0, azimuth: float = 315.0, altitude: float = 30.0) -> Lidar: ...

def lidar_histogram(self, input_lidar: Lidar, output_html_file: str, parameter: str = "elevation", clip_percent: float = 1.0) -> None: ...

def lidar_idw_interpolation(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation",  returns_included: str = "all", cell_size: float = 1.0,  idw_weight: float = 1.0, search_radius: float = 2.5, excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...

def lidar_info(self, input_lidar: Lidar, output_html_file: Optional[str], show_point_density: bool = True, show_vlrs: bool = True, show_geokeys: bool = True) -> str: ...

def lidar_join(self, inputs: List[Lidar]) -> Lidar: ...

def lidar_kappa(self, input_lidar1: Lidar, input_lidar2: Lidar, output_html_file: str, cell_size: float = 1.0, output_class_accuracy: bool = False) -> Raster: ...

def lidar_nearest_neighbour_gridding(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...

def lidar_point_density(self, input_lidar: Optional[Lidar], returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...

# Requires WbW-Pro license
def lidar_point_return_analysis(self, input: Lidar, create_output: bool = False) -> Optional[Lidar]: ...

def lidar_point_stats(self, input_lidar: Optional[Lidar], cell_size: float = 1.0, num_points: bool = False, num_pulses: bool = False, avg_points_per_pulse: bool = False, z_range: bool = False, intensity_range: bool = False, predominant_class: bool = False) : ...

def lidar_radial_basis_function_interpolation(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, num_points: int = 15, excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), func_type: str = "ThinPlateSpline", poly_order: str = "none", weight: float = 0.1) -> Raster: ...

def lidar_ransac_planes(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, classify: bool = False, only_last_returns: bool = False) -> Lidar: ...

def lidar_remove_outliers(self, input: Lidar, search_radius: float = 2.0, elev_diff: float = 50.0, use_median: bool = False, classify: bool = False) -> Lidar: ...

def lidar_rooftop_analysis(self, lidar_inputs: List[Lidar], building_footprints: Vector, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, norm_diff_threshold: float = 2.0, azimuth: float = 180.0, altitude: float = 30.0) -> Vector: ...

def lidar_segmentation(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, norm_diff_threshold: float = 2.0, max_z_diff: float = 1.0, classes: bool = False, ground: bool = False) -> Lidar: ...

def lidar_segmentation_based_filter(self, in_lidar: Lidar, search_radius: float = 5.0, norm_diff_threshold: float = 2.0, max_z_diff: float = 1.0, classify_points: bool = False) -> Lidar: ...

def lidar_shift(self, input: Lidar, x_shift: float = 0.0, y_shift: float = 0.0, z_shift: float = 0.0) -> Lidar: ...

# Requires WbW-Pro license
def lidar_sibson_interpolation(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", resolution: float = 1.0, returns_included: str = "all", excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Optional[Raster]: ...

def lidar_thin(self, input: Lidar, resolution: float = 1.0, selection_method: str = "first", save_filtered: bool = False) -> Tuple[Lidar, Optional[Lidar]]: ...

def lidar_thin_high_density(self, input: Lidar, density: float, resolution: float = 1.0, save_filtered: bool = False) -> Tuple[Lidar, Optional[Lidar]]: ...

def lidar_tile(self, input_lidar: Lidar, tile_width: float = 1000.0, tile_height: float = 1000.0, origin_x: float = 0.0, origin_y: float = 0.0, min_points_in_tile: int = 2, output_laz_format: bool = True) -> None: ...

def lidar_tile_footprint(self, input_lidar: Optional[Lidar], output_hulls: bool = False) -> Vector: ...

def lidar_tin_gridding(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, excluded_classes: Optional[List[int]] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Raster: ...

def lidar_tophat_transform(self, input: Lidar, search_radius: float) -> Lidar: ...

def line_detection_filter(self, raster: Raster, variant: str = "vertical", abs_values: bool = False, clip_tails: float = 0.0) -> Raster: ...

def line_intersections(self, input1: Vector, input2: Vector) -> Vector: ...

def line_thinning(self, raster: Raster) -> Raster: ...

def linearity_index(self, input: Vector) -> Vector: ...

def lines_to_polygons(self, input: Vector) -> Vector: ...

def list_unique_values(self, input: Vector, field_name: str) -> List[Tuple[str, int]]: ...

def list_unique_values_raster(self, raster: Raster) -> str: ...

# Requires WbW-Pro license
def local_hypsometric_analysis(self, dem: Raster, min_scale: int = 4, step_size: int = 1,  num_steps: int = 10, step_nonlinearity: float = 1.0) -> Tuple[Raster, Raster]: ...

# Requires WbW-Pro license
def logistic_regression(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str, scaling_method: str = "none", test_proportion: float = 0.2, create_output: bool = False) -> Optional[Raster]: ...

def long_profile(self, d8_pointer: Raster, streams_raster: Raster, dem: Raster, output_html_file: str, esri_pointer: bool = False) -> None: ...

def long_profile_from_points(self, d8_pointer: Raster, points: Vector, dem: Raster, output_html_file: str, esri_pointer: bool = False) -> None: ...

def longest_flowpath(self, dem: Raster, basins: Raster) -> Vector: ...

def lowest_position(self, input_rasters: List[Raster]) -> Raster: ...

# Requires WbW-Pro license
def low_points_on_headwater_divides(self, dem: Raster, streams: Raster) -> Vector: ...

def majority_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def map_off_terrain_objects(self, dem: Raster, max_slope: float = float('inf'), min_feature_size: int = 0) -> Raster: ...

def max_absolute_overlay(self, input_rasters: List[Raster]) -> Raster: ...

def max_anisotropy_dev(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

def max_anisotropy_dev_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...

def max_branch_length(self, dem: Raster, log_transform: bool = False) -> Raster: ...

def max_difference_from_mean(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

def max_downslope_elev_change(self, raster: Raster) -> Raster: ...

def max_elevation_dev_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...

def max_elevation_deviation(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

def max_overlay(self, input_rasters: List[Raster]) -> Raster: ...

def max_upslope_elev_change(self, raster: Raster) -> Raster: ...

def max_upslope_flowpath_length(self, dem: Raster) -> Raster: ...

def max_upslope_value(self, dem: Raster, values_raster: Raster) -> Raster: ...

def maximal_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def maximum_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def mdinf_flow_accum(self, dem: Raster, out_type: str = "SCA", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...

def mean_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def mean_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def median_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...

def medoid(self, input: Vector) -> Vector: ...

def merge_line_segments(self, input: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

def merge_table_with_csv(self, primary_vector: Vector, primary_key_field: str, foreign_csv_filename: str, foreign_key_field: str, import_field: str = "") -> None: ...

def merge_vectors(self, input_vectors: List[Vector]) -> Vector: ...

def min_absolute_overlay(self, input_rasters: List[Raster]) -> Raster: ...

# Requires WbW-Pro license
def min_dist_classification(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str, dist_threshold: float = float('inf')) -> Raster: ...

def min_downslope_elev_change(self, raster: Raster) -> Raster: ...

def min_max_contrast_stretch(self, raster: Raster, min_val: float, max_val: float, num_tones: int = 256) -> Raster: ...

def min_overlay(self, input_rasters: List[Raster]) -> Raster: ...

def minimal_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def minimum_bounding_box(self, input: Vector, min_criteria: str = "area", individual_feature_hulls: bool = True) -> Vector: ...

def minimum_bounding_circle(self, input: Vector, individual_feature_hulls: bool = True) -> Vector: ...

def minimum_bounding_envelope(self, input: Vector, individual_feature_hulls: bool = True) -> Vector: ...

def minimum_convex_hull(self, input: Vector, individual_feature_hulls: bool = True) -> Vector: ...

def minimum_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def modified_k_means_clustering(self, input_rasters: List[Raster], output_html_file: str = "", num_start_clusters: int = 1000, merge_distance: float = 1.0, max_iterations: int = 10, percent_changed_threshold: float = 2.0) -> Raster: ...

# Requires WbW-Pro license
def modify_lidar(self, statement: str, input_lidar: Optional[Lidar]) -> Optional[Lidar]: ...

def modify_nodata_value(self, input: Raster, new_value: float = -32768.0) : ...

def mosaic(self, images: List[Raster], resampling_method: str = "cc") -> Raster: ...

def mosaic_with_feathering(self, image1: Raster, image2: Raster, resampling_method: str = "cc", distance_weight: float = 4.0) -> Raster: ...

def multidirectional_hillshade(self, dem: Raster, altitude: float = 30.0, z_factor: float = 1.0, full_360_mode: bool = False) -> Raster: ...

def multipart_to_singlepart(self, input: Vector, exclude_holes: bool = False) -> Vector: ...

def multiply_overlay(self, input_rasters: List[Raster]) -> Raster: ...

# Requires WbW-Pro license
def multiscale_curvatures(self, dem: Raster, curv_type: str = 'profile', min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0, log_transform: bool = True, standardize: bool = False) -> Tuple[Raster, Raster]: ...

def multiscale_elevation_percentile(self, dem: Raster, num_significant_digits: int = 3, min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0) -> Tuple[Raster, Raster]: ...

def multiscale_roughness(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

def multiscale_roughness_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...

def multiscale_std_dev_normals(self, dem: Raster, min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0, html_signature_file: str = "") -> Tuple[Raster, Raster]: ...

def multiscale_std_dev_normals_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0) -> None: ...

def multiscale_topographic_position_image(self, local: Raster, meso: Raster, broad: Raster, hillshade: Optional[Raster] = None, lightness: float = 1.2) -> Raster: ...

def narrowness_index(self, raster: Raster) -> Raster: ...

def natural_neighbour_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None, clip_to_hull: bool = True) -> Raster: ...

def nearest_neighbour_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None, max_dist: float = float('inf')) -> Raster: ...

def new_raster_from_base_raster(self, base: Raster, out_val: float = float('nan'), data_type: str = "float") -> Raster: ...

def new_raster_from_base_vector(self, base: Vector, cell_size: float, out_val: float = float('nan'), data_type: str = "float") -> Raster: ...

# Requires WbW-Pro license
def nibble(self, input_raster: Raster, mask: Raster, use_nodata: bool = False, nibble_nodata: bool = True) -> Raster: ...

def normal_vectors(self, input: Lidar, search_radius: float = -1.0) -> Lidar: ...

def normalized_difference_index(self, nir_image: Raster, red_image: Raster, clip_percent: float = 0.0, correction_value: float = 0.0) -> Raster: ...

def normalize_lidar(self, input_lidar: Lidar, dtm: Raster) -> Lidar: ...

def num_downslope_neighbours(self, dem: Raster) -> Raster: ...

def num_inflowing_neighbours(self, dem: Raster) -> Raster: ...

def olympic_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def opening(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

# Requires WbW-Pro license
def openness(self, dem: Raster, dist: int = 20) -> Tuple[Raster, Raster]: ...

def otsu_thresholding(self, raster: Raster) -> Raster: ...

def paired_sample_t_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...

def panchromatic_sharpening(self, pan: Raster, colour_composite: Raster, red: Raster, green: Raster, blue: Raster, fusion_method: str = "brovey") -> Raster: ...

# Requires WbW-Pro license
def parallelepiped_classification(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str) -> Raster: ...

def patch_orientation(self, input: Vector) -> Vector: ...

def pennock_landform_classification(self, dem: Raster, slope_threshold: float = 3.0, prof_curv_threshold: float = 0.1, plan_curv_threshold: float = 0.0, z_factor: float = 1.0) -> Tuple[Raster, str]: ...

def percent_elev_range(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def percent_equal_to(self, input_rasters: List[Raster], comparison: Raster) -> Raster: ...

def percent_greater_than(self, input_rasters: List[Raster], comparison: Raster) -> Raster: ...

def percent_less_than(self, input_rasters: List[Raster], comparison: Raster) -> Raster: ...

def percentage_contrast_stretch(self, raster: Raster, clip: float = 1.0, tail: str = "both", num_tones: int = 256) -> Raster: ...

def percentile_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...

def perimeter_area_ratio(self, input: Vector) -> Vector: ...

def pick_from_list(self, input_rasters: List[Raster], pos_input: Raster) -> Raster: ...

# Requires WbW-Pro license
def piecewise_contrast_stretch(self, raster: Raster, transformation_statement: str, num_greytones: float = 1024.0) -> Raster: ...

# Requires WbW-Pro license
def phi_coefficient(self, raster1: Raster, raster2: Raster, output_html_file: str) -> None: ...

def plan_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def polygon_area(self, input: Vector) -> Vector: ...

def polygon_long_axis(self, input: Vector) -> Vector: ...

def polygon_perimeter(self, input: Vector) -> Vector: ...

def polygon_short_axis(self, input: Vector) -> Vector: ...

def polygonize(self, input_layers: List[Vector]) -> Vector: ...

def polygons_to_lines(self, input: Vector) -> Vector: ...

def prewitt_filter(self, raster: Raster, clip_tails: float = 0.0) -> Raster: ...

def principal_component_analysis(self, rasters: List[Raster], output_html_file: str, num_components: int = 2, standardized: bool = False) -> List[Raster]: ...

def print_geotiff_tags(self, file_name: str) : ...

def profile(self, lines_vector: Vector, surface: Raster, output_html_file: str) -> None: ...

def profile_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

# Requires WbW-Pro license
def prune_vector_streams(self, streams: Vector, dem: Raster, threshold: float, snap_distance: float = 0.001) -> Vector: ...

def qin_flow_accumulation(self, dem: Raster, out_type: str = "SCA", exponent: float = 10.0, max_slope: float = 45.0, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...

def quantiles(self, raster: Raster, num_quantiles: int = 5) -> Raster: ...

def quinn_flow_accumulation(self, dem: Raster, out_type: str = "SCA", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...

def radial_basis_function_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, radius: float = 0.0, min_points: int = 0, cell_size: float = 0.0, base_raster: Optional[Raster] = None, func_type: str = "ThinPlateSpline", poly_order: str = "none", weight: float = 0.1) -> Raster: ...

def radius_of_gyration(self, raster: Raster) -> Tuple[Raster, str]: ...

def raise_walls(self, dem: Raster, walls: Vector, breach_lines: Vector, wall_height: float = 100.0) -> Raster: ...

def random_field(self, base_raster: Optional[Raster] = None) -> Raster: ...

# Requires WbW-Pro license
def random_forest_classification_fit(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str, split_criterion: str = "Gini", n_trees: int = 200,  min_samples_leaf: int = 1, min_samples_split: int = 2, test_proportion: float = 0.2) -> List[int]: ...

# Requires WbW-Pro license
def random_forest_classification_predict(self, input_rasters: List[Raster], model_bytes: List[int]) -> Raster: ...

# Requires WbW-Pro license
def random_forest_regression_fit(self, input_rasters: List[Raster], training_data: Vector, field_name: str, n_trees: int = 200,  min_samples_leaf: int = 1, min_samples_split: int = 2, test_proportion: float = 0.2) -> List[int]: ...

# Requires WbW-Pro license
def random_forest_regression_predict(self, input_rasters: List[Raster], model_bytes: List[int]) -> Raster: ...

def random_sample(self, base_raster: Optional[Raster] = None, num_samples: int = 1000) -> Raster: ...

def range_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def raster_area(self, raster: Raster, units: str = "map units", zero_background: bool = False) -> Tuple[Raster, str]: ...

def raster_calculator(self, expression: str, input_rasters: List[Raster]) -> Raster: ...

def raster_cell_assignment(self, raster: Raster, what_to_assign: str = "column") -> Raster: ...

def raster_histogram(self, raster: Raster, output_html_file: str, num_bins: Optional[int] = None) -> None: ...

def raster_perimeter(self, raster: Raster, units: str = "map units", zero_background: bool = False) -> Tuple[Raster, str]: ...

def raster_streams_to_vector(self, streams: Raster, d8_pointer: Raster, esri_pointer: bool = False, all_vertices: bool = False) -> Vector: ...

def raster_summary_stats(self, input: Raster) -> str: ...

def raster_to_vector_lines(self, raster: Raster) -> Vector: ...

def raster_to_vector_points(self, raster: Raster) -> Vector: ...

def raster_to_vector_polygons(self, raster: Raster) -> Vector: ...

def rasterize_streams(self, streams: Vector, base_raster: Optional[Raster] = None, zero_background: bool = False, use_feature_id: bool = False) -> Raster: ...

def reciprocal(self, raster: Raster) -> Raster: ...

def reclass(self, raster: Raster, reclass_values: List[List[float]], assign_mode: bool = False) -> Raster: ...

def reclass_equal_interval(self, raster: Raster, interval_size: float, start_value: float = float('-inf'), end_value: float = float('inf')) -> Raster: ...

# Requires WbW-Pro license
def reconcile_multiple_headers(self, input: Vector, region_field_name: str, yield_field_name: str, radius: float, min_yield: float = float('-inf'),  max_yield: float = float('inf'), mean_tonnage: float = float('-inf')) -> Vector: ...

# Requires WbW-Pro license
def recover_flightline_info(self, input: Lidar, max_time_diff: float = 5.0, pt_src_id: bool = False, user_data: bool = False, rgb: bool = False) -> Lidar: ...

# Requires WbW-Pro license
def recreate_pass_lines(self, input: Vector, yield_field_name: str, max_change_in_heading: float = 25.0, ignore_zeros: bool = False) -> Tuple[Vector, Vector]: ...

def rectangular_grid_from_raster_base(self, base: Raster, width: float, height: float, x_origin: float = 0.0, y_origin: float = 0.0) -> Vector: ...

def rectangular_grid_from_vector_base(self, base: Vector, width: float, height: float, x_origin: float = 0.0, y_origin: float = 0.0) -> Vector: ...

def reinitialize_attribute_table(self, input: Vector) -> None: ...

def related_circumscribing_circle(self, input: Vector) -> Vector: ...

def relative_aspect(self, dem: Raster, azimuth: float = 0.0, z_factor: float = 1.0) -> Raster: ...

def relative_stream_power_index(self, specific_catchment_area: Raster, slope: Raster, exponent: float = 1.0) -> Raster: ...

def relative_topographic_position(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def remove_duplicates(self, input: Lidar, include_z: bool = False) -> Lidar: ...

# Requires WbW-Pro license
def remove_field_edge_points(self, input: Vector, radius: float,  max_change_in_heading: float = 25.0, flag_edges: bool = False) -> Vector: ...

def remove_off_terrain_objects(self, dem: Raster, filter_size: int = 11, slope_threshold: float = 15.0) -> Raster: ...

def remove_polygon_holes(self, input: Vector) -> Vector: ...

# Requires WbW-Pro license
def remove_raster_polygon_holes(self, input: Raster, threshold_size: int = sys.maxsize, use_diagonals: bool = False) -> Raster: ...

def remove_short_streams(self, d8_pntr: Raster, streams_raster: Raster, min_length: float = 0.0, esri_pntr: bool = False) -> Raster: ...

def remove_spurs(self, raster: Raster, max_iterations: int = 10) -> Raster: ...

def repair_stream_vector_topology(self, input: Vector, snap_dist: float) -> Vector: ...

def resample(self, input_rasters: List[Raster], cell_size: float = 0.0, base_raster: Optional[Raster] = None, method: str = "cc") -> Raster: ...

def rescale_value_range(self, raster: Raster, out_min_val: float, out_max_val: float, clip_min: float = float('inf'), clip_max: float = float('-inf')) -> Raster: ...

def rgb_to_ihs(self, red: Optional[Raster] = None, green: Optional[Raster] = None, blue: Optional[Raster] = None, composite: Optional[Raster] = None) -> Tuple[Raster, Raster, Raster]: ...

def rho8_flow_accum(self, raster: Raster, out_type: str = "SCA", log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False, esri_pntr: bool = False) -> Raster: ...

def rho8_pointer(self, dem: Raster, esri_pntr: bool = False) -> Raster: ...

# Requires WbW-Pro license
def ridge_and_valley_vectors(self, dem: Raster, filter_size: int = 11, ep_threshold: float = 30.0, slope_threshold: float = 0.0, min_length: int = 20) -> Tuple[Vector, Vector]: ...

# Requires WbW-Pro license
def ring_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

# Requires WbW-Pro license
def river_centerlines(self, raster: Raster, min_length: int = 3, search_radius: int = 9) -> Vector: ...

def roberts_cross_filter(self, raster: Raster, clip_amount: float = 0.0) -> Raster: ...

def root_mean_square_error(self, input: Raster, reference: Raster) -> str: ...

# Requires WbW-Pro license
def rotor(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def ruggedness_index(self, input: Raster) -> Raster: ...

def scharr_filter(self, raster: Raster, clip_tails: float = 0.0) -> Raster: ...

def sediment_transport_index(self, specific_catchment_area: Raster, slope: Raster, sca_exponent: float = 0.4, slope_exponent: float = 1.3) -> Raster: ...

def select_tiles_by_polygon(self, input_directory: str, output_directory: str, polygons: Vector) -> None: ...

def set_nodata_value(self, raster: Raster, back_value: float = 0.0) -> Raster: ...

# Requires WbW-Pro license
def shadow_animation(self, dem: Raster, output_html_file: str,  palette: WbPalette = WbPalette.Soft, max_dist: float = float('inf'), date: str = "21/06/2021", time_interval: int = 30, location: str = "43.5448/-80.2482/-4", image_height: int = 600, delay: int = 250, label: str = "") -> None: ...

# Requires WbW-Pro license
def shadow_image(self, dem: Raster, palette: WbPalette = WbPalette.Soft, max_dist: float = float('inf'), date: str = "21/06/2021", time: str = "13:00", location: str = "43.5448/-80.2482/-4") -> Raster: ...

def shape_complexity_index_raster(self, raster: Raster) -> Raster: ...

def shape_complexity_index_vector(self, input: Vector) -> Vector: ...

# Requires WbW-Pro license
def shape_index(self, dem: Raster, z_factor: float = 1.0) -> Raster: ...

def shreve_stream_magnitude(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

# Requires WbW-Pro license
def sieve(self, input_raster: Raster, threshold: int = 1, zero_background: bool = False) -> Raster: ...

def sigmoidal_contrast_stretch(self, raster: Raster, cutoff: float = 0.0, gain: float = 1.0, num_tones: int = 256) -> Raster: ...

def singlepart_to_multipart(self, input: Vector, field_name: str) -> Vector: ...

def sink(self, dem: Raster, zero_background: bool = False) -> Raster: ...

# Requires WbW-Pro license
def skyline_analysis(self, dem: Raster, points: Vector, output_html_file: str, max_dist: float = float('inf'), observer_hgt_offset: float = 0.0, output_as_polygons: bool = True, az_fraction: float = 1.0) -> Vector: ...

# Requires WbW-Pro license
def sky_view_factor(self, dem: Raster, az_fraction: float = 5.0, max_dist: float = float('inf'), observer_hgt_offset: float = 0.05) -> Raster: ...

def slope(self, dem: Raster, units: str = "degrees", z_factor: float = 1.0) -> Raster: ...

# Requires WbW-Pro license
def slope_vs_aspect_plot(self, dem: Raster, output_html_file: str, aspect_bin_size: float = 2.0, min_slope: float = 0.1, z_factor: float = 1.0) -> None: ...

def slope_vs_elev_plot(self, dem_rasters: List[Raster], output_html_file: str, watershed_rasters: List[Raster]) -> None: ...

def smooth_vectors(self, input: Vector, filter_size: int = 3) -> Vector: ...

# Requires WbW-Pro license
def smooth_vegetation_residual(self, dem: Raster, max_scale: int = 1, dev_threshold: float = 1.0, scale_threshold: int = 5) -> Raster: ...

def snap_pour_points(self, pour_pts: Vector, flow_accum: Raster, snap_dist: float = 0.0) -> Vector: ...

def sobel_filter(self, raster: Raster, variant: str = "3x3", clip_tails: float = 0.0) -> Raster: ...

# Requires WbW-Pro license
def sort_lidar(self, sort_criteria: str, input_lidar: Optional[Lidar]) -> Optional[Lidar]: ...

def spherical_std_dev_of_normals(self, dem: Raster, filter_size: int = 11) -> Raster: ...

def split_colour_composite(self, composite_image: Raster) -> Tuple[Raster, Raster, Raster]: ...

# Requires WbW-Pro license
def split_lidar(self, split_criterion: str, input_lidar: Optional[Lidar], interval: float = 5.0, min_pts: int = 5) -> None: ...

def split_vector_lines(self, input: Vector, segment_length: float) -> Vector: ...

def split_with_lines(self, input: Vector, split_vector: Vector) -> Vector: ...

def standard_deviation_contrast_stretch(self, raster: Raster, clip: float = 2.0, num_tones: int = 256) -> Raster: ...

def standard_deviation_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def standard_deviation_of_slope(self, dem: Raster, filter_size: int = 11, z_factor: float = 1.0) -> Raster: ...

def standard_deviation_overlay(self, input_rasters: List[Raster]) -> Raster: ...

def stochastic_depression_analysis(self, dem: Raster, rmse: float, range: float, iterations: int = 100) -> Raster: ...

def strahler_order_basins(self, d8_pointer: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...

def strahler_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def stream_link_class(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def stream_link_identifier(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def stream_link_length(self, d8_pointer: Raster, streams_id_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def stream_link_slope(self, d8_pointer: Raster, streams_id_raster: Raster, dem: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def stream_slope_continuous(self, d8_pointer: Raster, streams_raster: Raster, dem: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

def subbasins(self, d8_pntr: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...

def sum_overlay(self, input_rasters: List[Raster]) -> Raster: ...

def surface_area_ratio(self, dem: Raster) -> Raster: ...

# Requires WbW-Pro license
def svm_classification(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str, scaling_method: str = "none", c_value: float = 50.0, kernel_gamma: float = 0.5, tolerance: float = 0.1, test_proportion: float = 0.2, create_output: bool = False) -> Optional[Raster]: ...

# Requires WbW-Pro license
def svm_regression(self, input_rasters: List[Raster], training_data: Vector, class_field_name: str, scaling_method: str = "none", c_value: float = 50.0, epsilon_value: float = 10.0, kernel_gamma: float = 0.5, test_proportion: float = 0.2, create_output: bool = False) -> Optional[Raster]: ...

def symmetrical_difference(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

def tangential_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def thicken_raster_line(self, raster: Raster) -> Raster: ...

def time_in_daylight(self, dem: Raster, az_fraction: float = 5.0, max_dist: float = float('inf'), latitude: float = 0.0, longitude: float = 0.0, utc_offset_str: str = "UTC+00:00", start_day: int = 1, end_day: int = 365, start_time: str = "sunrise", end_time: str = "sunset") -> Raster: ...

def tin_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None, max_triangle_edge_length: float = float('inf')) -> Raster: ...

def tophat_transform(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, variant: str = "white") -> Raster: ...

# Requires WbW-Pro license
def topological_breach_burn(self, streams: Vector, dem: Raster, snap_distance: float = 0.001) -> Tuple[Raster, Raster, Raster, Raster]: ...

def topological_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def topographic_hachures(self, dem: Raster, contour_interval: float = 10.0, base_contour: float = 0.0, deflection_tolerance: float = 10.0, filter_size: int = 9, separation: float = 2.0, distmin: float = 0.5, distmax: float = 2.0, discretization: float = 0.5, turnmax: float = 45.0, slopemin: float = 0.5, depth: int = 16) -> Vector: ...

# Requires WbW-Pro license
def topo_render(self, dem: Raster, palette: WbPalette = WbPalette.Soft, reverse_palette: bool = False,  azimuth: float = 315.0, altitude: float = 30.0, clipping_polygon: Optional[Vector] = None, background_hgt_offset: float = 10.0, background_clr: Tuple[int, int, int, int] = (255, 255, 255, 255), attenuation_parameter: float = 0.3,  ambient_light: float = 0.2, z_factor: float = 1.0) -> Raster: ...

# Requires WbW-Pro license
def topographic_position_animation(self, dem: Raster, output_html_file: str = "topo_pos.html", palette: WbPalette = WbPalette.Soft,  min_scale: int = 1, num_steps: int = 1, step_nonlinearity: float = 1.0, image_height: int = 600, delay: int = 250, label: str = "", use_dev_max: bool = False) -> None: ...

def total_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def total_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

def trace_downslope_flowpaths(self, seed_points: Vector, d8_pointer: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def travelling_salesman_problem(self, input: Vector, duration: int = 60) -> Vector: ...

def trend_surface(self, raster: Raster, output_html_file: str, polynomial_order: int = 1) -> Raster: ...

def trend_surface_vector_points(self, input: Vector, cell_size: float, output_html_file: str, field_name: str = "FID", polynomial_order: int = 1) -> Raster: ...

def tributary_identifier(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

def turning_bands_simulation(self, base_raster: Optional[Raster] = None, range: float = 1.0, iterations: int = 1000) -> Raster: ...

def two_sample_ks_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...

def union(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

def unnest_basins(self, d8_pointer: Raster, pour_points: Vector, esri_pntr: bool = False) -> List[Raster]: ...

def unsharp_masking(self, raster: Raster, sigma: float = 0.75, amount: float = 100.0, threshold: float = 0.0) -> Raster: ...

# Requires WbW-Pro license
def unsphericity(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def update_nodata_cells(self, input1: Raster, input2: Raster) -> Raster: ...

def upslope_depression_storage(self, dem: Raster) -> Raster: ...

def user_defined_weights_filter(self, raster: Raster, weights: List[List[float]], kernel_center: str = "center", normalize_weights: bool = False) -> Raster: ...

def vector_hex_binning(self, vector_points: Vector, width: float, orientation: str = "horizontal") -> Vector: ...

def vector_lines_to_raster(self, input: Vector, field_name: str = "FID", zero_background: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None) -> Raster: ...

def vector_points_to_raster(self, input: Vector, field_name: str = "FID", assign_op: str = "last", zero_background: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None) -> Raster: ...

def vector_polygons_to_raster(self, input: Vector, field_name: str = "FID", zero_background: bool = False, cell_size: float = 0.0, base_raster: Optional[Raster] = None) -> Raster: ...

# Requires WbW-Pro license
def vertical_excess_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

def vector_stream_network_analysis(self, streams: Vector, dem: Raster, max_ridge_cutting_height: float = 10.0, snap_distance: float = 0.001) -> Tuple[Vector, Vector, Vector, Vector]: ...

def viewshed(self, dem: Raster, station_points: Vector, station_height: float = 2.0) -> Raster: ...

def visibility_index(self, dem: Raster, station_height: float = 2.0, resolution_factor: int = 8) -> Raster: ...

def voronoi_diagram(self, input_points: Vector) -> Vector: ...

def watershed(self, d8_pointer: Raster, pour_points: Vector, esri_pntr: bool = False) -> Raster: ...

def watershed_from_raster_pour_points(self, d8_pointer: Raster, pour_points: Raster, esri_pntr: bool = False) -> Raster: ...

def weighted_overlay(self, factors: List[Raster], weights: List[float], cost: Optional[List[Raster]] = None, constraints: Optional[List[Raster]] = None, scale_max: float = 1.0) -> Raster: ...

def weighted_sum(self, input_rasters: List[Raster], weights: List[float]) -> Raster: ...

def wetness_index(self, specific_catchment_area: Raster, slope: Raster) -> Raster: ...

def wilcoxon_signed_rank_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...

def write_function_memory_insertion(self, image1: Raster, image2: Raster, image3: Raster) -> Raster: ...

# Requires WbW-Pro license
def yield_filter(self, input: Vector, yield_field_name: str, pass_field_name: str,  swath_width: float = 6.096, z_score_threshold: float = 2.5, min_yield: float = 0.0, max_yield: float = float('inf')) -> Vector: ...

# Requires WbW-Pro license
def yield_map(self, input: Vector, pass_field_name: str, swath_width: float = 6.096, max_change_in_heading: float = 25.0) -> Vector: ...

# Requires WbW-Pro license
def yield_normalization(self, input: Vector, yield_field_name: str,  radius: float, standardize: bool = False, min_yield: float = 0.0, max_yield: float = float('inf')) -> Vector: ...

def z_scores(self, raster: Raster) -> Raster: ...

def zonal_statistics(self, data_raster: Raster, feature_definitions_raster: Raster, stat_type: str = "mean", zero_is_background: bool = False) -> Tuple[Raster, str]: ...

class WbPalette

Atlas: int
HighRelief: int
Arid: int
Earthtones: int
Soft: int
Muted: int
LightQuant: int
Turbo: int
Purple: int
Viridis: int
GreenYellow: int
PinkYellowGreen: int
BlueYellowRed: int
Deep: int
Imhof: int
White: int
Grey: int```

WbW function documentation

Each of the following functions are methods of WbEnvironment class. Tools may be called using the convention in the following example:

from whitebox_workflows import WbEnvironment

wbe = WbEnvironment()
# Set up the environment, e.g. working directory, verbose mode, num_procs
raster = wbe.read_raster('my_raster.tif') # Read some kind of data
result = wbe.mean_filter(raster) # Call some kind of function
...

