Examining the effects of vented dams on land use and land cover in the Shambhavi Catchment: a multitemporal sentinel imagery analysis

dc.contributor.authorChandana, S.
dc.contributor.authorAishwarya Hegde, A.
dc.contributor.authorUmesh, P.
dc.contributor.authorChandan, M.C.
dc.date.accessioned2026-02-08T16:49:53Z
dc.date.issued2024
dc.description.abstractThe 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.
dc.identifier.citationDevelopments in Environmental Science, 2024, Vol.16, , p. 431-454
dc.identifier.isbn9780080952017
dc.identifier.isbn9780080556093
dc.identifier.isbn9780444529879
dc.identifier.isbn9780080465227
dc.identifier.isbn9780080451329
dc.identifier.issn14748177
dc.identifier.urihttps://doi.org/10.1016/j.foodchem.2025.147253
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/33548
dc.publisherElsevier Ltd
dc.subjectchange detection
dc.subjectenvironmental management
dc.subjectgeomatics
dc.subjectGoogle Earth Engine
dc.subjecthydrology
dc.subjectimpact assessment
dc.subjectIndia
dc.subjectKarnataka
dc.subjectland use land cover
dc.subjectmachine learning
dc.subjectmultitemporal
dc.subjectnatural resource management
dc.subjectrandom forest
dc.subjectSentinel-2
dc.subjectsupervised classification
dc.subjectsurface water
dc.subjectVented dams
dc.titleExamining the effects of vented dams on land use and land cover in the Shambhavi Catchment: a multitemporal sentinel imagery analysis

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