Chandana, S.Aishwarya Hegde, A.Umesh, P.Chandan, M.C.2026-02-082024Developments in Environmental Science, 2024, Vol.16, , p. 431-4549780080952017978008055609397804445298799780080465227978008045132914748177https://doi.org/10.1016/j.foodchem.2025.147253https://idr.nitk.ac.in/handle/123456789/33548The rapid expansion of the global economy has given rise to concerning ecological consequences, notably a dramatic increase in land cover change (LCC). This section presents how to use the Google Earth Engine (GEE) cloud platform to explore the administrative divisions of the Southern Indian Dakshina Kannada (DK) district, which were chosen for their LCC susceptibility. Leveraging GEE, we generated a time series dataset tracking LCC over a 4-year period (2019–22). Our findings demonstrate an impressive overall accuracy (OA) of 96.30% for 2019 and 95.47% for 2022. A significant revelation in our study is the 13.64% reduction in forested areas, accompanied by a 0.68% increase in urban development within the district. This research attempt offers vital insights into the impact of dam construction on LCC, aiding informed decisions on water resource management. This research promotes a sustainable and ecologically conscious approach to holistic development in the study region and beyond. © 2024 Elsevier B.V.change detectionenvironmental managementgeomaticsGoogle Earth Enginehydrologyimpact assessmentIndiaKarnatakaland use land covermachine learningmultitemporalnatural resource managementrandom forestSentinel-2supervised classificationsurface waterVented dams