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Fire is among major threats to the world’s forests, leading to tremendous biodiversity losses. Forest fire in India has greatly increased in the last few decades; the state of Arunachal Pradesh, a recognized Himalayan biodiversity hotspot, is extremely prone to this disaster. The objective of this study was to develop GIS integrated mapping of direct and indirect factors, leading to a predictive model to identify settlements/villages for the strategic allocation of resources towards damage mitigation and control. Initial hotspots were generated by integrating factors of socio-economy, geography and climate, using differential weightage. The intersection of these with fire data, led to identification of final hotspots within the predictive model. The model was improved by linking it to a mobile App and the WebGIS portal. Of the 5258 settlements/villages, a total of 560 were found to be at high fire risk. Percentage correlation increased from 63 to 74, after data revision through the App. A focused intervention on predicted villages was undertaken, resulting in a decrease of 31% of fire incidence in comparison of last five years (2015–20) data. Such advanced information about fire disaster with optimal use of limited resources was greatly helpful, and helped protect the rich Himalayan biodiversity.

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This page is a summary of: Predictive modeling of forest fire using geospatial tools and strategic allocation of resources: eForestFire, Stochastic Environmental Research and Risk Assessment, September 2020, Springer Science + Business Media,
DOI: 10.1007/s00477-020-01872-3.
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