Wildfire Fuel Risk Analysis

What This Tool Does

Wildfire Fuel Risk Analysis combines moisture, fuel structure, optional terrain, and optional weather context to map wildfire risk and produce planning zones.

Typical Questions This Tool Helps Answer

• Which landscape units have the highest combined fuel loading and terrain-amplified fire spread potential this season?
• Where does ladder fuel continuity create high crown-fire transition risk that warrants priority treatment or prescribed burn planning?
• Which high-risk fuel zones overlap structures, evacuation routes, or suppression assets and should be addressed first?

When To Use

• Fuel-risk screening over large areas
• Mitigation prioritization and treatment planning
• Scenario comparisons with different weather or sensitivity profiles

What You Need

Key Settings

Available fuel models: temperate_mixed_forest, boreal_sparse, mediterranean_shrub, tropical_dense, grassland_savanna, none.

What You Get

risk_zones fields: ZONE_ID, MEAN_RISK, MAX_RISK, DOM_FUEL, RISK_TIER, PIXEL_COUNT.

Runtime Output Keys

result.outputs["moisture_index"]
result.outputs["fuel_load_class"]
result.outputs["ladder_fuel_continuity"]
result.outputs["risk_matrix"]
result.outputs["risk_zones"]
result.outputs["summary"]
result.outputs["html_report"]

Common Questions

Q: Which output should incident teams review first? A: Start with summary.high_risk_fraction and summary.extreme_risk_fraction, then map priority blocks using risk_zones.RISK_TIER.

Q: What is a common interpretation mistake? A: Treating dominant fuel class (DOM_FUEL) as final risk severity without considering moisture and terrain/weather amplification.

Q: Which settings most change extreme-risk area? A: dry_moisture_threshold, wet_moisture_threshold, and dryness_severity_factor usually have the largest effect.

Q: How should operations use these outputs? A: Use high-risk zones to prioritize fuels treatment and response pre-positioning, then refine by access and suppression constraints.

Results Delivery Checklist

• Band indices were verified against the optical bundle
• Optional inputs were confirmed to align to analysis grid
• Risk matrix and risk zones were visually reviewed
• Summary fractions and weather factors were checked

Purpose

Wildfire Fuel Risk Analysis quantifies landscape fuel characteristics (woody biomass, canopy structure, continuity) and integrates with climate/topography to assess wildfire ignition and spread risk. Enables prevention planning and fire management prioritization.

Background

Wildfire fuel-risk analysis combines vegetation condition, structural continuity, and terrain-weather amplification into a comparative hazard surface. A useful conceptual model is:

$$R(x)=w_m M(x)+w_f F(x)+w_l L(x)+w_t T(x)+w_w W(x)$$

Where $M$ is moisture stress, $F$ is fuel load class pressure, $L$ is ladder-fuel continuity, $T$ is terrain amplification, and $W$ is weather modulation.

Fuel and Moisture Interaction

Moisture indicators (for example NDMI/NDWI-style proxies) help distinguish currently receptive fuels from structurally loaded but less active areas. In practice, high structural load with low dryness may rank lower than moderate load under acute dryness.

Zone-Level Aggregation Logic

Risk-zone polygons summarize cell-level behavior into operational blocks. Zone statistics (mean/max risk, dominant fuel label, tier assignment) are intended for treatment sequencing and patrol planning, not parcel-level deterministic prediction.

Methodological Considerations

• Validate input band semantics and index modes before interpreting moisture-driven risk variation.
• Treat weather fields as scenario controls; report assumptions with each run.
• Re-run with sensitivity perturbations on moisture thresholds and severity factors before final prioritization.

Practical Interpretation Pitfalls

Common errors include treating dominant fuel label as final risk severity, ignoring uncertainty introduced by missing optional context layers, and over-committing to a single threshold configuration.

Inputs

Required runtime argument:

• optical_bundle

Common runtime arguments:

• red_band_index, nir_band_index, swir_band_index
• biomass_proxy, slope, aspect
• fuel_model, profile, zone_block_cells
• output_prefix (required)
• air_temperature_c, relative_humidity_pct, wind_speed_kmh

Parameters

Defaults and behavior:

• profile defaults to balanced.
• fuel_model defaults to none.
• zone_block_cells defaults to 20.
• swir_band_index is optional; when absent, the workflow uses an NDWI-style moisture proxy.

Outputs

Primary runtime outputs:

• moisture_index
• fuel_load_class
• ladder_fuel_continuity
• risk_matrix
• risk_zones
• summary
• html_report

risk_zones includes ZONE_ID, MEAN_RISK, MAX_RISK, DOM_FUEL, RISK_TIER, and PIXEL_COUNT.

Example

import whitebox_workflows as wbw

env = wbw.WbEnvironment()
env.wildfire_fuel_loading_and_risk_matrix(
	optical_bundle="optical_scene.tif",
	red_band_index=0,
	nir_band_index=1,
	swir_band_index=2,
	biomass_proxy="forest_structure_biomass_proxy.tif",
	slope="slope.tif",
	aspect="aspect.tif",
	fuel_model="temperate_mixed_forest",
	profile="balanced",
	zone_block_cells=20,
	output_prefix="wildfire_risk"
)

References

• Tool implementation: wbtools_pro/src/tools/workflow_products/wildfire_fuel_loading_and_risk_matrix.rs
• Wildland fire behavior interpretation practice (fuel/moisture/terrain/weather decomposition)

Parameter Interaction Notes

Prioritize sensitivity checks on profile/threshold settings that materially change output distributions and decision classes.

QA and Acceptance Criteria

Minimum acceptance:

• Band indices validated against the optical bundle layout.
• Optional rasters aligned to the analysis grid.
• summary includes risk fractions and weather/modulation context fields.
• risk_zones contains tier and dominant-fuel attributes expected by dispatch/planning workflows.

Advanced Operational Guidance

For production use, preserve run metadata, lock approved profiles, and version output packages for reproducibility and auditability.

Implementation Patterns

Common pattern: baseline run, sensitivity run on 1-2 parameters, then locked-profile production run for delivery.

Related Tools

See upstream conditioning and downstream validation tools in the same bundle for end-to-end workflow consistency.

When To Use This Workflow

Use Wildfire Fuel Risk Analysis when you need repeatable, source-contract-aligned outputs for planning, reporting, and stakeholder review.