As climate change drives more frequent and severe wildfires, accurate forest fuel
mapping is critical for hazard assessment and adaptation planning. This study combines high-resolution Airborne Laser Scanning (LiDAR) data with machine learning to produce detailed
fuel maps for the Xaxli'p Survival Territory in British Columbia. Using Random Forest
models, we mapped fuel layers prioritized by the Xaxli'p community, providing more precise
and locally relevant data than conventional wildfire fuel classifications. To train the models,
we compared two types of field data, structured, measurement-based samples and
community-based visual estimates, evaluating their effectiveness in predicting fuel
distribution. The findings show that integrating remote sensing with local knowledge can
improve wildfire hazard mapping in a local context. To support practical use, we developed
an interactive web map tailored for the Xaxli'p community to inform wildfire mitigation and
climate adaptation efforts. This research offers a scalable, community-driven approach to
wildfire hazard assessment and land stewardship.
