Forest fire risk assessment for effective geoenvironmental planning and management using geospatial techniques

Abstract

Forest are essential natural resources having the role of supporting economic activity, which plays a significant role in regulating the climate and the carbon cycle. Forest ecosystems increasingly threatened by fires caused by a range of natural and anthropogenic factors. Hence, spatial assessment of fire risk is critical to reducing the impacts of wildland fires. In the current research, the evaluation of forest fire risk (FFR) assessment performed by geospatial data of Melgaht Tiger Reserve Forest (MTRS), Maharashtra, India. We have used eleven natural and anthropogenic parameters (slope, altitude, topographic position index (TPI), aspect, rainfall, land surface temperature (LST), air temperature, wind speed, normalized differential vegetation index (NDVI), distance to road and distance to settlement) for FFR assessment based on the Analytic hierarchy process (AHP) and Frequency ratio (FR) models in a GIS framework. The results from AHP and FR models shown similar trends. The AHP model was significantly higher accuracy than the FR model. AHP and FR models based FHR maps were classified into five classes (very low, low, moderate, high, and very high). According to the generated FFR maps, the very high-risk class was found at some forest blocks (Mangtya, Kund, Gudfata, Katharmal, Amyar). The sensitivity analysis showed that some parameters (wind speed, air temperature, LST, slope, altitude, distance to settlement, and distance to the road) were more sensitive to forest fire risk. The FFR results were justified by the forest fire sample points (Forest Survey of India) and burn images (2010–2018). This work will provide a basic guideline for effective geo-environmental planning and management of Melgaht Tiger Reserve Forest.