Sentinel 2 based burn severity mapping and assessing post-fire impacts on forests and buildings in the Mizoram, a north-eastern Himalayan region

The Increasing frequency and severity of forest fires worldwide highlights the need for more effective Burnt area mapping. Finding the effects of fire on vegetation and putting mitigation methods in place, depends on post-fire evaluation. In this study, the location of the burned regions and the severity of the fire were determined using high-resolution multi-spectral images from Sentinel 2 on Google Earth Engine (GEE) platform. Three widely used fire severity indices—differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR)—based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR—were computed and compared based on their accuracy using very high-resolution planet imagery fire points and equal number of random non fire points. Maps also validated with active fires, ground based photos and crowdsourced images. The accuracy (AUC) of the RdNBR map was 85%, RBR - 84% and dNBR −82%. The RdNBR index demonstrated highest level of accuracy. Then the loss to vegetation using pre-fire and post-fire NDVI was analysed. The analysis of pre-fire and post-fire NDVI provided insights into the extent of vegetation loss. The analysis of vegetation loss offered valuable information regarding the impact of fire on the affected areas. Google building dataset was used to monitor the percent of buildings under threat due to these fires. Around 8.77% of buildings were found in high severity region. Accurate mapping aids post-fire evaluation, guided mitigation strategies, and enhanced forest management and ecological restoration.