Abstract
Detecting and analyzing Forest fire occurrence is crucial for mitigating immediate impacts and developing long-term adaptive strategies. Despite the increasing frequency of Forest fires across India, there is a notable lack of comprehensive studies focusing specifically on Forest fire patterns, occurrence, and spatial distribution in Maharashtra. To date, no systematic assessment has been conducted to understand the regional dynamics of Forest fires in this region. In this context, the objectives of the study were to detect fire occurrence, identify high-risk zones, and establish a baseline for enhanced Forest fire management. This study investigates the spatiotemporal patterns of Forest fires in Maharashtra from 2005 to 2024 using MODIS data from FIRMS. Maharashtra's Forest fire activity peaked in March and April, with 2213 incidents in March 2009, 3517 in March 2012, and 3267 in March 2018, driven by dry pre-monsoon conditions. The analysis revealed that March and April had the highest fire incidents, which was attributed to pre-monsoon conditions. Gadchiroli emerged as the most fire-prone district, averaging 679 fires annually, peaking at 1588 fires in 2012 and 1386 fires in 2018. Chandrapur has an annual average of 258 fires, peaking at 530 fires in 2012 and 551 fires in 2021. The Forest fire frequency map shows high activity in eastern Maharashtra (Vidarbha region) and moderate activity in the Western Ghats. This study directly contributes to SDG 13 by providing critical data and analysis that enhances the understanding of climate-driven Forest fire patterns in Maharashtra. By identifying high-risk zones and seasonal fire trends, this study enables proactive policy planning and resource allocation to mitigate climate impacts. It also helps to establish a baseline that informs targeted fire management, essential to mitigating environmental damage, preserving biodiversity, reducing socioeconomic losses, reducing greenhouse gas emissions from biomass burning, supporting ecosystem resilience, and informing community-level adaptation strategies in fire-prone regions, and provides indicator-ready data for SDG target 13.1 (climate-resilience) and SDG target 15.2 (sustainable forest management).