1. adaptive_filter
2. add_point_coordinates_to_table
3. aggregate_raster
4. anova
5. ascii_to_las
6. aspect
7. attribute_correlation
8. attribute_histogram
9. attribute_scattergram
10. available_functions
11. average_flowpath_slope
12. average_normal_vector_angular_deviation
13. average_overlay
14. average_upslope_flowpath_length
15. balance_contrast_enhancement
16. basins
17. bilateral_filter
18. block_maximum
19. block_minimum
20. bool_and
21. bool_not
22. bool_or
23. bool_xor
24. boundary_shape_complexity
25. breach_depressions_least_cost
26. breach_single_cell_pits
27. buffer_raster
28. burn_streams_at_roads
29. centroid_raster
30. centroid_vector
31. change_vector_analysis
32. check_in_license
33. circular_variance_of_aspect
34. classify_buildings_in_lidar
35. classify_overlap_points
36. clean_vector
37. clip
38. clip_lidar_to_polygon
39. clip_raster_to_polygon
40. closing
41. clump
42. compactness_ratio
43. conservative_smoothing_filter
44. construct_vector_tin
45. contours_from_points
46. contours_from_raster
47. convert_nodata_to_zero
48. corner_detection
49. correct_vignetting
50. cost_allocation
51. cost_distance
52. cost_pathway
53. count_if
54. create_colour_composite
55. create_plane
56. crispness_index
57. cross_tabulation
58. csv_points_to_vector
59. cumulative_distribution
60. d8_flow_accum
61. d8_mass_flux
62. d8_pointer
63. depth_in_sink
64. deviation_from_mean_elevation
65. deviation_from_regional_direction
66. diff_of_gaussians_filter
67. difference
68. difference_from_mean_elevation
69. dinf_flow_accum
70. dinf_mass_flux
71. dinf_pointer
72. direct_decorrelation_stretch
73. directional_relief
74. dissolve
75. distance_to_outlet
76. diversity_filter
77. downslope_distance_to_stream
78. downslope_flowpath_length
79. downslope_index
80. edge_contamination
81. edge_density
82. edge_preserving_mean_filter
83. edge_proportion
84. elev_relative_to_min_max
85. elev_relative_to_watershed_min_max
86. elevation_above_pit
87. elevation_above_stream
88. elevation_above_stream_euclidean
89. elevation_percentile
90. eliminate_coincident_points
91. elongation_ratio
92. embankment_mapping
93. emboss_filter
94. erase
95. erase_polygon_from_lidar
96. erase_polygon_from_raster
97. euclidean_allocation
98. euclidean_distance
99. export_table_to_csv
100. exposure_towards_wind_flux
101. extend_vector_lines
102. extract_by_attribute
103. extract_nodes
104. extract_raster_values_at_points
105. extract_streams
106. extract_valleys
107. farthest_channel_head
108. fast_almost_gaussian_filter
109. fd8_flow_accum
110. fd8_pointer
111. feature_preserving_smoothing
112. fetch_analysis
113. fill_burn
114. fill_depressions
115. fill_depressions_planchon_and_darboux
116. fill_depressions_wang_and_liu
117. fill_missing_data
118. fill_pits
119. filter_lidar_classes
120. filter_lidar_scan_angles
121. filter_raster_features_by_area
122. find_flightline_edge_points
123. find_lowest_or_highest_points
124. find_main_stem
125. find_noflow_cells
126. find_parallel_flow
127. find_patch_edge_cells
128. find_ridges
129. flatten_lakes
130. flightline_overlap
131. flip_image
132. flood_order
133. flow_accum_full_workflow
134. flow_length_diff
135. gamma_correction
136. gaussian_contrast_stretch
137. gaussian_curvature
138. gaussian_filter
139. geomorphons
140. hack_stream_order
141. heat_map
142. height_above_ground
143. hexagonal_grid_from_raster_base
144. hexagonal_grid_from_vector_base
145. high_pass_filter
146. high_pass_median_filter
147. highest_position
148. hillshade
149. hillslopes
150. histogram_equalization
151. histogram_matching
152. histogram_matching_two_images
153. hole_proportion
154. horizon_angle
155. horton_ratios
156. horton_stream_order
157. hypsometric_analysis
158. hypsometrically_tinted_hillshade
159. idw_interpolation
160. ihs_to_rgb
161. image_autocorrelation
162. image_correlation
163. image_correlation_neighbourhood_analysis
164. image_regression
165. image_stack_profile
166. impoundment_size_index
167. individual_tree_detection
168. insert_dams
169. integral_image_transform
170. intersect
171. isobasins
172. jenson_snap_pour_points
173. join_tables
174. k_means_clustering
175. k_nearest_mean_filter
176. kappa_index
177. ks_normality_test
178. laplacian_filter
179. laplacian_of_gaussians_filter
180. las_to_ascii
181. las_to_shapefile
182. layer_footprint_raster
183. layer_footprint_vector
184. lee_filter
185. length_of_upstream_channels
186. license_type
187. lidar_block_maximum
188. lidar_block_minimum
189. lidar_classify_subset
190. lidar_colourize
191. lidar_construct_vector_tin
192. lidar_digital_surface_model
193. lidar_elevation_slice
194. lidar_ground_point_filter
195. lidar_hex_bin
196. lidar_hillshade
197. lidar_histogram
198. lidar_idw_interpolation
199. lidar_info
200. lidar_join
201. lidar_kappa
202. lidar_nearest_neighbour_gridding
203. lidar_point_density
204. lidar_point_stats
205. lidar_radial_basis_function_interpolation
206. lidar_ransac_planes
207. lidar_remove_outliers
208. lidar_rooftop_analysis
209. lidar_segmentation
210. lidar_segmentation_based_filter
211. lidar_shift
212. lidar_thin
213. lidar_thin_high_density
214. lidar_tile
215. lidar_tile_footprint
216. lidar_tin_gridding
217. lidar_tophat_transform
218. line_detection_filter
219. line_intersections
220. line_thinning
221. linearity_index
222. lines_to_polygons
223. list_unique_values
224. list_unique_values_raster
225. long_profile
226. long_profile_from_points
227. longest_flowpath
228. lowest_position
229. majority_filter
230. map_off_terrain_objects
231. max_absolute_overlay
232. max_anisotropy_dev
233. max_anisotropy_dev_signature
234. max_branch_length
235. max_difference_from_mean
236. max_downslope_elev_change
237. max_elevation_dev_signature
238. max_elevation_deviation
239. max_overlay
240. max_procs
241. max_upslope_elev_change
242. max_upslope_flowpath_length
243. max_upslope_value
244. maximal_curvature
245. maximum_filter
246. mdinf_flow_accum
247. mean_curvature
248. mean_filter
249. median_filter
250. medoid
251. merge_line_segments
252. merge_table_with_csv
253. merge_vectors
254. min_absolute_overlay
255. min_downslope_elev_change
256. min_max_contrast_stretch
257. min_overlay
258. minimal_curvature
259. minimum_bounding_box
260. minimum_bounding_circle
261. minimum_bounding_envelope
262. minimum_convex_hull
263. minimum_filter
264. modified_k_means_clustering
265. modified_shepard_interpolation
266. modify_nodata_value
267. mosaic
268. mosaic_with_feathering
269. multidirectional_hillshade
270. multipart_to_singlepart
271. multiply_overlay
272. multiscale_elevation_percentile
273. multiscale_roughness
274. multiscale_roughness_signature
275. multiscale_std_dev_normals
276. multiscale_std_dev_normals_signature
277. multiscale_topographic_position_image
278. narrowness_index
279. natural_neighbour_interpolation
280. nearest_neighbour_interpolation
281. new_lidar
282. new_raster
283. new_raster_from_base_raster
284. new_raster_from_base_vector
285. new_vector
286. normal_vectors
287. normalize_lidar
288. normalized_difference_index
289. num_downslope_neighbours
290. num_inflowing_neighbours
291. olympic_filter
292. opening
293. otsu_thresholding
294. paired_sample_t_test
295. panchromatic_sharpening
296. patch_orientation
297. pennock_landform_classification
298. percent_elev_range
299. percent_equal_to
300. percent_greater_than
301. percent_less_than
302. percentage_contrast_stretch
303. percentile_filter
304. perimeter_area_ratio
305. pick_from_list
306. plan_curvature
307. polygon_area
308. polygon_long_axis
309. polygon_perimeter
310. polygon_short_axis
311. polygonize
312. polygons_to_lines
313. prewitt_filter
314. principal_component_analysis
315. print_geotiff_tags
316. profile
317. profile_curvature
318. qin_flow_accumulation
319. quantiles
320. quinn_flow_accumulation
321. radial_basis_function_interpolation
322. radius_of_gyration
323. raise_walls
324. random_field
325. random_sample
326. range_filter
327. raster_area
328. raster_calculator
329. raster_cell_assignment
330. raster_histogram
331. raster_perimeter
332. raster_streams_to_vector
333. raster_summary_stats
334. raster_to_vector_lines
335. raster_to_vector_points
336. raster_to_vector_polygons
337. rasterize_streams
338. read_lidar
339. read_lidars
340. read_raster
341. read_rasters
342. read_vector
343. read_vectors
344. reciprocal
345. reclass
346. reclass_equal_interval
347. rectangular_grid_from_raster_base
348. rectangular_grid_from_vector_base
349. reinitialize_attribute_table
350. related_circumscribing_circle
351. relative_aspect
352. relative_stream_power_index
353. relative_topographic_position
354. remove_duplicates
355. remove_off_terrain_objects
356. remove_polygon_holes
357. remove_short_streams
358. remove_spurs
359. repair_stream_vector_topology
360. resample
361. rescale_value_range
362. rgb_to_ihs
363. rho8_flow_accum
364. rho8_pointer
365. roberts_cross_filter
366. root_mean_square_error
367. ruggedness_index
368. scharr_filter
369. sediment_transport_index
370. select_tiles_by_polygon
371. set_nodata_value
372. shape_complexity_index_raster
373. shape_complexity_index_vector
374. shreve_stream_magnitude
375. sigmoidal_contrast_stretch
376. singlepart_to_multipart
377. sink
378. slope
379. slope_vs_elev_plot
380. smooth_vectors
381. snap_pour_points
382. sobel_filter
383. spherical_std_dev_of_normals
384. split_colour_composite
385. split_vector_lines
386. split_with_lines
387. standard_deviation_contrast_stretch
388. standard_deviation_filter
389. standard_deviation_of_slope
390. standard_deviation_overlay
391. stochastic_depression_analysis
392. strahler_order_basins
393. strahler_stream_order
394. stream_link_class
395. stream_link_identifier
396. stream_link_length
397. stream_link_slope
398. stream_slope_continuous
399. subbasins
400. sum_overlay
401. surface_area_ratio
402. symmetrical_difference
403. tangential_curvature
404. thicken_raster_line
405. time_in_daylight
406. tin_interpolation
407. tophat_transform
408. topographic_hachures
409. topological_stream_order
410. total_curvature
411. total_filter
412. trace_downslope_flowpaths
413. travelling_salesman_problem
414. trend_surface
415. trend_surface_vector_points
416. tributary_identifier
417. turning_bands_simulation
418. two_sample_ks_test
419. union
420. unnest_basins
421. unsharp_masking
422. update_nodata_cells
423. upslope_depression_storage
424. user_defined_weights_filter
425. vector_hex_binning
426. vector_lines_to_raster
427. vector_points_to_raster
428. vector_polygons_to_raster
429. vector_stream_network_analysis
430. verbose
431. version
432. viewshed
433. visibility_index
434. voronoi_diagram
435. watershed
436. watershed_from_raster_pour_points
437. weighted_overlay
438. weighted_sum
439. wetness_index
440. wilcoxon_signed_rank_test
441. working_directory
442. write_function_memory_insertion
443. write_lidar
444. write_raster
445. write_text
446. write_vector
447. z_scores
448. zonal_statistics

adaptive_filter

This tool performs a type of adaptive filter on a raster image. An adaptive filter can be used to reduce the level of random noise (shot noise) in an image. The algorithm operates by calculating the average value in a moving window centred on each grid cell. If the absolute difference between the window mean value and the centre grid cell value is beyond a user-defined threshold (threshold), the grid cell in the output image is assigned the mean value, otherwise it is equivalent to the original value. Therefore, the algorithm only modifies the image where grid cell values are substantially different than their neighbouring values.

Neighbourhood size, or filter size, is specified in the x and y dimensions using filterx and filtery. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

See Also

mean_filter

Function Signature

def adaptive_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, threshold: float = 2.0) -> Raster: ...

add_point_coordinates_to_table

Description

This tool modifies the attribute table of a vector of POINT VectorGeometryType by adding two fields, XCOORD and YCOORD, containing each point's X and Y coordinates respectively.

Parameters

input (Vector): The input Vector object

Returns

Vector: the returning value

Function Signature

def add_point_coordinates_to_table(self, input: Vector) -> Vector: ...

aggregate_raster

This tool can be used to reduce the grid resolution of a raster by a user specified amount. For example, using an aggregation factor (agg_factor) of 2 would result in a raster with half the number of rows and columns. The grid cell values (type) in the output image will consist of the mean, sum, maximum, minimum, or range of the overlapping grid cells in the input raster (four cells in the case of an aggregation factor of 2).

See Also

resample

Function Signature

def aggregate_raster(self, raster: Raster, aggregation_factor: int = 2, aggregation_type: str = "mean") -> Raster: ...

anova

This tool performs an Analysis of variance (ANOVA) test on the distribution of values in a raster (input) among a group of features (features). The ANOVA report is written to an output HTML report (output).

Function Signature

def anova(self, input_raster: Raster, features_raster: Raster, output_html_file: str) -> None: ...

ascii_to_las

This tool can be used to convert one or more ASCII files, containing LiDAR point data, into LAS files. The user must specify the name(s) of the input ASCII file(s) (inputs). Each input file will have a correspondingly named output file with a .las file extension. The output point data, each on a separate line, will take the format:

x,y,z,intensity,class,return,num_returns"

The x, y, and z patterns must always be specified. If the rn pattern is used, the nr pattern must also be specified. Examples of valid pattern string include:

'x,y,z,i'
'x,y,z,i,rn,nr'
'x,y,z,i,c,rn,nr,sa'
'z,x,y,rn,nr'
'x,y,z,i,rn,nr,r,g,b'

Use the las_to_ascii tool to convert a LAS file into a text file containing LiDAR point data.

See Also

las_to_ascii

Function Signature

def ascii_to_las(self, input_ascii_files: List[str], pattern: str, epsg_code: int) -> None: ...

aspect

This tool calculates slope aspect (i.e. slope orientation in degrees clockwise from north) for each grid cell in an input digital elevation model (DEM). The user must specify an input DEM (dem). The Z conversion factor is only important when the vertical and horizontal units are not the same in the DEM. When this is the case, the algorithm will multiply each elevation in the DEM by the Z conversion factor. If the DEM is in the geographic coordinate system (latitude and longitude), the following equation is used:

zfactor = 1.0 / (111320.0 x cos(mid_lat))

where mid_lat is the latitude of the centre of each raster row, in radians.

The tool uses Horn's (1981) 3rd-order finite difference method to estimate slope. Given the following clock-type grid cell numbering scheme (Gallant and Wilson, 2000),

| 7 | 8 | 1 |
| 6 | 9 | 2 |
| 5 | 4 | 3 |

aspect = 180 - arctan(f_y / f_x) + 90(f_x / |f_x|)

where,

f_x = (z₃ - z₅ + 2(z₂ - z₆) + z₁ - z₇) / 8 * Δx

and,

f_y = (z₇ - z₅ + 2(z₈ - z₄) + z₁ - z₃) / 8 * Δy

Δx and Δy are the grid resolutions in the x and y direction respectively

Reference

Gallant, J. C., and J. P. Wilson, 2000, Primary topographic attributes, in Terrain Analysis: Principles and Applications, edited by J. P. Wilson and J. C. Gallant pp. 51-86, John Wiley, Hoboken, N.J.

See Also

slope, plan_curvature, profile_curvature

Function Signature

def aspect(self, dem: Raster, z_factor: float = 1.0) -> Raster: ...

attribute_correlation

This tool can be used to estimate the Pearson product-moment correlation coefficient (r) for each pair among a group of attributes associated with the database file of a shapefile. The r-value is a measure of the linear association in the variation of the attributes. The coefficient ranges from -1, indicated a perfect negative linear association, to 1, indicated a perfect positive linear association. An r-value of 0 indicates no correlation between the test variables.

Notice that this index is a measure of the linear association; two variables may be strongly related by a non-linear association (e.g. a power function curve) which will lead to an apparent weak association based on the Pearson coefficient. In fact, non-linear associations are very common among spatial variables, e.g. terrain indices such as slope and contributing area. In such cases, it is advisable that the input images are transformed prior to the estimation of the Pearson coefficient, or that an alternative, non-parametric statistic be used, e.g. the Spearman rank correlation coefficient.

The user must specify the name of the input vector Shapefile (input). Correlations will be calculated for each pair of numerical attributes contained within the input file's attribute table and presented in a correlation matrix HMTL output (output).

See Also

image_correlation, attribute_scattergram, attribute_histogram

Function Signature

def attribute_correlation(self, input: Vector, output_html_file: str) -> None: ...

attribute_histogram

This tool can be used to create a histogram, which is a graph displaying the frequency distribution of data, for the values contained in a field of an input vector's attribute table. The user must specify the name of an input vector (input) and the name of one of the fields (field) contained in the associated attribute table. The tool output (output) is an HTML formatted histogram analysis report. If the specified field is non-numerical, the tool will produce a bar-chart of class frequency, similar to the tabular output of the list_unique_values tool.

See Also

list_unique_values, raster_histogram

Function Signature

def attribute_histogram(self, input: Vector, field_name: str, output_html_file: str) -> None: ...

attribute_scattergram

This tool can be used to create a scattergram for two numerical fields (fieldx and fieldy) contained within an input vector's attribute table (input). The user must specify the name of an input shapefile and the name of two of the fields contained it the associated attribute table. The tool output (output) is an HTML formatted report containing a graphical scattergram plot.

See Also

attribute_histogram, attribute_correlation

Function Signature

def attribute_scattergram(self, input: Vector, field_name_x: str, field_name_y: str, output_html_file: str, add_trendline: bool = False) -> None: ...

available_functions

This function will list all of the available functions associated with a WbEnvironment (wbe). The functions that are accessible will depend on the license level (WbW or WbWPro).

average_flowpath_slope

This tool calculates the average slope gradient (i.e. slope steepness in degrees) of the flowpaths that pass through each grid cell in an input digital elevation model (DEM). The user must specify the name of a DEM raster (dem). It is important that this DEM is pre-processed to remove all topographic depressions and flat areas using a tool such as breach_depressions_least_cost. Several intermediate rasters are created and stored in memory during the operation of this tool, which may limit the size of DEM that can be processed, depending on available system resources.

See Also

average_upslope_flowpath_length, breach_depressions_least_cost

Function Signature

def average_flowpath_slope(self, dem: Raster) -> Raster: ...

average_normal_vector_angular_deviation

This tool characterizes the spatial distribution of the average normal vector angular deviation, a measure of surface roughness. Working in the field of 3D printing, Ko et al. (2016) defined a measure of surface roughness based on quantifying the angular deviations in the direction of the normal vector of a real surface from its ideal (i.e. smoothed) form. This measure of surface complexity is therefore in units of degrees. Specifically, roughness is defined in this study as the neighborhood-averaged difference in the normal vectors of the original DEM and a smoothed DEM surface. Smoothed surfaces are derived by applying a Gaussian blur of the same size as the neighborhood (filter).

The multiscale_roughness tool calculates the same measure of surface roughness, except that it is designed to work with multiple spatial scales.

Reference

Ko, M., Kang, H., ulrim Kim, J., Lee, Y., & Hwang, J. E. (2016, July). How to measure quality of affordable 3D printing: Cultivating quantitative index in the user community. In International Conference on Human-Computer Interaction (pp. 116-121). Springer, Cham.

Lindsay, J. B., & Newman, D. R. (2018). Hyper-scale analysis of surface roughness. PeerJ Preprints, 6, e27110v1.

See Also

multiscale_roughness, spherical_std_dev_of_normals, circular_variance_of_aspect

Function Signature

def average_normal_vector_angular_deviation(self, dem: Raster, filter_size: int = 11) -> Raster: ...

average_overlay

This tool can be used to find the average value in each cell of a grid from a set of input images (inputs). It is therefore similar to the weighted_sum tool except that each input image is given equal weighting. This tool operates on a cell-by-cell basis. Therefore, each of the input rasters must share the same number of rows and columns and spatial extent. An error will be issued if this is not the case. At least two input rasters are required to run this tool. Like each of the WhiteboxTools overlay tools, this tool has been optimized for parallel processing.

See Also

weighted_sum

Function Signature

def average_overlay(self, input_rasters: List[Raster]) -> Raster: ...

average_upslope_flowpath_length

This tool calculates the average slope gradient (i.e. slope steepness in degrees) of the flowpaths that pass through each grid cell in an input digital elevation model (DEM). The user must specify the name of a DEM raster (dem). It is important that this DEM is pre-processed to remove all topographic depressions and flat areas using a tool such as breach_depressions_least_cost. Several intermediate rasters are created and stored in memory during the operation of this tool, which may limit the size of DEM that can be processed, depending on available system resources.

See Also

average_upslope_flowpath_length, breach_depressions_least_cost

Function Signature

def average_upslope_flowpath_length(self, dem: Raster) -> Raster: ...

balance_contrast_enhancement

This tool can be used to reduce colour bias in a colour composite image based on the technique described by Liu (1991). Colour bias is a common phenomena with colour images derived from multispectral imagery, whereby a higher average brightness value in one band results in over-representation of that band in the colour composite. The tool essentially applies a parabolic stretch to each of the three bands in a user specified RGB colour composite, forcing the histograms of each band to have the same minimum, maximum, and average values while maintaining their overall histogram shape. For greater detail on the operation of the tool, please see Liu (1991). Aside from the names of the input and output colour composite images, the user must also set the value of E, the desired output band mean, where 20 < E < 235.

Reference

Liu, J.G. (1991) Balance contrast enhancement technique and its application in image colour composition. International Journal of Remote Sensing, 12:10.

See Also

direct_decorrelation_stretch, histogram_matching, histogram_matching_two_images, histogram_equalization, gaussian_contrast_stretch

Function Signature

def balance_contrast_enhancement(self, image: Raster, band_mean: float = 100.0) -> Raster: ...

basins

This tool can be used to delineate all of the drainage basins contained within a local drainage direction, or flow pointer raster (d8_pntr), and draining to the edge of the data. The flow pointer raster must be derived using the d8_pointer tool and should have been extracted from a digital elevation model (DEM) that has been hydrologically pre-processed to remove topographic depressions and flat areas, e.g. using the breach_depressions_least_cost tool. By default, the flow pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools:

If the pointer file contains ESRI flow direction values instead, the esri_pntr parameter must be specified.

The basins and watershed tools are similar in function but while the watershed tool identifies the upslope areas that drain to one or more user-specified outlet points, the basins tool automatically sets outlets to all grid cells situated along the edge of the data that do not have a defined flow direction (i.e. they do not have a lower neighbour). Notice that these edge outlets need not be situated along the edges of the flow-pointer raster, but rather along the edges of the region of valid data. That is, the DEM from which the flow-pointer has been extracted may incompletely fill the containing raster, if it is irregular shaped, and NoData regions may occupy the peripherals. Thus, the entire region of valid data in the flow pointer raster will be divided into a set of mutually exclusive basins using this tool.

See Also

watershed, d8_pointer, breach_depressions_least_cost

Function Signature

def basins(self, d8_pntr: Raster, esri_pntr: bool = False) -> Raster: ...

bilateral_filter

This tool can be used to perform an edge-preserving smoothing filter, or bilateral filter, on an image. A bilateral filter can be used to emphasize the longer-range variability in an image, effectively acting to smooth the image, while reducing the edge blurring effect common with other types of smoothing filters. As such, this filter is very useful for reducing the noise in an image. Bilateral filtering is a non-linear filtering technique introduced by Tomasi and Manduchi (1998). The algorithm operates by convolving a kernel of weights with each grid cell and its neighbours in an image. The bilateral filter is related to Gaussian smoothing, in that the weights of the convolution kernel are partly determined by the 2-dimensional Gaussian (i.e. normal) curve, which gives stronger weighting to cells nearer the kernel centre. Unlike the gaussian_filter, however, the bilateral kernel weightings are also affected by their similarity to the intensity value of the central pixel. Pixels that are very different in intensity from the central pixel are weighted less, also based on a Gaussian weight distribution. Therefore, this non-linear convolution filter is determined by the spatial and intensity domains of a localized pixel neighborhood.

The heavier weighting given to nearer and similar-valued pixels makes the bilateral filter an attractive alternative for image smoothing and noise reduction compared to the much-used Mean filter. The size of the filter is determined by setting the standard deviation distance parameter (sigma_dist); the larger the standard deviation the larger the resulting filter kernel. The standard deviation can be any number in the range 0.5-20 and is specified in the unit of pixels. The standard deviation intensity parameter (sigma_int), specified in the same units as the z-values, determines the intensity domain contribution to kernel weightings.

References

Tomasi, C., & Manduchi, R. (1998, January). Bilateral filtering for gray and color images. In null (p. 839). IEEE.

See Also

edge_preserving_mean_filter

Function Signature

def bilateral_filter(self, raster: Raster, sigma_dist: float = 0.75, sigma_int: float = 1.0) -> Raster: ...

block_maximum

Creates a raster grid based on a set of vector points and assigns grid values using a block maximum scheme.

Function Signature

def block_maximum(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...

block_minimum

Creates a raster grid based on a set of vector points and assigns grid values using a block minimum scheme.

Function Signature

def block_minimum(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...

bool_and

This tool is a Boolean AND operator, i.e. it works on True or False (1 and 0) values. Grid cells for which the first and second input rasters (input1; input2) have True values are assigned 1 in the output raster, otherwise grid cells are assigned a value of 0. All non-zero values in the input rasters are considered to be True, while all zero-valued grid cells are considered to be False. Grid cells containing NoData values in either of the input rasters will be assigned a NoData value in the output raster.

See Also

bool_not, bool_or, bool_xor

Function Signature

def bool_and(self, input1: Raster, input2: Raster) -> Raster: ...

bool_not

This tool is a Boolean NOT operator, i.e. it works on True or False (1 and 0) values. Grid cells for which the first input raster (input1) has a True value and the second raster (input2) has a False value are assigned 0 in the output raster, otherwise grid cells are assigned a value of 0. All non-zero values in the input rasters are considered to be True, while all zero-valued grid cells are considered to be False. Grid cells containing NoData values in either of the input rasters will be assigned a NoData value in the output raster. Notice that the Not operator is asymmetrical, and the order of inputs matters.

See Also

bool_and, bool_or, bool_xor

Function Signature

def bool_not(self, input1: Raster, input2: Raster) -> Raster: ...

bool_or

This tool is a Boolean OR operator, i.e. it works on True or False (1 and 0) values. Grid cells for which the either the first or second input rasters (input1; input2) have a True value are assigned 1 in the output raster, otherwise grid cells are assigned a value of 0. All non-zero values in the input rasters are considered to be True, while all zero-valued grid cells are considered to be False. Grid cells containing NoData values in either of the input rasters will be assigned a NoData value in the output raster.

See Also

bool_and, bool_not, bool_xor

Function Signature

def bool_or(self, input1: Raster, input2: Raster) -> Raster: ...

bool_xor

This tool is a Boolean XOR operator, i.e. it works on True or False (1 and 0) values. Grid cells for which either the first or second input rasters (input1; input2) have a True value but not both are assigned 1 in the output raster, otherwise grid cells are assigned a value of 0. All non-zero values in the input rasters are considered to be True, while all zero-valued grid cells are considered to be False. Grid cells containing NoData values in either of the input rasters will be assigned a NoData value in the output raster. Notice that the Not operator is asymmetrical, and the order of inputs matters.

See Also

bool_and, bool_not, bool_or

Function Signature

def bool_xor(self, input1: Raster, input2: Raster) -> Raster: ...

boundary_shape_complexity

This tools calculates a type of shape complexity index for raster objects, focused on the complexity of the boundary of polygons. The index uses the line_thinning tool to estimate a skeletonized network for each input raster polygon. The Boundary Shape Complexity (BSC) index is then calculated as the percentage of the skeletonized network belonging to exterior links. Polygons with more complex boundaries will possess more branching skeletonized networks, with each spur in the boundary possessing a short exterior branch. The two longest exterior links in the network are considered to be part of the main network. Therefore, polygons of complex shaped boundaries will have a higher percentage of their skeleton networks consisting of exterior links. It is expected that simple convex hulls should have relatively low BSC index values.

Objects in the input raster (input) are designated by their unique identifiers. Identifier values should be positive, non-zero whole numbers.

See Also

shape_complexity_index_raster, line_thinning

Function Signature

def boundary_shape_complexity(self, raster: Raster) -> Raster: ...

breach_depressions_least_cost

This tool can be used to perform a type of optimal depression breaching to prepare a digital elevation model (DEM) for hydrological analysis. Depression breaching is a common alternative to depression filling (fill_depressions) and often offers a lower-impact solution to the removal of topographic depressions. This tool implements a method that is loosely based on the algorithm described by Lindsay and Dhun (2015), furthering the earlier algorithm with efficiency optimizations and other significant enhancements. The approach uses a least-cost path analysis to identify the breach channel that connects pit cells (i.e. grid cells for which there is no lower neighbour) to some distant lower cell. Prior to breaching and in order to minimize the depth of breach channels, all pit cells are rised to the elevation of the lowest neighbour minus a small heigh value. Here, the cost of a breach path is determined by the amount of elevation lowering needed to cut the breach channel through the surrounding topography.

The user must specify the name of the input DEM file (dem), the output breached DEM file (output), the maximum search window radius (dist), the optional maximum breach cost (max_cost), and an optional flat height increment value (flat_increment). Notice that if the flat_increment parameter is not specified, the small number used to ensure flow across flats will be calculated automatically, which should be preferred in most applications of the tool. The tool operates by performing a least-cost path analysis for each pit cell, radiating outward until the operation identifies a potential breach destination cell or reaches the maximum breach length parameter. If a value is specified for the optional max_cost parameter, then least-cost breach paths that would require digging a channel that is more costly than this value will be left unbreached. The flat increment value is used to ensure that there is a monotonically descending path along breach channels to satisfy the necessary condition of a downslope gradient for flowpath modelling. It is best for this value to be a small value. If left unspecified, the tool with determine an appropriate value based on the range of elevation values in the input DEM, which should be the case in most applications. Notice that the need to specify these very small elevation increment values is one of the reasons why the output DEM will always be of a 64-bit floating-point data type, which will often double the storage requirements of a DEM (DEMs are often store with 32-bit precision). Lastly, the user may optionally choose to apply depression filling (fill) on any depressions that remain unresolved by the earlier depression breaching operation. This filling step uses an efficient filling method based on flooding depressions from their pit cells until outlets are identified and then raising the elevations of flooded cells back and away from the outlets.

The tool can be run in two modes, based on whether the min_dist is specified. If the min_dist flag is specified, the accumulated cost (accum₂) of breaching from cell1 to cell2 along a channel issuing from pit is calculated using the traditional cost-distance function:

cost₁ = z₁ - (z_pit + l × s)

cost₂ = z₂ - [z_pit + (l + 1)s]

accum₂ = accum₁ + g(cost₁ + cost₂) / 2.0

where cost₁ and cost₂ are the costs associated with moving through cell1 and cell2 respectively, z₁ and z₂ are the elevations of the two cells, z_pit is the elevation of the pit cell, l is the length of the breach channel to cell1, g is the grid cell distance between cells (accounting for diagonal distances), and s is the small number used to ensure flow across flats. If the min_dist flag is not present, the accumulated cost is calculated as:

accum₂ = accum₁ + cost₂

That is, without the min_dist flag, the tool works to minimize elevation changes to the DEM caused by breaching, without considering the distance of breach channels. Notice that the value max_cost, if specified, should account for this difference in the way cost/cost-distances are calculated. The first cell in the least-cost accumulation operation that is identified for which cost₂ <= 0.0 is the target cell to which the breach channel will connect the pit along the least-cost path.

In comparison with the breach_depressions_least_cost tool, this breaching method often provides a more satisfactory, lower impact, breaching solution and is often more efficient. It is therefore advisable that users try the breach_depressions_least_cost tool to remove depressions from their DEMs first. This tool is particularly well suited to breaching through road embankments. There are instances when a breaching solution is inappropriate, e.g. when a very deep depression such as an open-pit mine occurs in the DEM and long, deep breach paths are created. Often restricting breaching with the max_cost parameter, combined with subsequent depression filling (fill) can provide an adequate solution in these cases. Nonetheless, there are applications for which full depression filling using the fill_depressions tool may be preferred.

Reference

Lindsay J, Dhun K. 2015. Modelling surface drainage patterns in altered landscapes using LiDAR. International Journal of Geographical Information Science, 29: 1-15. DOI: 10.1080/13658816.2014.975715

See Also

breach_depressions_least_cost, fill_depressions, cost_pathway

Function Signature

def breach_depressions_least_cost(self, dem: Raster, max_cost: float = float('inf'), max_dist: int = 100, flat_increment: float = float('nan'), fill_deps: bool = False, minimize_dist: bool = False) -> Raster: ...

breach_single_cell_pits

This tool calculates the average slope gradient (i.e. slope steepness in degrees) of the flowpaths that pass through each grid cell in an input digital elevation model (DEM). The user must specify the name of a DEM raster (dem). It is important that this DEM is pre-processed to remove all topographic depressions and flat areas using a tool such as breach_depressions_least_cost. Several intermediate rasters are created and stored in memory during the operation of this tool, which may limit the size of DEM that can be processed, depending on available system resources.

See Also

average_upslope_flowpath_length, breach_depressions_least_cost

Function Signature

def breach_single_cell_pits(self, dem: Raster) -> Raster: ...

buffer_raster

This tool can be used to identify an area of interest within a specified distance of features of interest in a raster data set.

The Euclidean distance (i.e. straight-line distance) is calculated between each grid cell and the nearest 'target cell' in the input image. Distance is calculated using the efficient method of Shih and Wu (2004). Target cells are all non-zero, non-NoData grid cells. Because NoData values in the input image are assigned the NoData value in the output image, the only valid background value in the input image is zero.

The user must specify the input and output image names, the desired buffer size (size), and, optionally, whether the distance units are measured in grid cells (i.e. gridcells flag). If the gridcells flag is not specified, the linear units of the raster's coordinate reference system will be used.

Reference

Shih FY and Wu Y-T (2004), Fast Euclidean distance transformation in two scans using a 3 x 3 neighborhood, Computer Vision and Image Understanding, 93: 195-205.

See Also

euclidean_distance

Function Signature

def buffer_raster(self, input: Raster, buffer_size: float, grid_cells_units: bool = False) -> Raster: ...

burn_streams_at_roads

This tool decrements (lowers) the elevations of pixels within an input digital elevation model (DEM) (dem) along an input vector stream network (streams) at the sites of road (roads) intersections. In addition to the input data layers, the user must specify the output raster DEM (output), and the maximum road embankment width (width), in map units. The road width parameter is used to determine the length of channel along stream lines, at the junctions between streams and roads, that the burning (i.e. decrementing) operation occurs. The algorithm works by identifying stream-road intersection cells, then traversing along the rasterized stream path in the upstream and downstream directions by half the maximum road embankment width. The minimum elevation in each stream traversal is identified and then elevations that are higher than this value are lowered to the minimum elevation during a second stream traversal.

Reference

Lindsay JB. 2016. The practice of DEM stream burning revisited. Earth Surface Processes and Landforms, 41(5): 658–668. DOI: 10.1002/esp.3888

See Also

raster_streams_to_vector, rasterize_streams

Function Signature

def burn_streams_at_roads(self, dem: Raster, streams: Vector, roads: Vector, road_width: float) -> Raster: ...

centroid_raster

This tool calculates the centroid, or average location, of raster polygon objects. For vector features, use the centroid_vector tool instead.

See Also

centroid_vector

Function Signature

def centroid_raster(self, input: Raster) -> Tuple[Raster, str]: ...

centroid_vector

This can be used to identify the centroid point of a vector polyline or polygon feature or a group of vector points. The output is a vector shapefile of points. For multi-part polyline or polygon features, the user can optionally specify whether to identify the centroid of each part. The default is to treat multi-part features a single entity.

For raster features, use the Centroid tool instead.

See Also

Centroid, medoid

Function Signature

def centroid_vector(self, input: Vector) -> Vector: ...

change_vector_analysis

Change Vector Analysis (CVA) is a change detection method that characterizes the magnitude and change direction in spectral space between two times. A change vector is the difference vector between two vectors in n-dimensional feature space defined for two observations of the same geographical location (i.e. corresponding pixels) during two dates. The CVA inputs include the set of raster images corresponding to the multispectral data for each date. Note that there must be the same number of image files (bands) for the two dates and they must be entered in the same order, i.e. if three bands, red, green, and blue are entered for date one, these same bands must be entered in the same order for date two.

CVA outputs two image files. The first image contains the change vector length, i.e. magnitude, for each pixel in the multi-spectral dataset. The second image contains information about the direction of the change event in spectral feature space, which is related to the type of change event, e.g. deforestation will likely have a different change direction than say crop growth. The vector magnitude is a continuous numerical variable. The change vector direction is presented in the form of a code, referring to the multi-dimensional sector in which the change vector occurs. A text output will be produced to provide a key describing sector codes, relating the change vector to positive or negative shifts in n-dimensional feature space.

It is common to apply a simple thresholding operation on the magnitude data to determine 'actual' change (i.e. change above some assumed level of error). The type of change (qualitatively) is then defined according to the corresponding sector code. Jensen (2015) provides a useful description of this approach to change detection.

Reference

Jensen, J. R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective.

See Also

write_function_memory_insertion

Function Signature

def change_vector_analysis(self, date1_rasters: List[Raster], date2_rasters: List[Raster]) -> Tuple[Raster, Raster, str]: ...

check_in_license

Check-in your floating license.

circular_variance_of_aspect

This tool can be used to calculate the circular variance (i.e. one minus the mean resultant length) of aspect for a digital elevation model (DEM). This is a measure of how variable slope aspect is within a local neighbourhood of a specified size (filter). circular_variance_of_aspect is therefore a measure of surface shape complexity, or texture. It will take a value of 0.0 for smooth sites and near 1.0 in areas of high surface roughness or complex topography.

The local neighbourhood size (filter) must be any odd integer equal to or greater than three. Grohmann et al. (2010) found that vector dispersion, a related measure of angular variance, increases monotonically with scale. This is the result of the angular dispersion measure integrating (accumulating) all of the surface variance of smaller scales up to the test scale. A more interesting scale relation can therefore be estimated by isolating the amount of surface complexity associated with specific scale ranges. That is, at large spatial scales, the metric should reflect the texture of large-scale landforms rather than the accumulated complexity at all smaller scales, including microtopographic roughness. As such, this tool normalizes the surface complexity of scales that are smaller than the filter size by applying Gaussian blur (with a standard deviation of one-third the filter size) to the DEM prior to calculating circular_variance_of_aspect. In this way, the resulting distribution is able to isolate and highlight the surface shape complexity associated with landscape features of a similar scale to that of the filter size.

