Fire severity is a quantitative measure of the ecological and structural impact that wildfire exerts on vegetation, soil, and landscape features across a defined geographic area. In GIS workflows, fire severity assessment leverages multitemporal remote sensing data — most commonly derived from Landsat, Sentinel-2, or MODIS satellite imagery — to calculate spectral indices such as the Normalized Burn Ratio (NBR) and differenced Normalized Burn Ratio (dNBR). These indices quantify changes in near-infrared and shortwave-infrared reflectance between pre-fire and post-fire imagery, enabling geospatial analysts to classify burned areas into severity categories ranging from unburned to high severity.

Spatial analysis tools within platforms such as ArcGIS Pro or QGIS allow practitioners to integrate fire severity rasters with additional geospatial datasets, including topographic models, fuel load inventories, and land cover classifications, producing comprehensive fire impact maps. These outputs directly support post-fire emergency response, erosion risk modeling, reforestation planning, and long-term ecosystem monitoring. By translating complex spectral data into actionable geospatial intelligence, GIS-driven fire severity mapping empowers land managers, ecologists, and public safety agencies to make informed, evidence-based decisions across affected landscapes.