Multi-criteria decision modelling for forest fire risk mapping in protected areas of Mayurbhanj District, Odisha: A Case study in a geomorphologically diverse touristic landscape
DOI:
https://doi.org/10.12775/EQ.2025.030Keywords
forest fire, MCDM, AHP-FAHP, protected areas, Simlipal Biosphere Reserve, risk assessment, geo-environmental hazardAbstract
Forest fires are one of the most serious environmental disasters that endanger the natural forest ecosystem. Forest fire catastrophes have recently received a lot of attention because of their escalating numbers and the effects of global climate change. Recognizing fire occurrence and their patterns is important in identifying fire risks to mitigate the potential fire-prone areas surrounding human settlements and potential protected areas. Additionally, smoke emissions from fires endanger public health and natural systems, plus the added impact of natural triggers such as rainfall may cause debris floods or landslides initiated from the burnt areas. This study seeks to highlight burnt area mapping of the environmentally protected area of Mayurbhanj District, Odisha, India, which was devastated in the year 2021 due to a massive forest fire event. The main aim of this study was to create a map that would be a reliable risk indicator of forest fire zones in a defined region of interest, which is important and famous as a unique Geotourism and recreational destination. The study of the forest fire probability (risk zones) involved the investigation of an array of pertinent natural and geomorphological independent variables, such as vegetation type, climate, topography, road buffer, historical fire data, etc. Multi-criteria decision model (MCDM), i.e., analytic hierarchy processes (AHP) and Fuzzy Analytic Hierarchy Processes (FAHP) were used to comparatively assign weightage as per their influence on the prevailing fire risk. Results indicate that 1,058 km² (30.79% of the study area) is highly susceptible to wildfires, posing a significant threat to biodiversity. Satellite-derived fire risk indices and historical MODIS fire data effectively delineate high-risk zones after the severe 2021 wildfire, highlighting the urgent need for mitigation. By leveraging modelling and geospatial analytics, this study presents a scalable wildfire risk management approach, offering valuable insights for policymakers and disaster mitigation authorities in fire-prone landscapes of touristic importance.
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