This tool makes extensive use of integral images (i.e. summed-area tables) and parallel processing to ensure computational efficiency. It may, however, require substantial memory resources when applied to larger DEMs.

References

Grohmann, C. H., Smith, M. J., & Riccomini, C. (2010). Multiscale analysis of topographic surface roughness in the Midland Valley, Scotland. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1200-1213.

See Also

aspect, spherical_std_dev_of_normals, multiscale_roughness, edge_density, surface_area_ratio, ruggedness_index

Function Signature

def circular_variance_of_aspect(self, dem: Raster, filter_size: int = 11) -> Raster: ...

classify_buildings_in_lidar

This tool can be used to assign the building class (classification value 6) to all points within an input LiDAR point cloud (input) that are contained within the polygons of an input buildings footprint vector (buildings). The tool performs a simple point-in-polygon operation to determine membership. The two inputs (i.e. the LAS file and vector) must share the same map projection. Furthermore, any error in the definition of the building footprints will result in misclassified points in the output LAS file (output). In particular, if the footprints extend slightly beyond the actual building, ground points situated adjacent to the building will be incorrectly classified. Thus, care must be taken in digitizing building footprint polygons. Furthermore, where there are tall trees that overlap significantly with the building footprint, these vegetation points will also be incorrectly assigned the building class value.

See Also

filter_lidar_classes, lidar_ground_point_filter, clip_lidar_to_polygon

Function Signature

def classify_buildings_in_lidar(self, in_lidar: Lidar, building_footprints: Vector) -> Lidar: ...

classify_overlap_points

This tool can be used to flag points within an input LiDAR file (input) that overlap with other nearby points from different flightlines, i.e. to identify overlap points. The flightline associated with a LiDAR point is assumed to be contained within the point's Point Source ID (PSID) property. If the PSID property is not set, or has been lost, users may with to apply the recover_flightline_info tool prior to running flightline_overlap.

Areas of multiple flightline overlap tend to have point densities that are far greater than areas of single flightlines. This can produce suboptimal results for applications that assume regular point distribution, e.g. in point classification operations.

The tool works by applying a square grid over the extent of the input LiDAR file. The grid cell size is determined by the user-defined resolution parameter. Grid cells containing multiple PSIDs, i.e. with more than one flightline, are then identified. Overlap points within these grid cells can then be flagged on the basis of a user-defined criterion. The flagging options include the following:

Note that the max scan angle criterion may not be appropriate when more than two flightlines overlap, since it will result in only flagging points from one of the multiple flightlines.

It is important to set the resolution parameter appropriately, as setting this value too high will yield the filtering of points in non-overlap areas, and setting the resolution to low will result in fewer than expected points being flagged. An appropriate resolution size value may require experimentation, however a value that is 2-3 times the nominal point spacing has been previously recommended. The nominal point spacing can be determined using the lidar_info tool.

By default, all flagged overlap points are reclassified in the output LiDAR file (output) to class 12. Alternatively, if the user specifies the filter parameter, then each overlap point will be excluded from the output file. Classified overlap points may also be filtered from LiDAR point clouds using the filter_lidar tool.

Note that this tool is intended to be applied to LiDAR tile data containing points that have been merged from multiple overlapping flightlines. It is commonly the case that airborne LiDAR data from each of the flightlines from a survey are merged and then tiled into 1 km² tiles, which are the target dataset for this tool.

See Also

flightline_overlap, recover_flightline_info, filter_lidar, lidar_info

Function Signature

def classify_overlap_points(self, in_lidar: Lidar, resolution: float = 1.0, overlap_criterion: str = "max scan angle", filter: bool = False) -> Lidar: ...

clean_vector

Description

This tool can be used to remove all features in Shapefiles that are of the null VectorGeometryType. It also removes line features with fewer than two vertices and polygon features with fewer than three vertices.

Parameters

input (Vector): The input Vector object

Returns

Vector: the returning value

Function Signature

def clean_vector(self, input: Vector) -> Vector: ...

clip

This tool will extract all the features, or parts of features, that overlap with the features of the clip vector file. The clipping operation is one of the most common vector overlay operations in GIS and effectively imposes the boundary of the clip layer on a set of input vector features, or target features. The operation is sometimes likened to a 'cookie-cutter'. The input vector file can be of any feature type (i.e. points, lines, polygons), however, the clip vector must consist of polygons.

See Also

erase

Function Signature

def clip(self, input: Vector, clip_layer: Vector) -> Vector: ...

clip_lidar_to_polygon

This tool can be used to isolate, or clip, all of the LiDAR points in a LAS file (input) contained within one or more vector polygon features. The user must specify the name of the input clip file (--polygons), which must be a vector of a Polygon base shape type. The clip file may contain multiple polygon features and polygon hole parts will be respected during clipping, i.e. LiDAR points within polygon holes will be removed from the output LAS file.

Use the erase_polygon_from_lidar tool to perform the complementary operation of removing points from a LAS file that are contained within a set of polygons.

See Also

erase_polygon_from_lidar, filter_lidar, clip, clip_raster_to_polygon

Function Signature

def clip_lidar_to_polygon(self, input: Lidar, polygons: Vector) -> Lidar: ...

clip_raster_to_polygon

This tool can be used to clip an input raster (input) to the extent of a vector polygon (shapefile). The user must specify the name of the input clip file (polygons), which must be a vector of a Polygon base shape type. The clip file may contain multiple polygon features. Polygon hole parts will be respected during clipping, i.e. polygon holes will be removed from the output raster by setting them to a NoData background value. Raster grid cells that fall outside of a polygons in the clip file will be assigned the NoData background value in the output file. By default, the output raster will be cropped to the spatial extent of the clip file, unless the maintain_dimensions parameter is used, in which case the output grid extent will match that of the input raster. The grid resolution of output raster is the same as the input raster.

It is very important that the input raster and the input vector polygon file share the same projection. The result is unlikely to be satisfactory otherwise.

See Also

erase_polygon_from_raster

Function Signature

def clip_raster_to_polygon(self, raster: Raster, polygons: Vector, maintain_dimensions: bool = False) -> Raster: ...

closing

This tool performs a closing operation on an input greyscale image (input). A closing is a mathematical morphology operation involving an erosion (minimum filter) of a dilation (maximum filter) set. closing operations, together with the opening operation, is frequently used in the fields of computer vision and digital image processing for image noise removal. The user must specify the size of the moving window in both the x and y directions (filterx and filtery).

See Also

opening, tophat_transform

Function Signature

def closing(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

clump

This tool re-categorizes data in a raster image by grouping cells that form discrete, contiguous areas into unique categories. Essentially this will produce a patch map from an input categorical raster, assigning each feature unique identifiers. The input raster should either be Boolean (1's and 0's) or categorical. The input raster could be created using the reclass tool or one of the comparison operators (GreaterThan, LessThan, EqualTo, NotEqualTo). Use the treat zeros as background cells options (zero_back) if you would like to only assigned contiguous groups of non-zero values in the raster unique identifiers. Additionally, inter-cell connectivity can optionally include diagonally neighbouring cells if the diag flag is specified.

See Also

reclass, GreaterThan, LessThan, EqualTo, NotEqualTo

Function Signature

def clump(self, raster: Raster, diag: bool = False, zero_background: bool = False) -> Raster: ...

compactness_ratio

The compactness ratio is an indicator of polygon shape complexity. The compactness ratio is defined as the polygon area divided by its perimeter. Unlike some other shape parameters (e.g. ShapeComplexityIndex), compactness ratio does not standardize to a simple Euclidean shape. Although widely used for landscape analysis, compactness ratio, like its inverse, the perimeter_area_ratio, exhibits the undesirable property of polygon size dependence (Mcgarigal et al. 2002). That is, holding shape constant, an increase in polygon size will cause a change in the compactness ratio.

The output data will be contained in the input vector's attribute table as a new field (COMPACT).

See Also

perimeter_area_ratio, ShapeComplexityIndex, related_circumscribing_circle

Function Signature

def compactness_ratio(self, input: Vector) -> Vector: ...

conservative_smoothing_filter

This tool performs a conservative smoothing filter on a raster image. A conservative smoothing filter can be used to remove short-range variability in an image, effectively acting to smooth the image. It is particularly useful for eliminating local spikes and reducing the noise in an image. The algorithm operates by calculating the minimum and maximum neighbouring values surrounding a grid cell. If the cell at the centre of the kernel is greater than the calculated maximum value, it is replaced with the maximum value in the output image. Similarly, if the cell value at the kernel centre is less than the neighbouring minimum value, the corresponding grid cell in the output image is replaced with the minimum value. This filter tends to alter an image very little compared with other smoothing filters such as the mean_filter, edge_preserving_mean_filter, bilateral_filter, median_filter, gaussian_filter, or olympic_filter.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

See Also

mean_filter, edge_preserving_mean_filter, bilateral_filter, median_filter, gaussian_filter, olympic_filter

Function Signature

def conservative_smoothing_filter(self, raster: Raster, filter_size_x: int = 3, filter_size_y: int = 3) -> Raster: ...

construct_vector_tin

This tool creates a vector triangular irregular network (TIN) for a set of vector points (input) using a 2D Delaunay triangulation algorithm. TIN vertex heights can be assigned based on either a field in the vector's attribute table (field), or alternatively, if the vector is of a z-dimension VectorGeometryTypeDimension, the point z-values may be used for vertex heights (use_z). For LiDAR points, use the lidar_construct_vector_tin tool instead.

Triangulation often creates very long, narrow triangles near the edges of the data coverage, particularly in convex regions along the data boundary. To avoid these spurious triangles, the user may optionally specify the maximum allowable edge length of a triangular facet (max_triangle_edge_length).

See Also

lidar_construct_vector_tin

Function Signature

def construct_vector_tin(self, input_points: Vector, field_name: str = "FID", use_z: bool = False, max_triangle_edge_length: float = float('inf')) -> Vector: ...

contours_from_points

This tool creates a contour coverage from a set of input points (input). The user must specify the contour interval (interval) and optionally, the base contour value (base). The degree to which contours are smoothed is controlled by the Smoothing Filter Size parameter (smooth). This value, which determines the size of a mean filter applied to the x-y position of vertices in each contour, should be an odd integer value, e.g. 3, 5, 7, 9, 11, etc. Larger values will result in smoother contour lines.

See Also

contours_from_raster

Function Signature

def contours_from_points(self, input: Vector, field_name: str = "", use_z_values: bool = False, max_triangle_edge_length: float = float('inf'), contour_interval: float = 10.0, base_contour: float = 0.0, smoothing_filter_size: int = 9) -> Vector: ...

contours_from_raster

This tool can be used to create a vector contour coverage from an input raster surface model (input), such as a digital elevation model (DEM). The user must specify the contour interval (interval) and optionally, the base contour value (base). The degree to which contours are smoothed is controlled by the Smoothing Filter Size parameter (smooth). This value, which determines the size of a mean filter applied to the x-y position of vertices in each contour, should be an odd integer value, e.g. 3, 5, 7, 9, 11, etc. Larger values will result in smoother contour lines. The tolerance parameter (tolerance) controls the amount of line generalization. That is, vertices in a contour line will be selectively removed from the line if they do not result in an angular deflection in the line's path of at least this threshold value. Increasing this value can significantly decrease the size of the output contour vector file, at the cost of generating straighter contour line segments.

See Also

raster_to_vector_polygons

Function Signature

def contours_from_raster(self, raster_surface: Raster, contour_interval: float = 10.0, base_contour: float = 0.0, smoothing_filter_size: int = 9, deflection_tolerance: float = 10.0) -> Vector: ...

convert_nodata_to_zero

Description

This tool can be used to change the value within the grid cells of a raster (input) that contain NoData to zero. The most common reason for using this tool is to change the background region of a raster image such that it can be included in analysis since NoData values are usually ignored by by most tools. This change, however, will result in the background no longer displaying transparently in most GIS. This change can be reversed using the set_nodata_value tool.

See Also

set_nodata_value, Raster.is_nodata

Parameters

raster (Raster): The input Raster object

Returns

Raster: the returning value

Function Signature

def convert_nodata_to_zero(self, raster: Raster) -> Raster: ...

corner_detection

This tool identifies corner patterns in boolean images using hit-and-miss pattern matching. Foreground pixels in the input image (input) are designated by any positive, non-zero values. Zero-valued and NoData-valued grid cells are interpreted by the algorithm as background values.

Reference

Fisher, R, Brown, N, Cammas, N, Fitzgibbon, A, Horne, S, Koryllos, K, Murdoch, A, Robertson, J, Sharman, T, Strachan, C, 2004. Hypertext Image Processing Resource. online: http://homepages.inf.ed.ac.uk/rbf/HIPR2/hitmiss.htm

Function Signature

def corner_detection(self, raster: Raster) -> Raster: ...

correct_vignetting

This tool can be used to reduce vignetting within an image. Vignetting refers to the reduction of image brightness away from the image centre (i.e. the principal point). Vignetting is a radiometric distortion resulting from lens characteristics. The algorithm calculates the brightness value in the output image (BVout) as:

BVout = BVin / [cos^n(arctan(d / f))]

Where d is the photo-distance from the principal point in millimetres, f is the focal length of the camera, in millimeters, and n is a user-specified parameter. Pixel distances are converted to photo-distances (in millimetres) using the specified image width, i.e. distance between left and right edges (mm). For many cameras, 4.0 is an appropriate value of the n parameter. A second pass of the image is used to rescale the output image so that it possesses the same minimum and maximum values as the input image.

If an RGB image is input, the analysis will be performed on the intensity component of the HSI transform.

Function Signature

def correct_vignetting(self, image: Raster, principal_point: Vector, focal_length: float = 304.8, image_width: float = 228.6, n_param: float = 4.0) -> Raster: ...

cost_allocation

This tool can be used to identify the 'catchment area' of each source grid cell in a cost-distance analysis. The user must specify the names of the input source and back-link raster files. Source cells (i.e. starting points for the cost-distance or least-cost path analysis) are designated as all positive, non-zero valued grid cells in the source raster. A back-link raster file can be created using the cost_distance tool and is conceptually similar to the D8 flow-direction pointer raster grid in that it describes the connectivity between neighbouring cells on the accumulated cost surface.

NoData values in the input back-link image are assigned NoData values in the output image.

See Also

cost_distance, cost_pathway, euclidean_allocation

Function Signature

def cost_allocation(self, source: Raster, backlink: Raster) -> Raster: ...

cost_distance

This tool can be used to perform cost-distance or least-cost pathway analyses. Specifically, this tool can be used to calculate the accumulated cost of traveling from the 'source grid cell' to each other grid cell in a raster dataset. It is based on the costs associated with traveling through each cell along a pathway represented in a cost (or friction) surface. If there are multiple source grid cells, each cell in the resulting cost-accumulation surface will reflect the accumulated cost to the source cell that is connected by the minimum accumulated cost-path. The user must specify the names of the raster file containing the source cells (source), the raster file containing the cost surface information (cost), the output cost-accumulation surface raster (out_accum), and the output back-link raster (out_backlink). Source cells are designated as all positive, non-zero valued grid cells in the source raster. The cost (friction) raster can be created by combining the various cost factors associated with the specific problem (e.g. slope gradient, visibility, etc.) using a raster calculator or the weighted_overlay tool.

While the cost-accumulation surface raster can be helpful for visualizing the three-dimensional characteristics of the 'cost landscape', it is actually the back-link raster that is used as inputs to the other two cost-distance tools, cost_allocation and cost_pathway, to determine the least-cost linkages among neighbouring grid cells on the cost surface. If the accumulated cost surface is analogous to a digital elevation model (DEM) then the back-link raster is equivalent to the D8 flow-direction pointer. In fact, it is created in a similar way and uses the same convention for designating 'flow directions' between neighbouring grid cells. The algorithm for the cost distance accumulation operation uses a type of priority-flood method similar to what is used for depression filling and flow accumulation operations.

NoData values in the input cost surface image are ignored during processing and assigned NoData values in the outputs. The output cost accumulation raster is of the float data type and continuous data scale.

See Also

cost_allocation, cost_pathway, weighted_overlay

Function Signature

def cost_distance(self, source: Raster, cost: Raster) -> Tuple[Raster, Raster]: ...

cost_pathway

This tool can be used to map the least-cost pathway connecting each destination grid cell in a cost-distance analysis to a source cell. The user must specify the names of the input destination and back-link raster files. Destination cells (i.e. end points for the least-cost path analysis) are designated as all positive, non-zero valued grid cells in the destination raster. A back-link raster file can be created using the cost_distance tool and is conceptually similar to the D8 flow-direction pointer raster grid in that it describes the connectivity between neighbouring cells on the accumulated cost surface. All background grid cells in the output image are assigned the NoData value.

NoData values in the input back-link image are assigned NoData values in the output image.

See Also

cost_distance, cost_allocation

Function Signature

def cost_pathway(self, destination: Raster, backlink: Raster, zero_background: bool = False) -> Raster: ...

count_if

This tool counts the number of occurrences of a specified value (value) in a stack of input rasters (inputs). Each grid cell in the output raster (output) will contain the number of occurrences of the specified value in the stack of corresponding cells in the input image. At least two input rasters are required to run this tool. Each of the input rasters must share the same number of rows and columns and spatial extent. An error will be issued if this is not the case.

See Also

pick_from_list

Function Signature

def count_if(self, input_rasters: List[Raster], comparison_value: float) -> Raster: ...

create_colour_composite

This tool can be used to create a colour-composite image from three bands of multi-spectral imagery. The user must input images to enter into the red, green, and blue channels of the resulting composite image. The output image uses the 32-bit aRGB colour model, and therefore, in addition to red, green and blue bands, the user may optionally specify a fourth image that will be used to determine pixel opacity (the 'a' channel). If no opacity image is specified, each pixel will be opaque. This can be useful for cropping an image to an irregular-shaped boundary. The opacity channel can also be used to create transparent gradients in the composite image.

A balance contrast enhancement (BCE) can optionally be performed on the bands prior to creation of the colour composite. While this operation will add to the runtime of create_colour_composite, if the individual input bands have not already had contrast enhancements, then it is advisable that the BCE option be used to improve the quality of the resulting colour composite image.

NoData values in any of the input images are assigned NoData values in the output image and are not taken into account when performing the BCE operation. Please note, not all images have NoData values identified. When this is the case, and when the background value is 0 (often the case with multispectral imagery), then the create_colour_composite tool can be told to ignore zero values using the zeros flag.

See Also

balance_contrast_enhancement, split_colour_composite

Function Signature

def create_colour_composite(self, red: Raster, green: Raster, blue: Raster, opacity: Raster = None, enhance: bool = True, treat_zeros_as_nodata: bool = False) -> Raster: ...

create_plane

This tool can be used to create a new raster with values that are determined by the equation of a simple plane. The user must specify the name of a base raster (base) from which the output raster coordinate and dimensional information will be taken. In addition the user must specify the values of the planar slope gradient (S; gradient; aspect) in degrees, the planar slope direction or aspect (A; 0 to 360 degrees), and an constant value (k; constant). The equation of the plane is as follows:

Z = tan(S) × sin(A - 180) × X + tan(S) × cos(A - 180) × Y + k

where X and Y are the X and Y coordinates of each grid cell in the grid. Notice that A is the direction, or azimuth, that the plane is facing

Function Signature

def create_plane(self, base_file: Raster, gradient: float, aspect: float, constant: float) -> Raster: ...

crispness_index

The Crispness Index (C) provides a means of quantifying the crispness, or fuzziness, of a membership probability (MP) image. MP images describe the probability of each grid cell belonging to some feature or class. MP images contain values ranging from 0 to 1.

The index, as described by Lindsay (2006), is the ratio between the sum of the squared differences (from the image mean) in the MP image divided by the sum of the squared differences for the Boolean case in which the total probability, summed for the image, is arranged crisply.

C is closely related to a family of relative variation coefficients that measure variation in an MP image relative to the maximum possible variation (i.e. when the total probability is arranged such that grid cells contain only 1s or 0s). Notice that 0 < C < 1 and a low C-value indicates a nearly uniform spatial distribution of any probability value, and C = 1 indicates a crisp spatial probability distribution, containing only 1's and 0's.

C is calculated as follows:

C = SS_mp ∕ SS_B = [∑(pij − p-bar)^2] ∕ [ ∑pij(1 − p-bar)^2 + p2(RC − ∑pij)]

Note that there is an error in the original published equation. Specifically, the denominator read:

∑pij(1 - p_bar)^2 + p_bar^2 (RC - ∑pij)

instead of the original:

∑pij(1 - p_bar^2) - p_bar^2 (RC - ∑pij)

References

Lindsay, J. B. (2006). Sensitivity of channel mapping techniques to uncertainty in digital elevation data. International Journal of Geographical Information Science, 20(6), 669-692.

Function Signature

def crispness_index(self, raster: Raster, output_html_file: str) -> None: ...

cross_tabulation

This tool can be used to perform a cross-tabulation on two input raster images (i1 and i2) containing categorical data, i.e. classes. It will output a contingency table in HTML format (output). A contingency table, also known as a cross tabulation or crosstab, is a type of table that displays the multivariate frequency distribution of the variables. These tables provide a basic picture of the interrelation between two categorical variables and can help find interactions between them. cross_tabulation can provide useful information about the nature of land-use/land-cover (LULC) changes between two dates of classified multi-spectral satellite imagery. For example, the extent of urban expansion could be described using the information about the extent of pixels in an 'urban' class in Date 2 that were previously assigned to other classes (e.g. agricultural LULC categories) in the Date 1 imagery.

Both input images must share the same grid, as the analysis requires a comparison of a pair of images on a cell-by-cell basis. If a grid cell contains a NoData value in either of the input images, the cell will be excluded from the analysis.

Function Signature

def cross_tabulation(self, raster1: Raster, raster2: Raster, output_html_file: str) -> None: ...

csv_points_to_vector

This tool can be used to import a series of points contained within a comma-separated values (*.csv) file (input_file) into a vector shapefile of a POINT VectorGeometryType. The input file must be an ASCII text file with a .csv extensions. The tool will automatically detect the field data type; for numeric fields, it will also determine the appropriate length and precision. The user must specify the x-coordinate (x_field_num) and y-coordiante (y_field_num) fields. All fields are imported as attributes in the output (output) vector file. The tool assumes that the first line of the file is a header line from which field names are retrieved.

See Also

merge_table_with_csv, export_table_to_csv

Function Signature

def csv_points_to_vector(self, input_file: str, x_field_num: int = 0, y_field_num: int = 1, epsg: int = 0) -> Vector: ...

cumulative_distribution

This tool converts the values in an input image (input) into a cumulative distribution function. Therefore, the output raster (output) will contain the cumulative probability value (0-1) of of values equal to or less than the value in the corresponding grid cell in the input image. NoData values in the input image are not considered during the transformation and remain NoData values in the output image.

See Also

z_scores

Function Signature

def cumulative_distribution(self, raster: Raster) -> Raster: ...

d8_flow_accum

This tool is used to generate a flow accumulation grid (i.e. catchment area) using the D8 (O'Callaghan and Mark, 1984) algorithm. This algorithm is an example of single-flow-direction (SFD) method because the flow entering each grid cell is routed to only one downslope neighbour, i.e. flow divergence is not permitted. The user must specify the name of the input digital elevation model (DEM) or flow pointer raster (input) derived using the D8 or Rho8 method (d8_pointer, rho8_pointer). If an input DEM is used, it must have been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using the breach_depressions_least_cost or fill_depressions tools. If a D8 pointer raster is input, the user must also specify the optional pntr flag. If the D8 pointer follows the Esri pointer scheme, rather than the default WhiteboxTools scheme, the user must also specify the optional esri_pntr flag.

In addition to the input DEM/pointer, the user must specify the output type. The output flow-accumulation can be 1) cells (i.e. the number of inflowing grid cells), catchment area (i.e. the upslope area), or specific contributing area (i.e. the catchment area divided by the flow width. The default value is cells. The user must also specify whether the output flow-accumulation grid should be log-tranformed (log), i.e. the output, if this option is selected, will be the natural-logarithm of the accumulated flow value. This is a transformation that is often performed to better visualize the contributing area distribution. Because contributing areas tend to be very high along valley bottoms and relatively low on hillslopes, when a flow-accumulation image is displayed, the distribution of values on hillslopes tends to be 'washed out' because the palette is stretched out to represent the highest values. Log-transformation provides a means of compensating for this phenomenon. Importantly, however, log-transformed flow-accumulation grids must not be used to estimate other secondary terrain indices, such as the wetness index, or relative stream power index.

Grid cells possessing the NoData value in the input DEM/pointer raster are assigned the NoData value in the output flow-accumulation image.

Reference

O'Callaghan, J. F., & Mark, D. M. 1984. The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing, 28(3), 323-344.

See Also:

FD8FlowAccumulation, quinn_flow_accumulation, qin_flow_accumulation, DInfFlowAccumulation, MDInfFlowAccumulation, rho8_pointer, d8_pointer, breach_depressions_least_cost, fill_depressions

Function Signature

def d8_flow_accum(self, raster: Raster, out_type: str = "sca", log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False, esri_pntr: bool = False) -> Raster: ...

d8_mass_flux

This tool can be used to perform a mass flux calculation using DEM-based surface flow-routing techniques. For example, it could be used to model the distribution of sediment or phosphorous within a catchment. Flow-routing is based on a D8 flow pointer (i.e. flow direction) derived from an input depresionless DEM (dem). The user must also specify the names of loading (loading), efficiency (efficiency), and absorption (absorption) rasters, as well as the output raster. Mass Flux operates very much like a flow-accumulation operation except that rather than accumulating catchment areas the algorithm routes a quantity of mass, the spatial distribution of which is specified within the loading image. The efficiency and absorption rasters represent spatial distributions of losses to the accumulation process, the difference being that the efficiency raster is a proportional loss (e.g. only 50% of material within a particular grid cell will be directed downslope) and the absorption raster is an loss specified as a quantity in the same units as the loading image. The efficiency image can range from 0 to 1, or alternatively, can be expressed as a percentage. The equation for determining the mass sent from one grid cell to a neighbouring grid cell is:

Outflowing Mass = (Loading - Absorption + Inflowing Mass) × Efficiency

This tool assumes that each of the three input rasters have the same number of rows and columns and that any NoData cells present are the same among each of the inputs.

See Also

DInfMassFlux

Function Signature

def d8_mass_flux(self, dem: Raster, loading: Raster, efficiency: Raster, absorption: Raster) -> Raster: ...

d8_pointer

This tool is used to generate a flow pointer grid using the simple D8 (O'Callaghan and Mark, 1984) algorithm. The user must specify the name (dem) of a digital elevation model (DEM) that has been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using either the breach_depressions_least_cost or fill_depressions tool. The local drainage direction raster output (output) by this tool serves as a necessary input for several other spatial hydrology and stream network analysis tools in the toolset. Some tools will calculate this flow pointer raster directly from the input DEM.

By default, D8 flow pointers use the following clockwise, base-2 numeric index convention:

Notice that grid cells that have no lower neighbours are assigned a flow direction of zero. In a DEM that has been pre-processed to remove all depressions and flat areas, this condition will only occur along the edges of the grid. If the pointer file contains ESRI flow direction values instead, the esri_pntr parameter must be specified.

Grid cells possessing the NoData value in the input DEM are assigned the NoData value in the output image.

Memory Usage

The peak memory usage of this tool is approximately 10 bytes per grid cell.

Reference

O'Callaghan, J. F., & Mark, D. M. (1984). The extraction of drainage networks from digital elevation data. Computer vision, graphics, and image processing, 28(3), 323-344.

See Also

DInfPointer, fd8_pointer, breach_depressions_least_cost, fill_depressions

Function Signature

def d8_pointer(self, dem: Raster, esri_pointer: bool = False) -> Raster: ...

depth_in_sink

This tool measures the depth that each grid cell in an input (dem) raster digital elevation model (DEM) lies within a sink feature, i.e. a closed topographic depression. A sink, or depression, is a bowl-like landscape feature, which is characterized by interior drainage and groundwater recharge. The depth_in_sink tool operates by differencing a filled DEM, using the same depression filling method as fill_depressions, and the original surface model.

In addition to the names of the input DEM (dem) and the output raster (output), the user must specify whether the background value (i.e. the value assigned to grid cells that are not contained within sinks) should be set to 0.0 (zero_background) Without this optional parameter specified, the tool will use the NoData value as the background value.

Reference

Antonić, O., Hatic, D., & Pernar, R. (2001). DEM-based depth in sink as an environmental estimator. Ecological Modelling, 138(1-3), 247-254.

See Also

fill_depressions

Function Signature

def depth_in_sink(self, dem: Raster, zero_background: bool = False) -> Raster: ...

deviation_from_mean_elevation

This tool can be used to calculate the difference between the elevation of each grid cell and the mean elevation of the centering local neighbourhood, normalized by standard deviation. Therefore, this index of topographic residual is essentially equivalent to a local z-score. This attribute measures the relative topographic position as a fraction of local relief, and so is normalized to the local surface roughness. DevFromMeanElev utilizes an integral image approach (Crow, 1984) to ensure highly efficient filtering that is invariant with filter size.

The user must input a digital elevation model (DEM) (dem) and the size of the neighbourhood in the x and y directions (filterx and filtery), measured in grid size.

While DeviationFromMeanElev calculates the deviation from mean elevation (DEV) at a single, user-defined scale, the max_elevation_deviation tool can be used to output the per-pixel maximum DEV value across a range of input scales.

See Also

DiffFromMeanElev, max_elevation_deviation

Function Signature

def deviation_from_mean_elevation(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

deviation_from_regional_direction

This tool calculates the degree to which each polygon in an input shapefile (input) deviates from the average, or regional, direction. The input file will have a new attribute inserted in the attribute table, DEV_DIR, which will contain the calculated values. The deviation values are in degrees. The orientation of each polygon is determined based on the long-axis of the minimum bounding box fitted to the polygon. The regional direction is based on the mean direciton of the polygons, weighted by long-axis length (longer polygons contribute more weight) and elongation, i.e., a function of the long and short axis lengths (greater elongation contributes more weight). Polygons with elongation values lower than the elongation threshold value (elongation_threshold), which has values between 0 and 1, will be excluded from the calculation of the regional direction.

See Also

patch_orientation, elongation_ratio

Function Signature

def deviation_from_regional_direction(self, input: Vector, elongation_threshold: float = 0.75) -> Vector: ...

diff_of_gaussians_filter

This tool can be used to perform a difference-of-Gaussians (DoG) filter on a raster image. In digital image processing, DoG is a feature enhancement algorithm that involves the subtraction of one blurred version of an image from another, less blurred version of the original. The blurred images are obtained by applying filters with Gaussian-weighted kernels of differing standard deviations to the input image (input). Blurring an image using a Gaussian-weighted kernel suppresses high-frequency spatial information and emphasizes lower-frequency variation. Subtracting one blurred image from the other preserves spatial information that lies between the range of frequencies that are preserved in the two blurred images. Thus, the difference-of-Gaussians is a band-pass filter that discards all but a specified range of spatial frequencies that are present in the original image.

The algorithm operates by differencing the results of convolving two kernels of weights with each grid cell and its neighbours in an image. The weights of the convolution kernels are determined by the 2-dimensional Gaussian (i.e. normal) curve, which gives stronger weighting to cells nearer the kernel centre. The size of the two convolution kernels are determined by setting the two standard deviation parameters (sigma1 and sigma2); the larger the standard deviation the larger the resulting filter kernel. The second standard deviation should be a larger value than the first, however if this is not the case, the tool will automatically swap the two parameters. Both standard deviations can range from 0.5-20.

The difference-of-Gaussians filter can be used to emphasize edges present in an image. Other edge-sharpening filters also operate by enhancing high-frequency detail, but because random noise also has a high spatial frequency, many of these sharpening filters tend to enhance noise, which can be an undesirable artifact. The difference-of-Gaussians filter can remove high-frequency noise while emphasizing edges. This filter can, however, reduce overall image contrast.

See Also

gaussian_filter, fast_almost_gaussian_filter, laplacian_filter, LaplacianOfGaussianFilter`

Function Signature

def diff_of_gaussians_filter(self, raster: Raster, sigma1: float = 2.0, sigma2: float = 4.0) -> Raster: ...

difference

This tool will remove all the overlapping features, or parts of overlapping features, between input and overlay vector files, outputting only the features that occur in one of the two inputs but not both. The Symmetrical Difference is related to the Boolean exclusive-or (XOR) operation in set theory and is one of the common vector overlay operations in GIS. The user must specify the names of the input and overlay vector files as well as the output vector file name. The tool operates on vector points, lines, or polygon, but both the input and overlay files must contain the same VectorGeometryType.

The Symmetrical Difference can also be derived using a combination of other vector overlay operations, as either (A union B) difference (A intersect B), or (A difference B) union (B difference A).

The attributes of the two input vectors will be merged in the output attribute table. Fields that are duplicated between the inputs will share a single attribute in the output. Fields that only exist in one of the two inputs will be populated by null in the output table. Multipoint VectorGeometryTypes however will simply contain a single output feature identifier (FID) attribute. Also, note that depending on the VectorGeometryType (polylines and polygons), Measure and Z ShapeDimension data will not be transferred to the output geometries. If the input attribute table contains fields that measure the geometric properties of their associated features (e.g. length or area), these fields will not be updated to reflect changes in geometry shape and size resulting from the overlay operation.

See Also

intersect, difference, union, clip, erase

Function Signature

def difference(self, input: Vector, overlay: Vector) -> Vector: ...

difference_from_mean_elevation

This tool can be used to calculate the difference between the elevation of each grid cell and the mean elevation of the centering local neighbourhood. This is similar to what a high-pass filter calculates for imagery data, but is intended to work with DEM data instead. This attribute measures the relative topographic position. DiffFromMeanElev utilizes an integral image approach (Crow, 1984) to ensure highly efficient filtering that is invariant with filter size.

The user must specify a digital elevation model (DEM) (dem) , and the size of the neighbourhood in the x and y directions (filterx and filtery), measured in grid size.

While DevFromMeanElev calculates the DIFF at a single, user-defined scale, the max_difference_from_mean tool can be used to output the per-pixel maximum DIFF value across a range of input scales.

See Also

DevFromMeanElev, max_difference_from_mean

Function Signature

def difference_from_mean_elevation(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

dinf_flow_accum

This tool is used to generate a flow accumulation grid (i.e. contributing area) using the D-infinity algorithm (Tarboton, 1997). This algorithm is an examples of a multiple-flow-direction (MFD) method because the flow entering each grid cell is routed to one or two downslope neighbour, i.e. flow divergence is permitted. The user must specify the name of the input digital elevation model or D-infinity pointer raster (input). If an input DEM is specified, the DEM should have been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using the breach_depressions_least_cost or fill_depressions tool.

In addition to the input DEM/pointer raster name, the user must specify the output type (out_type). The output flow-accumulation can be 1) specific catchment area (SCA), which is the upslope contributing area divided by the contour length (taken as the grid resolution), 2) total catchment area in square-metres, or 3) the number of upslope grid cells. The user must also specify whether the output flow-accumulation grid should be log-tranformed, i.e. the output, if this option is selected, will be the natural-logarithm of the accumulated area. This is a transformation that is often performed to better visualize the contributing area distribution. Because contributing areas tend to be very high along valley bottoms and relatively low on hillslopes, when a flow-accumulation image is displayed, the distribution of values on hillslopes tends to be 'washed out' because the palette is stretched out to represent the highest values. Log-transformation (log) provides a means of compensating for this phenomenon. Importantly, however, log-transformed flow-accumulation grids must not be used to estimate other secondary terrain indices, such as the wetness index, or relative stream power index.

Grid cells possessing the NoData value in the input DEM/pointer raster are assigned the NoData value in the output flow-accumulation image. The output raster is of the float data type and continuous data scale.

Reference

Tarboton, D. G. (1997). A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water resources research, 33(2), 309-319.

