FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping

arXiv:2403.15462v1 Announce Type: cross
Abstract: Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis …

abstract algorithm arxiv assessment behavior cs.lg data data sources diverse eess.iv ensemble fire fusion landsat management mapping multimodal multimodal data multimodel optical prediction radar risk sentinel synthetic type wildfire