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Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Author

Listed:
  • Zaheer Abbas

    (School of Geography, South China Normal University, Guangzhou 510631, China)

  • Guang Yang

    (School of Geography, South China Normal University, Guangzhou 510631, China)

  • Yuanjun Zhong

    (Lands and Resource Department of Guangdong Province, Surveying and Mapping Institute, Guangzhou 510500, China)

  • Yaolong Zhao

    (School of Geography, South China Normal University, Guangzhou 510631, China)

Abstract

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.

Suggested Citation

  • Zaheer Abbas & Guang Yang & Yuanjun Zhong & Yaolong Zhao, 2021. "Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China," Land, MDPI, vol. 10(6), pages 1-26, June.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:6:p:584-:d:567032
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    References listed on IDEAS

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    Cited by:

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    5. Bhatia, Samarth Y. & Gadiya, Kirtesh & Patil, Gopal R. & Krishna Mohan, Buddhiraju, 2024. "Thresholding-based cellular automata for transportation network derived future urban growth patterns in a peri-urban area," Transport Policy, Elsevier, vol. 148(C), pages 40-55.
    6. Jiangyun Li & Mingbao Chen, 2023. "Spatiotemporal Evolution and Influencing Factors of Social-Ecological System Vulnerability in the Guangdong–Hong Kong–Macau Greater Bay Area," Land, MDPI, vol. 12(4), pages 1-15, April.
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