See Also

DInfPointer, D8FlowAccumulation, <a href="tool_help.md#quinn_flow_accumulation">quinn_flow_accumulation</a>, <a href="tool_help.md#qin_flow_accumulation">qin_flow_accumulation</a>, FD8FlowAccumulation, MDInfFlowAccumulation`, rho8_pointer, breach_depressions_least_cost, fill_depressions

Function Signature

def dinf_flow_accum(self, dem: Raster, out_type: str = "sca", convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False) -> Raster: ...

dinf_mass_flux

This tool can be used to perform a mass flux calculation using DEM-based surface flow-routing techniques. For example, it could be used to model the distribution of sediment or phosphorous within a catchment. Flow-routing is based on a D-Infinity flow pointer derived from an input DEM (dem). The user must also specify the names of loading (loading), efficiency (efficiency), and absorption (absorption) rasters, as well as the output raster. Mass Flux operates very much like a flow-accumulation operation except that rather than accumulating catchment areas the algorithm routes a quantity of mass, the spatial distribution of which is specified within the loading image. The efficiency and absorption rasters represent spatial distributions of losses to the accumulation process, the difference being that the efficiency raster is a proportional loss (e.g. only 50% of material within a particular grid cell will be directed downslope) and the absorption raster is an loss specified as a quantity in the same units as the loading image. The efficiency image can range from 0 to 1, or alternatively, can be expressed as a percentage. The equation for determining the mass sent from one grid cell to a neighbouring grid cell is:

Outflowing Mass = (Loading - Absorption + Inflowing Mass) × Efficiency

This tool assumes that each of the three input rasters have the same number of rows and columns and that any NoData cells present are the same among each of the inputs.

See Also

d8_mass_flux

Function Signature

def dinf_mass_flux(self, dem: Raster, loading: Raster, efficiency: Raster, absorption: Raster) -> Raster: ...

dinf_pointer

This tool is used to generate a flow pointer grid (i.e. flow direction) using the D-infinity (Tarboton, 1997) algorithm. Dinf is a multiple-flow-direction (MFD) method because the flow entering each grid cell is routed one or two downslope neighbours, i.e. flow divergence is permitted. The user must specify the name of a digital elevation model (DEM; dem) that has been hydrologically corrected to remove all spurious depressions and flat areas (breach_depressions_least_cost, fill_depressions). DEM pre-processing is usually achieved using the breach_depressions_least_cost or fill_depressions tool1. Flow directions are specified in the output flow-pointer grid (output) as azimuth degrees measured from north, i.e. any value between 0 and 360 degrees is possible. A pointer value of -1 is used to designate a grid cell with no flow-pointer. This occurs when a grid cell has no downslope neighbour, i.e. a pit cell or topographic depression. Like aspect grids, Dinf flow-pointer grids are best visualized using a circular greyscale palette.

Grid cells possessing the NoData value in the input DEM are assigned the NoData value in the output image. The output raster is of the float data type and continuous data scale.

Reference

Tarboton, D. G. (1997). A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water resources research, 33(2), 309-319.

See Also

DInfFlowAccumulation, breach_depressions_least_cost, fill_depressions

Function Signature

def dinf_pointer(self, dem: Raster) -> Raster: ...

direct_decorrelation_stretch

The Direct Decorrelation Stretch (DDS) is a simple type of saturation stretch. The stretch is applied to a colour composite image and is used to improve the saturation, or colourfulness, of the image. The DDS operates by reducing the achromatic (grey) component of a pixel's colour by a scale factor (k), such that the red (r), green (g), and blue (b) components of the output colour are defined as:

r_k = r - k min(r, g, b)

g_k = g - k min(r, g, b)

b_k = b - k min(r, g, b)

The achromatic factor (k) can range between 0 (no effect) and 1 (full saturation stretch), although typical values range from 0.3 to 0.7. A linear stretch is used afterwards to adjust overall image brightness. Liu and Moore (1996) recommend applying a colour balance stretch, such as balance_contrast_enhancement before using the DDS.

Reference

Liu, J.G., and Moore, J. (1996) Direct decorrelation stretch technique for RGB colour composition. International Journal of Remote Sensing, 17:5, 1005-1018.

See Also

create_colour_composite, balance_contrast_enhancement

Function Signature

def direct_decorrelation_stretch(self, image: Raster, achromatic_factor: float = 0.5, clip_percent: float = 1.0) -> Raster: ...

directional_relief

This tool calculates the relief for each grid cell in a digital elevation model (DEM) in a specified direction. Directional relief is an index of the degree to which a DEM grid cell is higher or lower than its surroundings. It is calculated by subtracting the elevation of a DEM grid cell from the average elevation of those cells which lie between it and the edge of the DEM in a specified compass direction. Thus, positive values indicate that a grid cell is lower than the average elevation of the grid cells in a specific direction (i.e. relatively sheltered), whereas a negative directional relief indicates that the grid cell is higher (i.e. relatively exposed). The algorithm is based on a modification of the procedure described by Lapen and Martz (1993). The modifications include: (1) the ability to specify any direction between 0-degrees and 360-degrees (azimuth), and (2) the ability to use a distance-limited search (max_dist), such that the ray-tracing procedure terminates before the DEM edge is reached for longer search paths. The algorithm works by tracing a ray from each grid cell in the direction of interest and evaluating the average elevation along the ray. Linear interpolation is used to estimate the elevation of the surface where a ray does not intersect the DEM grid precisely at one of its nodes. The user must input a DEM raster file (dem) and a hypothetical wind direction. Furthermore, the user is able to constrain the maximum search distance for the ray tracing. If no maximum search distance is specified, each ray will be traced to the edge of the DEM. The units of the output image are the same as the input DEM.

Ray-tracing is a highly computationally intensive task and therefore this tool may take considerable time to operate for larger sized DEMs. This tool is parallelized to aid with computational efficiency. NoData valued grid cells in the input image will be assigned NoData values in the output image. The output raster is of the float data type and continuous data scale. Directional relief is best displayed using the blue-white-red bipolar palette to distinguish between the positive and negative values that are present in the output.

Reference

Lapen, D. R., & Martz, L. W. (1993). The measurement of two simple topographic indices of wind sheltering-exposure from raster digital elevation models. Computers & Geosciences, 19(6), 769-779.

See Also

fetch_analysis, horizon_angle, relative_aspect

Function Signature

def directional_relief(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf')) -> Raster: ...

dissolve

This tool can be used to remove the interior, or shared, boundaries within a vector polygon coverage. You can either dissolve all interior boundaries or dissolve those boundaries along polygons with the same value of a user-specified attribute within the vector's attribute table. It may be desirable to use the VectorCleaning tool to correct any topological errors resulting from the slight misalignment of nodes along shared boundaries in the vector coverage before performing the dissolve operation.

See Also

clip, erase, polygonize

Function Signature

def dissolve(self, input: Vector, dissolve_field: str = "", snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

distance_to_outlet

Description

This tool calculates the distance of stream grid cells to the channel network outlet cell for each grid cell belonging to a raster stream network. The user must input a raster containing streams data (streams_raster), where stream grid cells are denoted by all positive non-zero values, and a D8 flow pointer (i.e. flow direction) raster (d8_pointer). The pointer image is used to traverse the stream network and must only be created using the D8 algorithm. Stream cells are designated in the streams image as all grid cells with values greater than zero. Thus, all non-stream or background grid cells are commonly assigned either zeros or NoData values. Background cells will be assigned the NoData value in the output image, unless the zero_background parameter is True, in which case non-stream cells will be assigned zero values in the output.

By default, the pointer raster is assumed to use the clockwise indexing method used by Whitebox. If the pointer file contains ESRI flow direction values instead, the esri_pointer parameter must be True.

See Also

downslope_distance_to_stream, length_of_upstream_channels

Parameters

d8_pointer (Raster): The D8 pointer (flow direction) raster.

streams_raster (Raster): The raster object containing the streams data.

esri_pointer (bool): Determines whether the d8_pointer raster contains pointer data in the Esri format. Default is False.

zero_background (bool): Determines whether the background value in the output raster are assigned zero (True) or NoData values (False). Default is False.

Returns

Raster: returning value

Function Signature

def distance_to_outlet(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

diversity_filter

This tool assigns each cell in the output grid the number of different values in a moving window centred on each grid cell in the input raster. The input image should contain integer values but floating point data are allowable and will be handled by multiplying pixel values by 1000 and rounding. Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values, e.g. 3, 5, 7, 9... If the kernel filter size is the same in the x and y dimensions, the silent filter flag may be used instead (command-line interface only).

See Also

majority_filter

Function Signature

def diversity_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

downslope_distance_to_stream

This tool can be used to calculate the distance from each grid cell in a raster to the nearest stream cell, measured along the downslope flowpath. The user must specify the name of an input digital elevation model (dem) and streams raster (streams). The DEM must have been pre-processed to remove artifact topographic depressions and flat areas (see breach_depressions_least_cost). The streams raster should have been created using one of the DEM-based stream mapping methods, i.e. contributing area thresholding. Stream cells are designated in this raster as all non-zero values. The output of this tool, along with the elevation_above_stream tool, can be useful for preliminary flood plain mapping when combined with high-accuracy DEM data.

By default, this tool calculates flow-path using the D8 flow algorithm. However, the user may specify (dinf) that the tool should use the D-infinity algorithm instead.

See Also

elevation_above_stream, distance_to_outlet

Function Signature

def downslope_distance_to_stream(self, dem: Raster, streams: Raster, use_dinf: bool = False) -> Raster: ...

downslope_flowpath_length

This tool can be used to calculate the downslope flowpath length from each grid cell in a raster to an outlet cell either at the edge of the grid or at the outlet point of a watershed. The user must specify the name of a flow pointer grid (d8_pntr) derived using the D8 flow algorithm (d8_pointer). This grid should be derived from a digital elevation model (DEM) that has been pre-processed to remove artifact topographic depressions and flat areas (breach_depressions_least_cost, fill_depressions). The user may also optionally provide watershed (watersheds) and weights (weights) images. The optional watershed image can be used to define one or more irregular-shaped watershed boundaries. Flowpath lengths are measured within each watershed in the watershed image (each defined by a unique identifying number) as the flowpath length to the watershed's outlet cell.

The optional weight image is multiplied by the flow-length through each grid cell. This can be useful when there is a need to convert the units of the output image. For example, the default unit of flowpath lengths is the same as the input image(s). Thus, if the input image has X-Y coordinates measured in metres, the output image will likely contain very large values. A weight image containing a value of 0.001 for each grid cell will effectively convert the output flowpath lengths into kilometres. The weight image can also be used to convert the flowpath distances into travel times by multiplying the flow distance through a grid cell by the average velocity.

NoData valued grid cells in any of the input images will be assigned NoData values in the output image. The output raster is of the float data type and continuous data scale.

See Also

d8_pointer, elevation_above_stream, breach_depressions_least_cost, fill_depressions, watershed

Function Signature

def downslope_flowpath_length(self, d8_pointer: Raster, watersheds: Raster, weights: Raster, esri_pntr: bool = False) -> Raster: ...

downslope_index

This tool can be used to calculate the downslope index described by Hjerdt et al. (2004). The downslope index is a measure of the slope gradient between a grid cell and some downslope location (along the flowpath passing through the upslope grid cell) that represents a specified vertical drop (i.e. a potential head drop). The index has been shown to be useful for hydrological, geomorphological, and biogeochemical applications.

The user must input a digital elevaton model (DEM) raster. This DEM should be have been pre-processed to remove artifact topographic depressions and flat areas. The user must also specify the head potential drop (d), and the output type. The output type can be either 'tangent', 'degrees', 'radians', or 'distance'. If 'distance' is selected as the output type, the output grid actually represents the downslope flowpath length required to drop d meters from each grid cell. Linear interpolation is used when the specified drop value is encountered between two adjacent grid cells along a flowpath traverse.

Notice that this algorithm is affected by edge contamination. That is, for some grid cells, the edge of the grid will be encountered along a flowpath traverse before the specified vertical drop occurs. In these cases, the value of the downslope index is approximated by replacing d with the actual elevation drop observed along the flowpath. To avoid this problem, the entire watershed containing an area of interest should be contained in the DEM.

Grid cells containing NoData values in any of the input images are assigned the NoData value in the output raster. The output raster is of the float data type and continuous data scale.

Reference

Hjerdt, K.N., McDonnell, J.J., Seibert, J. Rodhe, A. (2004) A new topographic index to quantify downslope controls on local drainage, Water Resources Research, 40, W05602, doi:10.1029/2004WR003130.

Function Signature

def downslope_index(self, dem: Raster, vertical_drop: float, output_type: str = "tangent") -> Raster: ...

edge_contamination

This tool identifs grid cells in a DEM for which the upslope area extends beyond the raster data extent, so-called 'edge-contamined cells'. If a significant number of edge contaminated cells intersect with your area of interest, it is likely that any estimate of upslope area (i.e. flow accumulation) will be under-estimated.

The user must specify the name (dem) of the input digital elevation model (DEM) and the output file (output). The DEM must have been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using either the breach_depressions_least_cost (also breach_depressions_least_cost) or fill_depressions tool.

Additionally, the user must specify the type of flow algorithm used for the analysis (-flow_type), which must be one of 'd8', 'mfd', or 'dinf', based on each of the D8FlowAccumulation, FD8FlowAccumulation, DInfFlowAccumulation methods respectively.

See Also

D8FlowAccumulation, FD8FlowAccumulation, DInfFlowAccumulation

Function Signature

def edge_contamination(self, dem: Raster, flow_type: str = "mfd", z_factor: float = -1.0) -> Raster: ...

edge_density

This tool calculates the density of edges, or breaks-in-slope within an input digital elevation model (DEM). A break-in-slope occurs between two neighbouring grid cells if the angular difference between their normal vectors is greater than a user-specified threshold value (norm_diff). edge_density calculates the proportion of edge cells within the neighbouring window, of square filter dimension filter, surrounding each grid cell. Therefore, EdgeDensity is a measure of how complex the topographic surface is within a local neighbourhood. It is therefore a measure of topographic texture. It will take a value near 0.0 for smooth sites and 1.0 in areas of high surface roughness or complex topography.

The distribution of edge_density is highly dependent upon the value of the norm_diff used in the calculation. This threshold may require experimentation to find an appropriate value and is likely dependent upon the topography and source data. Nonetheless, experience has shown that edge_density provides one of the best measures of surface texture of any of the available roughness tools.

See Also

circular_variance_of_aspect, multiscale_roughness, surface_area_ratio, ruggedness_index

Function Signature

def edge_density(self, dem: Raster, filter_size: int = 11, normal_diff_threshold: float = 5.0, z_factor: float = 1.0) -> Raster: ...

edge_preserving_mean_filter

This tool performs a type of edge-preserving mean filter operation on an input image (input). The filter, a type of low-pass filter, can be used to emphasize the longer-range variability in an image, effectively acting to smooth the image and to reduce noise in the image. The algorithm calculates the average value in a moving window centred on each grid cell, including in the averaging only the set of neighbouring values for which the absolute value difference with the centre value is less than a specified threshold value (threshold). It is, therefore, similar to the bilateral_filter, except all neighbours within the threshold difference are equally weighted and neighbour distance is not accounted for. Filter kernels are always square, and filter size, is specified using the filter parameter. This dimensions should be odd, positive integer values, e.g. 3, 5, 7, 9...

This tool works with both greyscale and red-green-blue (RGB) input images. RGB images are decomposed into intensity-hue-saturation (IHS) and the filter is applied to the intensity channel. If an RGB image is input, the threshold value must be in the range 0.0-1.0 (more likely less than 0.15), where a value of 1.0 would result in an ordinary mean filter (mean_filter). NoData values in the input image are ignored during filtering.

See Also

mean_filter, bilateral_filter, edge_preserving_mean_filter, gaussian_filter, median_filter, rgb_to_ihs

Function Signature

def edge_preserving_mean_filter(self, raster: Raster, filter_size: int = 11, threshold: float = 15.0) -> Raster: ...

edge_proportion

This tool will measure the edge proportion, i.e. the proportion of grid cells in a patch that are located along the patch's boundary, for an input raster image (input). Edge proportion is an indicator of polygon shape complexity and elongation. The user must specify the name of the output raster file (output), which will be raster layer containing the input features assigned the edge proportion. The user may also optionally choose to output text data for easy input to a spreadsheet or database.

Objects in the input raster are designated by their unique identifiers. Identifier values must be positive, non-zero whole numbers.

See Also

shape_complexity_index_raster, linearity_index, elongation_ratio

Function Signature

def edge_proportion(self, raster: Raster) -> Tuple[Raster, str]: ...

elev_relative_to_min_max

This tool can be used to express the elevation of a grid cell in a digital elevation model (DEM) as a percentage of the relief between the DEM minimum and maximum values. As such, it provides a basic measure of relative topographic position.

See Also

elev_relative_to_watershed_min_max, elevation_above_stream, ElevAbovePit

Function Signature

def elev_relative_to_min_max(self, dem: Raster) -> Raster: ...

elev_relative_to_watershed_min_max

This tool can be used to express the elevation of a grid cell in a digital elevation model (DEM) as a percentage of the relief between the watershed minimum and maximum values. As such, it provides a basic measure of relative topographic position. The user must input a DEM (dem) and watersheds (watersheds) raster files.

See Also

elev_relative_to_min_max, elevation_above_stream, ElevAbovePit

Function Signature

def elev_relative_to_watershed_min_max(self, dem: Raster, watersheds: Raster) -> Raster: ...

elevation_above_pit

This tool will calculate the elevation of each grid cell in a digital elevation model (DEM) above the nearest downslope pit cell or grid edge cell, depending on which is encountered first during the flow-path traverse. The resulting image is therefore a measure of relative landscape position. The user must input a D8 flow pointer grid and a DEM file. The flow pointer grid must be derived using the D8 flow algorithm.

See Also

elevation_above_stream

Function Signature

def elevation_above_pit(self, dem: Raster) -> Raster: ...

elevation_above_stream

This tool can be used to calculate the elevation of each grid cell in a raster above the nearest stream cell, measured along the downslope flowpath. This terrain index, a measure of relative topographic position, is essentially equivalent to the 'height above drainage' (HAND), as described by Renno et al. (2008). The user must specify the name of an input digital elevation model (dem) and streams raster (streams). The DEM must have been pre-processed to remove artifact topographic depressions and flat areas (see breach_depressions_least_cost). The streams raster should have been created using one of the DEM-based stream mapping methods, i.e. contributing area thresholding. Stream cells are designated in this raster as all non-zero values. The output of this tool, along with the downslope_distance_to_stream tool, can be useful for preliminary flood plain mapping when combined with high-accuracy DEM data.

The difference between elevation_above_stream and elevation_above_stream_euclidean is that the former calculates distances along drainage flow-paths while the latter calculates straight-line distances to streams channels.

Reference

Renno, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., Tomasella, J., & Waterloo, M. J. (2008). HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia. Remote Sensing of Environment, 112(9), 3469-3481.

See Also

elevation_above_stream_euclidean, downslope_distance_to_stream, ElevAbovePit, breach_depressions_least_cost

Function Signature

def elevation_above_stream(self, dem: Raster, streams: Raster) -> Raster: ...

elevation_above_stream_euclidean

This tool can be used to calculate the elevation of each grid cell in a raster above the nearest stream cell, measured along the straight-line distance. This terrain index, a measure of relative topographic position, is related to the 'height above drainage' (HAND), as described by Renno et al. (2008). HAND is generally estimated with distances measured along drainage flow-paths, which can be calculated using the elevation_above_stream tool. The user must specify the name of an input digital elevation model (dem) and streams raster (streams). Stream cells are designated in this raster as all non-zero values. The output of this tool, along with the downslope_distance_to_stream tool, can be useful for preliminary flood plain mapping when combined with high-accuracy DEM data.

The difference between elevation_above_stream and elevation_above_stream_euclidean is that the former calculates distances along drainage flow-paths while the latter calculates straight-line distances to streams channels.

Reference

Renno, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., Tomasella, J., & Waterloo, M. J. (2008). HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia. Remote Sensing of Environment, 112(9), 3469-3481.

See Also

elevation_above_stream, downslope_distance_to_stream, ElevAbovePit

Function Signature

def elevation_above_stream_euclidean(self, dem: Raster, streams: Raster) -> Raster: ...

elevation_percentile

Elevation percentile (EP) is a measure of local topographic position (LTP). It expresses the vertical position for a digital elevation model (DEM) grid cell (z₀) as the percentile of the elevation distribution within the filter window, such that:

EP = count_i∈C(z_i > z₀) x (100 / n_C)

where z₀ is the elevation of the window's center grid cell, z_i is the elevation of cell i contained within the neighboring set C, and n_C is the number of grid cells contained within the window.

EP is unsigned and expressed as a percentage, bound between 0% and 100%. Quantile-based estimates (e.g., the median and interquartile range) are often used in nonparametric statistics to provide data variability estimates without assuming the distribution is normal. Thus, EP is largely unaffected by irregularly shaped elevation frequency distributions or by outliers in the DEM, resulting in a highly robust metric of LTP. In fact, elevation distributions within small to medium sized neighborhoods often exhibit skewed, multimodal, and non-Gaussian distributions, where the occurrence of elevation errors can often result in distribution outliers. Thus, based on these statistical characteristics, EP is considered one of the most robust representation of LTP.

The algorithm implemented by this tool uses the relatively efficient running-histogram filtering algorithm of Huang et al. (1979). Because most DEMs contain floating point data, elevation values must be rounded to be binned. The sig_digits parameter is used to determine the level of precision preserved during this binning process. The algorithm is parallelized to further aid with computational efficiency.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

References

Newman, D. R., Lindsay, J. B., and Cockburn, J. M. H. (2018). Evaluating metrics of local topographic position for multiscale geomorphometric analysis. Geomorphology, 312, 40-50.

Huang, T., Yang, G.J.T.G.Y. and Tang, G., 1979. A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(1), pp.13-18.

See Also

DevFromMeanElev, DiffFromMeanElev

Function Signature

def elevation_percentile(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...

eliminate_coincident_points

This tool can be used to remove any coincident, or nearly coincident, points from a vector points file. The user must specify the name of the input file, which must be of a POINTS VectorGeometryType, the output file name, and the tolerance distance. All points that are within the specified tolerance distance will be eliminated from the output file. A tolerance distance of 0.0 indicates that points must be exactly coincident to be removed.

See Also

LidarRemoveDuplicates

Function Signature

def eliminate_coincident_points(self, input: Vector, tolerance_dist: float) -> Vector: ...

elongation_ratio

This tool can be used to calculate the elongation ratio for vector polygons. The elongation ratio values calculated for each vector polygon feature will be placed in the accompanying database file (.dbf) as an elongation field (ELONGATION).

The elongation ratio (E) is:

E = 1 - S / L

Where S is the short-axis length, and L is the long-axis length. Axes lengths are determined by estimating the minimum bounding box.

The elongation ratio provides similar information as the Linearity Index. The ratio is not an adequate measure of overall polygon narrowness, because a highly sinuous but narrow polygon will have a low linearity (elongation) owing to the compact nature of these polygon.

Function Signature

def elongation_ratio(self, input: Vector) -> Vector: ...

embankment_mapping

This tool can be used to map and/or remove road embankments from an input fine-resolution digital elevation model (dem). Fine-resolution LiDAR DEMs can represent surface features such as road and railway embankments with high fidelity. However, transportation embankments are problematic for several environmental modelling applications, including soil an vegetation distribution mapping, where the pre-embankment topography is the contolling factor. The algorithm utilizes repositioned (search_dist) transportation network cells, derived from rasterizing a transportation vector (road_vec), as seed points in a region-growing operation. The embankment region grows based on derived morphometric parameters, including road surface width (min_road_width), embankment width (typical_width and max_width), embankment height (max_height), and absolute slope (spillout_slope). The tool can be run in two modes. By default the tool will simply map embankment cells, with a Boolean output raster. If, however, the remove_embankments flag is specified, the tool will instead output a DEM for which the mapped embankment grid cells have been excluded and new surfaces have been interpolated based on the surrounding elevation values (see below).

Hillshade from original DEM:

Hillshade from embankment-removed DEM:

References

Van Nieuwenhuizen, N, Lindsay, JB, DeVries, B. 2021. Automated mapping of transportation embankments in fine-resolution LiDAR DEMs. Remote Sensing. 13(7), 1308; https://doi.org/10.3390/rs13071308

See Also:

remove_off_terrain_objects, smooth_vegetation_residual

Function Signature

def embankment_mapping(self, dem: Raster, roads_vector: Vector, search_dist: float = 2.5, min_road_width: float = 6.0, typical_embankment_width: float = 30.0, typical_embankment_max_height: float = 2.0, embankment_max_width: float = 60.0, max_upwards_increment: float = 0.05, spillout_slope: float = 4.0, remove_embankments: bool = False) -> Tuple[Raster, Union[Raster, None]]: ...

emboss_filter

This tool can be used to perform one of eight 3x3 emboss filters on a raster image. Like the sobel_filter and prewitt_filter, the emboss_filter is often applied in edge-detection applications. While these other two common edge-detection filters approximate the slope magnitude of the local neighbourhood surrounding each grid cell, the emboss_filter can be used to estimate the directional slope. The kernel weights for each of the eight available filters are as follows:

North (n)

Northeast (ne)

East (e)

Southeast (se)

South (s)

Southwest (sw)

West (w)

Northwest (nw)

The user must specify the direction, options include 'n', 's', 'e', 'w', 'ne', 'se', 'nw', 'sw'. The user may also optionally clip the output image distribution tails by a specified amount (e.g. 1%).

See Also

sobel_filter, prewitt_filter

Function Signature

def emboss_filter(self, raster: Raster, direction: str = "n", clip_amount: float = 0.0) -> Raster: ...

erase

This tool will remove all the features, or parts of features, that overlap with the features of the erase vector file. The erasing operation is one of the most common vector overlay operations in GIS and effectively imposes the boundary of the erase layer on a set of input vector features, or target features.

See Also

clip

Function Signature

def erase(self, input: Vector, erase_layer: Vector) -> Vector: ...

erase_polygon_from_lidar

This tool can be used to isolate, or clip, all of the LiDAR points in a LAS file (input) contained within one or more vector polygon features. The user must specify the name of the input clip file (--polygons), which must be a vector of a Polygon base shape type. The clip file may contain multiple polygon features and polygon hole parts will be respected during clipping, i.e. LiDAR points within polygon holes will be removed from the output LAS file.

Use the erase_polygon_from_lidar tool to perform the complementary operation of removing points from a LAS file that are contained within a set of polygons.

See Also

erase_polygon_from_lidar, filter_lidar, clip, clip_raster_to_polygon

Function Signature

def erase_polygon_from_lidar(self, input: Lidar, polygons: Vector) -> Lidar: ...

erase_polygon_from_raster

This tool can be used to set values an input raster (input) to a NoData background value with a vector erasing polygon (polygons). The input erase polygon file must be a vector of a Polygon base shape type. The erase file may contain multiple polygon features. Polygon hole parts will be respected during clipping, i.e. polygon holes will not be removed from the output raster. Raster grid cells that fall inside of a polygons in the erase file will be assigned the NoData background value in the output file.

See Also

clip_raster_to_polygon

Function Signature

def erase_polygon_from_raster(self, raster: Raster, polygons: Vector) -> Raster: ...

euclidean_allocation

This tool assigns grid cells in the output image the value of the nearest target cell in the input image, measured by the Euclidean distance (i.e. straight-line distance). Thus, euclidean_allocation essentially creates the Voronoi diagram for a set of target cells. Target cells are all non-zero, non-NoData grid cells in the input image. Distances are calculated using the same efficient algorithm (Shih and Wu, 2003) as the euclidean_distance tool.

Reference

Shih FY and Wu Y-T (2004), Fast Euclidean distance transformation in two scans using a 3 x 3 neighborhood, Computer Vision and Image Understanding, 93: 195-205.

See Also

euclidean_distance, voronoi_diagram, cost_allocation

Function Signature

def euclidean_allocation(self, input: Raster) -> Raster: ...

euclidean_distance

This tool will estimate the Euclidean distance (i.e. straight-line distance) between each grid cell and the nearest 'target cell' in the input image. Target cells are all non-zero, non-NoData grid cells. Distance in the output image is measured in the same units as the horizontal units of the input image.

Algorithm Description

The algorithm is based on the highly efficient distance transform of Shih and Wu (2003). It makes four passes of the image; the first pass initializes the output image; the second and third passes calculate the minimum squared Euclidean distance by examining the 3 x 3 neighbourhood surrounding each cell; the last pass takes the square root of cell values, transforming them into true Euclidean distances, and deals with NoData values that may be present. All NoData value grid cells in the input image will contain NoData values in the output image. As such, NoData is not a suitable background value for non-target cells. Background areas should be designated with zero values.

Reference

Shih FY and Wu Y-T (2004), Fast Euclidean distance transformation in two scans using a 3 x 3 neighborhood, Computer Vision and Image Understanding, 93: 195-205.

See Also

euclidean_allocation, cost_distance

Function Signature

def euclidean_distance(self, input: Raster) -> Raster: ...

export_table_to_csv

This tool can be used to export a vector's attribute table to a comma separated values (CSV) file. CSV files stores tabular data (numbers and text) in plain-text form such that each row corresponds to a record and each column to a field. Fields are typically separated by commas within records. The user must specify the name of the vector (and associated attribute file), the name of the output CSV file, and whether or not to include the field names as a header column in the output CSV file.

See Also

merge_table_with_csv

Function Signature

def export_table_to_csv(self, input: Vector, output_csv_file: str, headers: bool = True) -> None: ...

exposure_towards_wind_flux

This tool creates a new raster in which each grid cell is assigned the exposure of the land-surface to a hypothetical wind flux. It can be conceptualized as the angle between a plane orthogonal to the wind and a plane that represents the local topography at a grid cell (Bohner and Antonic, 2007). The user must input a digital elevation model (dem), as well as the dominant wind azimuth (azimuth) and a maximum search distance (max_dist) used to calclate the horizon angle. Notice that the specified azimuth represents a regional average wind direction.

Exposure towards the sloped wind flux essentially combines the relative terrain aspect and the maximum upwind slope (i.e. horizon angle). This terrain attribute accounts for land-surface orientation, relative to the wind, and shadowing effects of distant topographic features but does not account for deflection of the wind by topography. This tool should not be used on very extensive areas over which Earth's curvature must be taken into account. DEMs in projected coordinate systems are preferred.

Algorithm Description:

Exposure is measured based on the equation presented in Antonic and Legovic (1999):

cos(E) = cos(S) sin(H) + sin(S) cos(H) cos(Az - A)

Where, E is angle between a plane defining the local terrain and a plane orthogonal to the wind flux, S is the terrain slope, A is the terrain aspect, Az is the azimuth of the wind flux, and H is the horizon angle of the wind flux, which is zero when only the horizontal component of the wind flux is accounted for.

Exposure images are best displayed using a greyscale or bipolar palette to distinguish between the positive and negative values that are present in the output.

References

Antonić, O., & Legović, T. 1999. Estimating the direction of an unknown air pollution source using a digital elevation model and a sample of deposition. Ecological modelling, 124(1), 85-95.

Böhner, J., & Antonić, O. 2009. Land-surface parameters specific to topo-climatology. Developments in Soil Science, 33, 195-226.

See Also

relative_aspect

Function Signature

def exposure_towards_wind_flux(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf'), z_factor: float = 1.0) -> Raster: ...

extend_vector_lines

This tool can be used to extend vector lines by a specified distance. The user must input the names of the input and output shapefiles, the distance to extend features by, and whether to extend both ends, line starts, or line ends. The input shapefile must be of a POLYLINE base shape type and should be in a projected coordinate system.

Function Signature

def extend_vector_lines(self, input: Vector, distance: float, extend_direction: str = "both") -> Vector: ...

extract_by_attribute

This tool extracts features from an input vector into an output file based on attribute properties. The user must specify the name of the input (input) and output (output) files, along with the filter statement (statement). The conditional statement is a single-line logical condition containing one or more attribute variables contained in the file's attribute table that evaluates to TRUE/FALSE. In addition to the common comparison and logical
operators, i.e. < > <= >= == (EQUAL TO) != (NOT EQUAL TO) || (OR) && (AND), conditional statements may contain a
any valid mathematical operation and the null value.

The following are examples of valid conditional statements:

HEIGHT >= 300.0

CROP == "corn"

(ELEV >= 525.0) && (HGT_AB_GR <= 5.0)

math::ln(CARBON) > 1.0

VALUE == null

Function Signature

def extract_by_attribute(self, input: Vector, statement: str) -> Vector: ...

extract_nodes

This tool converts vector lines or polygons into vertex points. The user must specify the name of the input vector, which must be of a polyline or polygon base shape type, and the name of the output point-type vector.

Function Signature

def extract_nodes(self, input: Vector) -> Vector: ...

extract_raster_values_at_points

This tool can be used to extract the values of one or more rasters (inputs) at the sites of a set of vector points. By default, the data is output to the attribute table of the input points (points) vector; however, if the out_text parameter is specified, the tool will additionally output point values as text data to standard output (stdout). Attribute fields will be added to the table of the points file, with field names, VALUE1, VALUE2, VALUE3, etc. each corresponding to the order of input rasters.

If you need to plot a chart of values from a raster stack at a set of points, the image_stack_profile may be more suitable for this application.

See Also

image_stack_profile, find_lowest_or_highest_points

Function Signature

def extract_raster_values_at_points(self, rasters: List[Raster], points: Vector) -> Tuple[Vector, str]: ...

extract_streams

Description

This tool can be used to extract, or map, the likely stream cells from an input flow-accumulation image (flow_accumulation). The algorithm applies a threshold to the input flow accumulation image such that streams are considered to be all grid cells with accumulation values greater than the specified threshold (threshold). As such, this threshold represents the minimum area (area is used here as a surrogate for discharge) required to initiate and maintain a channel. Smaller threshold values result in more extensive stream networks and vice versa. Unfortunately there is very little guidance regarding an appropriate method for determining the channel initiation area threshold in practice. As such, it is frequently determined either by examining map or imagery data, using field work, or by experimentation until a suitable or desirable channel network is identified. Notice that the threshold value will be unique for each landscape and dataset (including source and grid resolution), further complicating its a priori determination. There is also evidence that in some landscape the threshold is a combined upslope area-slope function. Generally, a lower threshold is appropriate in humid climates and a higher threshold is appropriate in areas underlain by more resistant bedrock. Climate and bedrock resistance are two factors related to drainage density, i.e. the extent to which a landscape is dissected by drainage channels.

The background value of the output raster will be the NoData value unless zero_background is set to True.

See Also

extract_valleys

Parameters

flow_accumulation (Raster): The input flow accumulation Raster object.

threshold (float): The minimum accumulation value required to be part of a stream channel. Default is 0.0, but should be set higher.

zero_background (bool): Whether the output raster uses 0.0 for non-channel cells (True) or NoData (False). Default is False.

Returns:

Raster

Function Signature

def extract_streams(self, flow_accumulation: Raster, threshold: float = 0.0, zero_background: bool = False) -> Raster: ...

extract_valleys

This tool can be used to extract channel networks from an input digital elevation models (dem) using one of three techniques that are based on local topography alone.

The Lindsay (2006) 'lower-quartile' method (variant='LQ') algorithm is a type of 'valley recognition' method. Other channel mapping methods, such as the Johnston and Rosenfeld (1975) algorithm, experience problems because channel profiles are not always 'v'-shaped, nor are they always apparent in small 3 x 3 windows. The lower-quartile method was developed as an alternative and more flexible valley recognition channel mapping technique. The lower-quartile method operates by running a filter over the DEM that calculates the percentile value of the centre cell with respect to the distribution of elevations within the filter window. The roving window is circular, the diameter of which should reflect the topographic variation of the area (e.g. the channel width or average hillslope length). If this variant is selected, the user must specify the filter_size parameter, in pixels, and this value should be an odd number (e.g. 3, 5, 7, etc.). The appropriateness of the selected window diameter will depend on the grid resolution relative to the scale of topographic features. Cells that are within the lower quartile of the distribution of elevations of their neighbourhood are flagged. Thus, the algorithm identifies grid cells that are in relatively low topographic positions at a local scale. This approach to channel mapping is only appropriate in fluvial landscapes. In regions containing numerous lakes and wetlands, the algorithm will pick out the edges of features.

