Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China
Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/10/6/584 |
| _version_ | 1797531698482642944 |
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| author | Zaheer Abbas Guang Yang Yuanjun Zhong Yaolong Zhao |
| author_facet | Zaheer Abbas Guang Yang Yuanjun Zhong Yaolong Zhao |
| author_sort | Zaheer Abbas |
| collection | DOAJ |
| description | Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland. |
| first_indexed | 2024-03-10T10:48:25Z |
| format | Article |
| id | doaj.art-08d59aca18f84246a9bd1ab42c736f96 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T10:48:25Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-08d59aca18f84246a9bd1ab42c736f962023-11-21T22:25:19ZengMDPI AGLand2073-445X2021-06-0110658410.3390/land10060584Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, ChinaZaheer Abbas0Guang Yang1Yuanjun Zhong2Yaolong Zhao3School of Geography, South China Normal University, Guangzhou 510631, ChinaSchool of Geography, South China Normal University, Guangzhou 510631, ChinaLands and Resource Department of Guangdong Province, Surveying and Mapping Institute, Guangzhou 510500, ChinaSchool of Geography, South China Normal University, Guangzhou 510631, ChinaLand use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland.https://www.mdpi.com/2073-445X/10/6/584change analysistransition potential modelingCA-ANNpredictionintensity analysisGreater Bay Area |
| spellingShingle | Zaheer Abbas Guang Yang Yuanjun Zhong Yaolong Zhao Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China Land change analysis transition potential modeling CA-ANN prediction intensity analysis Greater Bay Area |
| title | Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China |
| title_full | Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China |
| title_fullStr | Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China |
| title_full_unstemmed | Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China |
| title_short | Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China |
| title_sort | spatiotemporal change analysis and future scenario of lulc using the ca ann approach a case study of the greater bay area china |
| topic | change analysis transition potential modeling CA-ANN prediction intensity analysis Greater Bay Area |
| url | https://www.mdpi.com/2073-445X/10/6/584 |
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