GIS & Machine Learning Based Approaches to Assess Forest and Biodiversity Vulnerability Under Climate Stress: A Case Study from Assam, India

The study presents an integrated geospatial and machine learning framework to assess forest and biodiversity vulnerability in Assam, India, under projected climate stress scenarios. Using multi-source remote sensing datasets and climate variables, the work applies GIS-based spatial modeling and machine learning algorithms to identify climate hotspots, evaluate forest resilience, and highlight biodiversity-rich yet highly vulnerable landscapes. The findings underscore how data-driven approaches can inform adaptive forest management, enhance biodiversity conservation, and support climate-resilient policy frameworks in India’s ecologically fragile regions.