The Johnston and Rosenfeld (1975) algorithm (variant='JandR') is a type of 'valley recognition' method and operates as follows: channel cells are flagged in a 3 x 3 window if the north and south neighbours are higher than the centre grid cell or if the east and west neighbours meet this same criterion. The group of cells that are flagged after one pass of the roving window constituted the drainage network. This method is best applied to DEMs that are relatively smooth and do not exhibit high levels of short-range roughness. As such, it may be desirable to use a smoothing filter before applying this tool. The feature_preserving_smoothing is a good option for removing DEM roughness while preserving the topographic information contain in breaks-in-slope (i.e. edges).

The Peucker and Douglas (1975) algorithm (variant='PandD') is one of the simplest and earliest algorithms for topography-based network extraction. Their 'valley recognition' method operates by passing a 2 x 2 roving window over a DEM and flagging the highest grid cell in each group of four. Once the window has passed over the entire DEM, channel grid cells are left unflagged. This method is also best applied to DEMs that are relatively smooth and do not exhibit high levels of short-range roughness. Pre-processing the DEM with the feature_preserving_smoothing tool may also be useful when applying this method.

Each of these methods of extracting valley networks result in line networks that can be wider than a single grid cell. As such, it is often desirable to thin the resulting network using a line-thinning algorithm. The option to perform line-thinning is provided by the tool as a post-processing step (line_thin=True).

References

Johnston, E. G., & Rosenfeld, A. (1975). Digital detection of pits, peaks, ridges, and ravines. IEEE Transactions on Systems, Man, and Cybernetics, (4), 472-480.

Lindsay, J. B. (2006). Sensitivity of channel mapping techniques to uncertainty in digital elevation data. International Journal of Geographical Information Science, 20(6), 669-692.

Peucker, T. K., & Douglas, D. H. (1975). Detection of surface-specific points by local parallel processing of discrete terrain elevation data. Computer Graphics and image processing, 4(4), 375-387.

See Also

feature_preserving_smoothing

Function Signature

def extract_valleys(self, dem: Raster, variant: str = "lq", line_thin: bool = False, filter_size: int = 5) -> Raster: ...

farthest_channel_head

Description

This tool calculates the upstream distance to the farthest stream head for each grid cell belonging to a raster stream network. The user must input a raster containing streams data (streams), where stream grid cells are denoted by all positive non-zero values, and a D8 flow pointer (i.e. flow direction) raster (d8_pointer). The pointer image is used to traverse the stream network and must only be created using the D8 algorithm. Stream cells are designated in the streams image as all values greater than zero. Thus, all non-stream or background grid cells are commonly assigned either zeros or NoData values. Background cells will be assigned the NoData value in the output image, unless zero_background=True, in which case non-stream cells will be assigned zero values in the output.

By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools. If the pointer file contains ESRI flow direction values instead, the user should specify esri_pntr=True.

See Also

length_of_upstream_channels, find_main_stem

Parameters

d8_pointer (Raster): The D8 pointer (flow direction) raster.

streams_raster (Raster): The raster object containing the streams data.

esri_pointer (bool): Determines whether the d8_pointer raster contains pointer data in the Esri format. Default is False.

zero_background (bool): Determines whether the background value in the output raster are assigned zero (True) or NoData values (False). Default is False.

Returns

Raster: returning value

Function Signature

def farthest_channel_head(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

fast_almost_gaussian_filter

The tool is somewhat modified from Dr. Kovesi's original Matlab code in that it works with both greyscale and RGB images (decomposes to HSI and uses the intensity data) and it handles the case of rasters that contain NoData values. This adds complexity to the original 20 additions and 5 multiplications assertion of the original paper.

Also note, for small values of sigma (< 1.8), you should probably just use the regular GaussianFilter tool.

Reference

P. Kovesi 2010 Fast Almost-Gaussian Filtering, Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on.

Function Signature

def fast_almost_gaussian_filter(self, raster: Raster, sigma: float = 1.8) -> Raster: ...

fd8_flow_accum

This tool is used to generate a flow accumulation grid (i.e. contributing area) using the FD8 algorithm (Freeman, 1991), sometimes referred to as FMFD. This algorithm is an examples of a multiple-flow-direction (MFD) method because the flow entering each grid cell is routed to each downslope neighbour, i.e. flow divergence is permitted. The user must specify the name (dem) of the input digital elevation model (DEM). The DEM must have been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using either the breach_depressions_least_cost (also breach_depressions_least_cost) or fill_depressions tool. A value must also be specified for the exponent parameter (exponent), a number that controls the degree of dispersion in the resulting flow-accumulation grid. A lower value yields greater apparent flow dispersion across divergent hillslopes. Some experimentation suggests that a value of 1.1 is appropriate (Freeman, 1991), although this is almost certainly landscape-dependent.

In addition to the input DEM, the user must specify the output type (out_type). The output flow-accumulation can be 1) cells (i.e. the number of inflowing grid cells), catchment area (i.e. the upslope area), or specific contributing area (i.e. the catchment area divided by the flow width. The default value is cells. The user must also specify whether the output flow-accumulation grid should be log-tranformed (log), i.e. the output, if this option is selected, will be the natural-logarithm of the accumulated flow value. This is a transformation that is often performed to better visualize the contributing area distribution. Because contributing areas tend to be very high along valley bottoms and relatively low on hillslopes, when a flow-accumulation image is displayed, the distribution of values on hillslopes tends to be 'washed out' because the palette is stretched out to represent the highest values. Log-transformation provides a means of compensating for this phenomenon. Importantly, however, log-transformed flow-accumulation grids must not be used to estimate other secondary terrain indices, such as the wetness index, or relative stream power index.

The non-dispersive threshold (threshold) is a flow-accumulation value (measured in upslope grid cells, which is directly proportional to area) above which flow dispersion is no longer permitted. Grid cells with flow-accumulation values above this threshold will have their flow routed in a manner that is similar to the D8 single-flow-direction algorithm, directing all flow towards the steepest downslope neighbour. This is usually done under the assumption that flow dispersion, whilst appropriate on hillslope areas, is not realistic once flow becomes channelized.

Reference

Freeman, T. G. (1991). Calculating catchment area with divergent flow based on a regular grid. Computers and Geosciences, 17(3), 413-422.

See Also

D8FlowAccumulation, quinn_flow_accumulation, qin_flow_accumulation, DInfFlowAccumulation, MDInfFlowAccumulation, rho8_pointer

Function Signature

def fd8_flow_accum(self, dem: Raster, out_type: str = "sca", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...

fd8_pointer

This tool is used to generate a flow pointer grid (i.e. flow direction) using the FD8 (Freeman, 1991) algorithm. FD8 is a multiple-flow-direction (MFD) method because the flow entering each grid cell is routed one or more downslope neighbours, i.e. flow divergence is permitted. The user must specify the name of a digital elevation model (DEM; dem) that has been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using the breach_depressions_least_cost or fill_depressions tools.

By default, D8 flow pointers use the following clockwise, base-2 numeric index convention:

In the case of the FD8 algorithm, some portion of the flow entering a grid cell will be sent to each downslope neighbour. Thus, the FD8 flow-pointer value is the sum of each of the individual pointers for all downslope neighbours. For example, if a grid cell has downslope neighbours to the northeast, east, and south the corresponding FD8 flow-pointer value will be 1 + 2 + 8 = 11. Using the naming convention above, this is the only combination of flow-pointers that will result in the combined value of 11. Using the base-2 naming convention allows for the storage of complex combinations of flow-points using a single numeric value, which is the reason for using this somewhat odd convention.

Reference

Freeman, T. G. (1991). Calculating catchment area with divergent flow based on a regular grid. Computers and Geosciences, 17(3), 413-422.

See Also

FD8FlowAccumulation, d8_pointer, DInfPointer, breach_depressions_least_cost, fill_depressions

Function Signature

def fd8_pointer(self, dem: Raster) -> Raster: ...

feature_preserving_smoothing

Description

This tool implements a highly modified form of the DEM de-noising algorithm described by Sun et al. (2007). It is very effective at removing surface roughness from digital elevation models (DEMs), without significantly altering breaks-in-slope. As such, this tool should be used for smoothing DEMs rather than either smoothing with low-pass filters (e.g. mean, median, Gaussian filters) or grid size coarsening by resampling. The algorithm works by 1) calculating the surface normal 3D vector of each grid cell in the DEM, 2) smoothing the normal vector field using a filtering scheme that applies more weight to neighbours with lower angular difference in surface normal vectors, and 3) uses the smoothed normal vector field to update the elevations in the input DEM.

Sun et al.'s (2007) original method was intended to work on input point clouds and fitted triangular irregular networks (TINs). The algorithm has been modified to work with input raster DEMs instead. In so doing, this algorithm calculates surface normal vectors from the planes fitted to 3 x 3 neighbourhoods surrounding each grid cell, rather than the triangular facet. The normal vector field smoothing and elevation updating procedures are also based on raster filtering operations. These modifications make this tool more efficient than Sun's original method, but will also result in a slightly different output than what would be achieved with Sun's method.

The user must specify the values of three key parameters, including the filter size (filter), the normal difference threshold (norm_diff), and the number of iterations (num_iter). Lindsay et al. (2019) found that the degree of smoothing was less impacted by the filter size than it was either the normal difference threshold and the number of iterations. A filter size of 11, the default value, tends to work well in many cases. To increase the level of smoothing applied to the DEM, consider increasing the normal difference threshold, i.e. the angular difference in normal vectors between the center cell of a filter window and a neighbouring cell. This parameter determines which neighbouring values are included in a filtering operation and higher values will result in a greater number of neighbouring cells included, and therefore smoother surfaces. Similarly, increasing the number of iterations from the default value of 3 to upwards of 5-10 will result in significantly greater smoothing.

Before smoothing treatment:

After smoothing treatment with FPS:

For a video tutorial on how to use the feature_preserving_smoothing tool, please see this YouTube video.

Reference

Lindsay JB, Francioni A, Cockburn JMH. 2019. LiDAR DEM smoothing and the preservation of drainage features. Remote Sensing, 11(16), 1926; DOI: 10.3390/rs11161926.

Sun, X., Rosin, P., Martin, R., & Langbein, F. (2007). Fast and effective feature-preserving mesh denoising. IEEE Transactions on Visualization & Computer Graphics, (5), 925-938.

Parameters

dem (Raster): The input digital elevation model (DEM)

filter_size (int): The filter size used for smoothing. Default is 11.

normal_diff_threshold (float): The maximum allowable difference in the angle of the normals between two grid cells on the same facet. Default is 8.0.

iterations (int): The number of iterations used during smoothing. Default is 3.

max_elevation_diff (float): The maximum allowable vertical distance that a cell's elevation is allowed to be changed by

z_factor (float): Used to convert elevation units so that they match the horizontal units. Unless the two units differ, this should be set to 1.0. Default is 1.0.

Returns

Raster: return value

Function Signature

def feature_preserving_smoothing(self, dem: Raster, filter_size: int = 11, normal_diff_threshold: float = 8.0, iterations: int = 3, max_elevation_diff: float = float('inf'), z_factor: float = 1.0) -> Raster: ...

fetch_analysis

This tool creates a new raster in which each grid cell is assigned the distance, in meters, to the nearest topographic obstacle in a specified direction. It is a modification of the algorithm described by Lapen and Martz (1993). Unlike the original algorithm, Fetch Analysis is capable of analyzing fetch in any direction from 0-360 degrees. The user must input a digital elevation model (DEM) raster file, a hypothetical wind direction, and a value for the height increment parameter. The algorithm searches each grid cell in a path following the specified wind direction until the following condition is met:

Z_test >= Z_core + DI

where Z_core is the elevation of the grid cell at which fetch is being determined, Z_test is the elevation of the grid cell being tested as a topographic obstacle, D is the distance between the two grid cells in meters, and I is the height increment in m/m. Lapen and Martz (1993) suggest values for I in the range of 0.025 m/m to 0.1 m/m based on their study of snow re-distribution in low-relief agricultural landscapes of the Canadian Prairies. If the directional search does not identify an obstacle grid cell before the edge of the DEM is reached, the distance between the DEM edge and Zcore is entered. Edge distances are assigned negative values to differentiate between these artificially truncated fetch values and those for which a valid topographic obstacle was identified. Notice that linear interpolation is used to estimate the elevation of the surface where a ray (i.e. the search path) does not intersect the DEM grid precisely at one of its nodes.

Ray-tracing is a highly computationally intensive task and therefore this tool may take considerable time to operate for larger sized DEMs. This tool is parallelized to aid with computational efficiency. NoData valued grid cells in the input image will be assigned NoData values in the output image. Fetch Analysis images are best displayed using the blue-white-red bipolar palette to distinguish between the positive and negative values that are present in the output.

Reference

Lapen, D. R., & Martz, L. W. (1993). The measurement of two simple topographic indices of wind sheltering-exposure from raster digital elevation models. Computers & Geosciences, 19(6), 769-779.

See Also

directional_relief, horizon_angle, relative_aspect

Function Signature

def fetch_analysis(self, dem: Raster, azimuth: float = 0.0, height_increment: float = 0.05) -> Raster: ...

fill_burn

Burns streams into a DEM using the FillBurn (Saunders, 1999) method which produces a hydro-enforced DEM. This tool uses the algorithm described in:

Lindsay JB. 2016. The practice of DEM stream burning revisited. Earth Surface Processes and Landforms, 41(5): 658-668. DOI: 10.1002/esp.3888

And:

Saunders, W. 1999. Preparation of DEMs for use in environmental modeling analysis, in: ESRI User Conference. pp. 24-30.

Function Signature

def fill_burn(self, dem: Raster, streams: Vector) -> Raster: ...

fill_depressions

This tool can be used to fill all of the depressions in a digital elevation model (DEM) and to remove the flat areas. This is a common pre-processing step required by many flow-path analysis tools to ensure continuous flow from each grid cell to an outlet located along the grid edge. The fill_depressions algorithm operates by first identifying single-cell pits, that is, interior grid cells with no lower neighbouring cells. Each pit cell is then visited from highest to lowest and a priority region-growing operation is initiated. The area of monotonically increasing elevation, starting from the pit cell and growing based on flood order, is identified. Once a cell, that has not been previously visited and possessing a lower elevation than its discovering neighbour cell, is identified the discovering neighbour is labelled as an outlet (spill point) and the outlet elevation is noted. The algorithm then back-fills the labelled region, raising the elevation in the output DEM (output) to that of the outlet. Once this process is completed for each pit cell (noting that nested pit cells are often solved by prior pits) the flat regions of filled pits are optionally treated (fix_flats) with an applied small slope gradient away from outlets (note, more than one outlet cell may exist for each depression). The user may optionally specify the size of the elevation increment used to solve flats (flat_increment), although it is best to not specify this optional value and to let the algorithm determine the most suitable value itself. The flat-fixing method applies a small gradient away from outlets using another priority region-growing operation (i.e. based on a priority queue operation), where priorities are set by the elevations in the input DEM (input). This in effect ensures a gradient away from outlet cells but also following the natural pre-conditioned topography internal to depression areas. For example, if a large filled area occurs upstream of a damming road-embankment, the filled DEM will possess flow directions that are similar to the un-flooded valley, with flow following the valley bottom. In fact, the above case is better handled using the breach_depressions_least_cost tool, which would simply cut through the road embankment at the likely site of a culvert. However, the flat-fixing method of fill_depressions does mean that this common occurrence in LiDAR DEMs is less problematic.

The breach_depressions_least_cost, while slightly less efficient than either other hydrological preprocessing methods, often provides a lower impact solution to topographic depressions and should be preferred in most applications. In comparison with the breach_depressions_least_cost tool, the depression filling method often provides a less satisfactory, higher impact solution. It is advisable that users try the breach_depressions_least_cost tool to remove depressions from their DEMs before using fill_depressions. Nonetheless, there are applications for which full depression filling using the fill_depressions tool may be preferred.

Note that this tool will not fill in NoData regions within the DEM. It is advisable to remove such regions using the fill_missing_data tool prior to application.

See Also

breach_depressions_least_cost, breach_depressions_least_cost, sink, depth_in_sink, fill_missing_data

Function Signature

def fill_depressions(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan'), max_depth: float = float('inf')) -> Raster: ...

fill_depressions_planchon_and_darboux

This tool can be used to fill all of the depressions in a digital elevation model (DEM) and to remove the flat areas using the Planchon and Darboux (2002) method. This is a common pre-processing step required by many flow-path analysis tools to ensure continuous flow from each grid cell to an outlet located along the grid edge. This tool is currently not the most efficient depression-removal algorithm available in WhiteboxTools; fill_depressions and breach_depressions_least_cost are both more efficient and often produce better, lower-impact results.

The user may optionally specify the size of the elevation increment used to solve flats (flat_increment), although it is best not to specify this optional value and to let the algorithm determine the most suitable value itself.

Reference

Planchon, O. and Darboux, F., 2002. A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena, 46(2-3), pp.159-176.

See Also

fill_depressions, breach_depressions_least_cost

Function Signature

def fill_depressions_planchon_and_darboux(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...

fill_depressions_wang_and_liu

This tool can be used to fill all of the depressions in a digital elevation model (DEM) and to remove the flat areas. This is a common pre-processing step required by many flow-path analysis tools to ensure continuous flow from each grid cell to an outlet located along the grid edge. The fill_depressions_wang_and_liu algorithm is based on the computationally efficient approach of examining each cell based on its spill elevation, starting from the edge cells, and visiting cells from lowest order using a priority queue. As such, it is based on the algorithm first proposed by Wang and Liu (2006). However, it is currently not the most efficient depression-removal algorithm available in WhiteboxTools; fill_depressions and breach_depressions_least_cost are both more efficient and often produce better, lower-impact results.

If the input DEM has gaps, or missing-data holes, that contain NoData values, it is better to use the fill_missing_data tool to repair these gaps. This tool will interpolate values across the gaps and produce a more natural-looking surface than the flat areas that are produced by depression filling. Importantly, the fill_depressions tool algorithm implementation assumes that there are no 'donut hole' NoData gaps within the area of valid data. Any NoData areas along the edge of the grid will simply be ignored and will remain NoData areas in the output image.

The user may optionally specify the size of the elevation increment used to solve flats (flat_increment), although it is best not to specify this optional value and to let the algorithm determine the most suitable value itself.

Reference

Wang, L. and Liu, H. 2006. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, 20(2): 193-213.

See Also

fill_depressions, breach_depressions_least_cost, breach_depressions_least_cost, fill_missing_data

Function Signature

def fill_depressions_wang_and_liu(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...

fill_missing_data

This tool can be used to fill in small gaps in a raster or digital elevation model (DEM). The gaps, or holes, must have recognized NoData values. If gaps do not currently have this characteristic, use the set_nodata_value tool and ensure that the data are stored using a raster format that supports NoData values. All valid, non-NoData values in the input raster will be assigned the same value in the output image.

The algorithm uses an inverse-distance weighted (IDW) scheme based on the valid values on the edge of NoData gaps to estimate gap values. The user must specify the filter size (filter), which determines the size of gap that is filled, and the IDW weight (weight).

The filter size, specified in grid cells, is used to determine how far the algorithm will search for valid, non-NoData values. Therefore, setting a larger filter size allows for the filling of larger gaps in the input raster.

The no_edges flag can be used to exclude NoData values that are connected to the edges of the raster. It is usually the case that irregularly shaped DEMs have large regions of NoData values along the containing raster edges. This flag can be used to exclude these regions from the gap-filling operation, leaving only interior gaps for filling.

See Also

set_nodata_value

Function Signature

def fill_missing_data(self, dem: Raster, filter_size: int = 11, weight: float = 2.0, exclude_edge_nodata: bool = False) -> Raster: ...

fill_pits

This tool can be used to remove pits from a digital elevation model (DEM). Pits are single grid cells with no downslope neighbours. They are important because they impede overland flow-paths. This tool will remove any pits in the input DEM that can be resolved by raising the elevation of the pit such that flow will continue past the pit cell to one of the downslope neighbours. Notice that this tool can be a useful pre-processing technique before running one of the more robust depression breaching (breach_depressions_least_cost) or filling (fill_depressions) techniques, which are designed to remove larger depression features.

See Also

breach_depressions_least_cost, fill_depressions, breach_single_cell_pits

Function Signature

def fill_pits(self, dem: Raster) -> Raster: ...

filter_lidar_classes

This tool can be used to remove points within a LAS LiDAR file that possess certain specified class values. The user must input the names of the input (input) and output (output) LAS files and the class values to be excluded (exclude_cls). Class values are specified by their numerical values, such that:

Thus, to filter out low and high noise points from a point cloud, specify exclude_cls='7,18'. Class ranges may also be specified, e.g. exclude_cls='3-5,7,18'. Notice that usage of this tool assumes that the LAS file has underwent a comprehensive point classification, which not all point clouds have had. Use the lidar_info tool determine the distribution of various class values in your file.

See Also

lidar_info

Function Signature

def filter_lidar_classes(self, input: Lidar, exclusion_classes: List[int]) -> Lidar: ...

filter_lidar_scan_angles

Function Signature

def filter_lidar_scan_angles(self, in_lidar: Lidar, threshold: int) -> Lidar: ...

filter_raster_features_by_area

This tool takes an input raster (input) containing integer-labelled features, such as the output of the clump tool, and removes all features that are smaller than a user-specified size (threshold), measured in grid cells. The user must specify the replacement value for removed features using the background parameter, which can be either zero or nodata.

See Also

clump

Function Signature

def filter_raster_features_by_area(self, input: Raster, threshold: int, zero_background: bool = False) -> Raster: ...

find_flightline_edge_points

Function Signature

def find_flightline_edge_points(self, in_lidar: Lidar) -> Lidar: ...

find_lowest_or_highest_points

This tool locates the lowest and/or highest cells in a raster and outputs these locations to a vector points file. The user must specify the name of the input raster (input) and the name of the output vector file (output). The user also has the option (out_type) to locate either the lowest value, highest value, or both values. The output vector's attribute table will contain fields for the points XY coordinates and their values.

See Also

extract_raster_values_at_points

Function Signature

def find_lowest_or_highest_points(self, raster: Raster, output_type: str = "lowest") -> Vector: ...

find_main_stem

This tool can be used to identify the main channel in a stream network. The user must input a D8 pointer (flow direction) raster (d8_pointer), and a streams raster (streams_raster). The pointer raster is used to traverse the stream network and should only be created using the d8_pointer tool. By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools:

If the pointer file contains ESRI flow direction values instead, you must set esri_pointer=True parameter must be specified.

The streams raster should have been created using one of the DEM-based stream mapping methods, i.e. contributing area thresholding. Stream grid cells are designated in the streams image as all positive, non-zero values. All non-stream cells will be assigned the NoData value in the output image, unless the user sets zero_background=True.

The algorithm operates by traversing each stream and identifying the longest stream-path draining to each outlet. When a confluence is encountered, the traverse follows the branch with the larger distance-to-head.

See Also

d8_pointer

Function Signature

def find_main_stem(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

find_noflow_cells

This tool can be used to find cells with undefined flow, i.e. no valid flow direction, based on the D8 flow direction algorithm (d8_pointer). These cells are therefore either at the bottom of a topographic depression or in the interior of a flat area. In a digital elevation model (DEM) that has been pre-processed to remove all depressions and flat areas (breach_depressions_least_cost), this condition will only occur along the edges of the grid, otherwise no-flow grid cells can be situation in the interior. The user must specify the name (dem) of the DEM.

See Also

d8_pointer, breach_depressions_least_cost

Function Signature

def find_noflow_cells(self, dem: Raster) -> Raster: ...

find_parallel_flow

This tool can be used to find cells in a stream network grid that possess parallel flow directions based on an input D8 flow-pointer grid (d8_pointer). Because streams rarely flow in parallel for significant distances, these areas are likely errors resulting from the biased assignment of flow direction based on the D8 method.

See Also

d8_pointer

Function Signature

def find_parallel_flow(self, d8_pntr: Raster, streams: Raster) -> Raster: ...

find_patch_edge_cells

This tool will identify all grid cells situated along the edges of patches or class features within an input raster (input). Edge cells in the output raster (output) will have the patch identifier value assigned in the corresponding grid cell. All non-edge cells will be assigned zero in the output raster. Patches (or classes) are designated by positive, non-zero values in the input image. Zero-valued and NoData-valued grid cells are interpreted as background cells by the tool.

See Also

edge_proportion

Function Signature

def find_patch_edge_cells(self, raster: Raster) -> Raster: ...

find_ridges

This tool can be used to identify ridge cells in a digital elevation model (DEM). Ridge cells are those that have lower neighbours either to the north and south or the east and west. Line thinning can optionally be used to create single-cell wide ridge networks by specifying the line_thin parameter.

Function Signature

def find_ridges(self, dem: Raster, line_thin: bool = True) -> Raster: ...

flatten_lakes

This tool can be used to set the elevations contained in a set of input vector lake polygons (lakes) to a consistent value within an input (dem) digital elevation model (DEM). Lake flattening is a common pre-processing step for DEMs intended for use in hydrological applications. This algorithm determines lake elevation automatically based on the minimum perimeter elevation for each lake polygon. The minimum perimeter elevation is assumed to be the lake outlet elevation and is assigned to the entire interior region of lake polygons, excluding island geometries. Note, this tool will not provide satisfactory results if the input vector polygons contain wide river features rather than true lakes. When this is the case, the tool will lower the entire river to the elevation of its mouth, leading to the creation of an artificial gorge.

See Also

fill_depressions

Function Signature

def flatten_lakes(self, dem: Raster, lakes: Vector) -> Raster: ...

flightline_overlap

This tool can be used to map areas of overlapping flightlines in an input LiDAR (LAS) file (input). The output raster file (output) will contain the number of different flightlines that are contained within each grid cell. The user must specify the desired cell size (resolution). The flightline associated with a LiDAR point is assumed to be contained within the point's Point Source ID property. Thus, the tool essentially counts the number of different Point Source ID values among the points contained within each grid cell. If the Point Source ID property is not set, or has been lost, users may with to apply the recover_flightline_info tool prior to running flightline_overlap.

It is important to set the resolution parameter appropriately, as setting this value too high will yield the mis-characterization of non-overlap areas, and setting the resolution to low will result in fewer than expected overlap areas. An appropriate resolution size value may require experimentation, however a value that is 2-3 times the nominal point spacing has been previously recommended. The nominal point spacing can be determined using the lidar_info tool.

Note that this tool is intended to be applied to LiDAR tile data containing points that have been merged from multiple overlapping flightlines. It is commonly the case that airborne LiDAR data from each of the flightlines from a survey are merged and then tiled into 1 km² tiles, which are the target dataset for this tool.

Like many of the LiDAR related tools, the input and output file parameters are optional. If left unspecified, the tool will locate all valid LiDAR files within the current Whitebox working directory and use these for calculation (specifying the output raster file name based on the associated input LiDAR file). This can be a helpful way to run the tool on a batch of user inputs within a specific directory.

See Also

classify_overlap_points, recover_flightline_info, lidar_info

Function Signature

def flightline_overlap(self, input_lidar: Lidar, resolution: float = 1.0) -> Raster: ...

flip_image

This tool can be used to flip, or reflect, an image (input) either vertically, horizontally, or both. The axis of reflection is specified using the direction parameter. The input image is not reflected in place; rather, the reflected image is stored in a separate output file.

Function Signature

def flip_image(self, raster: Raster, direction: str = "v") -> Raster: ...

flood_order

This tool takes an input digital elevation model (DEM) and creates an output raster where every grid cell contains the flood order of that cell within the DEM. The flood order is the sequence of grid cells that are encountered during a search, starting from the raster grid edges and the lowest grid cell, moving inward at increasing elevations. This is in fact similar to how the highly efficient Wang and Liu (2006) depression filling algorithm and the Breach Depressions (Fast) operates. The output flood order raster contains the sequential order, from lowest edge cell to the highest pixel in the DEM.

Like the fill_depressions tool, flood_order will read the entire DEM into memory. This may make the algorithm ill suited to processing massive DEMs except where the user's computer has substantial memory (RAM) resources.

Reference

Wang, L., and Liu, H. (2006). An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, 20(2), 193-213.

See Also

fill_depressions

Function Signature

def flood_order(self, dem: Raster) -> Raster: ...

flow_accum_full_workflow

Resolves all of the depressions in a DEM, outputting a breached DEM, an aspect-aligned non-divergent flow pointer, and a flow accumulation raster.

Function Signature

def flow_accum_full_workflow(self, dem: Raster, out_type: str = "sca", log_transform: bool = False, clip: bool = False, esri_pntr: bool = False) -> Tuple[Raster, Raster, Raster]: ...

flow_length_diff

FlowLengthDiff calculates the local maximum absolute difference in downslope flowpath length, which is useful in mapping drainage divides and ridges.

See Also

max_branch_length

Function Signature

def flow_length_diff(self, d8_pointer: Raster, esri_pointer: bool = False, log_transform: bool = False) -> Raster: ...

gamma_correction

This tool performs a gamma colour correction transform on an input image (input), such that each input pixel value (z_in^{) is mapped to the corresponding output value (z_out) as:}

z_out = z_in^gamma

The user must specify the value of the gamma parameter. The input image may be of either a greyscale or RGB colour composite data type.

Function Signature

def gamma_correction(self, raster: Raster, gamma_value: float = 0.5) -> Raster: ...

gaussian_contrast_stretch

This tool performs a Gaussian stretch on a raster image. The observed histogram of the input image is fitted to a Gaussian histogram, i.e. normal distribution. A histogram matching technique is used to map the values from the input image onto the output Gaussian distribution. The user must input the number of tones (num_tones) used.

This tool is related to the more general histogram_matching tool, which can be used to fit any frequency distribution to an input image, and other contrast enhancement tools such as histogram_equalization, min_max_contrast_stretch, percentage_contrast_stretch, sigmoidal_contrast_stretch, and standard_deviation_contrast_stretch.

See Also

piecewise_contrast_stretch, histogram_equalization, min_max_contrast_stretch, percentage_contrast_stretch, sigmoidal_contrast_stretch, standard_deviation_contrast_stretch, histogram_matching

Function Signature

def gaussian_contrast_stretch(self, raster: Raster, num_tones: int = 256) -> Raster: ...

gaussian_curvature

This tool calculates the Gaussian curvature from a digital elevation model (DEM). Gaussian curvature is the product of maximal and minimal curvatures, and retains values in each point of the topographic surface after its bending without breaking, stretching, and compressing (Florinsky, 2017). Gaussian curvature is measured in units of m^-2.

The user must input a DEM (dem).The Z conversion factor (zfactor) is only important when the vertical and horizontal units are not the same in the DEM. When this is the case, the algorithm will multiply each elevation in the DEM by the Z Conversion Factor. Curvature values are often very small and as such the user may opt to log-transform the output raster (log). Transforming the values applies the equation by Shary et al. (2002):

Θ' = sign(Θ) ln(1 + 10ⁿ|Θ|)

where Θ is the parameter value and n is dependent on the grid cell size.

For DEMs in projected coordinate systems, the tool uses the 3rd-order bivariate Taylor polynomial method described by Florinsky (2016). Based on a polynomial fit of the elevations within the 5x5 neighbourhood surrounding each cell, this method is considered more robust against outlier elevations (noise) than other methods. For DEMs in geographic coordinate systems (i.e. angular units), the tool uses the 3x3 polynomial fitting method for equal angle grids also described by Florinsky (2016).

References

Florinsky, I. (2016). Digital terrain analysis in soil science and geology. Academic Press.

Florinsky, I. V. (2017). An illustrated introduction to general geomorphometry. Progress in Physical Geography, 41(6), 723-752.

Shary P. A., Sharaya L. S. and Mitusov A. V. (2002) Fundamental quantitative methods of land surface analysis. Geoderma 107: 1–32.

See Also

tangential_curvature, profile_curvature, plan_curvature, mean_curvature, minimal_curvature, maximal_curvature

Function Signature

def gaussian_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

gaussian_filter

This tool can be used to perform a Gaussian filter on a raster image. A Gaussian filter can be used to emphasize the longer-range variability in an image, effectively acting to smooth the image. This can be useful for reducing the noise in an image. The algorithm operates by convolving a kernel of weights with each grid cell and its neighbours in an image. The weights of the convolution kernel are determined by the 2-dimensional Gaussian (i.e. normal) curve, which gives stronger weighting to cells nearer the kernel centre. It is this characteristic that makes the Gaussian filter an attractive alternative for image smoothing and noise reduction than the mean_filter. The size of the filter is determined by setting the standard deviation parameter (sigma), which is in units of grid cells; the larger the standard deviation the larger the resulting filter kernel. The standard deviation can be any number in the range 0.5-20.

gaussian_filter works with both greyscale and red-green-blue (RGB) colour images. RGB images are decomposed into intensity-hue-saturation (IHS) and the filter is applied to the intensity channel. NoData values in the input image are ignored during processing.

Like many low-pass filters, Gaussian filtering can significantly blur well-defined edges in the input image. The edge_preserving_mean_filter and bilateral_filter offer more robust feature preservation during image smoothing. gaussian_filter is relatively slow compared to the fast_almost_gaussian_filter tool, which offers a fast-running approximatation to a Gaussian filter for larger kernel sizes.

See Also

fast_almost_gaussian_filter, mean_filter, median_filter, rgb_to_ihs

Function Signature

def gaussian_filter(self, raster: Raster, sigma: float = 0.75) -> Raster: ...

geomorphons

This tool can be used to perform a geomorphons landform classification based on an input digital elevation model (dem). The geomorphons concept is based on line-of-sight analysis for the eight topographic profiles in the cardinal directions surrounding each grid cell in the input DEM. The relative sizes of the zenith angle of a profile's maximum elevation angle (i.e. horizon angle) and the nadir angle of a profile's minimum elevation angle are then used to generate a ternary (base-3) digit: 0 when the nadir angle is less than the zenith angle, 1 when the two angles differ by less than a user-defined flatness threshold (threshold), and 2 when the nadir angle is greater than the zenith angle. A ternary number is then derived from the digits assigned to each of the eight profiles, with digits sequenced counter-clockwise from east. This ternary number forms the geomorphons code assigned to the grid cell. There are 3⁸ = 6561 possible codes, although many of these codes are equivalent geomorphons through rotations and reflections. Some of the remaining geomorphons also rarely if ever occur in natural topography. Jasiewicz et al. (2013) identified 10 common landform types by reclassifying related geomorphons codes. The user may choose to output these common forms (forms) rather than the the raw ternary code. These landforms include:

One of the main advantages of the geomrophons method is that, being based on minimum/maximum elevation angles, the scale used to estimate the landform type at a site adapts to the surrounding terrain. In principle, choosing a large value of search distance (search) should result in identification of a landform element regardless of its scale.

An experimental feature has been added to correct for global inclination. Global inclination biases the flatness threshold angle becasue it is measured relative to the z-axis, especially in locally flat areas. Including the residuals flag "flattens" the input by converting elevation to residuals of a 2-d linear model.

Reference

Jasiewicz, J., and Stepinski, T. F. (2013). Geomorphons — a pattern recognition approach to classification and mapping of landforms. Geomorphology, 182, 147-156.

See Also

PennockLandformClass

Function Signature

def geomorphons(self, dem: Raster, search_distance: int = 1, flatness_threshold: float = 1.0, flatness_distance: int = 0, skip_distance: int = 0, output_forms: bool = True, analyze_residuals: bool = False) -> Raster: ...

hack_stream_order

This tool can be used to assign the Hack stream order to each link in a stream network. According to this common stream numbering system, the main stream is assigned an order of one. All tributaries to the main stream (i.e. the trunk) are assigned an order of two; tributaries to second-order links are assigned an order of three, and so on. The trunk or main stream of the stream network can be defined either based on the furthest upstream distance, at each bifurcation (i.e. network junction).

