Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

The objectives of this study are to evaluate landscape-scale fuel and terrain controls on fire rate spread (ROS) estimates derived from repetitive airborne thermal infrared (ATIR) imagery sequences collected during the 2017 Thomas Detwiler extreme wildfire events in California. Environmental covariate data were prefire National Agriculture Imagery Program (NAIP) orthoimagery USGS digital elevation models (DEMs). Active fronts vectors expanding fires delineated ATIR imagery. Then, statistical relationships between rates landscape covariates analyzed using bivariate multivariate regression. Directional slope is found be most statistically significant with ROS for five that its relationship best characterized as an exponential growth function (adj. R2 max = 0.548, min 0.075). Imaged-derived alone weak predictors 0.363, 0.002) but, when included models, increased predictability variance explanation (+14%) compared directional alone.