Dataset of 'MODIS FIRMS and Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.'

Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Likewise, Sikkim suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, population density and proximity to roads are the major determinants of the forest fire. This indicates the role of human activities on the incidences of a forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.78, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.全球范围内，受人类对荒野地区的侵占与气候变化影响，森林火灾事件的发生率呈上升趋势。同样，锡金地区在干燥的冬季会频繁出现季节性森林火灾。为应对该问题，研究团队利用森林火灾库存数据库与环境特征地图，制作了一套融合地理信息系统（GIS，Geographic Information System）与MaxEnt机器学习方法的森林火灾预测图。研究表明，在各类环境特征因子中，人口密度与道路邻近度是影响森林火灾发生的主要驱动因素，这印证了人类活动对森林火灾发生的重要作用。模型验证指标包括受试者工作特征（ROC，Receiver Operating Characteristic）曲线、相关系数与科恩kappa（Cohen’s Kappa）系数，结果显示该模型具备优异的预测性能（AUC=0.95，COR=0.78，κ=0.78）。本研究生成的森林火灾预测图成果，可助力森林管理相关方制定科学合理的火灾防控减灾措施。