Stream order is often used in hydro-geomorphic and ecological studies to quantify the relative size and importance of a stream segment to the overall river system. Unlike some other stream ordering systems, e.g. Horton-Strahler stream order (strahler_stream_order) and Shreve's stream magnitude (shreve_stream_magnitude), Hack's stream ordering method increases from the catchment outlet towards the channel heads. This has the main advantage that the catchment outlet is likely to be accurately located while the channel network extent may be less accurately mapped.

The user must input a streams raster image (streams_raster) and D8 pointer image (d8_pntr). Stream cells are designated in the streams image as all positive, nonzero values. Thus all non-stream or background grid cells are commonly assigned either zeros or NoData values. The pointer image is used to traverse the stream network and should only be created using the D8 algorithm (d8_pointer). Background cells will be assigned the NoData value in the output image, unless the zero_background=True, in which case non-stream cells will be assigned zero values in the output.

By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools. If the pointer file contains ESRI flow direction values instead, the user should specify esri_pntr=True.

Reference

Hack, J. T. (1957). Studies of longitudinal stream profiles in Virginia and Maryland (Vol. 294). US Government Printing Office.

See Also

horton_stream_order, strahler_stream_order, shreve_stream_magnitude, topological_stream_order

Function Signature

def hack_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

heat_map

This tool is used to generate a raster heat map, or kernel density estimation surface raster from a set of vector points (input). Heat mapping is a visualization and modelling technique used to create the continuous density surface associated with the occurrences of a point phenomenon. Heat maps can therefore be used to identify point clusters by mapping the concentration of event occurrence. For example, heat maps have been used extensively to map the spatial distributions of crime events (i.e. crime mapping) or disease cases.

By default, the tool maps the density of raw occurrence events, however, the user may optionally specify an associated weights field (weights) from the point file's attribute table. When a weights field is specified, these values are simply multiplied by each of the individual components of the density estimate. Weights must be numeric.

The bandwidth parameter (--bandwidth) determines the radius of the kernel used in calculation of the density surface. There are guidelines that statisticians use in determining an appropriate bandwidth for a particular population and data set, but often this parameter is determined through experimentation. The bandwidth of the kernel is a free parameter which exhibits a strong influence on the resulting estimate.

The user must specify the kernel function type (kernel). Options include 'uniform', 'triangular', 'epanechnikov', 'quartic', 'triweight', 'tricube', 'gaussian', 'cosine', 'logistic', 'sigmoid', and 'silverman'; 'quartic' is the default kernel type. Descriptions of each function can be found at the link above.

The characteristics of the output raster (resolution and extent) are determined by one of two optional parameters, cell_size and base. If the user optionally specifies the output grid cell size parameter (cell_size) then the coordinates of the output raster extent are determined by the input vector (i.e. the bounding box) and the specified cell size determines the number of rows and columns. If the user instead specifies the optional base raster file parameter (base), the output raster's coordinates (i.e. north, south, east, west) and row and column count, and therefore, resolution, will be the same as the base file.

Reference

Geomatics (2017) QGIS Heatmap Using Kernel Density Estimation Explained, online resource: https://www.geodose.com/2017/11/qgis-heatmap-using-kernel-density.html visited 02/06/2022.

Function Signature

def heat_map(self, points: Vector, field_name: str, bandwidth: float = 0.0, cell_size: float = 0.0, base_raster: Raster = None, kernel_function: str = "quartic") -> Raster: ...

height_above_ground

This tool normalizes an input LiDAR point cloud (input) such that point z-values in the output LAS file (output) are converted from elevations to heights above the ground, specifically the height above the nearest ground-classified point. The input LAS file must have ground-classified points, otherwise the tool will return an error. The lidar_tophat_transform tool can be used to perform the normalization if a ground classification is lacking.

See Also

lidar_tophat_transform

Function Signature

def height_above_ground(self, input: Lidar) -> Lidar: ...

hexagonal_grid_from_raster_base

This tool can be used to create a hexagonal vector grid. The extent of the hexagonal grid is based on the extent of an input raster base file (base). The user must also specify the hexagonal cell width (width) and whether the hexagonal orientation (orientation) is horizontal or vertical. To use a vector base image instead of a raster, use the hexagonal_grid_from_vector_base tool.

See Also

hexagonal_grid_from_vector_base

Function Signature

def hexagonal_grid_from_raster_base(self, base: Raster, width: float, orientation: str = "h") -> Vector: ...

hexagonal_grid_from_vector_base

This tool can be used to create a hexagonal vector grid. The extent of the hexagonal grid is based on the extent of an input vector base file (base). The user must also specify the hexagonal cell width (width) and whether the hexagonal orientation (orientation) is horizontal or vertical. To use a raster base image instead of a vector, use the hexagonal_grid_from_raster_base tool.

See Also

hexagonal_grid_from_raster_base

Function Signature

def hexagonal_grid_from_vector_base(self, base: Vector, width: float, orientation: str = "h") -> Vector: ...

high_pass_filter

This tool performs a high-pass filter on a raster image. High-pass filters can be used to emphasize the short-range variability in an image. The algorithm operates essentially by subtracting the value at the grid cell at the centre of the window from the average value in the surrounding neighbourhood (i.e. window.)

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

See Also

high_pass_median_filter, mean_filter

Function Signature

def high_pass_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

high_pass_median_filter

This tool performs a high-pass median filter on a raster image. High-pass filters can be used to emphasize the short-range variability in an image. The algorithm operates essentially by subtracting the value at the grid cell at the centre of the window from the median value in the surrounding neighbourhood (i.e. window.)

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

See Also

high_pass_filter, median_filter

Function Signature

def high_pass_median_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...

highest_position

This tool identifies the stack position (index) of the maximum value within a raster stack on a cell-by-cell basis. For example, if five raster images (inputs) are input to the tool, the output raster (output) would show which of the five input rasters contained the highest value for each grid cell. The index value in the output raster is the zero-order number of the raster stack, i.e. if the highest value in the stack is contained in the first image, the output value would be 0; if the highest stack value were the second image, the output value would be 1, and so on. If any of the cell values within the stack is NoData, the output raster will contain the NoData value for the corresponding grid cell. The index value is related to the order of the input images.

Warning

Each of the input rasters must have the same spatial extent and number of rows and columns.

See Also

lowest_position, pick_from_list

Function Signature

def highest_position(self, input_rasters: List[Raster]) -> Raster: ...

hillshade

This tool performs a hillshade operation (also called shaded relief) on an input digital elevation model (DEM). The user must input a DEM. Other parameters that must be specified include the illumination source azimuth (azimuth), or sun direction (0-360 degrees), the illumination source altitude (altitude; i.e. the elevation of the sun above the horizon, measured as an angle from 0 to 90 degrees) and the Z conversion factor (zfactor). The Z conversion factor is only important when the vertical and horizontal units are not the same in the DEM. When this is the case, the algorithm will multiply each elevation in the DEM by the Z conversion factor. If the DEM is in the geographic coordinate system (latitude and longitude), the following equation is used:

zfactor = 1.0 / (111320.0 x cos(mid_lat))

where mid_lat is the latitude of the centre of the raster, in radians.

The hillshade value (HS) of a DEM grid cell is calculate as:

HS = tan(s) / [1 - tan(s)²]^0.5 x [sin(Alt) / tan(s) - cos(Alt) x sin(Az - a)]

where s and a are the local slope gradient and aspect (orientation) respectively and Alt and Az are the illumination source altitude and azimuth respectively. Slope and aspect are calculated using Horn's (1981) 3rd-order finate difference method.

Reference

Gallant, J. C., and J. P. Wilson, 2000, Primary topographic attributes, in Terrain Analysis: Principles and Applications, edited by J. P. Wilson and J. C. Gallant pp. 51-86, John Wiley, Hoboken, N.J.

See Also

hypsometrically_tinted_hillshade, multidirectional_hillshade, aspect, slope

Function Signature

def hillshade(self, dem: Raster, azimuth: float = 315.0, altitude: float = 30.0, z_factor: float = 1.0) -> Raster: ...

hillslopes

This tool decrements (lowers) the elevations of pixels within an input digital elevation model (DEM) (dem) along an input vector stream network (streams) at the sites of road (roads) intersections. In addition to the input data layers, the user must specify the output raster DEM (output), and the maximum road embankment width (width), in map units. The road width parameter is used to determine the length of channel along stream lines, at the junctions between streams and roads, that the burning (i.e. decrementing) operation occurs. The algorithm works by identifying stream-road intersection cells, then traversing along the rasterized stream path in the upstream and downstream directions by half the maximum road embankment width. The minimum elevation in each stream traversal is identified and then elevations that are higher than this value are lowered to the minimum elevation during a second stream traversal.

Reference

Lindsay JB. 2016. The practice of DEM stream burning revisited. Earth Surface Processes and Landforms, 41(5): 658–668. DOI: 10.1002/esp.3888

See Also

raster_streams_to_vector, rasterize_streams

Function Signature

def hillslopes(self, d8_pntr: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...

histogram_equalization

This tool alters the cumulative distribution function (CDF) of a raster image to match, as closely as possible, the CDF of a uniform distribution. Histogram equalization works by first calculating the histogram of the input image. This input histogram is then converted into a CDF. Each grid cell value in the input image is then mapped to the corresponding value in the uniform distribution's CDF that has an equivalent (or as close as possible) cumulative probability value. Histogram equalization provides a very effective means of performing image contrast adjustment in an efficient manner with little need for human input.

The user must specify the name of the input image to perform histogram equalization on. The user must also specify the number of tones, corresponding to the number of histogram bins used in the analysis.

histogram_equalization is related to the histogram_matching_two_images tool (used when an image's CDF is to be matched to a reference CDF derived from a reference image). Similarly, histogram_matching, and gaussian_contrast_stretch are similarly related tools frequently used for image contrast adjustment, where the reference CDFs are uniform and Gaussian (normal) respectively.

Notes:

• The algorithm can introduces gaps in the histograms (steps in the CDF). This is to be expected because the histogram is being distorted. This is more prevalent for integer-level images.
• Histogram equalization is not appropriate for images containing categorical (class) data.

See Also

piecewise_contrast_stretch, histogram_matching, histogram_matching_two_images, gaussian_contrast_stretch

Function Signature

def histogram_equalization(self, raster: Raster, num_tones: int = 256) -> Raster: ...

histogram_matching

This tool alters the cumulative distribution function (CDF) of a raster image to match, as closely as possible, the CDF of a reference histogram. Histogram matching works by first calculating the histogram of the input image. This input histogram and reference histograms are each then converted into CDFs. Each grid cell value in the input image is then mapped to the corresponding value in the reference CDF that has an equivalent (or as close as possible) cumulative probability value. Histogram matching provides the most flexible means of performing image contrast adjustment.

The reference histogram must be specified to the tool in the form of a text file (.txt), provided using the histo_file flag. This file must contain two columns (delimited by a tab, space, comma, colon, or semicolon) where the first column contains the x value (i.e. the values that will be assigned to the grid cells in the output image) and the second column contains the frequency or probability. Note that 1) the file must not contain a header row, 2) each x value/frequency pair must be on a separate row. It is possible to create this type of histogram using the wide range of distribution tools available in most spreadsheet programs (e.g. Excel or LibreOffice's Calc program). You must save the file as a text-only (ASCII) file.

histogram_matching is related to the histogram_matching_two_images tool, which can be used when a reference CDF can be derived from a reference image. histogram_equalization and gaussian_contrast_stretch are similarly related tools frequently used for image contrast adjustment, where the reference CDFs are uniform and Gaussian (normal) respectively.

Notes:

• The algorithm can introduces gaps in the histograms (steps in the CDF). This is to be expected because the histogram is being distorted. This is more prevalent for integer-level images.
• Histogram matching is not appropriate for images containing categorical (class) data.
• This tool is not intended for images containing RGB data. If this is the case, the colour channels should be split using the split_colour_composite tool.

See Also

histogram_matching_two_images, histogram_equalization, gaussian_contrast_stretch, split_colour_composite

Function Signature

def histogram_matching(self, image: Raster, histogram: List[List[float]], histo_is_cumulative: bool = False) -> Raster: ...

histogram_matching_two_images

This tool alters the cumulative distribution function (CDF) of a raster image to match, as closely as possible, the CDF of a reference image. Histogram matching works by first calculating the histograms of the input image (i.e. the image to be adjusted) and the reference image. These histograms are then converted into CDFs. Each grid cell value in the input image is then mapped to the corresponding value in the reference CDF that has the an equivalent (or as close as possible) cumulative probability value. A common application of this is to match the images from two sensors with slightly different responses, or images from the same sensor, but the sensor's response is known to change over time.The size of the two images (rows and columns) do not need to be the same, nor do they need to be geographically overlapping.

histogram_matching_two_images is related to the histogram_matching tool, which can be used when a reference CDF is used directly rather than deriving it from a reference image. histogram_equalization and gaussian_contrast_stretch are similarly related tools, where the reference CDFs are uniform and Gaussian (normal) respectively.

The algorithm may introduces gaps in the histograms (steps in the CDF). This is to be expected because the histograms are being distorted. This is more prevalent for integer-level images. Histogram matching is not appropriate for images containing categorical (class) data. It is also not intended for images containing RGB data, in which case, the colour channels should be split using the split_colour_composite tool.

See Also

histogram_matching, histogram_equalization, gaussian_contrast_stretch, split_colour_composite

Function Signature

def histogram_matching_two_images(self, image1: Raster, image2: Raster) -> Raster: ...

hole_proportion

This calculates the proportion of the total area of a polygon's holes (i.e. islands) relative to the area of the polygon's hull. It can be a useful measure of shape complexity, or how discontinuous a patch is. The user must specify the name of the input vector file and the output data will be contained within the input vector's database file as a new field (HOLE_PROP).

See Also

ShapeComplexityIndex, elongation_ratio, perimeter_area_ratio

Function Signature

def hole_proportion(self, input: Vector) -> Vector: ...

horizon_angle

This tool calculates the horizon angle (Sx), i.e. the maximum slope along a specified azimuth (0-360 degrees) for each grid cell in an input digital elevation model (DEM). Horizon angle is sometime referred to as the maximum upwind slope in wind exposure/sheltering studies. Positive values can be considered sheltered with respect to the azimuth and negative values are exposed. Thus, Sx is a measure of exposure to a wind from a specific direction. The algorithm works by tracing a ray from each grid cell in the direction of interest and evaluating the slope for each location in which the DEM grid is intersected by the ray. Linear interpolation is used to estimate the elevation of the surface where a ray does not intersect the DEM grid precisely at one of its nodes.

The user is able to constrain the maximum search distance (max_dist) for the ray tracing by entering a valid maximum search distance value (in the same units as the X-Y coordinates of the input raster DEM). If the maximum search distance is left blank, each ray will be traced to the edge of the DEM, which will add to the computational time.

Maximum upwind slope should not be calculated for very extensive areas over which the Earth's curvature must be taken into account. Also, this index does not take into account the deflection of wind by topography. However, averaging the horizon angle over a window of directions can yield a more robust measure of exposure, compensating for the deflection of wind from its regional average by the topography. For example, if you are interested in measuring the exposure of a landscape to a northerly wind, you could perform the following calculation:

Sx(N) = [Sx(345)+Sx(350)+Sx(355)+Sx(0)+Sx(5)+Sx(10)+Sx(15)] / 7.0

Ray-tracing is a highly computationally intensive task and therefore this tool may take considerable time to operate for larger sized DEMs. Maximum upwind slope is best displayed using a Grey scale palette that is inverted.

Horizon angle is best visualized using a white-to-black palette and rescaled from approximately -10 to 70 (see below for an example of horizon angle calculated at a 150-degree azimuth).

See Also

time_in_daylight

Function Signature

def horizon_angle(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf')) -> Raster: ...

horton_ratios

This function can be used to calculate Horton's so-called laws of drainage network composition for a input stream network. The user must specify an input DEM (which has been suitably hydrologically pre-processed to remove any topographic depressions) and a raster stream network. The function will output a 4-element tuple containing the bifurcation ratio (Rb), the length ratio (Rl), the area ratio (Ra), and the slope ratio (Rs). These indices are related to drainage network geometry and are used in some geomorphological analysis. The calculation of the ratios is based on the method described by Knighton (1998) Fluvial Forms and Processes: A New Perspective.

Code Example

from whitebox_workflows import WbEnvironment

# Set up the WbW environment
wbe = WbEnvironment()
wbe.verbose = True
wbe.working_directory = '/path/to/data'

# Read the inputs
dem = wbe.read_raster('DEM.tif')
streams = wbe.read_raster('streams.tif')

# Calculate the Horton ratios
(bifurcation_ratio, length_ratio, area_ratio, slope_ratio) = wbe.horton_ratios(dem, streams)

# Outputs
print(f"Bifurcation ratio (Rb): {bifurcation_ratio:.3f}")
print(f"Length ratio (Rl): {length_ratio:.3f}")
print(f"Area ratio (Ra): {area_ratio:.3f}")
print(f"Slope ratio (Rs): {slope_ratio:.3f}")

See Also

horton_stream_order

Function Signature

def horton_ratios(self, dem: Raster, streams_raster: Raster) -> Tuple[float, float, float, float]: ...

horton_stream_order

This tool can be used to assign the Horton stream order to each link in a stream network. Stream ordering is often used in hydro-geomorphic and ecological studies to quantify the relative size and importance of a stream segment to the overall river system. There are several competing stream ordering schemes. Based on to this common stream numbering system, headwater stream links are assigned an order of one. Stream order only increases downstream when two links of equal order join, otherwise the downstream link is assigned the larger of the two link orders.

Strahler order and Horton order are similar approaches to assigning stream network hierarchy. Horton stream order essentially starts with the Strahler order scheme, but subsequently replaces each of the assigned stream order value along the main trunk of the network with the order value of the outlet. The main channel is not treated differently compared with other tributaries in the Strahler ordering scheme.

The user must specify input a streams raster image (streams_raster) and D8 pointer image (d8_pntr). Stream cells are designated in the streams image as all positive, nonzero values. Thus all non-stream or background grid cells are commonly assigned either zeros or NoData values. The pointer image is used to traverse the stream network and should only be created using the D8 algorithm (d8_pointer). Background cells will be assigned the NoData value in the output image, unless the user specifies zero_background=True, in which case non-stream cells will be assigned zero values in the output.

By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools. If the pointer file contains ESRI flow direction values instead, the user must set esri_pntr=True.

Reference Horton, R. E. (1945). Erosional development of streams and their

drainage basins; hydrophysical approach to quantitative morphology. Geological society of America bulletin, 56(3), 275-370.

See Also

hack_stream_order, shreve_stream_magnitude, strahler_stream_order, topological_stream_order

Function Signature

def horton_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...

hypsometric_analysis

This tool can be used to derive the hypsometric curve, or area-altitude curve, of one or more input digital elevation models (DEMs) ('inputs'). A hypsometric curve is a histogram or cumulative distribution function of elevations in a geographical area.

See Also

SlopeVsElevationPlot

Function Signature

def hypsometric_analysis(self, dem_rasters: List[Raster], output_html_file: str, watershed_rasters: List[Raster] = None) -> None: ...

hypsometrically_tinted_hillshade

This tool creates a hypsometrically tinted shaded relief (Swiss hillshading) image from an input digital elevation model (DEM). The tool combines a colourized version of the DEM with varying illumination provided by a hillshade image, to produce a composite relief model that can be used to visual topography for more effective interpretation of landscapes. The output of the tool is a 24-bit red-green-blue (RGB) colour image.

The user must input a DEM. Other parameters that must be specified include the illumination source azimuth (azimuth), or sun direction (0-360 degrees), the illumination source altitude (altitude; i.e. the elevation of the sun above the horizon, measured as an angle from 0 to 90 degrees), the hillshade weight (hs_weight; 0-1), image brightness (brightness; 0-1), and atmospheric effects (atmospheric; 0-1). The hillshade weight can be used to increase or subdue the relative prevalence of the hillshading effect in the output image. The image brightness parameter is used to create an overall brighter or darker version of the terrain rendering; note however, that very high values may over-saturate the well-illuminated portions of the terrain. The atmospheric effects parameter can be used to introduce a haze or atmosphere effect to the output image. It is intended to reproduce the effect of viewing mountain valley bottoms through a thicker and more dense atmosphere. Values greater than zero will introduce a slightly blue tint, particularly at lower altitudes, blur the hillshade edges slightly, and create a random haze-like speckle in lower areas. The user must also specify the Z conversion factor (zfactor). The Z conversion factor is only important when the vertical and horizontal units are not the same in the DEM. When this is the case, the algorithm will multiply each elevation in the DEM by the Z conversion factor. If the DEM is in the geographic coordinate system (latitude and longitude), the following equation is used:

zfactor = 1.0 / (111320.0 x cos(mid_lat))

where mid_lat is the latitude of the centre of the raster, in radians.

See Also

hillshade, multidirectional_hillshade, aspect, slope

Function Signature

def hypsometrically_tinted_hillshade(self, dem: Raster, solar_altitude: float = 45.0, hillshade_weight: float = 0.5, brightness: float = 0.5, atmospheric_effects: float = 0.0, palette: str = "atlas", reverse_palette: bool = False, full_360_mode: bool = False, z_factor: float = 1.0) -> Raster: ...

idw_interpolation

points or a fixed neighbourhood size. This tool is currently configured to perform the later only, using a FixedRadiusSearch structure. Using a fixed number of neighbours will require use of a KD-tree structure. I've been testing one Rust KD-tree library but its performance does not appear to be satisfactory compared to the FixedRadiusSearch. I will need to explore other options here.

Another change that will need to be implemented is the use of a nodal function. The original Whitebox GAT tool allows for use of a constant or a quadratic. This tool only allows the former.

Function Signature

def idw_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, weight: float = 2.0, radius: float = 0.0, min_points: int = 0, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...

ihs_to_rgb

This tool transforms three intensity, hue, and saturation (IHS; sometimes HSI or HIS) raster images into three equivalent multispectral images corresponding with the red, green, and blue channels of an RGB composite. Intensity refers to the brightness of a color, hue is related to the dominant wavelength of light and is perceived as color, and saturation is the purity of the color (Koutsias et al., 2000). There are numerous algorithms for performing a red-green-blue (RGB) to IHS transformation. This tool uses the transformation described by Haydn (1982). Note that, based on this transformation, the input IHS values must follow the ranges:

0 < I < 1

0 < H < 2PI

0 < S < 1

The output red, green, and blue images will have values ranging from 0 to 255. The user must specify the names of the intensity, hue, and saturation images (intensity, hue, saturation). These images will generally be created using the rgb_to_ihs tool. The user must also specify the names of the output red, green, and blue images (red, green, blue). Image enhancements, such as contrast stretching, are often performed on the individual IHS components, which are then inverse transformed back in RGB components using this tool. The output RGB components can then be used to create an improved color composite image.

References

Haydn, R., Dalke, G.W. and Henkel, J. (1982) Application of the IHS color transform to the processing of multisensor data and image enhancement. Proc. of the Inter- national Symposium on Remote Sensing of Arid and Semiarid Lands, Cairo, 599-616.

Koutsias, N., Karteris, M., and Chuvico, E. (2000). The use of intensity-hue-saturation transformation of Landsat-5 Thematic Mapper data for burned land mapping. Photogrammetric Engineering and Remote Sensing, 66(7), 829-840.

See Also

rgb_to_ihs, balance_contrast_enhancement, direct_decorrelation_stretch

Function Signature

def ihs_to_rgb(self, intensity: Raster, hue: Raster, saturation: Raster) -> Tuple[Raster, Raster, Raster]: ...

image_autocorrelation

Spatial autocorrelation describes the extent to which a variable is either dispersed or clustered through space. In the case of a raster image, spatial autocorrelation refers to the similarity in the values of nearby grid cells. This tool measures the spatial autocorrelation of a raster image using the global Moran's I statistic. Moran's I varies from -1 to 1, where I = -1 indicates a dispersed, checkerboard type pattern and I = 1 indicates a clustered (smooth) surface. I = 0 occurs for a random distribution of values. image_autocorrelation computes Moran's I for the first lag only, meaning that it only takes into account the variability among the immediate neighbors of each grid cell.

The user must specify the names of one or more input raster images. In addition, the user must specify the contiguity type (contiguity; Rook's, King's, or Bishop's), which describes which neighboring grid cells are examined for the analysis. The following figure describes the available cases:

Rook's contiguity

Kings's contiguity

Bishops's contiguity

The tool outputs an HTML report (output) which, for each input image (input), reports the Moran's I value and the variance, z-score, and p-value (significance) under normal and randomization sampling assumptions.

Use the image_correlation tool instead when there is need to determine the correlation among multiple raster inputs.

**NoData **values in the input image are ignored during the analysis.

See Also

image_correlation, image_correlation_neighbourhood_analysis

Function Signature

def image_autocorrelation(self, rasters: List[Raster], output_html_file: str, contiguity_type: str = "bishop") -> None: ...

image_correlation

This tool can be used to estimate the Pearson product-moment correlation coefficient (r) between two or more input images (inputs). The r-value is a measure of the linear association in the variation of the input variables (images, in this case). The coefficient ranges from -1.0, indicated a perfect negative linear association, to 1.0, indicated a perfect positive linear association. An r-value of 0.0 indicates no correlation between the test variables.

Note that this index is a measure of the linear association; two variables may be strongly related by a non-linear association (e.g. a power function curve) which will lead to an apparent weak association based on the Pearson coefficient. In fact, non-linear associations are very common among spatial variables, e.g. terrain indices such as slope and contributing area. In such cases, it is advisable that the input images are transformed prior to the estimation of the Pearson coefficient, or that an alternative, non-parametric statistic be used, e.g. the Spearman rank correlation coefficient.

The user must specify the names of two or more input images (inputs). All input images must share the same grid, as the coefficient requires a comparison of a pair of images on a grid-cell-by-grid-cell basis. If more than two image names are selected, the correlation coefficient will be calculated for each pair of images and reported in the HTML output report (output) as a correlation matrix. Caution must be exercised when attempted to estimate the significance of a correlation coefficient derived from image data. The very high N-value (essentially the number of pixels in the image pair) means that even small correlation coefficients can be found to be statistically significant, despite being practically insignificant.

NoData values in either of the two input images are ignored during the calculation of the correlation between images.

See Also

image_correlation_neighbourhood_analysis, image_regression, image_autocorrelation

Function Signature

def image_correlation(self, rasters: List[Raster], output_html_file: str) -> None: ...

image_correlation_neighbourhood_analysis

This tool can be used to perform nieghbourhood-based (i.e. using roving search windows applied to each grid cell) correlation analysis on two input rasters (input1 and input2). The tool outputs a correlation value raster (output1) and a significance (p-value) raster (output2). Additionally, the user must specify the size of the search window (filter) and the correlation statistic (stat). Options for the correlation statistic include pearson, kendall, and spearman. Notice that Pearson's r is the most computationally efficient of the three correlation metrics but is unsuitable when the input distributions are non-linearly associated, in which case, either Spearman's Rho or Kendall's tau-b correlations are more suited. Both Spearman and Kendall correlations evaluate monotonic associations without assuming linearity in the relation. Kendall's tau-b is by far the most computationally expensive of the three statistics and may not be suitable to larger sized search windows.

See Also

image_correlation, image_regression

Function Signature

def image_correlation_neighbourhood_analysis(self, raster1: Raster, raster2: Raster, filter_size: int = 11, correlation_stat: str = "pearson") -> Tuple[Raster, Raster]: ...

image_regression

This tool performs a bivariate linear regression analysis on two input raster images. The first image (i1) is considered to be the independent variable while the second image (i2) is considered to be the dependent variable in the analysis. Both input images must share the same grid, as the coefficient requires a comparison of a pair of images on a grid-cell-by-grid-cell basis. The tool will output an HTML report (output) summarizing the regression model, an Analysis of Variance (ANOVA), and the significance of the regression coefficients. The regression residuals can optionally be output as a new raster image (out_residuals) and the user can also optionally specify to standardize the residuals (standardize).

Note that the analysis performs a linear regression; two variables may be strongly related by a non-linear association (e.g. a power function curve) which will lead to an apparently weak fitting regression model. In fact, non-linear relations are very common among spatial variables, e.g. terrain indices such as slope and contributing area. In such cases, it is advisable that the input images are transformed prior to the analysis.

NoData values in either of the two input images are ignored during the calculation of the correlation between images.

Example usage

import whitebox_workflow

See Also

image_correlation, image_correlation_neighbourhood_analysis

Function Signature

def image_regression(self, independent_variable: Raster, dependent_variable: Raster, output_html_file: str, standardize_residuals: bool = False, output_scattergram: bool = False, num_samples: int = 1000) -> Raster: ...

image_stack_profile

This tool can be used to plot an image stack profile (i.e. a signature) for a set of points (points) and a multispectral image stack (inputs). The tool outputs an interactive SVG line graph embedded in an HTML document. If the input points vector contains multiple points, each input point will be associated with a single line in the output plot. The order of vertices in each signature line is determined by the order of images specified in the inputs parameter. At least two input images are required to run this operation. Note that this tool does not require multispectral images as inputs; other types of data may also be used as the image stack. Also note that the input images should be single-band, continuous greytone rasters. RGB colour images are not good candidates for this tool.

If you require the raster values to be saved in the vector points file's attribute table, or if you need the raster values to be output as text, you may use the extract_raster_values_at_points tool instead.

See Also

extract_raster_values_at_points

Function Signature

def image_stack_profile(self, images: List[Raster], points: Vector, output_html_file: str) -> None: ...

impoundment_size_index

This tool can be used to calculate the impoundment size index (ISI) from a digital elevation model (DEM). The ISI is a land-surface parameter related to the size of the impoundment that would result from inserting a dam of a user-specified maximum length (damlength) into each DEM grid cell. The tool requires the user to specify the name of one or more of the possible outputs, which include the mean flooded depth (out_mean), the maximum flooded depth (out_max), the flooded volume (out_volume), the flooded area (out_area), and the dam height (out_dam_height).

Please note that this tool performs an extremely complex and computationally intensive flow-accumulation operation. As such, it may take a substantial amount of processing time and may encounter issues (including memory issues) when applied to very large DEMs. It is not necessary to pre-process the input DEM (dem) to remove topographic depressions and flat areas. The internal flow-accumulation operation will not be confounded by the presence of these features.

Reference

Lindsay, JB (2015) Modelling the spatial pattern of potential impoundment size from DEMs. Online resource: Whitebox Blog

See Also

insert_dams, stochastic_depression_analysis

Function Signature

def impoundment_size_index(self, dem: Raster, max_dam_length: float, output_mean: bool = False, output_max: bool = False, output_volume: bool = False, output_area: bool = False, output_height: bool = False) -> Tuple[Union[Raster, None], Union[Raster, None], Union[Raster, None], Union[Raster, None], Union[Raster, None]]: ...

individual_tree_detection

This tool can be used to identify points in a LiDAR point cloud that are associated with the tops of individual trees. The tool takes a LiDAR point cloud as an input (input_lidar) and it is best if the input file has been normalized using the lidar_tophat_transform function, such that points record height above the ground surface. Note that the input_lidar parameter is optional and if left unspecified the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This 'batch mode' operation is common among many of the LiDAR processing tools. Output vectors are saved to disc automatically for each processed LiDAR file when operating in batch mode and the function returns None. When an individual input_lidar Lidar object is specified, the tool will return a Vector object, containing the tree top points.

The tool will evaluate the points within a local neighbourhood around each point in the input point cloud and determine if it is the highest point within the neighbourhood. If a point is the highest local point, it will be entered into the output vector file. The neighbourhood size can vary, with higher canopy positions generally associated with larger neighbourhoods. The user specifies the min_search_radius and min_height parameters, which default to 1 m and 0 m respectively. If the min_height parameter is greater than zero, all points that are less than this value above the ground (assuming the input point cloud measures this height parameter) are ignored, which can be a useful mechanism for removing shorter trees and other vegetation from the analysis. If the user specifies the max_search_radius and max_height parameters, the search radius will be determined by linearly interpolation based on point height and the min/max search radius and height parameter values. Points that are above the max_height parameter will be processed with search neighbourhoods sized max_search_radius. If the max radius and height parameters are unspecified, they are set to the same values as the minimum radius and height parameters, i.e., the neighbourhood size does not increase with canopy height.

If the point cloud contains point classifications, it may be useful to exclude all non-vegetation points. To do this simply set the only_use_veg parameter to True. This parameter should only be set to True when you know that the input file contains point classifications, otherwise the tool may generate an empty output vector file.

See Also

lidar_tophat_transform

Function Signature

def individual_tree_detection(self, input_lidar: Lidar, min_search_radius: float = 1.0, min_height: float = 0.0, max_search_radius: Optional[float] = None, max_height: Optional[float] = None, only_use_veg = False) -> Optional[Vector]: ...

insert_dams

This tool can be used to insert dams at one or more user-specified points (dam_pts), and of a maximum length (damlength), within an input digital elevation model (DEM) (dem). This tool can be thought of as providing the impoundment feature that is calculated internally during a run of the the impoundment size index (ISI) tool for a set of points of interest. from a (DEM).

Reference

Lindsay, JB (2015) Modelling the spatial pattern of potential impoundment size from DEMs. Online resource: Whitebox Blog

See Also

impoundment_size_index, stochastic_depression_analysis

Function Signature

def insert_dams(self, dem: Raster, dam_points: Vector, dam_length: float) -> Raster: ...

integral_image_transform

This tool transforms an input raster image into an integral image, or summed area table. Integral images are the two-dimensional equivalent to a cumulative distribution function. Each pixel contains the sum of all pixels contained within the enclosing rectangle above and to the left of a pixel. Images with a very large number of grid cells will likely experience numerical overflow errors when converted to an integral image. Integral images are used in a wide variety of computer vision and digital image processing applications, including texture mapping. They allow for the efficient calculation of very large filters and are the basis of several of WhiteboxTools's image filters.

Reference

Crow, F. C. (1984, January). Summed-area tables for texture mapping. In ACM SIGGRAPH computer graphics (Vol. 18, No. 3, pp. 207-212). ACM.

Function Signature

def integral_image_transform(self, raster: Raster) -> Raster: ...

intersect

The result of the intersect vector overlay operation includes all the feature parts that occur in both input layers, excluding all other parts. It is analogous to the OR logical operator and multiplication in arithmetic. This tool is one of the common vector overlay operations in GIS. The user must specify the names of the input and overlay vector files as well as the output vector file name. The tool operates on vector points, lines, or polygon, but both the input and overlay files must contain the same VectorGeometryType.

The intersect tool is similar to the clip tool. The difference is that the overlay vector layer in a clip operation must always be polygons, regardless of whether the input layer consists of points or polylines.

The attributes of the two input vectors will be merged in the output attribute table. Note, duplicate fields should not exist between the inputs layers, as they will share a single attribute in the output (assigned from the first layer). Multipoint VectorGeometryTypes will simply contain a single output feature identifier (FID) attribute. Also, note that depending on the VectorGeometryType (polylines and polygons), Measure and Z ShapeDimension data will not be transferred to the output geometries. If the input attribute table contains fields that measure the geometric properties of their associated features (e.g. length or area), these fields will not be updated to reflect changes in geometry shape and size resulting from the overlay operation.

See Also

difference, union, symmetrical_difference, clip, erase

Function Signature

def intersect(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...

isobasins

This tool can be used to divide a landscape into a group of nearly equal-sized watersheds, known as isobasins. The user must specify the name (dem) of a digital elevation model (DEM), the output raster name (output), and the isobasin target area (size) specified in units of grid cells. The DEM must have been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using either the breach_depressions_least_cost or fill_depressions tool. Several temporary rasters are created during the execution and stored in memory of this tool.

The tool can optionally (connections) output a CSV table that contains the upstream/downstream connections among isobasins. That is, this table will identify the downstream basin of each isobasin, or will list N/A in the event that there is no downstream basin, i.e. if it drains to an edge. Additionally, the CSV file will contain information about the number of grid cells in each isobasin and the isobasin outlet's row and column number and
flow direction. The output CSV file will have the same name as the output raster, but with a *.csv file extension.

