Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model
ABSTRACTLand use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in t...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2231137 |
| _version_ | 1797785728535494656 |
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| author | Mame Henriette Astou Sambou Jean Albergel Expédit Wilfrid Vissin Stefan Liersch Hagen Koch Zoltan Szantoi Wassim Baba Moussé Landing Sane Ibrahima Toure |
| author_facet | Mame Henriette Astou Sambou Jean Albergel Expédit Wilfrid Vissin Stefan Liersch Hagen Koch Zoltan Szantoi Wassim Baba Moussé Landing Sane Ibrahima Toure |
| author_sort | Mame Henriette Astou Sambou |
| collection | DOAJ |
| description | ABSTRACTLand use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in two significant watersheds of the Senegal River basin (SRB) in West Africa: Bafing and Faleme. This study used Landsat images from 1986, 2006 and 2020 and the Random Forest classification method to analyze past LULCCs in these two watersheds. The results revealed: In Bafing, vegetation, settlement, agricultural areas and water increased, while the bareground decreased significantly between 1986-2020. In Faleme, two periods have different trends. Between 1986-2006, vegetation, settlement, agricultural areas and water increased, while bareground decreased. Between 2006-2020, settlement increased, while vegetation, agricultural areas, water and bareground decreased. To predict LULCCs in 2050 under business-as-usual assumptions, the Multilayer Perceptron and Marcov Chain model (MLP-MC) was used. The MLP-MC shows better results on Bafing than on Faleme but without questioning its application on the two watersheds. Bafing has seen a trend towards ”more people, more trees”, while Faleme has seen a trend towards ”more people, more deforestation”. These results contribute to develop appropriate land management policies and strategies to achieve or to maintain sustainable development in the SRB. |
| first_indexed | 2024-03-13T00:59:01Z |
| format | Article |
| id | doaj.art-6963fdffa4424fb5b52e381887a43e89 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-03-13T00:59:01Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-6963fdffa4424fb5b52e381887a43e892023-07-06T13:31:43ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542023-12-0156110.1080/22797254.2023.2231137Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain modelMame Henriette Astou Sambou0Jean Albergel1Expédit Wilfrid Vissin2Stefan Liersch3Hagen Koch4Zoltan Szantoi5Wassim Baba6Moussé Landing Sane7Ibrahima Toure8Climate Change and Water Resources, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Université d’Abomey-Calavi, Cotonou, BeninMontpellier SupAgro, IRD, University of Montpellier, Montpellier, FranceLaboratoire Pierre PAGNEY, Climat, Eau, Ecosystème Et Développement (LACEEDE), Université d’Abomey-Calavi (République du Bénin), Abomey Calavi, BeninClimate Resilience, Hydroclimatic Risks, Potsdam Institute for Climate Impact Research (PIK), Postdam, GermanyClimate Resilience, Hydroclimatic Risks, Potsdam Institute for Climate Impact Research (PIK), Postdam, GermanyLaboratoire Pierre PAGNEY, Climat, Eau, Ecosystème et Développement (LACEEDE), Stellenbosch University, Abomey Calavi, BeninScience, Applications & Climate Department, European Space Agency, Frascati, ItalyFaculty of Sciences and Technology, Department of Physics, Cheikh Anta Diop University, Dakar, SenegalFaculty of Sciences and Technology, Department of Physics, Cheikh Anta Diop University, Dakar, SenegalABSTRACTLand use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in two significant watersheds of the Senegal River basin (SRB) in West Africa: Bafing and Faleme. This study used Landsat images from 1986, 2006 and 2020 and the Random Forest classification method to analyze past LULCCs in these two watersheds. The results revealed: In Bafing, vegetation, settlement, agricultural areas and water increased, while the bareground decreased significantly between 1986-2020. In Faleme, two periods have different trends. Between 1986-2006, vegetation, settlement, agricultural areas and water increased, while bareground decreased. Between 2006-2020, settlement increased, while vegetation, agricultural areas, water and bareground decreased. To predict LULCCs in 2050 under business-as-usual assumptions, the Multilayer Perceptron and Marcov Chain model (MLP-MC) was used. The MLP-MC shows better results on Bafing than on Faleme but without questioning its application on the two watersheds. Bafing has seen a trend towards ”more people, more trees”, while Faleme has seen a trend towards ”more people, more deforestation”. These results contribute to develop appropriate land management policies and strategies to achieve or to maintain sustainable development in the SRB.https://www.tandfonline.com/doi/10.1080/22797254.2023.2231137Land use land cover changemulti-temporal analysisMLP-MCRandom forestSenegal river basin |
| spellingShingle | Mame Henriette Astou Sambou Jean Albergel Expédit Wilfrid Vissin Stefan Liersch Hagen Koch Zoltan Szantoi Wassim Baba Moussé Landing Sane Ibrahima Toure Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model European Journal of Remote Sensing Land use land cover change multi-temporal analysis MLP-MC Random forest Senegal river basin |
| title | Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model |
| title_full | Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model |
| title_fullStr | Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model |
| title_full_unstemmed | Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model |
| title_short | Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model |
| title_sort | prediction of land use and land cover change in two watersheds in the senegal river basin west africa using the multilayer perceptron and markov chain model |
| topic | Land use land cover change multi-temporal analysis MLP-MC Random forest Senegal river basin |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2231137 |
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