Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery
"Accurate land use classification plays a critical role in agricultural monitoring, resource management, and policy planning. Remote sensing, particularly the use of high-resolution multispectral imagery, has emerged as a powerful tool for mapping and assessing agricultural production systems with e...
| Autores principales: | , , , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
International Rice Research Institute
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/173243 |
| _version_ | 1855540716103532544 |
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| author | Villano, Lorena Garcia, Cornelia Rala, Arnel Raviz, Jeny Laborte, Alice |
| author_browse | Garcia, Cornelia Laborte, Alice Rala, Arnel Raviz, Jeny Villano, Lorena |
| author_facet | Villano, Lorena Garcia, Cornelia Rala, Arnel Raviz, Jeny Laborte, Alice |
| author_sort | Villano, Lorena |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | "Accurate land use classification plays a critical role in agricultural monitoring, resource management, and policy planning. Remote sensing, particularly the use of high-resolution multispectral imagery, has emerged as a powerful tool for mapping and assessing agricultural production systems with enhanced precision. In Cambodia, where rice farming dominates the landscape, understanding spatial variations in land use and cropping patterns is essential for improving agricultural productivity and sustainability.
This study aims to classify land use and assess agricultural production systems in selected sites in Takeo and Prey Veng provinces, Cambodia, using high-resolution satellite imagery from Pleiades (0.5 m) and SPOT 7 (1.5 m). By integrating satellite-derived data with field-based validation techniques, this study seeks to improve classification accuracy and enhance our understanding of land use dynamics in these regions.
The study employs Object-Based Image Analysis (OBIA) and a Support Vector Machine (SVM) classifier within the Orfeo Toolbox (OTB) in QGIS. This approach leverages spectral, textural, and spatial attributes to enhance classification accuracy while minimizing misclassification errors commonly associated with pixel-based methods. The classification results are further validated using ground truth data collected through field surveys and supplementary sources such as Google Earth and the RIICE project’s rice area maps.
The findings provide insights into the spatial distribution of key land cover types, including rice fields, fallow croplands, built-up areas, and tree cover. Additionally, the study highlights challenges in differentiating specific land use classes due to spectral similarities and seasonal variations. The results contribute to improved land use planning and decision-making for agricultural development in Cambodia." |
| format | Informe técnico |
| id | CGSpace173243 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Rice Research Institute |
| publisherStr | International Rice Research Institute |
| record_format | dspace |
| spelling | CGSpace1732432025-11-06T17:25:27Z Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery Villano, Lorena Garcia, Cornelia Rala, Arnel Raviz, Jeny Laborte, Alice land use food systems deltas agricultural productivity sustainability "Accurate land use classification plays a critical role in agricultural monitoring, resource management, and policy planning. Remote sensing, particularly the use of high-resolution multispectral imagery, has emerged as a powerful tool for mapping and assessing agricultural production systems with enhanced precision. In Cambodia, where rice farming dominates the landscape, understanding spatial variations in land use and cropping patterns is essential for improving agricultural productivity and sustainability. This study aims to classify land use and assess agricultural production systems in selected sites in Takeo and Prey Veng provinces, Cambodia, using high-resolution satellite imagery from Pleiades (0.5 m) and SPOT 7 (1.5 m). By integrating satellite-derived data with field-based validation techniques, this study seeks to improve classification accuracy and enhance our understanding of land use dynamics in these regions. The study employs Object-Based Image Analysis (OBIA) and a Support Vector Machine (SVM) classifier within the Orfeo Toolbox (OTB) in QGIS. This approach leverages spectral, textural, and spatial attributes to enhance classification accuracy while minimizing misclassification errors commonly associated with pixel-based methods. The classification results are further validated using ground truth data collected through field surveys and supplementary sources such as Google Earth and the RIICE project’s rice area maps. The findings provide insights into the spatial distribution of key land cover types, including rice fields, fallow croplands, built-up areas, and tree cover. Additionally, the study highlights challenges in differentiating specific land use classes due to spectral similarities and seasonal variations. The results contribute to improved land use planning and decision-making for agricultural development in Cambodia." 2024-12-20 2025-02-20T07:18:26Z 2025-02-20T07:18:26Z Report https://hdl.handle.net/10568/173243 en Open Access application/pdf International Rice Research Institute Villano, L., Garcia, C., Rala, A., Raviz, J., Laborte, A. (2024). Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery. Technical report, CGIAR Initiative on Asian Mega-Deltas: International Rice Research Institute. 19 p. |
| spellingShingle | land use food systems deltas agricultural productivity sustainability Villano, Lorena Garcia, Cornelia Rala, Arnel Raviz, Jeny Laborte, Alice Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery |
| title | Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery |
| title_full | Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery |
| title_fullStr | Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery |
| title_full_unstemmed | Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery |
| title_short | Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery |
| title_sort | land use mapping of selected sites in the cambodia mekong mega delta using high resolution satellite imagery |
| topic | land use food systems deltas agricultural productivity sustainability |
| url | https://hdl.handle.net/10568/173243 |
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