See Also

watershed, basins, breach_depressions_least_cost, fill_depressions

Function Signature

def isobasins(self, dem: Raster, target_size: float, connections: bool = False, csv_file: str = "" ) -> Raster: ...

jenson_snap_pour_points

This tool measures the depth that each grid cell in an input (dem) raster digital elevation model (DEM) lies within a sink feature, i.e. a closed topographic depression. A sink, or depression, is a bowl-like landscape feature, which is characterized by interior drainage and groundwater recharge. The depth_in_sink tool operates by differencing a filled DEM, using the same depression filling method as fill_depressions, and the original surface model.

In addition to the names of the input DEM (dem) and the output raster (output), the user must specify whether the background value (i.e. the value assigned to grid cells that are not contained within sinks) should be set to 0.0 (zero_background) Without this optional parameter specified, the tool will use the NoData value as the background value.

Reference

Antonić, O., Hatic, D., & Pernar, R. (2001). DEM-based depth in sink as an environmental estimator. Ecological Modelling, 138(1-3), 247-254.

See Also

fill_depressions

Function Signature

def jenson_snap_pour_points(self, pour_pts: Vector, streams: Raster, snap_dist: float = 0.0) -> Vector: ...

join_tables

This tool can be used to join (i.e. merge) a vector's attribute table with a second table. The user must specify the name of the vector file (and associated attribute file) as well as the primary key within the table. The primary key (pkey flag) is the field within the table that is being appended to that serves as the identifier. Additionally, the user must specify the name of a second vector from which the data appended into the first table will be derived. The foreign key (fkey flag), the identifying field within the second table that corresponds with the data contained within the primary key in the table, must be specified. Both the primary and foreign keys should either be strings (text) or integer values. Fields containing decimal values are not good candidates for keys. Lastly, the names of the field within the second file to include in the merge operation can also be input (import_field). If the import_field field is not input, all fields in the attribute table of the second file, that are not the foreign key nor FID, will be imported to the first table.

Merging works for one-to-one and many-to-one database relations. A one-to-one relations exists when each record in the attribute table corresponds to one record in the second table and each primary key is unique. Since each record in the attribute table is associated with a geospatial feature in the vector, an example of a one-to-one relation may be where the second file contains AREA and PERIMETER fields for each polygon feature in the vector. This is the most basic type of relation. A many-to-one relation would exist when each record in the first attribute table corresponds to one record in the second file and the primary key is NOT unique. Consider as an example a vector and attribute table associated with a world map of countries. Each country has one or more more polygon features in the shapefile, e.g. Canada has its mainland and many hundred large islands. You may want to append a table containing data about the population and area of each country. In this case, the COUNTRY columns in the attribute table and the second file serve as the primary and foreign keys respectively. While there may be many duplicate primary keys (all of those Canadian polygons) each will correspond to only one foreign key containing the population and area data. This is a many-to-one relation. The join_tables tool does not support one-to-many nor many-to-many relations.

See Also

merge_table_with_csv, reinitialize_attribute_table, export_table_to_csv

Function Signature

def join_tables(self, primary_vector: Vector, primary_key_field: str, foreign_vector: Vector, foreign_key_field: str, import_field: str = "") -> None: ...

k_means_clustering

This tool can be used to perform a k-means clustering operation on two or more input images (inputs), typically several bands of multi-spectral satellite imagery. The tool creates two outputs, including the classified image (output and a classification HTML report (out_html). The user must specify the number of class (classes), which should be known a priori, and the strategy for initializing class clusters (initialize). The initialization strategies include "diagonal" (clusters are initially located randomly along the multi-dimensional diagonal of spectral space) and "random" (clusters are initially located randomly throughout spectral space). The algorithm will continue updating cluster center locations with each iteration of the process until either the user-specified maximum number of iterations (max_iterations) is reached, or until a stability criteria (class_change) is achieved. The stability criteria is the percent of the total number of pixels in the image that are changed among the class values between consecutive iterations. Lastly, the user must specify the minimum allowable number of pixels in a cluster (min_class_size).

Note, each of the input images must have the same number of rows and columns and the same spatial extent because the analysis is performed on a pixel-by-pixel basis. NoData values in any of the input images will result in the removal of the corresponding pixel from the analysis.

See Also

modified_k_means_clustering

Function Signature

def k_means_clustering(self, input_rasters: List[Raster], output_html_file: str = "", num_clusters: int = 5, max_iterations: int = 10, percent_changed_threshold: float = 2.0, initialization_mode: str = "dia", min_class_size: int = 10) -> Raster: ...

k_nearest_mean_filter

This tool performs a k-nearest mean filter on a raster image. A mean filter can be used to emphasize the longer-range variability in an image, effectively acting to smooth or blur the image. This can be useful for reducing the noise in an image. The algorithm operates by calculating the average of a specified number (k) values in a moving window centred on each grid cell. The k values used in the average are those cells in the window with the nearest intensity values to that of the centre cell. As such, this is a type of edge-preserving smoothing filter. The bilateral_filter and edge_preserving_mean_filter are examples of more sophisticated edge-preserving smoothing filters.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

NoData values in the input image are ignored during filtering.

See Also

mean_filter, bilateral_filter, edge_preserving_mean_filter

Function Signature

def k_nearest_mean_filter(self, raster: Raster, filter_size_x: int = 3, filter_size_y: int = 3, k: int = 5) -> Raster: ...

kappa_index

This tool calculates the Kappa index of agreement (KIA), or Cohen's Kappa, for two categorical input raster images (input1 and input2). The KIA is a measure of inter-rater reliability (i.e. classification accuracy) and is widely applied in many fields, notably remote sensing. For example, The KIA is often used as a means of assessing the accuracy of an image classification analysis. The KIA can be interpreted as the percentage improvement that the underlying classification has over and above a random classifier (i.e. random assignment to categories). The user must specify the output HTML file (output). The input images must be of a categorical data type, i.e. contain classes. As a measure of classification accuracy, the KIA is more robust than the overall percent agreement because it takes into account the agreement occurring by chance. A KIA of 0 would indicate that the classifier is no better than random class assignment. In addition to the KIA, this tool will also output the producer's and user's accuracy, the overall accuracy, and the error matrix.

See Also

cross_tabulation

Function Signature

def kappa_index(self, class_raster: Raster, reference_raster: Raster, output_html_file: str = "") -> None: ...

ks_normality_test

This tool will perform a Kolmogorov-Smirnov (K-S) test for normality to evaluate whether the frequency distribution of values within a raster image are drawn from a Gaussian (normal) distribution. The user must specify the name of the raster image. The test can be performed optionally on the entire image or on a random sub-sample of pixel values of a user-specified size. In evaluating the significance of the test, it is important to keep in mind that given a sufficiently large sample, extremely small and non-notable differences can be found to be statistically significant. Furthermore statistical significance says nothing about the practical significance of a difference.

See Also

two_sample_ks_test

Function Signature

def ks_normality_test(self, raster: Raster, output_html_file: str, num_samples: int) -> None: ...

laplacian_filter

This tool can be used to perform a Laplacian filter on a raster image. A Laplacian filter can be used to emphasize the edges in an image. As such, this filter type is commonly used in edge-detection applications. The algorithm operates by convolving a kernel of weights with each grid cell and its neighbours in an image. Four 3x3 sized filters and one 5x5 filter are available for selection. The weights of the kernels are as follows:

3x3(1)

3x3(2)

3x3(3)

3x3(4)

5x5(1)

5x5(2)

The user must specify the variant, including '3x3(1)', '3x3(2)', '3x3(3)', '3x3(4)', '5x5(1)', and '5x5(2)'. The user may also optionally clip the output image distribution tails by a specified amount (e.g. 1%).

See Also

prewitt_filter, sobel_filter

Function Signature

def laplacian_filter(self, raster: Raster, variant: str = "3x3(1)", clip_amount: float = 0.0) -> Raster: ...

laplacian_of_gaussians_filter

The Laplacian-of-Gaussian (LoG) is a spatial filter used for edge enhancement and is closely related to the difference-of-Gaussians filter (DiffOfGaussianFilter). The formulation of the LoG filter algorithm is based on the equation provided in the Hypermedia Image Processing Reference (HIPR) 2. The LoG operator calculates the second spatial derivative of an image. In areas where image intensity is constant, the LoG response will be zero. Near areas of change in intensity the LoG will be positive on the darker side, and negative on the lighter side. This means that at a sharp edge, or boundary, between two regions of uniform but different intensities, the LoG response will be:

• zero at a long distance from the edge,
• positive just to one side of the edge,
• negative just to the other side of the edge,
• zero at some point in between, on the edge itself.

The user may optionally choose to reflecting the data along image edges. NoData values in the input image are similarly valued in the output. The output raster is of the float data type and continuous data scale.

Reference

Fisher, R. 2004. Hypertext Image Processing Resources 2 (HIPR2). Available online: http://homepages.inf.ed.ac.uk/rbf/HIPR2/roberts.htm

See Also

DiffOfGaussianFilter

Function Signature

def laplacian_of_gaussians_filter(self, raster: Raster, sigma: float = 0.75) -> Raster: ...

las_to_ascii

This tool can be used to convert one or more LAS file, containing LiDAR data, into ASCII files. The user must specify the name(s) of the input LAS file(s) (inputs). Each input file will have a correspondingly named output file with a .csv file extension. CSV files are comma separated value files and contain tabular data with each column corresponding to a field in the table and each row a point value. Fields are separated by commas in the ASCII formatted file. The output point data, each on a separate line, will take the format:

X,Y,Z,INTENSITY,CLASS,RETURN,NUM_RETURN,SCAN_ANGLE

If the LAS file has a point format that contains RGB data, the final three columns will contain the RED, GREEN, and BLUE values respectively. Use the ascii_to_las tool to convert a text file containing LiDAR point data into a LAS file.

See Also

ascii_to_las

Function Signature

def las_to_ascii(self, input_lidar: Optional[Lidar]) -> None: ...

las_to_shapefile

This tool converts one or more LAS files into a POINT vector. When the input parameter is not specified, the tool grids all LAS files contained within the working directory. The attribute table of the output Shapefile will contain fields for the z-value, intensity, point class, return number, and number of return.

This tool can be used in place of the LasToMultipointShapefile tool when the number of points are relatively low and when the desire is to represent more than simply the x,y,z position of points. Notice however that because each point in the input LAS file will be represented as a separate record in the output Shapefile, the output file will be many time larger than the equivalent output of the LasToMultipointShapefile tool. There is also a practical limit on the total number of records that can be held in a single Shapefile and large LAS files approach this limit. In these cases, the LasToMultipointShapefile tool should be preferred instead.

See Also

LasToMultipointShapefile

Function Signature

def las_to_shapefile(self, input_lidar: Optional[Lidar], output_multipoint: bool = False) -> Vector: ...

layer_footprint_raster

This tool creates a vector polygon footprint of the area covered by an input raster grid (input). It will create a vector rectangle corresponding to the bounding box of the input raster.

If input data are irregular shape (i.e. there a boundary of NoData cells) the resulting vector will still correspond to the full grid extent, ignoring the irregular boundary. If this is not the desired effect, you may consider the minimum_bounding_envelope tool instead.

See Also

layer_footprint_vector, minimum_bounding_envelope

Function Signature

def layer_footprint_raster(self, input: Raster) -> Vector: ...

layer_footprint_vector

This tool creates a vector polygon footprint of the area covered by a vector layer. It will create a vector rectangle corresponding to the bounding box. The user must specify the name of the input file (input).

If input data are irregular shape the resulting vector will still correspond to the full grid extent, ignoring the irregular boundary. If this is not the desired effect, you should use the minimum_bounding_envelope tool instead.

See Also

layer_footprint_raster, minimum_bounding_envelope

Function Signature

def layer_footprint_vector(self, input: Vector) -> Vector: ...

lee_filter

The Lee Sigma filter is a low-pass filter used to smooth the input image (input). The user must specify the dimensions of the filter (filterx and filtery) as well as the sigma (sigma) and M (m) parameter.

Reference

Lee, J. S. (1983). Digital image smoothing and the sigma filter. Computer vision, graphics, and image processing, 24(2), 255-269.

See Also

mean_filter, gaussian_filter

Function Signature

def lee_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sigma: float = 10.0, m_value: float = 5.0) -> Raster: ...

length_of_upstream_channels

This tool calculates, for each stream grid cell in an input streams raster (streams_raster) the total length of channels upstream. The user must specify the name of a raster containing streams data (streams_raster), where stream grid cells are denoted by all positive non-zero values, and a D8 flow pointer (i.e. flow direction) raster (d8_pointer). The pointer image is used to traverse the stream network and must only be created using the D8 algorithm. Stream cells are designated in the streams image as all values greater than zero. Thus, all non-stream or background grid cells are commonly assigned either zeros or NoData values. Background cells will be assigned the NoData value in the output image, unless the user specifies zero_background=True, in which case non-stream cells will be assigned zero values in the output.

By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools. If the pointer file contains ESRI flow direction values instead, set esri_pntr=True.

See Also

farthest_channel_head, find_main_stem

Function Signature

def length_of_upstream_channels(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...

license_type

Returns the license type, WbW or WbW-Pro

lidar_block_maximum

This function superimposes a raster grid overtop of an input LiDAR point cloud (input_lidar) of a user-specified resolution (cell_size) and identifies the highest point in each block. The output raster therefore appoximates a digital surface model (DSM), representing the elevation of the ground surface in open areas and the elevations of off-terrain objects (OTOs), such as buildings and vegetation. While this function will be faster, it is recommended that if you use the lidar_digital_surface_model instead if you are trying to create a DSM. This method will generally produce better results.

Like many of the LiDAR functions, the input LiDAR point cloud (input_lidar) is optional. If an input LiDAR file is not specified, the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to process a large number of LiDAR files contained within a directory. This batch processing mode enables the function to run in a more optimized parallel manner. When run in this batch mode, no output LiDAR object will be created. Instead the function will create an output file (saved to disc) with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

See Also

lidar_block_minimum, lidar_digital_surface_model, filterfilter_lidar_by_percentile_lidar

Function Signature

def lidar_block_maximum(self, input_lidar: Optional[Lidar], cell_size: float = 1.0) -> Raster: ...

lidar_block_minimum

This function superimposes a raster grid overtop of an input LiDAR point cloud (input_lidar) of a user-specified resolution (cell_size) and identifies the lowest point in each block. The output raster therefore appoximates a bare-earth digital elevation model (DEM), or a digital terrain model (DTM), although it is likely to contain several off-terrain objects (OTOs), such as buildings. Under heavier forest cover, the minimum-surface will also very likely contain some blocks that are not coinincident with the ground surface, but rather will represent the elevation of the lower position of tree trunks and low vegetation.

Like many of the LiDAR functions, the input LiDAR point cloud (input_lidar) is optional. If an input LiDAR file is not specified, the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to process a large number of LiDAR files contained within a directory. This batch processing mode enables the function to run in a more optimized parallel manner. When run in this batch mode, no output LiDAR object will be created. Instead the function will create an output file (saved to disc) with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

See Also

lidar_block_maximum, filterfilter_lidar_by_percentile_lidar

Function Signature

def lidar_block_minimum(self, input_lidar: Optional[Lidar], cell_size: float = 1.0) -> Raster: ...

lidar_classify_subset

This tool classifies points within a user-specified LiDAR point cloud (base) that correspond with points in a subset cloud (subset). The subset point cloud may have been derived by filtering the original point cloud. The user must specify the names of the two input LAS files (i.e. the full and subset clouds) and the class value (subset_class) to assign the matching points. This class value will be assigned to points in the base cloud, overwriting their input class values in the output LAS file (output). Class values should be numerical (integer valued) and should follow the LAS specifications below:

The user may optionally specify a class value to be assigned to non-subset (i.e. non-matching) points (nonsubset_class) in the base file. If this parameter is not specified, output non-sutset points will have the same class value as the base file.

Function Signature

def lidar_classify_subset(self, base_lidar: Lidar, subset_lidar: Lidar, subset_class_value: int, nonsubset_class_value: int) -> Lidar: ...

lidar_colourize

This tool can be used to add red-green-blue (RGB) colour values to the points contained within an input LAS file (in_lidar), based on the pixel values of an overlapping input colour image (in_image). Ideally, the image has been acquired at the same time as the LiDAR point cloud. If this is not the case, one may expect that transient objects (e.g. cars) in both input data sets will be incorrectly coloured. The input image should overlap in extent with the LiDAR data set and the two data sets should share the same projection. You may use the lidar_tile_footprint tool to determine the spatial extent of the LAS file.

See Also

colourize_based_on_class, colourize_based_on_point_returns, lidar_tile_footprint

Function Signature

def lidar_colourize(self, in_lidar: Lidar, in_image: Raster) -> Lidar: ...

lidar_construct_vector_tin

This tool creates a vector triangular irregular network (TIN) for a set of LiDAR points (input) using a 2D Delaunay triangulation algorithm. LiDAR points may be excluded from the triangulation operation based on a number of criteria, include the point return number (returns), point classification value (exclude_cls), or a minimum (minz) or maximum (maxz) elevation.

For vector points, use the construct_vector_tin tool instead.

See Also

construct_vector_tin

Function Signature

def lidar_construct_vector_tin(self, input_lidar: Optional[Lidar], returns_included: str = "all", excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Vector: ...

lidar_digital_surface_model

This tool creates a digital surface model (DSM) from a LiDAR point cloud. A DSM reflects the elevation of the tops of all off-terrain objects (i.e. non-ground features) contained within the data set. For example, a DSM will model the canopy top as well as building roofs. This is in stark contrast to a bare-earth digital elevation model (DEM), which models the ground surface without off-terrain objects present. Bare-earth DEMs can be derived from LiDAR data by interpolating last-return points using one of the other LiDAR interpolators (e.g. lidar_tin_gridding). The algorithm used for interpolation in this tool is based on gridding a triangulation (TIN) fit to top-level points in the input LiDAR point cloud. All points in the input LiDAR data set that are below other neighbouring points, within a specified search radius (radius), and that have a large inter-point slope, are filtered out. Thus, this tool will remove the ground surface beneath as well as any intermediate points within a forest canopy, leaving only the canopy top surface to be interpolated. Similarly, building wall points and any ground points beneath roof overhangs will also be remove prior to interpolation. Note that because the ground points beneath overhead wires and utility lines are filtered out by this operation, these features tend to be appear as 'walls' in the output DSM. If these points are classified in the input LiDAR file, you may wish to filter them out before using this tool (filter_lidar_classes).

The following images show the differences between creating a DSM using the lidar_digital_surface_model and by interpolating first-return points only using the lidar_tin_gridding tool respectively. Note, the images show time_in_daylight, which is a more effective way of hillshading DSMs than the traditional hillshade method. Compare how the DSM created lidar_digital_surface_model tool (above) has far less variability in areas of tree-cover, more effectively capturing the canopy top. As well, notice how building rooftops are more extensive and straighter in the lidar_digital_surface_model DSM image. This is because this method eliminates ground returns beneath roof overhangs before the triangulation operation.

The user must specify the grid resolution of the output raster (resolution), and optionally, the name of the input LiDAR file (input) and output raster (output). Note that if an input LiDAR file (input) is not specified by the user, the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to interpolate a DSM for a large number of LiDAR files. Not only does this batch processing mode enable the tool to run in a more optimized parallel manner, but it will also allow the tool to include a small buffer of points extending into adjacent tiles when interpolating an individual file. This can significantly reduce edge-effects when the output tiles are later mosaicked together. When run in this batch mode, the output file (output) also need not be specified; the tool will instead create an output file with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

Users may also exclude points from the interpolation if they fall below or above the minimum (minz) or maximum (maxz) thresholds respectively. This can be a useful means of excluding anomalously high or low points. Note that points that are classified as low points (LAS class 7) or high noise (LAS class 18) are automatically excluded from the interpolation operation.

Triangulation will generally completely fill the convex hull containing the input point data. This can sometimes result in very long and narrow triangles at the edges of the data or connecting vertices on either side of void areas. In LiDAR data, these void areas are often associated with larger waterbodies, and triangulation can result in very unnatural interpolated patterns within these areas. To avoid this problem, the user may specify a the maximum allowable triangle edge length (max_triangle_edge_length) and all grid cells within triangular facets with edges larger than this threshold are simply assigned the NoData values in the output DSM. These NoData areas can later be better dealt with using the fill_missing_data tool after interpolation.

See Also

lidar_tin_gridding, filter_lidar_classes, fill_missing_data, time_in_daylight

Function Signature

def lidar_digital_surface_model(self, input_lidar: Optional[Lidar], cell_size: float = 1.0, search_radius: float = 0.5, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Raster: ...

lidar_elevation_slice

This tool can be used to either extract or classify the elevation values (z) of LiDAR points within a specified elevation range (slice). In addition to the names of the input and output LiDAR files (input and output), the user must specify the lower (minz) and upper (maxz) bounds of the elevation range. By default, the tool will only output points within the elevation slice, filtering out all points lying outside of this range. If the class parameter is used, the tool will operate by assigning a class value (inclassval) to the classification bit of points within the slice and another class value (outclassval) to those points falling outside the range.

See Also

lidar_remove_outliers, lidar_classify_subset

Function Signature

def lidar_elevation_slice(self, input: Lidar, minz: float = float('-inf'), maxz: float = float('inf'), classify: bool = False, in_class_value: int = 2, out_class_value: int = 1) -> Lidar: ...

lidar_ground_point_filter

This tool can be used to perform a slope-based classification, or filtering (i.e. removal), of non-ground points within a LiDAR point-cloud. The user must specify the name of the input and output LiDAR files (input and output). Inter-point slopes are compared between pair of points contained within local neighbourhoods of size radius. Neighbourhoods with fewer than the user-specified minimum number of points (min_neighbours) are extended until the minimum point number is equaled or exceeded. Points that are above neighbouring points by the minimum (height_threshold) and have an inter-point slope greater than the user-specifed threshold (slope_threshold) are considered non-ground points and are either optionally (classify) excluded from the output point-cloud or assigned the unclassified (value 1) class value.

Slope-based ground-point classification methods suffer from the challenge of uses a constant slope threshold under varying terrain slopes. Some researchers have developed schemes for varying the slope threshold based on underlying terrain slopes. lidar_ground_point_filter instead allow the user to optionally (slope_norm) normalize the underlying terrain (i.e. flatten the terrain) using a white top-hat transform. A constant slope threshold may then be used without contributing to poorer performance under steep topography. Note, that this option, while useful in rugged terrain, is computationally intensive. If the point-cloud is of a relatively flat terrain, this option may be excluded.

While this tool is appropriately applied to LiDAR point-clouds, the remove_off_terrain_objects tool can be used to remove off-terrain objects from rasterized LiDAR digital elevation models (DEMs).

Reference

Vosselman, G. (2000). Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing, 33(B3/2; PART 3), 935-942.

See Also

improved_ground_point_filter, remove_off_terrain_objects

Function Signature

def lidar_ground_point_filter(self, input_lidar: Optional[Lidar], search_radius: float = 2.0, min_neighbours: int = 0, slope_threshold: float = 45.0, height_threshold: float = 1.0, classify: bool = False, slope_norm: bool = True, height_above_ground: bool = False) -> Lidar: ...

lidar_hex_bin

The practice of binning point data to form a type of 2D histogram, density plot, or what is sometimes called a heatmap, is quite useful as an alternative for the cartographic display of of very dense points sets. This is particularly the case when the points experience significant overlap at the displayed scale. The lidar_point_density tool can be used to perform binning based on a regular grid (raster output). This tool, by comparison, bases the binning on a hexagonal grid.

The tool is similar to the CreateHexagonalVectorGrid tool, however instead will create an output hexagonal grid in which each hexagonal cell possesses a COUNT attribute which specifies the number of points from an input points file (LAS file) that are contained within the hexagonal cell. The tool will also calculate the minimum and maximum elevations and intensity values and outputs these data to the attribute table.

In addition to the names of the input points file and the output Shapefile, the user must also specify the desired hexagon width (w), which is the distance between opposing sides of each hexagon. The size (s) each side of the hexagon can then be calculated as, s = w / [2 x cos(PI / 6)]. The area of each hexagon (A) is, A = 3s(w / 2). The user must also specify the orientation of the grid with options of horizontal (pointy side up) and vertical (flat side up).

See Also

vector_hex_binning, lidar_point_density, CreateHexagonalVectorGrid

Function Signature

def lidar_hex_bin(self, input_lidar: Lidar, width: float, orientation: str = "h") -> Vector: ...

lidar_hillshade

Function Signature

def lidar_hillshade(self, input: Lidar, search_radius: float = -1.0, azimuth: float = 315.0, altitude: float = 30.0) -> Lidar: ...

lidar_histogram

This tool can be used to plot a histogram of data derived from a LiDAR file. The user must specify the name of the input LAS file (input), the name of the output HTML file (output), the parameter (parameter) to be plotted, and the amount (in percent) to clip the upper and lower tails of the f requency distribution (clip). The LiDAR parameters that can be plotted using lidar_histogram include the point elevations, intensity values, scan angles, and class values.

Use the lidar_point_stats tool instead to examine the spatial distribution of LiDAR points.

See Also

lidar_point_stats

Function Signature

def lidar_histogram(self, input_lidar: Lidar, output_html_file: str, parameter: str = "elevation", clip_percent: float = 1.0) -> None: ...

lidar_idw_interpolation

This tool interpolates LiDAR files using inverse-distance weighting (IDW) scheme. The user must specify the value of the IDW weight parameter (weight). The output grid can be based on any of the stored LiDAR point parameters (parameter), including elevation (in which case the output grid is a digital elevation model, DEM), intensity, class, return number, number of returns, scan angle, RGB (colour) values, and user data values. Similarly, the user may specify which point return values (returns) to include in the interpolation, including all points, last returns (including single return points), and first returns (including single return points).

The user must specify the grid resolution of the output raster (resolution), and optionally, the name of the input LiDAR file (input) and output raster (output). Note that if an input LiDAR file (input) is not specified by the user, the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to interpolate a DEM for a large number of LiDAR files. Not only does this batch processing mode enable the tool to run in a more optimized parallel manner, but it will also allow the tool to include a small buffer of points extending into adjacent tiles when interpolating an individual file. This can significantly reduce edge-effects when the output tiles are later mosaicked together. When run in this batch mode, the output file (output) also need not be specified; the tool will instead create an output file with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

Users may excluded points from the interpolation based on point classification values, which follow the LAS classification scheme. Excluded classes are specified using the exclude_cls parameter. For example, to exclude all vegetation and building classified points from the interpolation, use --exclude_cls='3,4,5,6'. Users may also exclude points from the interpolation if they fall below or above the minimum (minz) or maximum (maxz) thresholds respectively. This can be a useful means of excluding anomalously high or low points. Note that points that are classified as low points (LAS class 7) or high noise (LAS class 18) are automatically excluded from the interpolation operation.

The tool will search for the nearest input LiDAR point to each grid cell centre, up to a maximum search distance (radius). If a grid cell does not have a LiDAR point within this search distance, it will be assigned the NoData value in the output raster. In LiDAR data, these void areas are often associated with larger waterbodies. These NoData areas can later be better dealt with using the fill_missing_data tool after interpolation.

See Also

lidar_tin_gridding, lidar_nearest_neighbour_gridding, lidar_sibson_interpolation

Function Signature

def lidar_idw_interpolation(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, idw_weight: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...

lidar_info

This tool can be used to print basic information about the data contained within a LAS file, used to store LiDAR data. The reported information will include including data on the header, point return frequency, and classification data and information about the variable length records (VLRs) and geokeys. If the output_html_file is specified, the function will write the output information as a HTML file that will be automatically displayed. If this parameter is unspecified, the function will return a string containing the information instead.

Function Signature

def lidar_info(self, input_lidar: Lidar, output_html_file: str = None, show_point_density: bool = True, show_vlrs: bool = True, show_geokeys: bool = True) -> str: ...

lidar_join

This tool can be used to merge multiple LiDAR LAS files into a single output LAS file. Due to their large size, LiDAR data sets are often tiled into smaller, non-overlapping tiles. Sometimes it is more convenient to combine multiple tiles together for data processing and lidar_join can be used for this purpose.

See Also

lidar_tile

Function Signature

def lidar_join(self, inputs: List[Lidar]) -> Lidar: ...

lidar_kappa

This tool performs a kappa index of agreement (KIA) analysis on the classification values of two LiDAR (LAS) files. The output report HTML file should be displayed automatically but can also be displayed afterwards in any web browser. As a measure of overall classification accuracy, the KIA is more robust than the percent agreement calculation because it takes into account the agreement occurring by random chance. In addition to the KIA, the tool will output the producer's and user's accuracy, the overall accuracy, and the error matrix. The KIA is often used as a means of assessing the accuracy of an image classification analysis; however the LidarKappaIndex tool performs the analysis on a point-to-point basis, comparing the class values of the points in one input LAS file with the corresponding nearest points in the second input LAS file.

The user must also specify the name and resolution of an output raster file, which is used to show the spatial distribution of class accuracy. Each grid cell contains the overall accuracy, i.e. the points correctly classified divided by the total number of points contained within the cell, expressed as a percentage.

Function Signature

def lidar_kappa(self, input_lidar1: Lidar, input_lidar2: Lidar, output_html_file: str, cell_size: float = 1.0, output_class_accuracy: bool = False) -> Raster: ...

lidar_nearest_neighbour_gridding

This tool grids LiDAR files using nearest-neighbour (NN) scheme, that is, each grid cell in the output image will be assigned the parameter value of the point nearest the grid cell centre. This method should not be confused for the similarly named natural-neighbour interpolation (a.k.a Sibson's method). Nearest neighbour gridding is generally regarded as a poor way of interpolating surfaces from low-density point sets and results in the creation of a Voronoi diagram. However, this method has several advantages when applied to LiDAR data. NN gridding is one of the fastest methods for generating raster surfaces from large LiDAR data sets. NN gridding is one of the few interpolation methods, along with triangulation, that will preserve vertical breaks-in-slope, such as occur at the edges of building. This characteristic can be important when using some post-processing methods, such as the remove_off_terrain_objects tool. Furthermore, because most LiDAR data sets have remarkably high point densities compared with other types of geographic data, this approach does often produce a satisfactory result; this is particularly true when the point density is high enough that there are multiple points in the majority of grid cells.

The output grid can be based on any of the stored LiDAR point parameters (parameter), including elevation (in which case the output grid is a digital elevation model, DEM), intensity, class, return number, number of returns, scan angle, RGB (colour) values, time, and user data values. Similarly, the user may specify which point return values (returns) to include in the interpolation, including all points, last returns (including single return points), and first returns (including single return points).

The user must specify the grid resolution of the output raster (resolution), and optionally, the name of the input LiDAR file (input) and output raster (output). Note that if an input LiDAR file (input) is not specified by the user, the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to interpolate a DEM for a large number of LiDAR files. Not only does this batch processing mode enable the tool to run in a more optimized parallel manner, but it will also allow the tool to include a small buffer of points extending into adjacent tiles when interpolating an individual file. This can significantly reduce edge-effects when the output tiles are later mosaicked together. When run in this batch mode, the output file (output) also need not be specified; the tool will instead create an output file with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

Users may excluded points from the interpolation based on point classification values, which follow the LAS classification scheme. Excluded classes are specified using the exclude_cls parameter. For example, to exclude all vegetation and building classified points from the interpolation, use --exclude_cls='3,4,5,6'. Users may also exclude points from the interpolation if they fall below or above the minimum (minz) or maximum (maxz) thresholds respectively. This can be a useful means of excluding anomalously high or low points. Note that points that are classified as low points (LAS class 7) or high noise (LAS class 18) are automatically excluded from the interpolation operation.

The tool will search for the nearest input LiDAR point to each grid cell centre, up to a maximum search distance (radius). If a grid cell does not have a LiDAR point within this search distance, it will be assigned the NoData value in the output raster. In LiDAR data, these void areas are often associated with larger waterbodies. These NoData areas can later be better dealt with using the fill_missing_data tool after interpolation.

See Also

lidar_tin_gridding, lidar_idw_interpolation, lidar_tin_gridding, remove_off_terrain_objects, fill_missing_data

Function Signature

def lidar_nearest_neighbour_gridding(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...

lidar_point_density

Function Signature

def lidar_point_density(self, input_lidar: Optional[Lidar], returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...

lidar_point_stats

This tool creates several rasters summarizing the distribution of LiDAR points in a LAS data file. The user must specify the name of an input LAS file (input) and the output raster grid resolution (resolution). Additionally, the user must specify one or more of the possible output rasters to create using the various available flags, which include:

If no output raster flags are specified, all of the output rasters will be created. All output rasters will have the same base name as the input LAS file but will have a suffix that reflects the statistic type (e.g. _num_pnts, _num_pulses, _avg_points_per_pulse, etc.). Output files will be in the GeoTIFF (*.tif) file format.

When the input/output parameters are not specified, the tool works on all LAS files contained within the working directory.

Notes:

1. The num_pulses output is actually the number of pulses with at least one return; specifically it is the sum of the early returns (first and only) in a grid cell. In areas of low reflectance, such as over water surfaces, the system may have emitted a significantly higher pulse rate but far fewer returns are observed.
2. The memory requirement of this tool is high, particulalry if the grid resolution is fine and the spatial extent is large.

See Also

lidar_block_minimum, lidar_block_maximum

Function Signature

def lidar_point_stats(self, input_lidar: Optional[Lidar], cell_size: float = 1.0, num_points: bool = False, num_pulses: bool = False, avg_points_per_pulse: bool = False, z_range: bool = False, intensity_range: bool = False, predominant_class: bool = False) : ...

lidar_radial_basis_function_interpolation

Function Signature

def lidar_radial_basis_function_interpolation(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, num_points: int = 15, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), func_type: str = "thinplatespline", poly_order: str = "none", weight: float = 0.1) -> Raster: ...

lidar_ransac_planes

This tool uses the random sample consensus (RANSAC) method to identify points within a LiDAR point cloud that belong to planar surfaces. RANSAC is a common method used in the field of computer vision to identify a subset of inlier points in a noisy data set containing abundant outlier points. Because LiDAR point clouds often contain vegetation points that do not form planar surfaces, this tool can be used to largely strip vegetation points from the point cloud, leaving behind the ground returns, buildings, and other points belonging to planar surfaces. If the classify flag is used, non-planar points will not be removed but rather will be assigned a different class (1) than the planar points (0).

The algorithm selects a random sample, of a specified size (num_samples) of the points from within the neighbourhood (radius) surrounding each LiDAR point. The sample is then used to parameterize a planar best-fit model. The distance between each neighbouring point and the plane is then evaluated; inliers are those neighbouring points within a user-specified distance threshold (threshold). Models with at least a minimum number of inlier points (model_size) are then accepted. This process of selecting models is iterated a number of user-specified times (num_iter).

One of the challenges with identifying planar surfaces in LiDAR point clouds is that these data are usually collected along scan lines. Therefore, each scan line can potentially yield a vertical planar surface, which is one reason that some vegetation points remain after applying the RANSAC plane-fitting method. To cope with this problem, the tool allows the user to specify a maximum planar slope (max_slope) parameter. Planes that have slopes greater than this threshold are rejected by the algorithm. This has the side-effect of removing building walls however.

References

Fischler MA and Bolles RC. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381–395.

See Also

lidar_segmentation, lidar_ground_point_filter

Function Signature

def lidar_ransac_planes(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, classify: bool = False, only_last_returns: bool = False) -> Lidar: ...

lidar_remove_outliers

This tool will filter out points from a LiDAR point cloud if the absolute elevation difference between a point and the averge elevation of its neighbourhood, calculated without the point, exceeds a threshold (elev_diff).

Function Signature

def lidar_remove_outliers(self, input: Lidar, search_radius: float = 2.0, elev_diff: float = 50.0, use_median: bool = False, classify: bool = False) -> Lidar: ...

lidar_rooftop_analysis

This tool can be used to identify roof segments in a LiDAR point cloud.

