In much of Canada, provincial and territorial fuel classification layers are built on vegetation inventory information that can be many years out of date. The inputs behind those layers are often unvalidated and the conditions they describe may not reflect current reality. …Forests change considerably over time. As an example, past harvest activity has restructured stands, and bark beetle infestations have converted millions of hectares of mature lodgepole pine into standing dead fuel. …Provincial layers typically describe fuel type classifications but say little about the structural attributes of those fuels, and nothing about their current seasonal condition. …In the WUI, the difference between a fuel-free buffer and a continuous shrub corridor can be measured in meters. Legacy maps cannot resolve these issues. The consequences of missing them are not abstract.

Forsite’s Fuel ID tool was built to address these gaps. Fuel ID encompasses a series of machine-learning approaches that use satellite imagery and, where available, LiDAR data to generate current, validated fuel information across the full canopy-to-surface profile. It is not a single product — it is a flexible system that generates resolution-appropriate outputs depending on available data inputs and the operational question being asked.