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Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia

Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective...

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Autores principales: Sisay, Getahun, Gesesse, Berehan, Fürst, Christine, Kassie, Meseret, Kebede, Belaynesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559858/
https://www.ncbi.nlm.nih.gov/pubmed/37809465
http://dx.doi.org/10.1016/j.heliyon.2023.e20088
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author Sisay, Getahun
Gesesse, Berehan
Fürst, Christine
Kassie, Meseret
Kebede, Belaynesh
author_facet Sisay, Getahun
Gesesse, Berehan
Fürst, Christine
Kassie, Meseret
Kebede, Belaynesh
author_sort Sisay, Getahun
collection PubMed
description Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective natural resource management. This study aimed to model and characterize the spatiotemporal trajectories of landscape change between the 1984 and 2060 periods. The satellite image spectral information was segmented into seven LULC classes using a hybrid approach of image spectral recognition. The supervised classification technique of Support Vector Machine (SVM) was used to classify the satellite images, whilst the Land Change Modeler (LCM) Module in TerrSet software was used to assess the historical trend and future simulation of LULC dynamics. To predict future landscape changes, transition potential maps were generated using a Multi-layer Perceptron (MLP) neural network algorithm. The findings of the study demonstrated that the Goang Watershed has experienced significant LULC change since 1984. During the 1984–2001, 2001–2022, and 1984–2022 periods, farmland showed a dramatic increasing trend with 7.5 km(2)/yr(−1), 110.3 km(2)/yr(−1), and 64.3 km(2)/yr(−1), respectively. A similar trend was also observed in built-up areas with 0.5 km(2)/yr(−1), 3.2 km(2)/yr(−1), and 2 km(2)/yr(−1). The expansion of farmland and built-up area was at the expense of forest, shrubland, and grasslands. With a business-as-usual scenario, the extent of farmland will continue to increase between 2022 and 2060 while rapid reduction is expected by forest, shrubland, and grasslands. The alarming rate of farmland and built-up area expansion will put significant pressure on biodiversity and ecosystem services in the area. As a result, eco-friendly conservation approaches should be implemented as soon as possible to maintain ecosystem health and encourage sustainable development.
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spelling pubmed-105598582023-10-08 Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia Sisay, Getahun Gesesse, Berehan Fürst, Christine Kassie, Meseret Kebede, Belaynesh Heliyon Research Article Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective natural resource management. This study aimed to model and characterize the spatiotemporal trajectories of landscape change between the 1984 and 2060 periods. The satellite image spectral information was segmented into seven LULC classes using a hybrid approach of image spectral recognition. The supervised classification technique of Support Vector Machine (SVM) was used to classify the satellite images, whilst the Land Change Modeler (LCM) Module in TerrSet software was used to assess the historical trend and future simulation of LULC dynamics. To predict future landscape changes, transition potential maps were generated using a Multi-layer Perceptron (MLP) neural network algorithm. The findings of the study demonstrated that the Goang Watershed has experienced significant LULC change since 1984. During the 1984–2001, 2001–2022, and 1984–2022 periods, farmland showed a dramatic increasing trend with 7.5 km(2)/yr(−1), 110.3 km(2)/yr(−1), and 64.3 km(2)/yr(−1), respectively. A similar trend was also observed in built-up areas with 0.5 km(2)/yr(−1), 3.2 km(2)/yr(−1), and 2 km(2)/yr(−1). The expansion of farmland and built-up area was at the expense of forest, shrubland, and grasslands. With a business-as-usual scenario, the extent of farmland will continue to increase between 2022 and 2060 while rapid reduction is expected by forest, shrubland, and grasslands. The alarming rate of farmland and built-up area expansion will put significant pressure on biodiversity and ecosystem services in the area. As a result, eco-friendly conservation approaches should be implemented as soon as possible to maintain ecosystem health and encourage sustainable development. Elsevier 2023-09-16 /pmc/articles/PMC10559858/ /pubmed/37809465 http://dx.doi.org/10.1016/j.heliyon.2023.e20088 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Sisay, Getahun
Gesesse, Berehan
Fürst, Christine
Kassie, Meseret
Kebede, Belaynesh
Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia
title Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia
title_full Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia
title_fullStr Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia
title_full_unstemmed Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia
title_short Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia
title_sort modeling of land use/land cover dynamics using artificial neural network and cellular automata markov chain algorithms in goang watershed, ethiopia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559858/
https://www.ncbi.nlm.nih.gov/pubmed/37809465
http://dx.doi.org/10.1016/j.heliyon.2023.e20088
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