CorrectStreamVectorDirection

This tool can be used to resolve many of the topological errors and inconsistencies associated with manually digitized vector stream networks, i.e. hydrography data. A properly structured stream network should consist of a series of stream segments that connect a channel head to a downstream confluence, or an upstream confluence to a downstream confluence/outlet. This tool will join vector arcs that connect at arbitrary, non-confluence points along stream segments. It also splits an arc where a tributary stream connects at a mid-point, thereby creating a proper confluence where two upstream triburaries converge into a downstream segment. The tool also handles non-connecting tributaries caused by dangling arcs, i.e. overshoots and undershoots.

The user may optinally specify the name of the input vector stream network (--input) and the output file (--output). Note that if an input file is not specified by the user, the tool will search for all vector files (*.shp) files contained within the current working directory. This feature can be very useful when you need to process a large number of stream files contained within a single directory. The tool will process the files in parallel in this batch mode.

A distance threshold for snapping dangling arcs (--snap) must be specified by the user. This distance is in the input layer's x-y units. The tool works best on projected input data, however, if the input are in geographic coordinates (latitude and longitude), then specifying a small valued snap distance is advisable.

Notice that the attributes of the input layer will not be carried over to the output file because there is not a one-for-one feature correspondence between the two files due to the joins and splits of stream segments. Instead the output attribute table will only contain a feature ID (FID) entry.

Note: this tool should be used to pre-process vector streams that are input to the VectorStreamNetworkAnalysis tool.

Parameters:

Flag	Description
-i, --input	Name of the input lines vector file
--outlet	Name of the input outlet vector points file
-o, --output	Name of the output lines vector file
--snap, --dist	Snap distance, in xy units (metres)

Python function:

wbt.correct_stream_vector_direction(
    i, 
    outlet, 
    output, 
    dist="", 
    callback=default_callback
)

Command-line Interface:

>> ./whitebox_tools -r=CorrectStreamVectorDirection ^
--input=streams.shp --outlet=outlet.shp ^
--output=streams_fixed.shp --snap=2.0

Source code on GitHub

Author: Prof. John Lindsay

Created: 15/08/2023 (oringinally in Whitebox Toolset Extension)

Last Modified: 15/08/2023

DistanceToOutlet

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 specify the name of 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_pntr). 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 --zero_background parameter is used, 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 --esri_pntr parameter must be specified.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.distance_to_outlet(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=DistanceToOutlet -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=DistanceToOutlet -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 13/07/2017

Last Modified: 18/10/2019

ExtractStreams

This tool can be used to extract, or map, the likely stream cells from an input flow-accumulation image (--flow_accum). 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. As such, it is frequently determined either by examining map or imagery data 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 (--output) will be the NoData value unless the --zero_background flag is specified.

See Also: GreaterThan

Parameters:

Flag	Description
--flow_accum	Input raster D8 flow accumulation file
-o, --output	Output raster file
--threshold	Threshold in flow accumulation values for channelization
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.extract_streams(
    flow_accum, 
    output, 
    threshold, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=ExtractStreams -v --wd="/path/to/data/" ^
--flow_accum='d8accum.tif' -o='output.tif' --threshold=100.0 ^
--zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 28/06/2017

Last Modified: 30/01/2020

ExtractValleys

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 (--filter), 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 FeaturePreservingSmoothing 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 FeaturePreservingSmoothing 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).

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.

