Workflow Index
This index provides task-first entry points for WbW-QGIS workflows. Each entry links to the chapter section where the complete step-by-step example can be found, and lists the key Processing Toolbox tool IDs needed for the task.
Terrain Analysis
| Task | Chapter section | Key tools |
|---|---|---|
| Compute slope from DEM | Terrain Analysis — Step 2 | whitebox_workflows:slope |
| Compute aspect from DEM | Terrain Analysis — Step 3 | whitebox_workflows:aspect |
| Generate hillshade for visualisation | Terrain Analysis — Step 4 | whitebox_workflows:hillshade |
| Compute profile and plan curvature | Terrain Analysis — Step 5 | whitebox_workflows:profile_curvature, whitebox_workflows:plan_curvature |
| Classify terrain into landform elements | Terrain Analysis — Geomorphons | whitebox_workflows:geomorphons |
| Compute topographic wetness index | Terrain Analysis — Step 6 | whitebox_workflows:wetness_index |
| Fill depressions before terrain derivatives | Terrain Analysis — Step 1 | whitebox_workflows:fill_depressions |
Spatial Hydrology
| Task | Chapter section | Key tools |
|---|---|---|
| Condition DEM for hydrologic routing | Spatial Hydrology — Step 1 | whitebox_workflows:breach_depressions_least_cost |
| Derive D8 flow direction | Spatial Hydrology — Step 2 | whitebox_workflows:d8_pointer |
| Compute flow accumulation | Spatial Hydrology — Step 3 | whitebox_workflows:d8_flow_accumulation |
| Extract stream network from accumulation | Spatial Hydrology — Step 4 | whitebox_workflows:extract_streams |
| Snap pour points to channel raster | Spatial Hydrology — Step 5 | whitebox_workflows:snap_pour_points |
| Delineate watershed / catchment | Spatial Hydrology — Step 6 | whitebox_workflows:watershed |
| Compute Topographic Wetness Index | Spatial Hydrology — TWI | whitebox_workflows:wetness_index |
LiDAR Processing
| Task | Chapter section | Key tools |
|---|---|---|
| QA — inspect point cloud statistics | LiDAR Processing — Step 1 | whitebox_workflows:lidar_point_stats |
| Thin high-density point cloud | LiDAR Processing — Step 2 | whitebox_workflows:lidar_thin |
| Classify ground returns | LiDAR Processing — Step 3 | whitebox_workflows:lidar_ground_point_filter |
| Build DTM from ground-classified cloud | LiDAR Processing — Step 4 | whitebox_workflows:lidar_idw_interpolation |
| Build DSM from first returns | LiDAR Processing — Step 5 | whitebox_workflows:lidar_idw_interpolation |
| Derive canopy height model (CHM) | LiDAR Processing — Step 6 | whitebox_workflows:canopy_height_model |
| Normalise heights above ground | LiDAR Processing — Step 7 | whitebox_workflows:height_above_ground |
Remote Sensing
| Task | Chapter section | Key tools |
|---|---|---|
| Compute NDVI from multispectral image | Remote Sensing — Step 2 | whitebox_workflows:ndvi |
| Threshold vegetation and classify change bins | Remote Sensing — Step 3 | QGIS Raster Calculator, whitebox_workflows:reclass |
| NDVI/NBR-based change detection | Remote Sensing — Step 3 | QGIS Raster Calculator |
| Reduce bands with PCA | Remote Sensing — Step 4 | whitebox_workflows:principal_component_analysis |
| Segment image into homogeneous objects | Remote Sensing — Step 5 | whitebox_workflows:image_segmentation |
Raster Analysis
| Task | Chapter section | Key tools |
|---|---|---|
| Compute distance from a binary feature raster | Raster Analysis — Step 1 | whitebox_workflows:euclidean_distance |
| Reclassify raster into suitability scores | Raster Analysis — Steps 2–4 | whitebox_workflows:reclass_from_file |
| Combine reclassified factors by weight | Raster Analysis — Step 5 | QGIS Raster Calculator |
| Summarise raster values within polygons | Raster Analysis — Step 6 | QGIS Zonal Statistics |
| Smooth raster with focal mean | Raster Analysis — Focal Statistics | whitebox_workflows:mean_filter |
Vector Analysis
| Task | Chapter section | Key tools |
|---|---|---|
| Validate and repair polygon geometry | Vector Analysis — Step 1 | QGIS native:fixgeometries |
| Add area, perimeter, centroid attributes | Vector Analysis — Step 2 | whitebox_workflows:add_geometry_attributes |
| Join attributes from overlapping polygons | Vector Analysis — Step 3 | whitebox_workflows:spatial_join |
| Compute distance to nearest feature | Vector Analysis — Step 4 | whitebox_workflows:near |
| Select features by spatial predicate | Vector Analysis — Step 5 | QGIS Select by Expression |
| Simplify polygon boundaries | Vector Analysis — Simplify | whitebox_workflows:simplify_features |
| Convert GeoPackage to TopoJSON and back | Vector Analysis — TopoJSON Conversion Chain | whitebox_workflows:add_geometry_attributes, QGIS Export |
| Simplify shared boundaries and emit TopoJSON | Vector Analysis — TopoJSON Boundary-Preserving Generalization Chain | whitebox_workflows:simplify_features, QGIS Export |
| Convert TopoJSON transport input, enrich, and re-emit | Vector Analysis — TopoJSON Transport + Enrichment Return Chain | QGIS Export, whitebox_workflows:add_geometry_attributes, whitebox_workflows:spatial_join, whitebox_workflows:near |
Network Analysis
| Task | Chapter section | Key tools |
|---|---|---|
| Prepare road network geometry and costs | Network Analysis — Workflow A | whitebox_workflows:add_geometry_attributes, QGIS geometry tools |
| Compute shortest path routes | Network Analysis — Workflow B | QGIS Network Analysis shortest path tools |
| Delineate road service areas | Network Analysis — Workflow B | QGIS native:serviceareafromlayer |
| Build OD-style batch travel-cost summaries | Network Analysis — Workflow C | QGIS shortest path batch/model workflows |
| Compute Strahler and Shreve stream hierarchy | Network Analysis — Workflow D | whitebox_workflows:strahler_stream_order, whitebox_workflows:shreve_stream_magnitude |
| Convert raster stream network to vector | Network Analysis — Workflow D | whitebox_workflows:raster_streams_to_vector |
Linear Referencing
| Task | Chapter section | Key tools |
|---|---|---|
| Add measure (M) values to route network | Linear Referencing — Step 1 | QGIS native:setmvalue |
| Validate unique route IDs | Linear Referencing — Step 2 | QGIS Python Console check |
| Locate point events along routes | Linear Referencing — Step 3 | whitebox_workflows:locate_point_events |
| Locate line events along routes | Linear Referencing — Step 4 | whitebox_workflows:locate_line_events |
| Calibrate M-values against control points | Linear Referencing — Calibrate | whitebox_workflows:calibrate_route |
By Data Type
Raster input tasks
- Fill depressions → see Terrain Analysis / Spatial Hydrology
- Slope, aspect, curvature → see Terrain Analysis
- Flow direction, accumulation → see Spatial Hydrology
- Reclassification, suitability → see Raster Analysis
- Spectral indices, PCA, change → see Remote Sensing
Point cloud input tasks
- Ground classification, DTM/DSM/CHM → see LiDAR Processing
- Height normalisation → see LiDAR Processing
Vector input tasks
- Geometry validation, overlay, joins → see Vector Analysis
- Routing, service areas, and stream hierarchy → see Network Analysis
- Route events, calibration → see Linear Referencing