Remote Sensing-Based LULC Change Analysis for Forest Conservation in Painganga Wildlife Sanctuary

This study investigates the two decades of Land Use and Land Cover (LULC) dynamics in the Painganga Wildlife Sanctuary, located in Maharashtra, India, using multi-temporal satellite imagery and GIS techniques. Landsat data from the years 2004 to 2024 were used to classify the landscape into five major categories: Dense Forest, Deciduous Forest, Grassland, Barren Land, and Water Bodies. The supervised classification approach, specifically the Maximum Likelihood Classifier (MLC), was applied for accurate mapping. The results reveal significant spatial and temporal transformations in land cover patterns, including a marked decline in dense forest areas and a rise in barren and grassland regions. These changes indicate increasing anthropogenic pressure and possible forest degradation. Change detection analysis provided insights into the rate and extent of land transformation. This research demonstrates the importance of integrating remote sensing and GIS technologies for forest monitoring and management and highlights the urgent need for conservation interventions in the region.