Workflow Products

Topo Render

Function name: topo_render

PROExperimental

Creates a pseudo-3D topographic rendering using palette tinting, hillshade, shadows, and attenuation.

geomorphometry terrain rendering topographic

Parameters

NameDescriptionRequiredDefault demInput DEM raster path.Requireddem.tif palettePalette name (soft, atlas, high_relief, turbo, viridis, dem, grey, white).Optionalsoft reverse_paletteReverse palette order.OptionalFalse azimuthLight-source azimuth in degrees [0, 360].Optional315.0 altitudeLight-source altitude in degrees [0, 90].Optional30.0 clipping_polygonOptional polygon vector path; only DEM cells inside polygon(s) are rendered.Optional— background_hgt_offsetVertical offset from minimum DEM elevation to background plane.Optional10.0 background_clrBackground RGBA colour as array [r,g,b,a].Optional[255, 255, 255, 255] attenuation_parameterDistance attenuation exponent (>= 0).Optional0.3 ambient_lightAmbient light amount in [0, 1].Optional0.2 z_factorVertical exaggeration multiplier.Optional1.0 max_distMaximum shadow search distance in map units.Optional— outputOutput raster path.Optionaltopo_render.tif

Examples

Generate a pseudo-3D topographic render from a DEM. wbe.topo_render(altitude=30.0, azimuth=315.0, dem='dem.tif', output='topo_render.tif', palette='soft')

Wetland Hydrogeomorphic Classification

Function name: wetland_hydrogeomorphic_classification

PROProduction

Classify wetlands into hydrogeomorphic classes using DEM context and wetland masks.

workflow pro

Workflow Narrative

Wetland Hydrogeomorphic Classification

Problem It Solves

Which mapped wetland regions belong to key HGM classes, and where is class confidence high enough for permitting decisions?

Who It Is For

  • Wetland scientists, permitting consultants, and mitigation planners.

Primary User

Environmental consulting firms, permitting agencies, and mitigation banking organizations.

What It Does

  • Classifies wetland mask cells into HGM classes using terrain context.
  • Builds confidence surfaces for wetland classification reliability.
  • Polygonizes connected wetland regions with region-level attributes.

How It Works

  • Computes local terrain signatures in wetland-mask cells from DEM gradients and relief context.
  • Assigns HGM class codes using rule-based thresholds over those signatures.
  • Aggregates connected components into polygons and summarizes dominant class and mean confidence.
  • Indicative formula: class = rule(slope, relief, wetness_proxy), confidence from distance-to-threshold stability.

Why It Wins

  • Produces connected-region polygons with explicit confidence and class attributes, not just cell-level labels.

Typical Buying Trigger

Permitting or mitigation workflows require polygon deliverables with defensible classification metadata.

Typical Presets

  • default for full region polygon extraction.
  • lower max_polygon_features for very large AOIs.

Inputs

ParameterOptionalDescription dem, wetland_masknoDEM and wetland mask defining candidate wetland extent and hydrogeomorphic context. max_polygon_featuresnoMaximum number of polygon features to emit for mapped wetland units.

Outputs

ParameterTypeDescription hgm_classGeoTIFFCategorical hydrogeomorphic wetland class raster. confidenceGeoTIFFConfidence layer quantifying reliability of modeled outputs. wetland_polygonsGeoPackageVectorized wetland class polygons for mapping and reporting. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

hgm, conf, polys, summary = wbe.wetland_hydrogeomorphic_classification( dem="data/dem.tif", wetland_mask="data/wetlands.tif", max_polygon_features=10000, output_prefix="output/wetland_hgm", )

print(hgm) print(conf) print(polys) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Urban Expansion Impact Assessment

Function name: urban_expansion_impact_assessment

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Urban Expansion Impact Assessment

Problem It Solves

What ecological and stream-network impacts should we expect under this growth scenario, and where are priorities for mitigation?

Who It Is For

  • Urban planners, environmental assessors, and watershed impact analysts.

Primary User

Municipal planning departments, environmental consultancies, and stormwater authorities.

What It Does

  • Quantifies expansion-driven impact severity from baseline/scenario urban surfaces.
  • Derives habitat-loss raster products from change footprint logic.
  • Scores stream features against impact raster exposure.

How It Works

  • Builds urban change footprint by differencing scenario and baseline urban rasters.
  • Translates new/expanded footprint intensity into impact and habitat-loss scores.
  • Samples stream geometries against impact surfaces to compute attributed reach-level metrics.
  • Indicative formula: change = max(0, urban_scenario - urban_baseline); impact ~= change * habitat_weight.

Why It Wins

  • Links scenario change, habitat loss, and attributed stream impact in one report-ready package.

Typical Buying Trigger

Planning approvals require quantified environmental impact evidence under multiple development scenarios.

Typical Presets

  • baseline/scenario-only impact analysis.
  • add habitat_sensitivity for weighted ecological impact scoring.

Inputs

ParameterOptionalDescription baseline_urban, scenario_urban, streamsnoBaseline and scenario urban rasters plus stream network used for impact assessment. optional habitat_sensitivityyesOptional habitat sensitivity layer used to weight ecological impact severity.

