Mapping of post-wildfire burned area using a hybrid algorithm and satellite data: The case of the camp fire wildfire in California, USA

Abstract

On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a preand post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre-and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre-and post-wildfire map. The SVM-ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre-and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre-and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for preand post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre-and post-wildfire using Sentinel-2 respectively. In total, eight pre-and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future.