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

Parameter	Optional	Description
input (LAS/LAZ or Lidar)	no	Input LiDAR source used to derive forest structure and biomass products.
profile: fast \| balanced \| strict	no	Operational profile controlling sensitivity and QA strictness.
resolution	yes	Output raster resolution, default 2.0.
stand_block_cells	yes	Stand aggregation block size in cells, default 12.
biomass_cap	yes	Upper bound applied to biomass proxy estimates, default 25.0.
terrain_adaptation: off \| moderate \| strong	yes	Biomass terrain-adaptation mode, default moderate.

Outputs

Parameter	Type	Description
canopy_height_metrics	GeoTIFF	Canopy height metrics raster for forestry structure interpretation.
vertical_structure_class	GeoTIFF	Categorical vertical-structure class raster.
stand_structure_units	GeoPackage	Vector stand structure units for reporting and management.
biomass_proxy	GeoTIFF	Biomass proxy raster derived from structural LiDAR metrics.
confidence	GeoTIFF	Confidence layer quantifying reliability of modeled outputs.
summary	JSON	Machine-readable summary report containing run metadata, QA diagnostics, and key metrics.
html_report	HTML	Human-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.

Project Links

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