Assessing fire severity in forest ecosystems of Turkey using spectral indices derived from satellite images

Fire severity classifications are used to determine fire damage and regeneration potential in post-fire areas for an effective implementation of post-fire restoration applications. Since fire damage varies according to vegetation and fire characteristics, regional assessment of fire severity is crucial. Our objectives are 1) to test the performance of different satellite imagery, different spectral indices, and two field measured severity indices (CBI and GeoCBI) to assess fire severity, 2) to calculate classification thresholds for the best-performing spectral indices, and 3) to generate fire severity maps based on these classification thresholds. As study areas, we selected five large fires that occurred in pine forests in Turkey. According to our results, GeoCBI provided more reliable estimates of field measured fire severity than CBI. While Sentinel-2 and Landsat-8/OLI images performed similarly well, MODIS performed poorly. Classification thresholds were determined for Sentinel-2 and Landsat-8/OLI based spectral indices. Among these, Sentinel-2 RdNBR and Landsat-8/OLI dNBR showed the highest accuracy in terms of fire severity classification. This is the first study to assess fire severity in a forest ecosystem in Turkey using local thresholds. Our results can be used to assess and map fire severity in forest ecosystems similar to those used in this study.