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Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas

Author

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  • Jinling Zhang

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
    State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China)

  • Ying Hou

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    University of Chinese Academy of Sciences, Beijing 101408, China)

  • Yifan Dong

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
    Yunnan Key Laboratory for International Rivers and Transboundary Eco-Security, Kunming 650091, China)

  • Cun Wang

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    University of Chinese Academy of Sciences, Beijing 101408, China)

  • Weiping Chen

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    University of Chinese Academy of Sciences, Beijing 101408, China)

Abstract

Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could reveal the land use change trend in the cities of the regions. Taking the urban areas of Wuhan, a typical rapid urbanizing region in China, as the study area, this study built a Markov chain–artificial neural network (ANN)–cellular automaton (CA) coupled model. The model used land use classification spatial data with a spatial resolution of 5 m in 2010 and 2020, obtained by remote sensing image interpretation, and data on natural and socio-economic driving forces for land use change simulation. Using the coupled model, the land use patterns of Wuhan urban areas in 2020 were simulated, which were validated in comparison with the actual land use data in 2020. Finally, the model was used to simulate the land uses in the study area in 2030. The model validation indicates that the land use change simulation has a high accuracy of 90.7% and a high kappa coefficient of 0.87. The simulated land uses of the urban areas of Wuhan show that artificial surfaces will continue to expand, with an area increase of approximately 7% from 2020 to 2030. Moreover, the area of urban green spaces will also increase by approximately 7%, while that of water bodies, grassland, cropland, and forests will decrease by 12.6%, 13.6%, 34.9%, and 1.3%, respectively, from 2020 to 2030. This study provides a method of simulating the land use changes in the cities of rapid urbanizing regions and helps to reveal the patterns and driving mechanisms of land use change in Wuhan urban areas.

Suggested Citation

  • Jinling Zhang & Ying Hou & Yifan Dong & Cun Wang & Weiping Chen, 2022. "Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas," IJERPH, MDPI, vol. 19(14), pages 1-19, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8785-:d:866602
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    References listed on IDEAS

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    1. Rahel Hamad & Heiko Balzter & Kamal Kolo, 2018. "Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    2. Brian Pickard & Joshua Gray & Ross Meentemeyer, 2017. "Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models," Land, MDPI, vol. 6(3), pages 1-21, August.
    3. Mansour, Shawky & Al-Belushi, Mohammed & Al-Awadhi, Talal, 2020. "Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques," Land Use Policy, Elsevier, vol. 91(C).
    4. Xueru Zhang & Jie Zhou & Wei Song, 2020. "Simulating Urban Sprawl in China Based on the Artificial Neural Network-Cellular Automata-Markov Model," Sustainability, MDPI, vol. 12(11), pages 1-13, May.
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    Cited by:

    1. Auwalu Faisal Koko & Zexu Han & Yue Wu & Siyuan Zhang & Nan Ding & Jiayang Luo, 2023. "Spatiotemporal Analysis and Prediction of Urban Land Use/Land Cover Changes Using a Cellular Automata and Novel Patch-Generating Land Use Simulation Model: A Study of Zhejiang Province, China," Land, MDPI, vol. 12(8), pages 1-21, August.
    2. Jing Liu & Chunchun Hu & Xionghua Kang & Fei Chen, 2023. "A Loosely Coupled Model for Simulating and Predicting Land Use Changes," Land, MDPI, vol. 12(1), pages 1-19, January.

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