Outputs

ParameterTypeDescription impact_severityGeoTIFFImpact severity raster for baseline-versus-scenario urban change. habitat_lossGeoTIFFRaster estimate of habitat loss intensity under scenario comparison. affected_streamsGeoPackageVector stream segments flagged as impacted under scenario conditions. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

impact, habitat, streams, summary = wbe.urban_expansion_impact_assessment( baseline_urban="data/urban_2020.tif", scenario_urban="data/urban_2035.tif", streams="data/streams.gpkg", habitat_sensitivity="data/habitat_sensitivity.tif", output_prefix="output/urban_impact", )

print(impact) print(habitat) print(streams) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Wind Turbine Siting

Function name: wind_turbine_siting

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Wind Turbine Siting Analysis

Problem It Solves

Which candidate areas are most promising for wind siting at early-stage screening, and how confident are those rankings?

Who It Is For

  • Renewable energy siting teams and feasibility analysts.

Primary User

Wind developers, utility planning groups, and engineering consultancies.

What It Does

  • Creates suitability and confidence surfaces for wind siting screening.
  • Combines slope constraints, terrain exposure, and settlement-visibility signals.
  • Supports profile-based scoring behavior for speed vs quality tradeoffs.

How It Works

  • Derives slope and terrain-exposure factors from the DEM.
  • Applies distance/visibility penalties around settlement inputs.
  • Combines normalized factors with profile-dependent weights into siting score and confidence.
  • Indicative formula: score ~= w_sterrain_suitability + w_eexposure - w_v*visibility_penalty.

Why It Wins

  • Combines terrain and visibility constraints with confidence scoring to support transparent shortlist decisions.

Typical Buying Trigger

A development team needs to narrow a broad search region before expensive met mast or field campaigns.

Typical Presets

  • fast for broad regional pre-screening.
  • balanced for standard feasibility support.
  • quality for higher-confidence candidate ranking.

Inputs

ParameterOptionalDescription dem, settlementsnoTerrain model and settlement features used for slope/visibility siting constraints. visibility_radius_metersnoMaximum visibility analysis radius used during visual-impact screening. min_slope_degrees, max_slope_degreesnoSlope suitability bounds used to constrain turbine placement candidates. profile: fast | balanced | qualitynoSiting profile controlling screening speed versus quality/strictness of constraints.

Outputs

ParameterTypeDescription siting_scoreGeoTIFFCore siting suitability score raster produced by the model. confidenceGeoTIFFConfidence layer quantifying reliability of modeled outputs. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

When sweep_spec is supplied, the workflow also emits run_matrix_summary, sensitivity_report, sensitivity_report_html, and stability_map. The sensitivity report includes metrics.primary_metric, metrics.primary_relative_span, and metrics.stability_class (high, medium, low), while stability_map uses classes 3=high, 2=medium, 1=low.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

score, conf, summary = wbe.wind_turbine_siting( dem="data/dem.tif", settlements="data/settlements.gpkg", profile="balanced", output_prefix="output/wind_siting", )

print(score) print(conf) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Solar Site Suitability Analysis

Function name: solar_site_suitability_analysis

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Solar Site Suitability Analysis

Problem It Solves

Where are top solar candidates based on terrain suitability, and which shortlisted sites are strong enough for deeper engineering review?

Who It Is For

  • Solar development teams and regional screening analysts.

Primary User

Solar developers, renewable consultants, and utility planning units.

What It Does

  • Computes solar siting suitability and visual-impact proxies from terrain.
  • Selects and ranks candidate point sites.
  • Emits attributed candidate vectors for downstream review.

How It Works

  • Computes terrain suitability response from slope/aspect and neighborhood context.
  • Estimates visual-impact proxy from terrain prominence and local exposure contrasts.
  • Applies thresholding and ranking to emit top candidate points with attributes.
  • Indicative formula: suitability ~= f(slope, aspect, relief); candidates = arg top-k(suitability) above threshold.

Why It Wins

  • Emits ranked candidate vectors with visual-impact attributes, enabling immediate GIS review and filtering.

Typical Buying Trigger

Early project screening demands a short list of high-probability solar candidates across a large area.

Typical Presets

  • higher candidate_threshold for only top candidates.
  • lower threshold plus larger max_candidate_sites for exploratory workflows.

Inputs

ParameterOptionalDescription demnoDigital elevation model used as the terrain reference surface. candidate_thresholdnoMinimum suitability threshold required for candidate-site extraction. max_candidate_sitesnoUpper limit on the number of candidate sites emitted in vector output.

Outputs

ParameterTypeDescription suitability_scoreGeoTIFFCore suitability score raster produced by the model. visual_impactGeoTIFFVisual-impact raster used to screen candidate sites and stakeholder constraints. candidate_sitesGeoPackageVector candidate-site features passing selection thresholds. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

When sweep_spec is supplied, the workflow also emits run_matrix_summary, sensitivity_report, sensitivity_report_html, and stability_map. The sensitivity report includes metrics.primary_metric, metrics.primary_relative_span, and metrics.stability_class (high, medium, low), while stability_map uses classes 3=high, 2=medium, 1=low.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

suit, vis, sites, summary = wbe.solar_site_suitability_analysis( dem="data/dem.tif", candidate_threshold=0.7, max_candidate_sites=200, output_prefix="output/solar_siting", )

print(suit) print(vis) print(sites) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Corridor Mapping Intelligence

Function name: corridor_mapping_intelligence

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Corridor Mapping and Route Planning

Problem It Solves

What is the terrain-optimal route for this linear infrastructure, and what alternative corridor band exists within an acceptable cost margin?

