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Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling

dc.citedby10
dc.contributor.authorPande C.B.en_US
dc.contributor.authorDiwate P.en_US
dc.contributor.authorOrimoloye I.R.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorPratap Mishra A.en_US
dc.contributor.authorMoharir K.N.en_US
dc.contributor.authorPal S.C.en_US
dc.contributor.authorAlshehri F.en_US
dc.contributor.authorTolche A.D.en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57192711598en_US
dc.contributor.authorid57196487246en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid57219913061en_US
dc.contributor.authorid57193546415en_US
dc.contributor.authorid57208776491en_US
dc.contributor.authorid57224683617en_US
dc.contributor.authorid57198446685en_US
dc.date.accessioned2025-03-03T07:48:54Z
dc.date.available2025-03-03T07:48:54Z
dc.date.issued2024
dc.description.abstractThis research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season?s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018?2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model?s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study?s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management. ? 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2290350
dc.identifier.doi10.1080/19475705.2023.2290350
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85180724682
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85180724682&doi=10.1080%2f19475705.2023.2290350&partnerID=40&md5=425b46e89c09db24545690b205b403ea
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37226
dc.identifier.volume15
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleGeomatics, Natural Hazards and Risk
dc.subjectClassification (of information)
dc.subjectCrops
dc.subjectDecision making
dc.subjectEngines
dc.subjectEvapotranspiration
dc.subjectFarms
dc.subjectForestry
dc.subjectInformation management
dc.subjectIrrigation
dc.subjectLand use
dc.subjectMapping
dc.subjectSoil conservation
dc.subjectWater conservation
dc.subjectWater management
dc.subjectWater quality
dc.subjectWater supply
dc.subjectChange detection
dc.subjectClassification and regression tree models
dc.subjectEnergy
dc.subjectGoogle earth engine
dc.subjectGoogle earths
dc.subjectLand use and land cover
dc.subjectRemote-sensing
dc.subjectSatellite data
dc.subjectSDG
dc.subjectWinter seasons
dc.subjectRemote sensing
dc.titleImpact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modelingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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