GIS and ML-Driven Insights into Forest Vulnerability and Climate Hotspots in Assam, India

This study assesses the impact of regional climate variability on forest vulnerability in Assam using a GIS and Machine Learning (ML)-based approach. A grid-based Forest Vulnerability Index (FVI) was developed using eight key indicators, and climate change hotspots were mapped using temperature and precipitation anomalies. The results revealed that 87 forested grids are highly vulnerable, with significant overlaps between climate hotspots and biodiversity risk zones. The study highlights the urgent need for adaptive forest management, AI-driven monitoring, and policy interventions to mitigate climate-induced risks.