Geospatial analyses focused on quantifying fuel types fragmentation and its autocorrelation with megafire severity inform decision making in contexts such as forest management and human activities regulation. Fuel type fragmentation plays a crucial role in fire severity contribution. I evaluated the landscape and class level fragmentation of fuel types in two maps: FuelSat (derived from remote sensing) and a completely randomized map. Specifically, the C-2 (Boreal Spruce), C-3 (Mature Pine), slash, and standing grass were targeted for class level metrics calculation. Fire behavior could be described in two terms – burn probability and fire intensity. Flammability (burn probability) represent the likelihood of a given location on landscape burning, while the fire intensity is the rate of heat energy released by the fire. Burn probability and fire intensity of those four target classes were extracted from landscapes (FuelSat and Random). Boxplots were created to visualize the difference between burn probability and fire intensity of four classes from FuelSat and Random, respectively. Results indicated higher fragmentated fuel types would lower the fire intensity generally, but resulted in more extreme events. It was not evident that fuel type fragmentation has significant impacts on burn probability. Moran’s I was computed and did indicate there was autocorrelation between fuel fragmentation and fire intensity. It helps fill the gap in forest fire prediction by considering effects of fuel fragmentation.
Autocorrelation between Fuel Type Fragmentation and Fire Severity at the Elephant Hill wildfire in British Columbia
Canada and British Columbia
Earth and Environmental Sciences