Parameters:

Flag	Description
--dem	Input raster DEM file
-o, --output	Output raster file
--variant	Options include 'LQ' (lower quartile), 'JandR' (Johnston and Rosenfeld), and 'PandD' (Peucker and Douglas); default is 'LQ'
--line_thin	Optional flag indicating whether post-processing line-thinning should be performed
--filter	Optional argument (only used when variant='lq') providing the filter size, in grid cells, used for lq-filtering (default is 5)

Python function:

wbt.extract_valleys(
    dem, 
    output, 
    variant="LQ", 
    line_thin=True, 
    filter=5, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=ExtractValleys -v --wd="/path/to/data/" ^
--dem=pointer.tif -o=out.tif --variant='JandR' ^
--line_thin
>>./whitebox_tools -r=ExtractValleys -v ^
--wd="/path/to/data/" --dem=pointer.tif -o=out.tif ^
--variant='lq' --filter=7 --line_thin

Source code on GitHub

Author: Dr. John Lindsay

Created: 12/07/2017

Last Modified: 30/01/2020

FarthestChannelHead

This tool calculates the upstream distance to the farthest stream head for each grid cell belonging to a raster stream network. The user must specify the name of 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_pntr). 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 --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.farthest_channel_head(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=FarthestChannelHead -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=FarthestChannelHead -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 12/07/2017

Last Modified: 18/10/2019

FindMainStem

This tool can be used to identify the main channel in a stream network. The user must specify the names of a D8 pointer (flow direction) raster (--d8_pntr), and a streams raster (--streams). The pointer raster is used to traverse the stream network and should only be created using the D8Pointer. By default, the pointer raster is assumed to use the clockwise indexing method used by WhiteboxTools:

.	.	.
64	128	1
32	0	2
16	8	4

If the pointer file contains ESRI flow direction values instead, the --esri_pntr 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 --zero_background parameter is specified.

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: D8Pointer

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.find_main_stem(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=FindMainStem -v --wd="/path/to/data/" ^
--d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=FindMainStem -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 27/06/2017

Last Modified: 18/10/2019

HackStreamOrder

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 (StrahlerStreamOrder) and Shreve's stream magnitude (ShreveStreamMagnitude), 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 specify the names of a streams raster image (--streams) 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. Background cells will be assigned the NoData value in the output image, unless the --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

Reference:

Hack, J. T. (1957). Studies of longitudinal stream profiles in Virginia and Maryland (Vol. 294). US Government Printing Office.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.hack_stream_order(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=HackStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=HackStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 25/06/2017

Last Modified: 18/10/2019

HortonStreamOrder

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 the names of a streams raster image (--streams) 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 (D8Pointer). Background cells will be assigned the NoData value in the output image, unless the --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

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.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.horton_stream_order(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=HortonStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=HortonStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 25/06/2017

Last Modified: 18/10/2019

LengthOfUpstreamChannels

This tool calculates, for each stream grid cell in an input streams raster (--streams) the total length of channels upstream. The user must specify the name of 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_pntr). 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 --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.length_of_upstream_channels(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=LengthOfUpstreamChannels -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=LengthOfUpstreamChannels -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 11/07/2017

Last Modified: 18/10/2019

LongProfile

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 is 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). The user must specify the names of a D8 pointer (--d8_pntr) image (flow direction), a streams raster image (--streams), 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 (D8Pointer). The streams image should be derived using a flow accumulation based stream network extraction algorithm, also based on the D8 flow algorithm.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
--dem	Input raster DEM file
-o, --output	Output HTML file
--esri_pntr	D8 pointer uses the ESRI style scheme

Python function:

wbt.long_profile(
    d8_pntr, 
    streams, 
    dem, 
    output, 
    esri_pntr=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=LongProfile -v --wd="/path/to/data/" ^
--d8_pntr=D8.tif --streams=streams.tif --dem=dem.tif ^
-o=output.html --esri_pntr

Source code on GitHub

Author: Dr. John Lindsay

Created: 20/02/2018

Last Modified: 18/10/2019

LongProfileFromPoints

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). The user must specify the names of a D8 pointer (--d8_pntr) 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 (D8Pointer).

