Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia
This study investigates the trends and frequencies of Land Use Land Cover (LULC) changes in the Guder watershed, located in the Upper Blue Nile Basin (Ethiopia), for the periods 1985 and 2021, with projections for 2039 and 2057. The research utilizes an integrated approach combining remote sensing (...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Elsevier
2025
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| Acceso en línea: | https://hdl.handle.net/10568/173939 |
| _version_ | 1855534299819802624 |
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| author | Demessie, Sintayehu Fetene Dile, Yihun T. Bedadi, Bobe Tarkegn, Temesgen Gashaw Bayabil, Haimanote Kebede Dejene, Sintayehu Workeneh |
| author_browse | Bayabil, Haimanote Kebede Bedadi, Bobe Dejene, Sintayehu Workeneh Demessie, Sintayehu Fetene Dile, Yihun T. Tarkegn, Temesgen Gashaw |
| author_facet | Demessie, Sintayehu Fetene Dile, Yihun T. Bedadi, Bobe Tarkegn, Temesgen Gashaw Bayabil, Haimanote Kebede Dejene, Sintayehu Workeneh |
| author_sort | Demessie, Sintayehu Fetene |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This study investigates the trends and frequencies of Land Use Land Cover (LULC) changes in the Guder watershed, located in the Upper Blue Nile Basin (Ethiopia), for the periods 1985 and 2021, with projections for 2039 and 2057. The research utilizes an integrated approach combining remote sensing (RS) and GIS for spatial analysis, Google Earth Engine (GEE) for cloud-based data processing, Random Forest (RF) machine learning for historical LULC classification, and an artificial neural network (ANN) model via QGIS's MOLUSCE tool for future LULC predictions. This innovative methodological approch allows for the examination of spatial and temporal LULC change patterns and future projections in the watershed. The results indicate that cultivated land increased from 54.8 % in 1985 to 72.9 % in 2021, and the built-up area experienced a significant increase of 227.5 % during this period. Percentage of land covered by forests fell from 35.9 % in 1985 to 9 % in 2021. By 2039 and 2057, shrubland, forest, and grassland are expected to decrease, while built-up and cultivated land will increase. Specifically, shrubland will decrease from 12.4 % in 2021 to 10.1 % in 2039 and 8.7 % in 2057, grassland from 4.8 % in 2021 to 1.9 % in 2039 and 1.1 % in 2057, and forest from 9.0 % in 2021 to 8.9 % in 2039 and 7.9 % in 2057. Meanwhile, the built-up area will rise significantly from 0.8 % in 2021 to 3.6 % in 2057. These shifts profoundly impact environmental management in the watershed. The motivation behind the present research was to provide a thorough understanding on LULC dynamics to improve land management practices using machine learning and ANN models for current and future environmental changes. Strategic interventions are crucial to mitigate adverse trends and promote sustainable land management based on current scenarios and future projections. |
| format | Journal Article |
| id | CGSpace173939 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1739392025-11-11T19:01:23Z Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia Demessie, Sintayehu Fetene Dile, Yihun T. Bedadi, Bobe Tarkegn, Temesgen Gashaw Bayabil, Haimanote Kebede Dejene, Sintayehu Workeneh remote sensing watershed management land-use change-land use change environmental charges This study investigates the trends and frequencies of Land Use Land Cover (LULC) changes in the Guder watershed, located in the Upper Blue Nile Basin (Ethiopia), for the periods 1985 and 2021, with projections for 2039 and 2057. The research utilizes an integrated approach combining remote sensing (RS) and GIS for spatial analysis, Google Earth Engine (GEE) for cloud-based data processing, Random Forest (RF) machine learning for historical LULC classification, and an artificial neural network (ANN) model via QGIS's MOLUSCE tool for future LULC predictions. This innovative methodological approch allows for the examination of spatial and temporal LULC change patterns and future projections in the watershed. The results indicate that cultivated land increased from 54.8 % in 1985 to 72.9 % in 2021, and the built-up area experienced a significant increase of 227.5 % during this period. Percentage of land covered by forests fell from 35.9 % in 1985 to 9 % in 2021. By 2039 and 2057, shrubland, forest, and grassland are expected to decrease, while built-up and cultivated land will increase. Specifically, shrubland will decrease from 12.4 % in 2021 to 10.1 % in 2039 and 8.7 % in 2057, grassland from 4.8 % in 2021 to 1.9 % in 2039 and 1.1 % in 2057, and forest from 9.0 % in 2021 to 8.9 % in 2039 and 7.9 % in 2057. Meanwhile, the built-up area will rise significantly from 0.8 % in 2021 to 3.6 % in 2057. These shifts profoundly impact environmental management in the watershed. The motivation behind the present research was to provide a thorough understanding on LULC dynamics to improve land management practices using machine learning and ANN models for current and future environmental changes. Strategic interventions are crucial to mitigate adverse trends and promote sustainable land management based on current scenarios and future projections. 2025-04 2025-03-30T14:19:39Z 2025-03-30T14:19:39Z Journal Article https://hdl.handle.net/10568/173939 en Open Access application/pdf Elsevier Demessie, S.F.; Dile, Y.T.; Bedadi, B.; Tarkegn, T.G.; Bayabil, H.K.; Dejene, S.W. (2024) Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia. Environmental Challenges, Online first paper (2024-12-25) ISSN: 2667-0100 |
| spellingShingle | remote sensing watershed management land-use change-land use change environmental charges Demessie, Sintayehu Fetene Dile, Yihun T. Bedadi, Bobe Tarkegn, Temesgen Gashaw Bayabil, Haimanote Kebede Dejene, Sintayehu Workeneh Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia |
| title | Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia |
| title_full | Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia |
| title_fullStr | Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia |
| title_full_unstemmed | Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia |
| title_short | Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia |
| title_sort | assessing and projecting land use land cover changes using machine learning and artificial neural network models in guder watershed ethiopia |
| topic | remote sensing watershed management land-use change-land use change environmental charges |
| url | https://hdl.handle.net/10568/173939 |
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