{"title":"埃塞俄比亚Omo - Gibe盆地Ajora - Woybo流域土地利用和土地覆盖的历史和未来动态及其驱动因素","authors":"M. B. Toma, Mulugeta Dadi Belete, M. Ulsido","doi":"10.1111/nrm.12353","DOIUrl":null,"url":null,"abstract":"Land use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. As a result, reversing the predicted conditions is critical to ensuring the watershed long‐term viability.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia\",\"authors\":\"M. B. Toma, Mulugeta Dadi Belete, M. Ulsido\",\"doi\":\"10.1111/nrm.12353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Land use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. 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Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia
Land use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. As a result, reversing the predicted conditions is critical to ensuring the watershed long‐term viability.
期刊介绍:
Natural Resource Modeling is an international journal devoted to mathematical modeling of natural resource systems. It reflects the conceptual and methodological core that is common to model building throughout disciplines including such fields as forestry, fisheries, economics and ecology. This core draws upon the analytical and methodological apparatus of mathematics, statistics, and scientific computing.