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--points	Input vector points file
--dem	Input raster DEM file
-o, --output	Output HTML file
--esri_pntr	D8 pointer uses the ESRI style scheme

Python function:

wbt.long_profile_from_points(
    d8_pntr, 
    points, 
    dem, 
    output, 
    esri_pntr=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=LongProfileFromPoints -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --points=stream_head.shp ^
--dem=dem.tif -o=output.html --esri_pntr

Source code on GitHub

Author: Dr. John Lindsay

Created: 21/02/2018

Last Modified: 18/10/2019

PruneVectorStreams

Note this tool is part of a WhiteboxTools extension product. Please visit Whitebox Geospatial Inc. for information about purchasing a license activation key (https://www.whiteboxgeo.com/extension-pricing/).

This tool can be used to prune an input vector stream network (--streams), such that smaller headwater streams are removed from the output vector streams file (--output). Streams are pruned based on their modelled size, which is estimated using the tributary total upstream channel length (TUCL). TUCL is a measure of the upstream channel extent and therefore the catchment area. A tributary always connects a channel head and a downstream confluence/outlet in the stream network. This is in contrast to a stream link, which connects either a channel head and downstream confluence, in the case of headwater streams, or two confluences, in the case of interior links. Tributaries are often composed of multiple stream links and can have tributaries of their own. At each confluence in the stream network, the tributary identifier that carries on downstream is associated with the upstream link with the larger TUCL value (a surrogate for stream/drainage area size). The tool will determine TUCL and tributary structure by first performing a network analysis operation on the input vector stream network. This requires knowledge of the elevation distribution among stream links, which is provided by an input digital elevation model (DEM; --dem). The main purpose of the DEM is to distinguish between the the outlet stream link and exterior (i.e. headwater) stream links in initiating the tree-traversal algorithm used in the stream network analysis operation. Thus, the DEM need not be particularly fine-resolution.

The user must specify the --threshold tributary TUCL value, with larger values providing more extensive pruning. Because the tool uses tributary TUCL, rather than stream-link TUCL, the resulting pruned stream network will contain fewer streams but will preserve the spatially extent of the original network. That is, stream tributaries with TUCL values larger than the specified threshold will maintain their full length all the way to their channel heads. The main channel stem of networks will be preserved. User experimentation is likely required to determine an appropriate value for the threshold parameter, which will also depend of the application. However, this value should not be larger than the TUCL of the main stem, or else the output network will not contain any features. Tributary TUCL generally follows an exponential progression in a stream network from headwater tributaries towards the main branch. To review the range of TUCL values within a network, one may wish to apply the related VectorStreamNetworkAnalysis tool first, reviewing the TUCL values in the output attribute table. Be mindful however that this is a stream-link TUCL value and not a tributary TUCL (which is why the output of PruneVectorStreams cannot simply be replicated by thresholding the TUCL attribute output by VectorStreamNetworkAnalysis) although the range of values will be the same.

The user must lastly specify the --snap distance value, in meters. This parameter allows the tool to identify the linkage between stream segments when their end nodes are not perfectly aligned. One may also choose to run the RepairStreamVectorTopology tool prior to this tool to resolve any misalignment in the input streams vector.

Parameters:

Flag	Description
--streams	Name of the input streams vector file
--dem	Name of the input DEM raster file
-o, --output	Name of the output lines shapefile
--threshold	Tributary upstream channel length threshold, in xy units (metres)
--snap	Snap distance, in xy units (metres)

Python function:

wbt.prune_vector_streams(
    streams, 
    dem, 
    output, 
    threshold="", 
    snap=0.1, 
    callback=default_callback
)

Command-line Interface:

>> ./whitebox_tools -r=PruneVectorStreams --streams=rivers.shp ^
--dem=DEM.tif -o=network_analysis.shp --snap=1.0

Source code is unavailable due to proprietary license.

