In recent years, India’s Northeastern territory has been plagued by wildfires. Mizoram, as per FSI, has been among the most affected regions, with approximately 20,744-recorded wildfires. Forest fire directly or indirectly affects human health, climate change, and the environment. The forest fire risk zonation is generated utilizing remote sensing data, and Geographic Information System based on specified physical and socioeconomic factors. As part of the current study, a Geographical Information System is employed to determine forest fire risk based on predetermined physical and socioeconomic parameters. This study used three distinct models of fire risk zonation index namely FRI (Fire Risk Index), HFI (Hybrid Fire Index), and SFI (Structural Fire Index) to identify the zones in the study area under the risk of forest fires. The Indian state of Mizoram is categorized into five distinct hazard zones based on the probability of wildfire incidents. Fire alerts generated using risk models, and real-time hotspot datasets (forest fire spots) received from MODIS and USGS have been validated. According to the study’s findings, 18.84 km2 of the study area is at low risk of forest fire, 11.072 km2 is at moderate risk, and 5.38 km2 is at high risk. The probability from each metric varies since the input and weightage of the different parameters vary from one another. SFI, therefore, therefore predicts a lower frequency of high-risk wildfire-prone zones than HFI and FRI. In this research, the coefficient of discrimination (R2) is employed to assess the reliability of the projected fire indices being compared with real-time hot spots. Here, FRI possesses the highest accuracy (R2=0.892), HFI has moderate accuracy (R2=0.676), and FRI has the lowest accuracy (R2=0.629).

In the districts of Purulia and Bankura, this study offers a methodology for spatial assessment of vulnerable and risk-prone areas. These districts are adjacent in space and have identical geographic characteristics (other than the eastern portion of Bankura district). Vulnerability and risk assessment could be used to measure the interactions between individuals and their surroundings. This research aims to pinpoint the areas in these two districts that are particularly susceptible to natural, social, and meteorological disasters. The natural and climate-induced factors considered are rainfall distribution and vegetation conditions. The social factors are agricultural dependence, percentage of farmers, female population, labor dependence on agriculture, and literacy rate. The potential impacts of developmental and environmental degradation processes can be examined and assessed by classifying regions according to their vulnerability and risk levels. The fundamental factors impacting susceptibility and risk, which are recognized, and the associated thematic-based outputs are produced in this study based on the persistent phenomenon of drought within these two districts. The elements of vulnerability selected for this study are exposure, sensitivity, and adaptive capacity (IPCC AR4) and risk, which is the combined outcome of hazard, exposure, and vulnerability (IPCC AR5). The aim of this research is to create a simplified, scalable assessment model for evaluating both vulnerabilities and threats, which can help with drought mitigation. It has been observed from the results that the western portion of the study area (Arsha, Purulia-I, Baghmundi blocks of Purulia district) with relatively higher risk and vulnerability needs more attention for reducing the vulnerability and risk than the eastern part. As a result, this research can serve as a platform for district-level prioritizing efforts, emergency response protocols, and policy interventions aimed at reducing disaster susceptibility (mostly drought) in Bankura and Purulia districts.