Who It Is For

  • Infrastructure planners, environmental consultants, and natural resource agencies assessing route options for roads, pipelines, or utility lines.

Primary User

Forestry companies, energy utilities, and environmental regulatory consultancies evaluating new linear infrastructure corridors.

What It Does

  • Builds a terrain impedance cost surface from DEM-derived slope and roughness.
  • Finds the least-cost path between two user-specified endpoints using Dijkstra accumulation.
  • Delineates a corridor suitability band of near-optimal alternative routes.
  • Supports polygon exclusion zones (steep terrain, protected areas, existing infrastructure).

How It Works

  • Computes per-cell cost from slope (and optionally local roughness) normalised to [0, 1].
  • Runs Dijkstra from both start and end to accumulate cost surfaces.
  • Least-cost path traced back from end → start through predecessor pointers.
  • Corridor band: cells where acc_from_start + acc_from_end ≤ optimal_cost × (1 + tolerance).
  • Indicative formula: cost ~= w_slope × slope_norm + w_rough × roughness_norm (profile-controlled weights).

Why It Wins

  • Compared with OSS least-cost building blocks (cost_distance + cost_pathway + cost_allocation), this tool is an end-to-end siting workflow: it derives the terrain cost surface from the DEM, computes the optimal route, delineates near-optimal corridor alternatives, and packages decision-ready outputs in one run.
  • Why it wins vs OSS least-cost tools:
  • Requires fewer analyst preparation steps (no separate friction-raster engineering pipeline required).
  • Returns vector route geometry with engineering-friendly attributes, not just a path raster.
  • Produces a corridor suitability band for option-space review, not only a single least-cost trace.
  • Supports polygon exclusion constraints directly in the routing workflow.
  • Emits a machine-readable summary contract for reproducible QA/reporting integration.

Typical Buying Trigger

A feasibility study needs a first-pass route alignment and suitability band for stakeholder review before field surveys. Cost profiles: - slope_only — slope-only impedance; fastest, suitable for quick screening. - slope_roughness — balanced slope + roughness blend (default); recommended for road and pipeline siting. - conservative — equal slope/roughness weighting; preferred for sensitive terrain or pipelines.

Inputs

ParameterOptionalDescription demnoInput DEM raster path. start_featuresnoStart feature vector path (point or polygon). end_featuresnoEnd feature vector path (point or polygon). constraintsyesOptional exclusion zone vector path (polygons to avoid). cost_profileyesCost weighting profile: slope_only | slope_roughness | conservative. Default: slope_roughness. terminal_anchor_strategyyesPolygon terminal anchor mode: mixed | centroid_only | boundary_only. Default: mixed. corridor_toleranceyesFraction above optimal cost included in corridor suitability band. Default: 0.15. output_prefixyesOutput prefix for generated artifacts.

Outputs

ParameterTypeDescription cost_surfaceGeoTIFFNormalized terrain impedance surface [0-1]. accumulated_costGeoTIFFDijkstra accumulated cost from selected start anchor. optimal_routeGeoPackageLeast-cost route LineString with route metrics. corridor_suitabilityGeoTIFFBinary suitability band (1 = within tolerance). summaryJSONSummary contract with metrics, selected anchors, and harmonization metadata. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

cost, acc_cost, route, suitability, summary = wbe.corridor_mapping_intelligence( dem="data/dem.tif", start_features="data/start_access_points.gpkg", end_features="data/forest_block_targets.gpkg", cost_profile="slope_roughness", terminal_anchor_strategy="mixed", corridor_tolerance=0.15, output_prefix="output/access_road", )

print(route) # path to optimal_route.gpkg print(suitability) # path to corridor suitability raster print(summary) # path to summary JSON`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Landslide Susceptibility Assessment

Function name: landslide_susceptibility_assessment

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Landslide Susceptibility Assessment

Problem It Solves

Which areas present elevated slope-failure risk today, and where is trigger pressure most concerning?

Who It Is For

  • Hazard analysts, corridor planners, and geotechnical screening teams.

Primary User

Geological surveys, transportation agencies, and hazard consulting teams.

What It Does

  • Estimates terrain-driven landslide susceptibility from slope/curvature context.
  • Integrates optional rainfall pressure to strengthen trigger interpretation.
  • Produces susceptibility, trigger-pressure, and confidence rasters with contract summary diagnostics.

How It Works

  • Computes local slope and roughness/curvature proxies from DEM neighborhood derivatives.
  • Blends terrain susceptibility with optional rainfall intensity to form trigger pressure.
  • Applies profile-weighted scaling and thresholding to output susceptibility, confidence, and risk zones.
  • Indicative formula: susceptibility ~= w1slope_term + w2roughness_term; trigger ~= susceptibility * (1 + rainfall_term).

Why It Wins

  • Provides reproducible hazard screening outputs with structured summary metrics for downstream reporting.

