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Abstract
The Terai Arc Landscape (TAL) is an ecologically important region of the Indian subcontinent, where anthropogenic habitat loss and forest fragmentation are major issues. The most prominent threat is forest fires because of their impacts on the microhabitat and macrohabitat characteristics and the resulting disruption of ecological processes. Moreover, wildfire aggravates conflicts between humans and wildlife in the forest fringe areas. The lack of a proper forest fire monitoring system in the TAL is a major management issue that needs attention for long-term forest viability. Hence, the present study was undertaken using maximum entropy modeling to predict the areas across the TAL at risk of wildfire and to identify key variables associated with fire occurrence. Spatiotemporally independent fire incidence locations along with other environmental variables were used to build the model. The accuracy of the model was assessed using the area under the curve. To evaluate the importance of each variable, a jackknife procedure was adopted. Areas in the projected map were categorized into high fire, marginal fire, and no fire areas. An adaptive forest management strategy can be implemented in the modeled high fire areas to mitigate forest fire and wildlife conflict in the TAL.
Parent Publication
Citation
Verma, Amit Kumar; Kaliyathan, Namitha Nhandadiyil; Bisht, Narendra Singh; Sharma, Satinder Dev; Nautiyal, Raman. 2020. Forest fire prediction modeling in the Terai Arc Landscape of the lesser Himalayas using the maximum entropy method. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, [eds.]. Proceedings of the Fire Continuum-Preparing for the future of wildland fire; 2018 May 21-24; Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 219-230.
