Exploring Google Earth Engine, Machine Learning, and GIS for Land Use Land Cover Change Detection in the Federal Capital Territory, Abuja, between 2014 and 2023
This study aims to visualize various land use land cover (LULC) classes, estimate the net change in LULC types between the years 2014 and 2023, and use transition mapping to track LULC transitions within waterbody, vegetation, bareland, and buildup to better understand how land use types change fro...
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| Principais autores: | , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
Environmental Research Institute, Chulalongkorn University
2024-06-01
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| Serier: | Applied Environmental Research |
| Fag: | |
| Online adgang: | https://ph01.tci-thaijo.org/index.php/aer/article/view/254819 |
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