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Updated: Sep 11, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Geospatial analysis of climate-induced land cover changes and vegetation trends in western Jharkhand, India
Randhir Kumar1, Kiran Jalem2, Sagar Kumar Swain3
1ICAR-Indian Institute of Soil & Water Conservation, Research Center - Sunabeda, Koraput, Odisha, India.
Abstract:
Understanding how climate variability and anthropogenic activities reshape ecosystems is essential for sustainable development in environmentally sensitive regions. This study analyses land use changes and vegetation trends in western Jharkhand, India, using multi-source satellite data from 2000 to 2020. The findings reveal that agricultural land expanded from 53.74% to 59.47% and built-up areas increased from 0.99% to 1.61%, while dense vegetation declined from 37.56% to 36.28%, sparse vegetation from 5.17% to 0.42%, and barren land from 0.63% to 0.17%. These transformations are associated with a rise in land surface temperature (LST), particularly in urban and deforested zones. A statistically significant inverse correlation (R2 = 0.71-0.77) between the Normalized Difference Vegetation Index (NDVI) and LST confirms that increased vegetation density contributes to surface cooling. This relationship underscores the vulnerability of green cover to warming and land conversion, consistent with climate-induced vegetation stress observed globally. Spatial analysis shows that forested districts such as Lohardaga and Gumla maintained high NDVI values, while mining-affected regions like Palamu and East Singhbhum experienced substantial vegetation decline. The results support India's National Action Plan on Climate Change (NAPCC) by offering localized evidence of land degradation and heat accumulation. While the study is based on validated satellite products and limitations due to spatial resolution differences. The research provides actionable insights for land planners and climate policymakers to implement green infrastructure, afforestation, and land restoration strategies. Future work should integrate climatic variables such as rainfall and soil moisture, and apply seasonal NDVI assessments to build a more comprehensive understanding of vegetation-climate dynamics across similar vulnerable landscapes.
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