GIS-based landscape vulnerability assessment to forest fire susceptibility of Rudraprayag district, Uttarakhand, India

Abstract

The study aims to assess the landscape vulnerability to forest fire susceptibility of Rudraprayag district, India, using frequency ratio model. Firstly, forest-fire-affected pixels were identified by using normalized difference burning ratio and ground survey. A total of 19,834 forest fire pixels were identified; out of these, 14,876 (70%) pixels were used to generate forest fire susceptibility map and the remaining 4958 affected pixels (30%) were used to validate the susceptibility model. Twelve forest fire conditioning indicators were selected: slope angle, slope aspect, curvature, elevation, topographic wetness index, soil texture, land use/land cover, normalized difference moisture index, annual average rainfall, road buffer, distance from settlement and distance from drainage to build the forest fire susceptibility model. Receiver operating characteristic curve was used to validate the forest fire susceptibility map, and 85% prediction accuracy was found. Final landscape vulnerability to forest fire susceptibility was assessed by using overlay function in GIS environment. The result shows that 73% area of Rudraprayag district falls into low and moderate susceptibility classes and approximately 16% area falls into high and very high susceptibility classes. Landscape vulnerability analysis revealed that moderate and very high forest fire susceptibility occupies the inaccessible parts of the core forest area of the district.