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Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing

Author

Listed:
  • Zimu Jia

    (State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Bingran Ma

    (State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Jing Zhang

    (State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
    Chinese Academy for Environmental Planning, Ministry of Environmental Protection, Beijing 100012, China)

  • Weihua Zeng

    (State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China)

Abstract

A change in the usage of land is influenced by a variety of driving factors and policies on spatial constraints. On the basis of considering the conventional natural and socio-economic indicators, the landscape pattern indicators were considered as new driving forces in the conversion of land use and its effects at small regional extent (CLUE-S) model to simulate spatial and temporal changes of land-use in Beijing. Compared with traditional spatial restrictions characterized by small and isolated areas, such as forest parks and natural reserves, the ecological redline areas increase the spatial integrity and connectivity of ecological and environmental functions at a regional scale, which were used to analyze the distribution patterns and behaviors of land use conversion in the CLUE-S model. The observed results indicate that each simulation scenario has a Kappa coefficient of more than 0.76 beyond the threshold value of 0.6 and represents high agreements between the actual and simulated land use maps. The simulation scenarios including landscape pattern indicators are more accurate than those without consideration of these new driving forces. The simulation results from using ecological redline areas as space constraints have the highest precision compared with the unrestricted and traditionally restricted scenarios. Therefore, the CLUE-S model based on the restriction of ecological redline and the consideration of landscape pattern factors has shown better effectiveness in simulating the future land use change. The conversion of land use types mainly occurred between construction land and cropland during the period from 2010 to 2020. Meanwhile, a large number of grasslands are being changed to construction lands in the mountain towns of northwest Beijing and large quantities of water bodies have disappeared and been replaced by construction lands due to rapid urbanization in the eastern and southern plains. To improve the sustainable use of land resources, it is necessary to adopt the construction and development mode of satellite towns rather than encouraging a disorderly expansion of downtown areas.

Suggested Citation

  • Zimu Jia & Bingran Ma & Jing Zhang & Weihua Zeng, 2018. "Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1299-:d:142640
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    Cited by:

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    5. Yanhua Zhao & De Su & Yang Bao & Wei Yang & Yibo Sun, 2022. "A CLUMondo Model-Based Multi-Scenario Land-Use Change Simulation in the Yangtze River Delta Urban Agglomeration, China," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    6. Benhui Zhu & Shizuka Hashimoto, 2021. "Is Expansion or Regulation more Critical for Existing Protected Areas? A Case Study on China’s Eco-Redline Policy in Chongqing Capital," Land, MDPI, vol. 10(10), pages 1-25, October.
    7. Yang Li & Chunyan Xue & Hua Shao & Ge Shi & Nan Jiang, 2018. "Study of the Spatiotemporal Variation Characteristics of Forest Landscape Patterns in Shanghai from 2004 to 2014 Based on Multisource Remote Sensing Data," Sustainability, MDPI, vol. 10(12), pages 1-33, November.
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    9. Deddy Romulo Siagian & Rajendra P. Shrestha & Sangam Shrestha & John K. M. Kuwornu, 2019. "Factors Driving Rice Land Change 1989–2018 in the Deli Serdang Regency, Indonesia," Agriculture, MDPI, vol. 9(9), pages 1-22, August.
    10. Deng, Huangwei & Zhou, Xuefei & Liao, Zhenliang, 2024. "Ecological redline delineation based on the supply and demand of ecosystem services," Land Use Policy, Elsevier, vol. 140(C).
    11. Chunsheng Wu, 2022. "Study on the Spatial Differences in Land-Use Change and Driving Factors in Tibet," Land, MDPI, vol. 11(9), pages 1-17, September.
    12. Zhenbo Wang, 2018. "Land Spatial Development Based on Carrying Capacity, Land Development Potential, and Efficiency of Urban Agglomerations in China," Sustainability, MDPI, vol. 10(12), pages 1-15, December.

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