Author: Whitebox Geospatial Inc. (c)

Created: 04/05/2023

Last Modified: 04/05/2023

RasterStreamsToVector

This tool converts a raster stream file into a vector file. The user must specify: 1) the name of the raster streams file, 2) the name of the D8 flow pointer file, and 3) the name of the output vector file. Streams in the input raster streams file are denoted by cells containing any positive, non-zero integer. A field in the vector database file, called STRM_VAL, will correspond to this positive integer value. The database file will also have a field for the length of each link in the stream network. The flow pointer file must be calculated from a DEM with all topographic depressions and flat areas removed and must be calculated using the D8 flow pointer algorithm. The output vector will contain PolyLine features. By default, the tool will only maintain vertices in the output vector file where there is a change in the downstream flow direction, which can result in a significant reduction in the number of vertices in the output file, and therefore reduced file sizes. However, the user can optionally override this behaviour by specifying the --all_vertices input parameter. If specified, this parameter has the effect of presering all of the verticies associated with grid cells in the input raster within the output vector file.

Parameters:

Flag	Description
--streams	Input raster streams file
--d8_pntr	Input raster D8 pointer file
-o, --output	Output vector file
--esri_pntr	D8 pointer uses the ESRI style scheme
--all_vertices	Do you want to preserve all vertices in output (i.e. no straight-line generalization)

Python function:

wbt.raster_streams_to_vector(
    streams, 
    d8_pntr, 
    output, 
    esri_pntr=False, 
    all_vertices=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=RasterStreamsToVector -v ^
--wd="/path/to/data/" --streams=streams.tif --d8_pntr=D8.tif ^
-o=output.shp
>>./whitebox_tools -r=RasterStreamsToVector -v ^
--wd="/path/to/data/" --streams=streams.tif --d8_pntr=D8.tif ^
-o=output.shp --esri_pntr

Source code on GitHub

Author: Dr. John Lindsay

Created: 24/09/2018

Last Modified: 18/10/2019

RasterizeStreams

This tool can be used rasterize an input vector stream network (--streams) using on Lindsay (2016) method. The user must specify the name of an existing raster (--base), from which the output raster's grid resolution is determined.

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: RasterStreamsToVector

Parameters:

Flag	Description
--streams	Input vector streams file
--base	Input base raster file
-o, --output	Output raster file
--nodata	Use NoData value for background?
--feature_id	Use feature number as output value?

Python function:

wbt.rasterize_streams(
    streams, 
    base, 
    output, 
    nodata=True, 
    feature_id=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=RasterizeStreams -v ^
--wd="/path/to/data/" --streams=streams.shp --base=raster.tif ^
-o=output.tif

Source code on GitHub

Author: Dr. John Lindsay

Created: 11/03/2018

Last Modified: 22/10/2019

RemoveShortStreams

This tool can be used to remove stream links in a stream network that are shorter than a user-specified length (--min_length). The user must specify the names of a streams raster image (--streams) and D8 pointer image (--d8_pntr). Stream cells are designated in the streams raster as all positive, nonzero values. Thus all non-stream or background grid cells are commonly assigned either zeros or NoData values. The pointer raster is used to traverse the stream network and should only be created using the D8 algorithm. Background cells will be assigned the NoData value in the output image, unless the --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

See Also: ExtractStreams

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--min_length	Minimum tributary length (in map units) used for network pruning
--esri_pntr	D8 pointer uses the ESRI style scheme

Python function:

wbt.remove_short_streams(
    d8_pntr, 
    streams, 
    output, 
    min_length, 
    esri_pntr=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=RemoveShortStreams -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif

Source code on GitHub

Author: Dr. John Lindsay

Created: 28/06/2017

Last Modified: 30/01/2020

RepairStreamVectorTopology

The user may optionally specify the name of the input vector stream network (--input) and the output file (--output). Note that if an input file is not specified by the user, the tool will search for all vector files (*.shp) files contained within the current working directory. This feature can be very useful when you need to process a large number of stream files contained within a single directory. The tool will process the files in parallel in this batch mode.

Additionally, the tool possesses two Boolean flags, --reverse_backward_arcs and --correct_nonconfluence_joins which determine whether the tool will correct backward arcs (i.e., line segements that are oriented in the reverse direction to the streamflow) and non-confluence joins (i.e., upstream/downstream line segments that are not joined at confluence locations).