Typical Buying Trigger

A public or infrastructure program requires defensible first-pass landslide risk zoning.

Typical Presets

  • fast for rapid regional screening.
  • balanced for standard hazard screening.
  • conservative for stricter slope/curvature sensitivity.

Inputs

ParameterOptionalDescription demnoDigital elevation model used as the terrain reference surface. optional rainfall_intensityyesOptional rainfall forcing raster used to refine landslide trigger pressure estimation. profile: fast | balanced | conservativenoProcessing profile controlling sensitivity, quality strictness, and runtime tradeoffs. susceptibility_thresholdnoThreshold used to convert continuous susceptibility into risk-zone candidates.

Outputs

ParameterTypeDescription susceptibilityGeoTIFFLandslide susceptibility raster used to identify unstable terrain. trigger_pressureGeoTIFFTrigger-pressure raster indicating forcing required to initiate failures. confidenceGeoTIFFConfidence layer quantifying reliability of modeled outputs. risk_zonesGeoPackagePriority vector polygons highlighting intervention zones. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

sus, trig, conf, zones, summary = wbe.landslide_susceptibility_assessment( dem="data/dem.tif", rainfall_intensity="data/rainfall_intensity.tif", profile="balanced", susceptibility_threshold=0.65, max_zone_features=5000, output_prefix="output/landslide", )

print(sus) print(trig) print(conf) print(zones) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

River Corridor Health Assessment

Function name: river_corridor_health_assessment

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

River Corridor Health Assessment

Problem It Solves

Which river reaches are stable versus at-risk, and where should restoration action be prioritized?

Who It Is For

  • Watershed restoration teams, river health analysts, and permit support consultants.

Primary User

Watershed councils, conservation authorities, and environmental consulting groups.

What It Does

  • Derives erosion-pressure context from terrain.
  • Scores stream reaches by sampled corridor pressure.
  • Emits restoration-priority line outputs for intervention planning.

How It Works

  • Calculates per-pixel erosion pressure from slope and local roughness terms.
  • Samples stream vertices over the erosion raster and derives reach metrics (including high quantiles).
  • Converts reach scores into health classes and restoration action categories.
  • Indicative formula: erosion ~= aslope_norm + broughness_norm; health ~= 1 - mean(erosion_samples_along_reach).

Why It Wins

  • Combines raster pressure context and attributed reach-level restoration outputs in one workflow.

Typical Buying Trigger

A watershed plan needs rapid reach triage with GIS-ready outputs for restoration budgeting.

Typical Presets

  • fast for rapid watershed screening.
  • balanced for default corridor scoring.
  • conservative for roughness-sensitive erosion scoring.

Inputs

ParameterOptionalDescription demnoDigital elevation model used as the terrain reference surface. streamsnoVector stream network used for corridor health and restoration targeting. profile: fast | balanced | conservativenoProcessing profile controlling sensitivity, quality strictness, and runtime tradeoffs.

Outputs

ParameterTypeDescription erosion_pressureGeoTIFFErosion pressure raster for river corridor condition assessment. corridor_confidenceGeoTIFFConfidence surface for river corridor health interpretation. stream_health_scoreGeoPackageStream-reach health score output summarizing corridor condition. restoration_zonesGeoPackagePriority vector polygons highlighting restoration intervention zones. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

erosion, confidence, health, restoration, summary = wbe.river_corridor_health_assessment( dem="data/dem.tif", streams="data/streams.gpkg", profile="balanced", output_prefix="output/river_health", )

print(erosion) print(confidence) print(health) print(restoration) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Baseline Matching And Diagnostics Assessment

Function name: baseline_matching_and_diagnostics_assessment

No help documentation available for this tool.

Carbon Sequestration Verification Audit

Function name: carbon_sequestration_verification_audit

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Carbon Verification Audit

Problem It Solves

Where did vegetation-linked carbon proxy increase or decrease, and where is confidence high enough for audit triage?

Who It Is For

  • Carbon program analysts, forest monitoring teams, and environmental verification workflows.

Primary User

Carbon project developers, ESG/MRV teams, and land-management compliance programs.

What It Does

  • Quantifies baseline-to-current vegetation change using NDVI deltas.
  • Derives a carbon-proxy change surface with confidence scoring.
  • Optionally blends LiDAR biomass proxy inputs to strengthen stand-level interpretation.
  • Produces verification zone polygons and an audit-ready contract output for MRV workflows.

How It Works

  • Extracts baseline and current red/NIR bands from multiband bundles.
  • Computes per-date NDVI and signed delta: NDVI_current - NDVI_baseline.
  • Converts NDVI delta to a carbon-proxy index and computes confidence from vegetation signal and change magnitude.
  • Optionally blends in biomass proxy values to improve relative stand-level carbon interpretation.
  • Aggregates pixels into verification blocks and assigns zone-level change class labels (gain, loss, unchanged).
  • Indicative formula: NDVI = (NIR - Red) / (NIR + Red), carbon_proxy ~= 10 * (NDVI_current - NDVI_baseline).

Why It Wins

  • Combines change mapping, confidence scoring, zone-level vector outputs, and an audit contract in one reproducible run.

