WILDFIRE SUSCEPTIBILITY MAPPING IN BHUTAN USING GEOINFORMATICS TECHNOLOGY
Keywords:
Wildfire susceptibility analysis, remote sensing, GIS, logistic regression, frequency ratio, Thimphu and Paro, BhutanAbstract
The integration of geoinformatics technology with suitable geospatial models has been widely employed in many wildfire studies to develop and enhance wildfire management systems in different parts of the world. In Bhutan, wildfire is perceived as one of the most prominent causes of forest degradation and a serious threat to national conservation efforts. Thus, wildfire susceptibility analysis is seen as a necessary component of the wildfire management system for Bhutan. The main aim of the study is to apply the innovative approach of geoinformatics technology with the integration of GIS-based logistic regression (LR) and frequency ratio (FR) models to establish a wildfire susceptibility map. Herein, the study collected and prepared various influential wildfire factors, analyzed them, and established probability maps. The efficiency of each of the 2 models was then evaluated and compared with each other to determine an optimal model using the relative operating characteristics method.
The interpretations of the results revealed that the most significant predictor variables that played a major role in determining a wildfire occurrence in the study area are land surface temperature, proximity to roads, elevation, population density, enhanced vegetation index, distance to agricultural land, relative humidity, and aspect. The prediction and success rates of the LR model were 88.3% and 88.1%, while for the FR model they were 85.3% and 85.5%, respectively. The results indicated that both models are good predictors of wildfire with the LR model performing slightly better than the FR model. The predicted probability map from the optimum LR model was further classified into 5 categories of wildfire susceptibility zones: very low, low, moderate, high, and very high. The results from the study demonstrate that the integration of geoinformatics technology with GIS-based LR and FR models is an inevitable component of wildfire mapping that can effectively determine the most significant influential factors of a wildfire and its probability and eventually lead to the development of the wildfire susceptibility map.
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