Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread

The objectives of this study were to evaluate spatial sampling and statistical aspects landscape-level wildfire rate spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps ROS repetitive ATIR image sequences collected during the 2017 Thomas Detwiler events in California. Three separate landscape unit (LSU) sizes used extract remotely sensed environmental covariates known influence fire behavior. Statistical relationships between rates analyzed using (1) bivariate regression, (2) multiple stepwise (3) geographically weighted regression (GWR), (4) eigenvector filtering (ESF) (5) trees (RT), (6) random forest (RF) regression. GWR ESF regressions reveal that are substantially non-stationary suggest global association controls locally differentiated on scales. Directional slope is by far most strongly associated covariate for imaging size LSUs has little any relationships.