Typical Buying Trigger

Teams need standardized remote-sensing evidence packages ahead of formal carbon verification reporting.

Typical Presets

  • conservative for stricter gain/loss interpretation thresholds.
  • balanced for default operational monitoring.
  • aggressive for early-signal monitoring workflows.

Inputs

ParameterOptionalDescription baseline_bundle, current_bundlenoBaseline and current multiband rasters used for NDVI change analysis. baseline_red_band_index, baseline_nir_band_indexyesBaseline red/NIR band indices. Defaults: 0, 1. current_red_band_index, current_nir_band_indexyesCurrent red/NIR band indices. Defaults: 0, 1. biomass_proxyyesOptional LiDAR-derived biomass proxy raster for blended carbon-proxy interpretation. profile: conservative | balanced | aggressiveyesSensitivity profile controlling gain/loss thresholds and confidence blending behavior. zone_block_cellsyesPixel block size used for verification zone polygon aggregation. Defaults to 16. mrv_templateyesMRV profile: verra_vcs_vm0010, american_carbon_registry, gold_standard, or none. methodology_referenceyesOptional methodology lineage note for audit metadata (for example Verra references). output_prefixyesPrefix used to name output artifacts.

Outputs

ParameterTypeDescription ndvi_baselineGeoTIFFBaseline NDVI surface (*_ndvi_baseline.tif). ndvi_currentGeoTIFFCurrent NDVI surface (*_ndvi_current.tif). ndvi_deltaGeoTIFFSigned NDVI change surface (*_ndvi_delta.tif). carbon_proxyGeoTIFFCarbon-proxy change index (*_carbon_proxy.tif). change_confidenceGeoTIFFConfidence raster for change interpretation (*_change_confidence.tif). verification_zonesGeoPackageBlock-aggregated verification polygons with change class and summary attributes (*_verification_zones.gpkg). audit_contractJSONMachine-readable audit summary contract (*_audit_contract.json). compliance_evidence_packetJSONSubmission-oriented compliance evidence packet (*_compliance_evidence_packet.json). regulator_ready_tableCSVFlat regulator-ready summary table (*_regulator_ready_table.csv). html_reportHTMLHuman-readable report generated from the contract for stakeholder review.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.carbon_sequestration_verification_audit( baseline_bundle="data/baseline_multiband.tif", current_bundle="data/current_multiband.tif", baseline_red_band_index=0, baseline_nir_band_index=1, current_red_band_index=0, current_nir_band_index=1, biomass_proxy="data/biomass_proxy.tif", profile="balanced", zone_block_cells=16, mrv_template="verra_vcs_vm0010", methodology_reference="Verra VM0010 v1.3", output_prefix="output/carbon_audit", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Wildfire Fuel Loading And Risk Matrix

Function name: wildfire_fuel_loading_and_risk_matrix

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Wildfire Fuel Risk Analysis

Problem It Solves

Where are the highest-priority fuel and spread-risk areas, and what dominant fuel conditions drive those zones?

Who It Is For

  • Wildfire planning teams, utility risk programs, and fuels management analysts.

Primary User

Utility wildfire mitigation teams, land-management agencies, and hazard/risk operations groups.

What It Does

  • Classifies sparse, surface, ladder, and canopy fuel classes from optical and optional structure inputs.
  • Computes moisture index and ladder-fuel continuity diagnostics.
  • Builds a terrain-amplified wildfire risk matrix with zone-level risk tiers.
  • Emits risk-zone vector outputs and summary contracts for operations planning.

How It Works

  • Extracts required optical bands and computes NDMI (if SWIR available) or NDWI proxy fallback.
  • Uses NDVI, moisture thresholds, and optional biomass proxy to assign fuel class per cell.
  • Computes ladder continuity and combines fuel risk with optional slope/aspect spread amplifiers.
  • Produces a clipped risk score in [0,1] and polygonized risk zones with dominant fuel and risk tier.
  • Indicative formula: risk ~= (base_fuel_risk + dryness_boost) * slope_amp * aspect_amp.

Why It Wins

  • Packages moisture, structure, terrain amplification, and zone-level risk outputs in a single reproducible workflow.

Typical Buying Trigger

Seasonal mitigation planning and risk-tier prioritization require consistent spatial evidence outputs.

Typical Presets

  • conservative for lower sensitivity and tighter risk escalation.
  • balanced for default planning workflows.
  • aggressive for early-warning and preventative treatment prioritization.

Inputs

ParameterOptionalDescription optical_bundlenoMultispectral raster containing red/NIR and optionally SWIR bands. red_band_index, nir_band_indexyesRed and NIR band indices. Defaults: 0, 1. swir_band_indexyesOptional SWIR index; enables NDMI moisture estimation when available. biomass_proxyyesOptional LiDAR biomass/height proxy used to refine fuel class and ladder continuity signals. slope, aspectyesOptional terrain slope/aspect rasters used for spread amplification terms. profile: conservative | balanced | aggressiveyesSensitivity profile controlling class/risk thresholds. zone_block_cellsyesPixel block size used for risk-zone polygon aggregation. Defaults to 20. output_prefixyesPrefix used to name output artifacts.