Note: this tool should be used to pre-process vector streams that are input to the VectorStreamNetworkAnalysis tool.

Parameters:

Flag	Description
-i, --input	Name of the input lines vector file
-o, --output	Name of the output lines vector file
--snap, --dist	Snap distance, in xy units (metres)

Python function:

wbt.repair_stream_vector_topology(
    i, 
    output, 
    dist="", 
    callback=default_callback
)

Command-line Interface:

>> ./whitebox_tools -r=RepairStreamVectorTopology ^
--input=streams.shp --output=streams_fixed.shp --snap=2.0

Source code is unavailable due to proprietary license.

Author: Prof. John Lindsay

Created: 03/08/2021 (oringinally in Whitebox Toolset Extension)

Last Modified: 04/11/2023

ShreveStreamMagnitude

This tool can be used to assign the Shreve stream magnitude 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. Shreve stream magnitude is equal to the number of headwater links upstream of each link. Headwater stream links are assigned a magnitude of one.

The user must specify the names of a streams raster image (--streams) and D8 pointer image (--d8_pntr). Stream cells are designated in the streams raster 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. Background cells will be assigned the NoData value in the output image, unless the --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

Reference:

Shreve, R. L. (1966). Statistical law of stream numbers. The Journal of Geology, 74(1), 17-37.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.shreve_stream_magnitude(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=ShreveStreamMagnitude -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=ShreveStreamMagnitude -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 28/06/2017

Last Modified: 18/10/2019

StrahlerStreamOrder

This tool can be used to assign the Strahler 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.

Reference:

Strahler, A. N. (1957). Quantitative analysis of watershed geomorphology. Eos, Transactions American Geophysical Union, 38(6), 913-920.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.strahler_stream_order(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=StrahlerStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=StrahlerStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 25/06/2017

Last Modified: 18/10/2019

StreamLinkClass

This tool identifies all interior and exterior links, and source, link, and sink nodes in an input stream network (--streams). The input streams file is used to designate which grid cells contain a stream and the pointer image is used to traverse the stream network. 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 --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

Each feature is assigned the following identifier in the output image:

Value	Stream Type
1	Exterior Link
2	Interior Link
3	Source Node (head water)
4	Link Node
5	Sink Node

The user must specify the names of an input stream file, a pointer (flow direction) file (--d8_pntr) and the output raster file (--output). The flow pointer and streams rasters should be generated using the D8Pointer algorithm. This will require a depressionless DEM, processed using either the BreachDepressions or FillDepressions tool. flow direction) raster, and the output raster.

See Also: StreamLinkIdentifier

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.stream_link_class(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=StreamLinkClass -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=StreamLinkClass -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 12/07/2017

Last Modified: 10/10/2019

StreamLinkIdentifier

This tool can be used to assign each link in a stream network a unique numeric identifier. This grid is used by a number of other stream network analysis tools.

The input streams file (--streams) is used to designate which grid cells contain a stream and the pointer image is used to traverse the stream network. 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 --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

The user must specify the name of a flow pointer (flow direction) raster (--d8_pntr), a streams raster (--streams), and the output raster (--output). The flow pointer and streams rasters should be generated using the D8Pointer algorithm. This will require a depressionless DEM, processed using either the BreachDepressions or FillDepressions tool.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.stream_link_identifier(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=StreamLinkIdentifier -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=StreamLinkIdentifier -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 25/06/2017

Last Modified: 03/09/2020

StreamLinkLength

This tool can be used to measure the length of each link in a stream network. The user must specify the names of a stream link ID raster (--linkid), created using the StreamLinkIdentifier and D8 pointer raster (--d8_pntr). The flow pointer raster is used to traverse the stream network and should only be created using the D8Pointer algorithm. Stream cells are designated in the stream link ID raster as all non-zero, positive values. Background cells will be assigned the NoData value in the output image, unless the --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

