Authors : Meenu Sharma; Rakesh Verma; Sundeep Pandita; Rajwant; Ahsan Ul Haq

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/pryub74y

Scribd : https://tinyurl.com/cmsdw2p3

DOI : https://doi.org/10.38124/ijisrt/26feb977

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study evaluates vegetation cover changes in the Tawi watershed of Jammu District between 2019 and 2024 using Landsat 8 and Sentinel-2 satellite imagery and machine learning techniques. Central to the analysis is the calculation of the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and the estimation of tree cover to detect spatial and temporal vegetation dynamics. Data processing and analysis were conducted through Google Earth Engine (GEE), employing a machine learning-based classification workflow to enhance accuracy and monitoring efficiency. The methodology encompasses data acquisition, pre- and post-classification comparisons, identification of key challenges, and interpretation of findings. The integrated analysis suggests a strong inverse relationship between NDVI and LST values in which areas with lower vegetation cover (low NDVI) correspond to higher surface temperatures (high LST) particularly in April 2024. The linear correlation between NDVI and LST usually reflect “Weak negative”, “Moderate negative” and “Very weak negative” in the Tawi watershed. This trend reflects potential land cover changes such as vegetation loss, urban expansion, or soil exposure, contributing to higher heat absorption and reduced evapo-transpiration. The results reveal significant vegetation changes over the five-year period: No Vegetation (Unchanged) – 32%, Afforestation – 5%, Deforestation – 12%, and Vegetation (Unchanged) – 51%. These findings underscore the value of integrating remote sensing and machine learning for ecological monitoring. The study concludes with strategic recommendations for sustainable landscape management and advocates for resilient, technology-driven frameworks in future environmental assessments

Keywords : NDVI; Tree Cover; Tawi Watershed; Sentinel 2; Afforestation; Deforestation, LST

References :

1. Molnár, T., & Király, G. (2023). Forest monitoring based on Sentinel-2 satellite imagery, Google Earth Engine cloud computing, and machine learning. Preprints. doi, 10.
2. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.
3. Ihlen, V., & Zanter, K. (2019). Landsat 8 (L8) data users handbook. US Geological Survey, 670.
4. Karaburun, A. (2010). Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean Journal of applied sciences, 3(1), 77-85.
5. Chouhan, R., & Rao, N. (2011). Vegetation detection in multispectral remote sensing images: Protective role-analysis of vegetation in 2004 indian ocean tsunami. PDPM Indian Institute of Information Technology.
6. Scanlon, T. M., Albertson, J. D., Caylor, K. K., & Williams, C. A. (2002). Determining land surface fractional cover from NDVI and rainfall time series for a savanna ecosystem. Remote Sensing of Environment, 82(2-3), 376-388.
7. Kunkel, M. L., Flores, A. N., Smith, T. J., McNamara, J. P., & Benner, S. G. (2011). A simplified approach for estimating soil carbon and nitrogen stocks in semi-arid complex terrain. Geoderma, 165(1), 1-11.
8. Anbazhagan, S., & Paramasivam, C. R. (2016). Statistical correlation between land surface temperature (LST) and vegetation index (NDVI) using multi-temporal landsat TM data. International Journal of Advanced Earth Science and Engineering, 5(1), 333-346.
9. Smith, R. C. G., & Choudhury, B. J. (1990). On the correlation of indices of vegetation and surface temperature over south-eastern Australia. International Journal of Remote Sensing, 11(11), 2113-2120.
10. Julien, Y., Sobrino, J. A., & Verhoef, W. (2006). Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote sensing of environment, 103(1), 43-55.
11. Yuan, X., Wang, W., Cui, J., Meng, F., Kurban, A., & De Maeyer, P. (2017). Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7(1), 3287.
12. Leprieur, C., Kerr, Y. H., Mastorchio, S., & Meunier, J. C. (2000). Monitoring vegetation cover across semi-arid regions: comparison of remote observations from various scales. International Journal of Remote Sensing, 21(2), 281-300.
13. Liu, H. Q., & Huete, A. (1994, August). A system-based modification of the NDVI to minimize soil and atmospheric noise. In Proceedings of IGARSS'94-1994 IEEE International Geoscience and Remote Sensing Symposium (Vol. 1, pp. 128-130). IEEE.
14. Karunakaran, K and Ranga Rao, 1976. Status of Exploration for hydrocarbons in the Himalayan Region- Contribution ti the stratigraphy and structure- Himalayan Geology Seminar, New Delhi.
15. CGWB (2013). Groundwater Information Booklet of Jammu District. Ministry of Water Resources, Government of India.