Outputs

ParameterTypeDescription moisture_indexGeoTIFFNDMI/NDWI moisture response raster (*_moisture_index.tif). fuel_load_classGeoTIFFFuel class code raster (sparse/surface/ladder/canopy) (*_fuel_load_class.tif). ladder_fuel_continuityGeoTIFFLadder continuity index surface (*_ladder_fuel_continuity.tif). risk_matrixGeoTIFFTerrain-amplified wildfire risk score raster (*_risk_matrix.tif). risk_zonesGeoPackageAggregated risk polygons with tier and dominant fuel attributes (*_risk_zones.gpkg). summaryJSONMachine-readable summary contract (*_summary.json). html_reportHTMLHuman-readable report generated from the summary contract for review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.wildfire_fuel_loading_and_risk_matrix( optical_bundle="data/optical_multiband.tif", red_band_index=0, nir_band_index=1, swir_band_index=2, biomass_proxy="data/biomass_proxy.tif", slope="data/slope.tif", aspect="data/aspect.tif", profile="balanced", zone_block_cells=20, output_prefix="output/wildfire_risk", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Mine Site Reclamation Compliance Tracker

Function name: mine_site_reclamation_compliance_tracker

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Mine Reclamation Compliance Tracker

Problem It Solves

Are reclamation milestones being achieved spatially, and where are intervention priorities for compliance closure?

Who It Is For

  • Mine closure teams, compliance analysts, and environmental reporting operations.

Primary User

Mine operators, reclamation contractors, and regulatory compliance monitoring groups.

What It Does

  • Tracks vegetation recovery between baseline and current imagery.
  • Computes per-cell reclamation progress against target NDVI milestone.
  • Optionally evaluates slope stability milestone compliance.
  • Produces compliance-zone outputs and a contract-ready compliance summary.

How It Works

  • Extracts baseline/current red and NIR bands and computes per-date NDVI.
  • Computes recovery delta and normalized progress toward target NDVI threshold.
  • Optionally evaluates slope raster against maximum acceptable slope angle.
  • Assigns milestone statuses and an overall compliance grade summary.
  • Aggregates per-cell progress into compliance zones with pass/conditional/fail class attributes.
  • Indicative formula: progress ~= (NDVI_current - NDVI_baseline) / (NDVI_target - NDVI_baseline), clipped to [0,1].

Why It Wins

  • Integrates vegetation recovery, optional slope milestone evaluation, zone outputs, and compliance-grade contract reporting in one run.

Typical Buying Trigger

Regulatory milestone reporting cycles require reproducible evidence and zone-level compliance diagnostics.

Typical Presets

  • spectral-only for vegetation recovery milestone tracking.
  • full compliance mode with slope stability for stricter regulatory submissions.

Inputs

ParameterOptionalDescription baseline_bundle, current_bundlenoBaseline and current multiband rasters used for NDVI recovery analysis. baseline_red_band_index, baseline_nir_band_indexyesBaseline red/NIR band indices. Defaults: 0, 1. current_red_band_index, current_nir_band_indexyesCurrent red/NIR band indices. Defaults: 0, 1. slopeyesOptional slope raster (degrees) for stability milestone evaluation. reclamation_target_ndviyesTarget NDVI threshold used for vegetation milestone scoring. Defaults to 0.35. slope_stability_max_degyesMaximum acceptable slope for stability compliance. Defaults to 30.0. jurisdictionyesCompliance template: us_federal_mtbs, us_california_mining, us_pennsylvania_coal, aus_western_australia, canada_bc_mines, south_africa_dmre, none. site_nameyesOptional site name or permit identifier in contract outputs. has_hydrology_evidence, has_soil_ph_evidence, has_perennial_vegetation_evidenceyesBoolean evidence flags used by submission-readiness diagnostics. report_interval_monthsyesReported cadence in months (1-120) compared against template expectations. zone_block_cellsyesPixel block size used for compliance-zone aggregation. Defaults to 20. output_prefixyesPrefix used to name output artifacts.

Outputs

ParameterTypeDescription ndvi_baselineGeoTIFFBaseline NDVI raster (*_ndvi_baseline.tif). ndvi_currentGeoTIFFCurrent NDVI raster (*_ndvi_current.tif). vegetation_recoveryGeoTIFFNDVI recovery delta raster (*_vegetation_recovery.tif). reclamation_progressGeoTIFFNormalized progress-to-target raster (*_reclamation_progress.tif). compliance_zonesGeoPackageZone-level compliance polygons with progress/recovery attributes (*_compliance_zones.gpkg). compliance_contractJSONMachine-readable compliance summary contract (*_compliance_contract.json). validation_diagnosticsJSONEvidence completeness and warning diagnostics (*_validation_diagnostics.json). html_reportHTMLHuman-readable report generated from compliance contract outputs.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.mine_site_reclamation_compliance_tracker( baseline_bundle="data/mine_baseline.tif", current_bundle="data/mine_current.tif", baseline_red_band_index=0, baseline_nir_band_index=1, current_red_band_index=0, current_nir_band_index=1, slope="data/slope.tif", reclamation_target_ndvi=0.35, slope_stability_max_deg=30.0, jurisdiction="canada_bc_mines", site_name="Mine Site Alpha", has_hydrology_evidence=True, has_soil_ph_evidence=True, has_perennial_vegetation_evidence=True, report_interval_months=12, zone_block_cells=20, output_prefix="output/reclamation", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Terrain Constraint And Conflict Analysis

Function name: terrain_constraint_and_conflict_analysis

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Terrain Constraint and Conflict Analysis

Problem It Solves

Where are terrain constraints likely to create permitting, access, or design conflicts before field mobilization?