See Also: D8Pointer, StreamLinkSlope

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--linkid	Input raster streams link ID (or tributary ID) file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.stream_link_length(
    d8_pntr, 
    linkid, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=StreamLinkLength -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --linkid=streamsID.tif ^
--dem=dem.tif -o=output.tif
>>./whitebox_tools ^
-r=StreamLinkLength -v --wd="/path/to/data/" --d8_pntr=D8.tif ^
--linkid=streamsID.tif --dem=dem.tif -o=output.tif --esri_pntr ^
--zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 27/06/2017

Last Modified: 18/10/2019

StreamLinkSlope

This tool can be used to measure the average slope gradient, in percent, of each link in a raster stream network. To estimate the slope of individual grid cells in a raster stream network, use the StreamSlopeContinuous tool instead. The user must specify the names of a stream link identifier raster image (--linkid), a D8 pointer image (--d8_pntr), and a digital elevation model (--dem). The pointer image is used to traverse the stream network and must only be created using the D8 algorithm (D8Pointer). 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 --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--linkid	Input raster streams link ID (or tributary ID) file
-i, --dem	Input raster DEM file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.stream_link_slope(
    d8_pntr, 
    linkid, 
    dem, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=StreamLinkSlope -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --linkid=streamsID.tif ^
--dem=dem.tif -o=output.tif
>>./whitebox_tools ^
-r=StreamLinkSlope -v --wd="/path/to/data/" --d8_pntr=D8.tif ^
--linkid=streamsID.tif --dem=dem.tif -o=output.tif --esri_pntr ^
--zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 27/06/2017

Last Modified: 18/10/2019

StreamSlopeContinuous

This tool can be used to measure the slope gradient, in degrees, each grid cell in a raster stream network. To estimate the average slope for each link in a stream network, use the StreamLinkSlope tool instead. The user must specify the names of a stream raster image (--streams), a D8 pointer image (--d8_pntr), and a digital elevation model (--dem). The pointer image is used to traverse the stream network and must only be created using the D8 algorithm (D8Pointer). 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 --zero_background parameter is used, in which case non-stream cells will be assigned zero values in the output.

See Also: StreamLinkSlope, D8Pointer

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-i, --dem	Input raster DEM file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.stream_slope_continuous(
    d8_pntr, 
    streams, 
    dem, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=StreamSlopeContinuous -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --linkid=streamsID.tif ^
--dem=dem.tif -o=output.tif
>>./whitebox_tools ^
-r=StreamSlopeContinuous -v --wd="/path/to/data/" ^
--d8_pntr=D8.tif --streams=streamsID.tif --dem=dem.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 06/07/2017

Last Modified: 18/10/2019

TopologicalStreamOrder

This tool can be used to assign the topological stream order to each link in a stream network. According to this stream numbering system, the link directly draining to the outlet is assigned an order of one. Each of the two tributaries draining to the order-one link are assigned an order of two, and so on until the most distant link from the catchment outlet has been assigned an order. The topological order can therefore be thought of as a measure of the topological distance of each link in the network to the catchment outlet and is likely to be related to travel time.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.topological_stream_order(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=TopologicalStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=TopologicalStreamOrder -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 28/06/2017

Last Modified: 18/10/2019

TributaryIdentifier

This tool can be used to assigns a unique identifier to each tributary in a stream network. A tributary is a section of a stream network extending from a channel head downstream to a confluence with a larger stream. Relative stream size is estimated using stream length as a surrogate. Tributaries therefore extend from channel heads downstream until a confluence is encountered in which the intersecting stream is longer, or an outlet cell is detected.

