Forests in Maharashtra are undergoing significant transformations driven by both natural and anthropogenic pressures. This study employs multi-temporal remote sensing data from 2014 to 2024 to analyze forest disturbances across the state. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), along with MODIS-derived Land Surface Temperature (LST), were integrated to assess spatiotemporal dynamics. Landsat data were utilized for the pre-2017 period and Sentinel-2 data thereafter, harmonized to a common spatial scale to ensure temporal consistency. Seasonal composites (pre-monsoon, monsoon, and post-monsoon) were generated, and district-level zonal statistics were computed. A composite Forest Disturbance Index (FDI) was developed by combining NDVI, NDWI, and LST anomalies, with the Mann–Kendall trend test and Sen’s slope methods applied for temporal analysis. Results indicate declining NDVI and NDWI trends, especially in Gadchiroli, Chandrapur, and Thane districts, where vegetation decreased by 5–15% and LST increased by 1.5–2.2°C. Disturbance hotspots were linked to urbanization, mining, and forest fragmentation in the Western Ghats and Vidarbha regions. Pre-monsoon periods exhibited the greatest stress and fire risk, while partial recovery was observed during the monsoon season. The study highlights the increasing vulnerability of Maharashtra’s forests due to human activities and climatic variability, emphasizing the need for continuous monitoring and sustainable forest management strategies. Integrating multi-source satellite datasets through open-source platforms proved effective for mapping disturbances and supporting data-driven conservation planning.
Rai et al. (Wed,) studied this question.