FOREST FIRE SUSCEPTIBILITY MAPPING OF BHUTAN USING LOGISTIC REGRESSION AND FREQUENCY RATIO MODEL

Abstract

Forest fire is not only observed as one of the most significant sources of forest degradation in Bhutan but also a serious danger to national conservation efforts. As a result, forest fire susceptibility analysis is recognised as an important part of Bhutan's forest fire management strategy. The study's major goal is to create a forest fire susceptibility map for Bhutan using logistic regression (LR) and frequency ratio (FR) models. The study gathered number of fire influencing factors, evaluated them, and created susceptibility maps. Using the relative operating characteristics technique, the efficiency of each of the two models was analysed and compared to select the best model. The Receiver Operating Characteristics (ROC) curves with the area under the curve (AUC) was used to check the correctness of the maps produced by the modelling procedure. The prediction and success rates of the LR model were 88.8% and 87.5%, while for the FR model they were 85.4% and 85.1%, respectively. The results showed that both models are good predictors of forest fire with the LR model performing fairly better than the FR model. So, the LR model was chosen as an optimum model for forest fire susceptibility mapping. The susceptibility map obtained from the optimum LR model was classified into five categories such as; very low, low, moderate, high, and very high.. The findings of this study give useful spatial information for implementing forest management techniques.