Parameters:

Flag	Description
--d8_pntr	Input raster D8 pointer file
--streams	Input raster streams file
-o, --output	Output raster file
--esri_pntr	D8 pointer uses the ESRI style scheme
--zero_background	Flag indicating whether a background value of zero should be used

Python function:

wbt.tributary_identifier(
    d8_pntr, 
    streams, 
    output, 
    esri_pntr=False, 
    zero_background=False, 
    callback=default_callback
)

Command-line Interface:

>>./whitebox_tools -r=TributaryIdentifier -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif
>>./whitebox_tools -r=TributaryIdentifier -v ^
--wd="/path/to/data/" --d8_pntr=D8.tif --streams=streams.tif ^
-o=output.tif --esri_pntr --zero_background

Source code on GitHub

Author: Dr. John Lindsay

Created: 25/06/2017

Last Modified: 10/10/2019

VectorStreamNetworkAnalysis

This tool performs common stream network analysis operations on an input vector stream file (--streams). The network indices produced by this analysis are contained within the output vector's (--output) attribute table. The following table shows each of the network indices that are calculated.

Index Name	Description
OUTLET	Unique outlet identifying value, used as basin identifier
TRIB_ID	Unique tributary identifying value
DIST2MOUTH	Distance to outlet (i.e., mouth node)
DS_NODES	Number of downstream nodes
TUCL	Total upstream channel length; the channel equivalent to catchment area
MAXUPSDIST	Maximum upstream distance
HORTON	Horton stream order
STRAHLER	Strahler stream order
SHREVE	Shreve stream magnitude
HACK	Hack stream order
MAINSTREAM	Boolean value indicating whether link is the main stream trunk of its basin
IS_OUTLET	Boolean value indicating whether link is an outlet link

In addition to the input and output files, the user must also specify the snap distance used to associate points at confluences within the network (--snap). It is advisable that the input streams file be pre-processed prior to analysis using the RepairStreamVectorTopology tool.

Note: The input streams file for this tool should be pre-processed using the RepairStreamVectorTopology and CorrectStreamVectorDirection tools. This is an important step.

OUTLET:

HORTON:

SHREVE:

TRIB_ID:

Many of the network indices output by this tool for vector streams have raster equivalents in WhiteboxTools. For example, see the StrahlerStreamOrder, ShreveStreamMagnitude tools.

Reference:

Lindsay, JB, Yang, W, Hornby, DD. 2019. Drainage network analysis and structuring of topologically noisy vector stream data. ISPRS International Journal of Geo-Information. 8(9), 422; DOI: 10.3390/ijgi8090422

Parameters:

Flag	Description
--streams	Name of the input streams vector file
--dem	Name of the input DEM raster file
-o, --output	Name of the output lines shapefile
--cutting_height	Maximum ridge-cutting height (z units)
--snap	Snap distance, in xy units (metres)

Python function:

wbt.vector_stream_network_analysis(
    streams, 
    dem, 
    output, 
    cutting_height=10.0, 
    snap=0.1, 
    callback=default_callback
)

Command-line Interface:

>> ./whitebox_tools -r=VectorStreamNetworkAnalysis ^
--streams=rivers.shp --dem=DEM.tif -o=network_analysis.shp ^
--cutting_height=10.0 --snap=1.0

Source code is unavailable due to proprietary license.

Author: Prof. John Lindsay

Created: 28/07/2021 (oringinally in Whitebox Toolset Extension)

Last Modified: 01/09/2023

WhiteboxTools User Manual

Stream Network Analysis

CorrectStreamVectorDirection

DistanceToOutlet

ExtractStreams

ExtractValleys

FarthestChannelHead

FindMainStem

HackStreamOrder

HortonStreamOrder

LengthOfUpstreamChannels

LongProfile

LongProfileFromPoints

PruneVectorStreams

RasterStreamsToVector

RasterizeStreams

RemoveShortStreams

RepairStreamVectorTopology

ShreveStreamMagnitude

StrahlerStreamOrder

StreamLinkClass

StreamLinkIdentifier

StreamLinkLength

StreamLinkSlope

StreamSlopeContinuous

TopologicalStreamOrder

TributaryIdentifier

VectorStreamNetworkAnalysis