Who It Is For

  • Infrastructure siting teams and engineering predesign analysts.

Primary User

Renewable developers, utilities, and civil engineering consultancies.

What It Does

  • Builds a harmonized terrain conflict score from slope and optional risk overlays.
  • Produces conflict classes for early-stage siting and routing screening.

How It Works

  • Uses DEM-derived slope as the primary terrain constraint signal.
  • Harmonizes optional wetness, flood-risk, and landcover-penalty rasters to the DEM grid.
  • Blends constraints into a normalized conflict score and class map.
  • QA acceptance guidance:
  • status=pass indicates the conflict fraction remained below review threshold.
  • diagnostics.acceptance_thresholds.high_conflict_fraction_review is the baseline threshold for escalation.
  • Elevated summary.high_conflict_fraction should trigger engineering review before downstream routing/siting commitments.
  • MVP hardening assets:
  • Benchmark scaffold: tests/fixtures/terrain_siting_benchmark/
  • Promotion guide: docs/internal/development/TERRAIN_PRECISION_AG_BENCHMARK_PROMOTION_GUIDE_2026_04_14.md

Inputs

ParameterOptionalDescription demnoReference DEM raster for terrain conflict analysis. wetnessyesOptional wetness raster normalized to [0,1]. flood_riskyesOptional flood-risk raster normalized to [0,1]. landcover_penaltyyesOptional landcover penalty raster normalized to [0,1]. slope_limit_degyesSlope threshold where terrain conflict accelerates.

Outputs

ParameterTypeDescription conflict_scoreGeoTIFFContinuous terrain conflict score [0,1]. conflict_classGeoTIFFDiscrete conflict class raster for planning triage. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

conflict, classes, summary = wbe.terrain_constraint_and_conflict_analysis( dem="data/dem.tif", wetness="data/wetness.tif", flood_risk="data/flood_risk.tif", slope_limit_deg=15.0, output_prefix="output/terrain_conflict", )

print(conflict) print(classes) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Terrain Constructability And Cost Analysis

Function name: terrain_constructability_and_cost_analysis

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Terrain Constructability and Cost Analysis

Problem It Solves

Which areas are practically constructible at lower relative cost before detailed design?

Who It Is For

  • Engineering predesign teams and capital planning groups.

Primary User

Infrastructure developers, engineering firms, and utility planning teams.

What It Does

  • Converts terrain and optional risk/cost context into constructability and cost-class surfaces.
  • Supports quick predesign ranking of lower-cost, lower-risk development zones.

How It Works

  • Computes slope from DEM and harmonizes optional conflict, wetness, and access-cost rasters.
  • Blends penalties into a constructability score and relative cost class output.
  • QA acceptance guidance:
  • status=pass indicates high-cost fraction is within baseline tolerance.
  • diagnostics.acceptance_thresholds.high_cost_fraction_review defines review escalation threshold.
  • Use summary.high_cost_fraction with local access constraints before capital-stage budgeting decisions.
  • MVP hardening assets:
  • Benchmark scaffold: tests/fixtures/terrain_siting_benchmark/
  • Promotion guide: docs/internal/development/TERRAIN_PRECISION_AG_BENCHMARK_PROMOTION_GUIDE_2026_04_14.md

Inputs

ParameterOptionalDescription demnoReference DEM raster for constructability scoring. existing_conflictyesOptional prior terrain conflict raster normalized to [0,1]. wetnessyesOptional wetness raster normalized to [0,1]. access_costyesOptional access-friction raster normalized to [0,1].

Outputs

ParameterTypeDescription constructability_scoreGeoTIFFConstructability score raster [0,1] (higher is better). cost_classGeoTIFFRelative cost class raster (1 low cost to 5 high cost). summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

constructability, cost_class, summary = wbe.terrain_constructability_and_cost_analysis( dem="data/dem.tif", existing_conflict="output/terrain_conflict_conflict_score.tif", access_cost="data/access_friction.tif", output_prefix="output/terrain_constructability", )

print(constructability) print(cost_class) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Utility Corridor Encroachment Intelligence

Function name: utility_corridor_encroachment_intelligence

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Utility Corridor Encroachment Detection

Problem It Solves

Which corridor segments present the highest near-term encroachment risk, and which assets should be prioritized first?

Who It Is For

  • Utility vegetation-management teams and corridor maintenance planners.

Primary User

Transmission and distribution operators, utility contractors, and corridor asset-management groups.

What It Does

  • Builds corridor-adjacent encroachment risk surfaces from LiDAR-derived canopy structure.
  • Produces priority zones and per-asset risk summaries for maintenance planning.

How It Works

  • Classifies vegetation and ground structure, then computes height-above-ground and local point-density context.
  • Builds a corridor-relative risk surface that emphasizes canopy proximity, density, and confidence-weighted structure signals.
  • Applies profile-based sensitivity settings and emits thresholded, ranked priority zones for maintenance triage.
  • Indicative formula: risk ~= f(proximity_to_corridor, canopy_height, local_density, classification_confidence).

