Keywords
Abstract
Forest fires represent a major environmental hazard in dry deciduous forest ecosystems, where vegetation condition, climatic variability, terrain characteristics and anthropogenic pressure can jointly control spatial susceptibility. This study assesses forest-fire susceptibility in Jhargram and Paschim Medinipur districts of West Bengal, India, using an integrated Remote Sensing (RS), Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) framework. Eleven conditioning parameters—normalized difference vegetation index (NDVI), distance from settlement, distance from road, land surface temperature (LST), average wind speed, relative humidity, slope, normalized difference water index (NDWI), precipitation, elevation and aspect—were derived from Landsat 8, Sentinel-2, SRTM DEM, ERA5 and CHIRPS datasets. The Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) was employed to represent uncertainty in pairwise expert judgements and derive criterion weights, which were subsequently integrated through a GIS-based weighted overlay to generate the Forest Fire Susceptibility Zone (FFSZ) map. Distance from settlement, NDVI, distance from road and LST received the highest normalized weights of 0.1975, 0.1752, 0.1681 and 0.1279, respectively, indicating the combined importance of anthropogenic accessibility and environmental conditions. The resulting susceptibility map was classified into five classes ranging from very low to very high. High and very-high susceptibility zones were concentrated mainly in the northern and western forested lateritic uplands, whereas comparatively lower susceptibility occurred in the agricultural and more moisture-influenced eastern and southeastern parts. The study demonstrates the utility of integrating RS, GIS and Fuzzy-AHP for spatial characterization of forest-fire susceptibility in the study region.
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Copyright (c) 2026 Prasanna Mahata, Prahallad Barman, Nandita Mahata, Rahul Debnath (Author)