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The recent episodes of forest res in Brazil and Australia of 2019 are tragic reminders of the hazards of forest re. Globally incidents of forest re events are on the rise due to human encroachment into the wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest re during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest re prediction map has been prepared using a forest re inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of forest res. Model validation criteria like ROC curve, correlation coe cient, and Cohen's Kappa show a good predictive ability (AUC = 0.95, COR = 0.81, κ = 0.78). The outcomes of this study in the form of a forest re prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

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MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of SikkimHimalaya using machine learning algorithms.