Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling
This 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 irrig...
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2025
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| author | Pande C.B. Diwate P. Orimoloye I.R. Sidek L.M. Pratap Mishra A. Moharir K.N. Pal S.C. Alshehri F. Tolche A.D. |
| author2 | 57193547008 |
| author_facet | 57193547008 Pande C.B. Diwate P. Orimoloye I.R. Sidek L.M. Pratap Mishra A. Moharir K.N. Pal S.C. Alshehri F. Tolche A.D. |
| author_sort | Pande C.B. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This 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. |
| format | Article |
| id | my.uniten.dspace-37226 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Taylor and Francis Ltd. |
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| spelling | my.uniten.dspace-372262025-03-03T15:48:54Z Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling Pande C.B. Diwate P. Orimoloye I.R. Sidek L.M. Pratap Mishra A. Moharir K.N. Pal S.C. Alshehri F. Tolche A.D. 57193547008 57192711598 57196487246 35070506500 57219913061 57193546415 57208776491 57224683617 57198446685 Classification (of information) Crops Decision making Engines Evapotranspiration Farms Forestry Information management Irrigation Land use Mapping Soil conservation Water conservation Water management Water quality Water supply Change detection Classification and regression tree models Energy Google earth engine Google earths Land use and land cover Remote-sensing Satellite data SDG Winter seasons Remote sensing This 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. Final 2025-03-03T07:48:54Z 2025-03-03T07:48:54Z 2024 Article 10.1080/19475705.2023.2290350 2-s2.0-85180724682 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180724682&doi=10.1080%2f19475705.2023.2290350&partnerID=40&md5=425b46e89c09db24545690b205b403ea https://irepository.uniten.edu.my/handle/123456789/37226 15 1 2290350 All Open Access; Gold Open Access Taylor and Francis Ltd. Scopus |
| spellingShingle | Classification (of information) Crops Decision making Engines Evapotranspiration Farms Forestry Information management Irrigation Land use Mapping Soil conservation Water conservation Water management Water quality Water supply Change detection Classification and regression tree models Energy Google earth engine Google earths Land use and land cover Remote-sensing Satellite data SDG Winter seasons Remote sensing Pande C.B. Diwate P. Orimoloye I.R. Sidek L.M. Pratap Mishra A. Moharir K.N. Pal S.C. Alshehri F. Tolche A.D. Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling |
| title | Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling |
| title_full | Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling |
| title_fullStr | Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling |
| title_full_unstemmed | Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling |
| title_short | Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling |
| title_sort | impact of land use/land cover changes on evapotranspiration and model accuracy using google earth engine and classification and regression tree modeling |
| topic | Classification (of information) Crops Decision making Engines Evapotranspiration Farms Forestry Information management Irrigation Land use Mapping Soil conservation Water conservation Water management Water quality Water supply Change detection Classification and regression tree models Energy Google earth engine Google earths Land use and land cover Remote-sensing Satellite data SDG Winter seasons Remote sensing |
| url_provider | http://dspace.uniten.edu.my/ |