Why It Wins

  • Produces both spatial priority zones and asset-level risk tables, enabling field-ready scheduling rather than map-only screening.

Typical Buying Trigger

Seasonal vegetation cycles or reliability programs require objective, repeatable encroachment prioritization across large networks.

Typical Presets

  • fast for broad network screening.
  • balanced for default operational planning.
  • strict for conservative risk escalation and tighter action thresholds.
  • Operational controls:
  • profile: fast | balanced | strict.
  • priority_zone_threshold and max_zone_features: bound priority-zone density for operational triage.

Inputs

ParameterOptionalDescription input (LAS/LAZ)noInput LiDAR point cloud used to derive QA, terrain, structure, or encroachment products. optional corridors and asset_featuresyesOptional corridor and asset vectors used to focus encroachment risk analysis. profile: fast | balanced | strictnoOperational profile controlling sensitivity and QA strictness for risk workflows. priority_zone_thresholdnoRisk threshold used to classify high-priority encroachment zones. max_zone_featuresnoUpper cap on number of output zone features to control product size.

Outputs

ParameterTypeDescription encroachment_riskGeoTIFFEncroachment risk surface used for corridor maintenance prioritization. corridor_priority_zonesGeoPackageVector priority zones for field action planning. asset_risk_tableGeoPackageVector table/layer with per-asset encroachment risk summaries. classification_confidenceGeoTIFFConfidence surface for LiDAR-derived classification and risk outputs. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

risk, zones, assets, conf, summary = wbe.utility_corridor_encroachment_intelligence( input="data/corridor_points.laz", corridors="data/corridors.gpkg", asset_features="data/assets.gpkg", profile="balanced", priority_zone_threshold=0.75, max_zone_features=5000, output_prefix="output/utility_corridor", )

print(risk) print(zones) print(assets) print(conf) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Forestry Structure And Biomass Intelligence

Function name: forestry_structure_and_biomass_intelligence

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Forestry Structure and Biomass Analysis

Problem It Solves

Where are high-structure and high-biomass stands concentrated, and how confident are those estimates for inventory decisions?

Who It Is For

  • Forest inventory analysts, carbon accounting teams, and silviculture planning groups.

Primary User

Forestry agencies, carbon-market project developers, and natural resource consultancies.

What It Does

  • Generates canopy structure, vertical class, and biomass-proxy products from LiDAR.
  • Produces stand-level structure units and confidence diagnostics for inventory workflows.
  • Requires Pro runtime visibility (include_pro=True, tier='pro' or higher).

How It Works

  • Grids LiDAR to derive canopy-height, density, and ground surfaces, then computes CHM-style structure metrics.
  • Classifies canopy structure from adaptive height thresholds and density support into vertical strata classes.
  • Scales a terrain-adaptive biomass proxy with configurable cap control.
  • Aggregates stand-level structure units and confidence indicators for inventory and monitoring.
  • Indicative formula: biomass_proxy ~= g(canopy_height, density_support, structure_class, terrain_relief), clamped by biomass_cap.

Why It Wins

  • Combines structure classes, biomass proxy, and stand-unit outputs in one reproducible workflow suitable for operational reporting.

Typical Buying Trigger

Inventory refresh, carbon-baseline updates, or stand treatment planning needs consistent structure and biomass surfaces.

Typical Presets

  • fast for regional reconnaissance.
  • balanced for default inventory support.
  • strict for conservative structure/banding and confidence interpretation.
  • Operational controls:
  • profile: fast | balanced | strict.
  • terrain_adaptation: off | moderate | strong.
  • biomass_cap: bounds biomass-proxy scaling for predictable downstream comparisons.

Inputs

ParameterOptionalDescription input (LAS/LAZ or Lidar)noInput LiDAR source used to derive forest structure and biomass products. profile: fast | balanced | strictnoOperational profile controlling sensitivity and QA strictness. resolutionyesOutput raster resolution, default 2.0. stand_block_cellsyesStand aggregation block size in cells, default 12. biomass_capyesUpper bound applied to biomass proxy estimates, default 25.0. terrain_adaptation: off | moderate | strongyesBiomass terrain-adaptation mode, default moderate.

Outputs

ParameterTypeDescription canopy_height_metricsGeoTIFFCanopy height metrics raster for forestry structure interpretation. vertical_structure_classGeoTIFFCategorical vertical-structure class raster. stand_structure_unitsGeoPackageVector stand structure units for reporting and management. biomass_proxyGeoTIFFBiomass proxy raster derived from structural LiDAR metrics. confidenceGeoTIFFConfidence layer quantifying reliability of modeled outputs. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

lidar = wbw.Lidar("data/forest_points.laz")

height, vclass, stands, biomass, conf, summary = wbe.forestry_structure_and_biomass_intelligence( input=lidar, profile="balanced", resolution=2.0, stand_block_cells=12, biomass_cap=30.0, terrain_adaptation="moderate", output_prefix="output/forestry_structure", )

print(height) print(vclass) print(stands) print(biomass) print(conf) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.