See Also

classify_buildings_in_lidar, clip_lidar_to_polygon

Function Signature

def lidar_rooftop_analysis(self, lidar_inputs: List[Lidar], building_footprints: Vector, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, norm_diff_threshold: float = 2.0, azimuth: float = 180.0, altitude: float = 30.0) -> Vector: ...

lidar_segmentation

This tool can be used to segment a LiDAR point cloud based on differences in the orientation of fitted planar surfaces and point proximity. The algorithm begins by attempting to fit planar surfaces to all of the points within a user-specified radius (radius) of each point in the LiDAR data set. The planar equation is stored for each point for which a suitable planar model can be fit. A region-growing algorithm is then used to assign nearby points with similar planar models. Similarity is based on a maximum allowable angular difference (in degrees) between the two neighbouring points' plane normal vectors (norm_diff). The norm_diff parameter can therefore be thought of as a way of specifying the magnitude of edges mapped by the region-growing algorithm. By setting this value appropriately, it is possible to segment each facet of a building's roof. Segment edges for planar points may also be determined by a maximum allowable height difference (maxzdiff) between neighbouring points on the same plane. Points for which no suitable planar model can be fit are assigned to 'volume' (non-planar) segments (e.g. vegetation points) using a region-growing method that connects neighbouring points based solely on proximity (i.e. all volume points within radius distance are considered to belong to the same segment).

The resulting point cloud will have both planar segments (largely ground surfaces and building roofs and walls) and volume segments (largely vegetation). Each segment is assigned a random red-green-blue (RGB) colour in the output LAS file. The largest segment in any airborne LiDAR dataset will usually belong to the ground surface. This largest segment will always be assigned a dark-green RGB of (25, 120, 0) by the tool.

This tool uses the random sample consensus (RANSAC) method to identify points within a LiDAR point cloud that belong to planar surfaces. RANSAC is a common method used in the field of computer vision to identify a subset of inlier points in a noisy data set containing abundant outlier points. Because LiDAR point clouds often contain vegetation points that do not form planar surfaces, this tool can be used to largely strip vegetation points from the point cloud, leaving behind the ground returns, buildings, and other points belonging to planar surfaces. If the classify flag is used, non-planar points will not be removed but rather will be assigned a different class (1) than the planar points (0).

The algorithm selects a random sample, of a specified size (num_samples) of the points from within the neighbourhood (radius) surrounding each LiDAR point. The sample is then used to parameterize a planar best-fit model. The distance between each neighbouring point and the plane is then evaluated; inliers are those neighbouring points within a user-specified distance threshold (threshold). Models with at least a minimum number of inlier points (model_size) are then accepted. This process of selecting models is iterated a number of user-specified times (num_iter).

One of the challenges with identifying planar surfaces in LiDAR point clouds is that these data are usually collected along scan lines. Therefore, each scan line can potentially yield a vertical planar surface, which is one reason that some vegetation points may be assigned to planes during the RANSAC plane-fitting method. To cope with this problem, the tool allows the user to specify a maximum planar slope (max_slope) parameter. Planes that have slopes greater than this threshold are rejected by the algorithm. This has the side-effect of removing building walls however.

References

Fischler MA and Bolles RC. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381–395.

See Also

lidar_ransac_planes, lidar_ground_point_filter

Function Signature

def lidar_segmentation(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, norm_diff_threshold: float = 2.0, max_z_diff: float = 1.0, classes: bool = False, ground: bool = False) -> Lidar: ...

lidar_segmentation_based_filter

Function Signature

def lidar_segmentation_based_filter(self, in_lidar: Lidar, search_radius: float = 5.0, norm_diff_threshold: float = 2.0, max_z_diff: float = 1.0, classify_points: bool = False) -> Lidar: ...

lidar_shift

This tool can be used to shift the x,y,z coordinates of points within a LiDAR file. The user must specify the name of the input file (input) and the output file (output). Additionally, the user must specify the x,y,z shift values (x_shift, y_shift, z_shift). At least one non-zero shift value is needed to run the tool. Notice that shifting the x,y,z coordinates of LiDAR points is also possible using the modify_lidar tool, which can also be used for more sophisticated point property manipulation (e.g. rotations).

See Also

modify_lidar, lidar_elevation_slice, height_above_ground

Function Signature

def lidar_shift(self, input: Lidar, x_shift: float = 0.0, y_shift: float = 0.0, z_shift: float = 0.0) -> Lidar: ...

lidar_thin

Thins a LiDAR point cloud, reducing point density.

Function Signature

def lidar_thin(self, input: Lidar, resolution: float = 1.0, selection_method: str = "first", save_filtered: bool = False) -> Tuple[Lidar, Union[Lidar, None]]: ...

lidar_thin_high_density

Thins points from high density areas within a LiDAR point cloud.

Function Signature

def lidar_thin_high_density(self, input: Lidar, density: float, resolution: float = 1.0, save_filtered: bool = False) -> Tuple[Lidar, Union[Lidar, None]]: ...

lidar_tile

single LAS file. The user must specify the parameter of the tile grid, including its origin (origin_x and origin_y) and the tile width and height (width and height). Tiles containing fewer points than specified in the min_points parameter will not be output. This can be useful when tiling terrestrial LiDAR datasets because the low point density at the edges of the point cloud (i.e. most distant from the scan station) can result in poorly populated tiles containing relatively few points.

See Also

lidar_join, lidar_tile_footprint

Function Signature

def lidar_tile(self, input_lidar: Lidar, tile_width: float = 1000.0, tile_height: float = 1000.0, origin_x: float = 0.0, origin_y: float = 0.0, min_points_in_tile: int = 2, output_laz_format: bool = True) -> None: ...

lidar_tile_footprint

This tool can be used to create a vector polygon of the bounding box or convex hull of a LiDAR point cloud (i.e. LAS file). If the user specified an input file (input) and output file (output), the tool will calculate the footprint, containing all of the data points, and output this feature to a vector polygon file. If the input and output parameters are left unspecified, the tool will calculate the footprint of every LAS file contained within the working directory and output these features to a single vector polygon file. If this is the desired mode of operation, it is important to specify the working directory (wd) containing the group of LAS files; do not specify the optional input and output parameters in this case. Each polygon in the output vector will contain a LAS_NM field, specifying the source LAS file name, a NUM_PNTS field, containing the number of points within the source file, and Z_MIN and Z_MAX fields, containing the minimum and maximum elevations. This output can therefore be useful to create an index map of a large tiled LiDAR dataset.

By default, this tool identifies the axis-aligned minimum rectangular hull, or bounding box, containing the points in each of the input tiles. If the user specifies the hull flag, the tool will identify the minimum convex hull instead of the bounding box. This option is considerably more computationally intensive and will be a far longer running operation if many tiles are specified as inputs.

A note on LAZ file inputs: While WhiteboxTools does not currently support the reading and writing of the compressed LiDAR format LAZ, it is able to read LAZ file headers. This tool, when run in in the bounding box mode (rather than the convex hull mode), is able to take LAZ input files.

lidar_tile, LayerFootprint, minimum_bounding_box, minimum_convex_hull

Function Signature

def lidar_tile_footprint(self, input_lidar: Optional[Lidar], output_hulls: bool = False) -> Vector: ...

lidar_tin_gridding

This tool creates a raster grid based on a Delaunay triangular irregular network (TIN) fitted to LiDAR points. The output grid can be based on any of the stored LiDAR point parameters (parameter), including elevation (in which case the output grid is a digital elevation model, DEM), intensity, class, return number, number of returns, scan angle, RGB (colour) values, and user data values. Similarly, the user may specify which point return values (returns) to include in the interpolation, including all points, last returns (including single return points), and first returns (including single return points).

The user must specify the grid resolution of the output raster (resolution), and optionally, the name of the input LiDAR file (input) and output raster (output). Note that if an input LiDAR file (input) is not specified by the user, the tool will search for all valid LiDAR (*.las, *.laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to interpolate a DEM for a large number of LiDAR files. Not only does this batch processing mode enable the tool to run in a more optimized parallel manner, but it will also allow the tool to include a small buffer of points extending into adjacent tiles when interpolating an individual file. This can significantly reduce edge-effects when the output tiles are later mosaicked together. When run in this batch mode, the output file (output) also need not be specified; the tool will instead create an output file with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

Users may excluded points from the interpolation based on point classification values, which follow the LAS classification scheme. Excluded classes are specified using the exclude_cls parameter. For example, to exclude all vegetation and building classified points from the interpolation, use --exclude_cls='3,4,5,6'. Users may also exclude points from the interpolation if they fall below or above the minimum (minz) or maximum (maxz) thresholds respectively. This can be a useful means of excluding anomalously high or low points. Note that points that are classified as low points (LAS class 7) or high noise (LAS class 18) are automatically excluded from the interpolation operation.

Triangulation will generally completely fill the convex hull containing the input point data. This can sometimes result in very long and narrow triangles at the edges of the data or connecting vertices on either side of void areas. In LiDAR data, these void areas are often associated with larger waterbodies, and triangulation can result in very unnatural interpolated patterns within these areas. To avoid this problem, the user may specify a the maximum allowable triangle edge length (max_triangle_edge_length) and all grid cells within triangular facets with edges larger than this threshold are simply assigned the NoData values in the output DSM. These NoData areas can later be better dealt with using the fill_missing_data tool after interpolation.

See Also

lidar_idw_interpolation, lidar_nearest_neighbour_gridding, lidar_tin_gridding, filter_lidar_classes, fill_missing_data

Function Signature

def lidar_tin_gridding(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Raster: ...

lidar_tophat_transform

This tool performs a white top-hat transform on a LiDAR point cloud (input). A top-hat transform is a common digital image processing operation used for various tasks, such as feature extraction, background equalization, and image enhancement. When applied to a LiDAR point cloud, the white top-hat transform provides an estimate of height above ground, which is useful for modelling the vegetation canopy.

As an example, notice that the input point cloud on the top of the image below has a substantial amount of topographic variability. After applying the top-hat transform (bottom point cloud), all of this topographic variability has been removed and point elevations values effectively become height above ground.

The white top-hat transform is defined as the difference between a point's original elevation and its opening. The opening operation can be thought of as the local neighbourhood maximum of a previous local minimum surface. The user must specify the size of the neighbourhood using the radius parameter. Setting this parameter can require some experimentation. Generally, it is appropriate to use a radius of a few meters in non-urban landscapes. However, in urban areas, the radius may need to be set much larger, reflective of the size of the largest building.

If the input point cloud already has ground points classified, it may be better to use the height_above_ground, which simply measures the difference in height between each point and its nearest ground classified point within the search radius.

See Also

height_above_ground, tophat_transform, closing, opening

Function Signature

def lidar_tophat_transform(self, input: Lidar, search_radius: float) -> Lidar: ...

line_detection_filter

This tool can be used to perform one of four 3x3 line-detection filters on a raster image. These filters can be used to find one-cell-thick vertical, horizontal, or angled (135-degrees or 45-degrees) lines in an image. Notice that line-finding is a similar application to edge-detection. Common edge-detection filters include the Sobel and Prewitt filters. The kernel weights for each of the four line-detection filters are as follows:

'v' (Vertical)

'h' (Horizontal)

'45' (Northeast-Southwest)

'135' (Northwest-Southeast)

The user must specify the variant, including 'v', 'h', '45', and '135', for vertical, horizontal, northeast-southwest, and northwest-southeast directions respectively. The user may also optionally clip the output image distribution tails by a specified amount (e.g. 1%).

See Also

prewitt_filter, sobel_filter

Function Signature

def line_detection_filter(self, raster: Raster, variant: str = "v", abs_values: bool = False, clip_tails: float = 0.0) -> Raster: ...

line_intersections

This tool identifies points where the features of two vector line/polygon layers intersect. The user must specify the names of two input vector line files and the output file. The output file will be a vector of POINT VectorGeometryType. If the input vectors intersect at a line segment, the beginning and end vertices of the segment will be present in the output file. A warning is issued if intersection line segments are identified during analysis. If no intersections are found between the input line files, the output file will not be saved and a warning will be issued.

Each intersection point will contain PARENT1 and PARENT2 attribute fields, identifying the instersecting features in the first and second input line files respectively. Additionally, the output attribute table will contain all of the attributes (excluding FIDs) of the two parent line features.

Function Signature

def line_intersections(self, input1: Vector, input2: Vector) -> Vector: ...

line_thinning

This image processing tool reduces all polygons in a Boolean raster image to their single-cell wide skeletons. This operation is sometimes called line thinning or skeletonization. In fact, the input image need not be truly Boolean (i.e. contain only 1's and 0's). All non-zero, positive values are considered to be foreground pixels while all zero valued cells are considered background pixels. The remove_spurs tool is useful for cleaning up an image before performing a line thinning operation.

Note: Unlike other filter-based operations in WhiteboxTools, this algorithm can't easily be parallelized because the output raster must be read and written to during the same loop.

See Also

remove_spurs, thicken_raster_line

Function Signature

def line_thinning(self, raster: Raster) -> Raster: ...

linearity_index

This tool calculates the linearity index of polygon features based on a regression analysis. The index is simply the coefficient of determination (r-squared) calculated from a regression analysis of the x and y coordinates of the exterior hull nodes of a vector polygon. Linearity index is a measure of how well a polygon can be described by a straight line. It is a related index to the elongation_ratio, but is more efficient to calculate as it does not require finding the minimum bounding box. The Pearson correlation coefficient between linearity index and the elongation ratio for a large data set of lake polygons in northern Canada was found to be 0.656, suggesting a moderate level of association between the two measures of polygon linearity. Note that this index is not useful for identifying narrow yet sinuous polygons, such as meandering rivers.

The only required input is the name of the file. The linearity values calculated for each vector polygon feature will be placed in the accompanying attribute table as a new field (LINEARITY).

See Also

elongation_ratio, patch_orientation

Function Signature

def linearity_index(self, input: Vector) -> Vector: ...

lines_to_polygons

This tool converts vector polylines into polygons. Note that this tool will close polygons that are open and will ensure that the first part of an input line is interpreted as the polygon hull and subsequent parts are considered holes. The tool does not examine input lines for line crossings (self intersections), which are topological errors.

See Also

polygons_to_lines

Function Signature

def lines_to_polygons(self, input: Vector) -> Vector: ...

list_unique_values

This tool can be used to list each of the unique values contained within a categorical field of an input vector file's attribute table. The tool outputs an HTML formatted report (output) containing a table of the unique values and their frequency of occurrence within the data. The user must specify the name of an input shapefile (input) and the name of one of the fields (field) contained in the associated attribute table. The specified field should not contained floating-point numerical data, since the number of categories will likely equal the number of records, which may be quite large. The tool effectively provides tabular output that is similar to the graphical output provided by the attribute_histogram tool, which, however, can be applied to continuous data.

See Also

attribute_histogram

Function Signature

def list_unique_values(self, input: Vector, field_name: str) -> Tuple[str, int]: ...

list_unique_values_raster

This function can be used to list each of the unique values contained within a categorical raster (raster). The tool outputs string containing a comma-seperated variable (CSV) table of the unique values and their frequency of occurrence within the data. The input raster should not contain continuous floating-point numerical data, because the number of categories will likely equal the number of pixels, which may be quite large.

See Also

list_unique_values

Function Signature

def list_unique_values_raster(self, raster: Raster) -> str: ...

long_profile

This tool can be used to create a longitudinal profile plot. A longitudinal stream profile is a plot of elevation against downstream distance. Most long profiles use distance from channel head as the distance measure. This tool, however, uses the distance to the stream network outlet cell, or mouth, as the distance measure. The reason for this difference is that while for any one location within a stream network there is only ever one downstream outlet, there are usually many upstream channel heads. Thus plotted using the traditional downstream-distance method, the same point within a network will plot in many different long profile locations, whereas it will always plot on one unique location in the distance-to-mouth method. One consequence of this difference is that the long profile will be oriented from right-to-left rather than left-to-right, as would traditionally be the case.

The tool outputs an interactive SVG line graph embedded in an HTML document (output_html_file). The user must input a D8 pointer (flow direction) raster (d8_pointer), a streams raster image (streams_raster), and a digital elevation model (dem). Stream cells are designated in the streams image as all positive, nonzero values. Thus all non-stream or background grid cells are commonly assigned either zeros or NoData values. The pointer image is used to traverse the stream network and should only be created using the D8 algorithm (d8_pointer). The streams image should be derived using a flow accumulation based stream network extraction algorithm, also based on the D8 flow algorithm.

By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools. If the pointer file contains ESRI flow direction values instead, set esri_pointer=True.

See Also

long_profile_from_points, profile, d8_pointer

Function Signature

def long_profile(self, d8_pointer: Raster, streams_raster: Raster, dem: Raster, output_html_file: str, esri_pointer: bool = False) -> None: ...

long_profile_from_points

This tool can be used to create a longitudinal profile plot for a set of vector points (points). A longitudinal stream profile is a plot of elevation against downstream distance. Most long profiles use distance from channel head as the distance measure. This tool, however, uses the distance to the outlet cell, or mouth, as the distance measure.

The tool outputs an interactive SVG line graph embedded in an HTML document (output_html_file). The user input a D8 pointer (d8_pointer) image (flow direction), a vector points file (points), and a digital elevation model (dem). The pointer image is used to traverse the flow path issuing from each initiation point in the vector file; this pointer file should only be created using the D8 algorithm (d8_pointer).

By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools. If the pointer file contains ESRI flow direction values instead, the esri_pointer parameter must be specified.

See Also

long_profile, profile, d8_pointer

Function Signature

def long_profile_from_points(self, d8_pointer: Raster, points: Vector, dem: Raster, output_html_file: str, esri_pointer: bool = False) -> None: ...

longest_flowpath

This tool delineates the longest flowpaths for a group of subbasins or watersheds. Flowpaths are initiated along drainage divides and continue along the D8-defined flow direction until either the subbasin outlet or DEM edge is encountered. Each input subbasin/watershed will have an associated vector flowpath in the output image. longest_flowpath is similar to the r.lfp plugin tool for GRASS GIS. The length of the longest flowpath draining to an outlet is related to the time of concentration, which is a parameter used in certain hydrological models.

The user must input the filename of a digital elevation model (DEM), a basins raster, and the output vector. The DEM must be depressionless and should have been pre-processed using the breach_depressions_least_cost or fill_depressions tool. The basins raster must contain features that are delineated by categorical (integer valued) unique identifier values. All non-NoData, non-zero valued grid cells in the basins raster are interpreted as belonging to features. In practice, this tool is usual run using either a single watershed, a group of contiguous non-overlapping watersheds, or a series of nested subbasins. These are often derived using the watershed tool, based on a series of input outlets, or the subbasins tool, based on an input stream network. If subbasins are input to longest_flowpath, each traced flowpath will include only the non-overlapping portions within nested areas. Therefore, this can be a convenient method of delineating the longest flowpath to each bifurcation in a stream network.

The output vector file will contain fields in the attribute table that identify the associated basin unique identifier (BASIN), the elevation of the flowpath source point on the divide (UP_ELEV), the elevation of the outlet point (DN_ELEV), the length of the flowpath (LENGTH), and finally, the average slope (AVG_SLOPE) along the flowpath, measured as a percent grade.

See Also

max_upslope_flowpath_length, breach_depressions_least_cost, fill_depressions, watershed, subbasins

Function Signature

def longest_flowpath(self, dem: Raster, basins: Raster) -> Vector: ...

lowest_position

This tool identifies the stack position (index) of the minimum value within a raster stack on a cell-by-cell basis. For example, if five raster images (inputs) are input to the tool, the output raster (output) would show which of the five input rasters contained the lowest value for each grid cell. The index value in the output raster is the zero-order number of the raster stack, i.e. if the lowest value in the stack is contained in the first image, the output value would be 0; if the lowest stack value were the second image, the output value would be 1, and so on. If any of the cell values within the stack is NoData, the output raster will contain the NoData value for the corresponding grid cell. The index value is related to the order of the input images.

Warning

Each of the input rasters must have the same spatial extent and number of rows and columns.

See Also

highest_position, pick_from_list

Function Signature

def lowest_position(self, input_rasters: List[Raster]) -> Raster: ...

majority_filter

This tool performs a range filter on an input image (input). A range filter assigns to each cell in the output grid. The range (maximum - minimum) of the values contained within a moving window centred on each grid cell.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

See Also

total_filter

Function Signature

def majority_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

map_off_terrain_objects

This tool can be used to map off-terrain objects in a digital surface model (DSM) based on cell-to-cell differences in elevations and local slopes. The algorithm works by using a region-growing operation to connect neighbouring grid cells outwards from seed cells. Two neighbouring cells are considered connected if the slope between the two cells is less than the user-specified maximum slope value (max_slope). Mapped segments that are less than the minimum feature size (min_size), in grid cells, are assigned a common background value. Note that this method of mapping off-terrain objects, and thereby separating ground cells from non-ground objects in DSMs, works best with fine-resolution DSMs that have been interpolated using a non-smoothing method, such as triangulation (TINing) or nearest-neighbour interpolation.

See Also

remove_off_terrain_objects

Function Signature

def map_off_terrain_objects(self, dem: Raster, max_slope: float = float('inf'), min_feature_size: int = 0) -> Raster: ...

max_absolute_overlay

This tool can be used to find the maximum absolute (non-negative) value in each cell of a grid from a set of input images (inputs). NoData values in any of the input images will result in a NoData pixel in the output image.

Warning

Each of the input rasters must have the same spatial extent and number of rows and columns.

See Also

max_overlay, min_absolute_overlay, min_overlay

Function Signature

def max_absolute_overlay(self, input_rasters: List[Raster]) -> Raster: ...

max_anisotropy_dev

Calculates the maximum anisotropy (directionality) in elevation deviation over a range of spatial scales.

Function Signature

def max_anisotropy_dev(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

max_anisotropy_dev_signature

/.//

Function Signature

def max_anisotropy_dev_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...

max_branch_length

Maximum branch length (Bmax) is the longest branch length between a grid cell's flowpath and the flowpaths initiated at each of its neighbours. It can be conceptualized as the downslope distance that a volume of water that is split into two portions by a drainage divide would travel before reuniting.

If the two flowpaths of neighbouring grid cells do not intersect, Bmax is simply the flowpath length from the starting cell to its terminus at the edge of the grid or a cell with undefined flow direction (i.e. a pit cell either in a topographic depression or at the edge of a major body of water).

The pattern of Bmax derived from a DEM should be familiar to anyone who has interpreted upslope contributing area images. In fact, Bmax can be thought of as the complement of upslope contributing area. Whereas contributing area is greatest along valley bottoms and lowest at drainage divides, Bmax is greatest at divides and lowest along channels. The two topographic attributes are also distinguished by their units of measurements; Bmax is a length rather than an area. The presence of a major drainage divide between neighbouring grid cells is apparent in a Bmax image as a linear feature, often two grid cells wide, of relatively high values. This property makes Bmax a useful land surface parameter for mapping ridges and divides.

Bmax is useful in the study of landscape structure, particularly with respect to drainage patterns. The index gives the relative significance of a specific location along a divide, with respect to the dispersion of materials across the landscape, in much the same way that stream ordering can be used to assess stream size.

See Also

flow_length_diff

Reference

Lindsay JB, Seibert J. 2013. Measuring the significance of a divide to local drainage patterns. International Journal of Geographical Information Science, 27: 1453-1468. DOI: 10.1080/13658816.2012.705289

Function Signature

def max_branch_length(self, dem: Raster, log_transform: bool = False) -> Raster: ...

max_difference_from_mean

Calculates the maximum difference from mean elevation over a range of spatial scales.

Function Signature

def max_difference_from_mean(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

max_downslope_elev_change

This tool calculates the maximum elevation drop between each grid cell and its neighbouring cells within a digital elevation model (DEM). The user must input a DEM (dem).

See Also

max_upslope_elev_change, min_downslope_elev_change, num_downslope_neighbours

Function Signature

def max_downslope_elev_change(self, raster: Raster) -> Raster: ...

max_elevation_dev_signature

max_elevation_dev_signature

Tool documentation not located.

Function Signature

def max_elevation_dev_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...

max_elevation_deviation

This tool can be used to calculate the maximum deviation from mean elevation, DEVmax (Lindsay et al. 2015) for each grid cell in a digital elevation model (DEM) across a range specified spatial scales. DEV is an elevation residual index and is essentially equivalent to a local elevation z-score. This attribute measures the relative topographic position as a fraction of local relief, and so is normalized to the local surface roughness. The multi-scaled calculation of DEVmax utilizes an integral image approach (Crow, 1984) to ensure highly efficient filtering that is invariant with filter size, which is the algorithm characteristic that allows for this densely sampled multi-scale analysis. In this way, max_elevation_deviation allows users to estimate the locally optimal scale with which to estimate DEV on a pixel-by-pixel basis. This multi-scaled version of local topographic position can reveal significant terrain characteristics and can aid with soil, vegetation, landform, and other mapping applications that depend on geomorphometric characterization.

The user must input a digital elevation model (DEM) (dem). The range of scales that are evaluated in calculating DEVmax are determined by the user-specified min_scale, max_scale, and step parameters. All filter radii between the minimum and maximum scales, increasing by step, will be evaluated. The scale parameters are in units of grid cells and specify kernel size "radii" (r), such that:

d = 2r + 1

That is, a radii of 1, 2, 3... yields a square filters of dimension (d) 3 x 3, 5 x 5, 7 x 7...

DEV is estimated at each tested filter size and every grid cell is assigned the maximum DEV value across the evaluated scales.

Two output rasters will be generated, including the magnitude (DEVmax) and a second raster the assigns each pixel the scale at which DEVmax is encountered (DEVscale). The DEVscale raster can be very useful for revealing multi-scale landscape structure.

Reference

Lindsay J, Cockburn J, Russell H. 2015. An integral image approach to performing multi-scale topographic position analysis. Geomorphology, 245: 51-61.

See Also

DevFromMeanElev, max_difference_from_mean, multiscale_elevation_percentile

Function Signature

def max_elevation_deviation(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...

max_overlay

This tool can be used to find the maximum value in each cell of a grid from a set of input images (inputs). NoData values in any of the input images will result in a NoData pixel in the output image (output). It is similar to the Max mathematical tool, except that it will accept more than two input images.

Warning

Each of the input rasters must have the same spatial extent and number of rows and columns.

See Also

min_overlay, max_absolute_overlay

Function Signature

def max_overlay(self, input_rasters: List[Raster]) -> Raster: ...

max_procs

Determines the number of processors used by functions that are parallelized. If set to -1 (wbe.max_procs=-1), the default, all available processors will be used. To throttle tools, set max_procs to a positive whole number less than the number of system processors.

max_upslope_elev_change

a digital elevation model (DEM). The user must input DEM (dem).

See Also

max_downslope_elev_change

Function Signature

def max_upslope_elev_change(self, raster: Raster) -> Raster: ...

max_upslope_flowpath_length

This tool calculates the maximum length of the flowpaths that run through each grid cell (in map horizontal units) in an input digital elevation model (dem). The tool works by first calculating the D8 flow pointer (d8_pointer) from the input DEM. The DEM must be depressionless and should have been pre-processed using the breach_depressions_least_cost or fill_depressions tool. The user must also specify the name of output raster (output).

See Also

d8_pointer, breach_depressions_least_cost, fill_depressions, average_upslope_flowpath_length, downslope_flowpath_length, downslope_distance_to_stream

Function Signature

def max_upslope_flowpath_length(self, dem: Raster) -> Raster: ...

max_upslope_value

This tool calculates the maximum length of the flowpaths that run through each grid cell (in map horizontal units) in an input digital elevation model (dem). The tool works by first calculating the D8 flow pointer (d8_pointer) from the input DEM. The DEM must be depressionless and should have been pre-processed using the breach_depressions_least_cost or fill_depressions tool. The user must also specify the name of output raster (output).

See Also

d8_pointer, breach_depressions_least_cost, fill_depressions, average_upslope_flowpath_length, downslope_flowpath_length, downslope_distance_to_stream

Function Signature

def max_upslope_value(self, dem: Raster, values_raster: Raster) -> Raster: ...

maximal_curvature

This tool calculates the maximal curvature from a digital elevation model (DEM). Maximal curvature is the curvature of a principal section with the highest value of curvature at a given point of the topographic surface (Florinsky, 2017). The values of this curvature are unbounded, and positive values correspond to ridge positions while negative values are indicative of closed depressions (Florinsky, 2016). Maximal curvature is measured in units of m^-1.

The user must input a DEM (dem). The Z conversion factor (zfactor) is only important when the vertical and horizontal units are not the same in the DEM. When this is the case, the algorithm will multiply each elevation in the DEM by the Z Conversion Factor. Curvature values are often very small and as such the user may opt to log-transform the output raster (log). Transforming the values applies the equation by Shary et al. (2002):

Θ' = sign(Θ) ln(1 + 10ⁿ|Θ|)

where Θ is the parameter value and n is dependent on the grid cell size.

For DEMs in projected coordinate systems, the tool uses the 3rd-order bivariate Taylor polynomial method described by Florinsky (2016). Based on a polynomial fit of the elevations within the 5x5 neighbourhood surrounding each cell, this method is considered more robust against outlier elevations (noise) than other methods. For DEMs in geographic coordinate systems (i.e. angular units), the tool uses the 3x3 polynomial fitting method for equal angle grids also described by Florinsky (2016).

References

Florinsky, I. (2016). Digital terrain analysis in soil science and geology. Academic Press.

Florinsky, I. V. (2017). An illustrated introduction to general geomorphometry. Progress in Physical Geography, 41(6), 723-752.

Shary P. A., Sharaya L. S. and Mitusov A. V. (2002) Fundamental quantitative methods of land surface analysis. Geoderma 107: 1–32.

minimal_curvature, tangential_curvature, profile_curvature, plan_curvature, mean_curvature, gaussian_curvature

Function Signature

def maximal_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

maximum_filter

This tool assigns each cell in the output grid. The maximum value in a moving window centred on each grid cell in the input raster (input). A maximum filter is the equivalent of the mathematical morphological dilation operator.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values, e.g. 3, 5, 7, 9... If the kernel filter size is the same in the x and y dimensions, the silent filter flag may be used instead (command-line interface only).

This tool takes advantage of the redundancy between overlapping, neighbouring filters to enhance computationally efficiency. Like most of WhiteboxTools' filters, it is also parallelized for further efficiency.

See Also

minimum_filter

Function Signature

def maximum_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

mdinf_flow_accum

This tool is used to generate a flow accumulation grid (i.e. contributing area) using the MD-infinity algorithm (Seibert and McGlynn, 2007). This algorithm is an examples of a multiple-flow-direction (MFD) method because the flow entering each grid cell is routed to one or two downslope neighbour, i.e. flow divergence is permitted. The user must specify the name of the input digital elevation model (dem). The DEM should have been hydrologically corrected to remove all spurious depressions and flat areas. DEM pre-processing is usually achieved using the breach_depressions_least_cost or fill_depressions tool.

In addition to the input flow-pointer grid name, the user must specify the output type (out_type). The output flow-accumulation can be 1) specific catchment area (SCA), which is the upslope contributing area divided by the contour length (taken as the grid resolution), 2) total catchment area in square-metres, or 3) the number of upslope grid cells. The user must also specify whether the output flow-accumulation grid should be log-tranformed, i.e. the output, if this option is selected, will be the natural-logarithm of the accumulated area. This is a transformation that is often performed to better visualize the contributing area distribution. Because contributing areas tend to be very high along valley bottoms and relatively low on hillslopes, when a flow-accumulation image is displayed, the distribution of values on hillslopes tends to be 'washed out' because the palette is stretched out to represent the highest values. Log-transformation (log) provides a means of compensating for this phenomenon. Importantly, however, log-transformed flow-accumulation grids must not be used to estimate other secondary terrain indices, such as the wetness index, or relative stream power index.

Grid cells possessing the NoData value in the input DEM raster are assigned the NoData value in the output flow-accumulation image. The output raster is of the float data type and continuous data scale.

Reference

Seibert, J. and McGlynn, B.L., 2007. A new triangular multiple flow direction algorithm for computing upslope areas from gridded digital elevation models. Water resources research, 43(4).

See Also

D8FlowAccumulation, FD8FlowAccumulation, quinn_flow_accumulation, qin_flow_accumulation, DInfFlowAccumulation, MDInfFlowAccumulation, rho8_pointer, breach_depressions_least_cost

Function Signature

def mdinf_flow_accum(self, dem: Raster, out_type: str = "sca", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...

mean_curvature

This tool calculates the mean curvature, or the rate of change in slope along a flow line, from a digital elevation model (DEM). Curvature is the second derivative of the topographic surface defined by a DEM. Profile curvature characterizes the degree of downslope acceleration or deceleration within the landscape (Gallant and Wilson, 2000). The user must input a DEM (dem). WhiteboxTools reports curvature in radians multiplied by 100 for easier interpretation because curvature values are typically very small. The Z conversion factor (zfactor) is only important when the vertical and horizontal units are not the same in the DEM. When this is the case, the algorithm will multiply each elevation in the DEM by the Z Conversion Factor. If the DEM is in the geographic coordinate system (latitude and longitude), the following equation is used:

zfactor = 1.0 / (111320.0 x cos(mid_lat))

where mid_lat is the latitude of the centre of the raster, in radians.

The algorithm uses the same formula for the calculation of plan curvature as Gallant and Wilson (2000). Profile curvature is negative for slope increasing downhill (convex flow profile, typical of upper slopes) and positive for slope decreasing downhill (concave, typical of lower slopes).

Reference

Gallant, J. C., and J. P. Wilson, 2000, Primary topographic attributes, in Terrain Analysis: Principles and Applications, edited by J. P. Wilson and J. C. Gallant pp. 51-86, John Wiley, Hoboken, N.J.

See Also

profile_curvature, tangential_curvature, total_curvature, slope, aspect

Function Signature

def mean_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...

mean_filter

This tool performs a mean filter operation on a raster image. A mean filter, a type of low-pass filter, can be used to emphasize the longer-range variability in an image, effectively acting to smooth the image. This can be useful for reducing the noise in an image. This tool utilizes an integral image approach (Crow, 1984) to ensure highly efficient filtering that is invariant to filter size. The algorithm operates by calculating the average value in a moving window centred on each grid cell. Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filtery flags. These dimensions should be odd, positive integer values, e.g. 3, 5, 7, 9... If the kernel filter size is the same in the x and y dimensions, the silent filter flag may be used instead (command-line interface only).

Although commonly applied in digital image processing, mean filters are generally considered to be quite harsh, with respect to their impact on the image, compared to other smoothing filters such as the edge-preserving smoothing filters including the bilateral_filter, median_filter, olympic_filter, edge_preserving_mean_filter and even gaussian_filter.

This tool works with both greyscale and red-green-blue (RGB) images. RGB images are decomposed into intensity-hue-saturation (IHS) and the filter is applied to the intensity channel. NoData values in the input image are ignored during filtering. NoData values are assigned to all sites beyond the raster.

Reference

Crow, F. C. (1984, January). Summed-area tables for texture mapping. In ACM SIGGRAPH computer graphics (Vol. 18, No. 3, pp. 207-212). ACM.

See Also

bilateral_filter, edge_preserving_mean_filter, gaussian_filter, median_filter, rgb_to_ihs

Function Signature

def mean_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...

median_filter

This tool performs a median filter on a raster image. Median filters, a type of low-pass filter, can be used to emphasize the longer-range variability in an image, effectively acting to smooth the image. This can be useful for reducing the noise in an image. The algorithm operates by calculating the median value (middle value in a sorted list) in a moving window centred on each grid cell. Specifically, this tool uses the efficient running-median filtering algorithm of Huang et al. (1979). The median value is not influenced by anomolously high or low values in the distribution to the extent that the average is. As such, the median filter is far less sensitive to shot noise in an image than the mean filter.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the filterx and filteryflags. These dimensions should be odd, positive integer values (e.g. 3, 5, 7, 9, etc.).

Reference

Huang, T., Yang, G.J.T.G.Y. and Tang, G., 1979. A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(1), pp.13-18.

See Also

bilateral_filter, edge_preserving_mean_filter, gaussian_filter, mean_filter

Function Signature