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The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

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  • Jie Liu

    (College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
    Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China)

  • Lang Zhang

    (Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China)

  • Qingping Zhang

    (College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China)

Abstract

The development and evolution of an urban green space system is affected by both natural effects and human intervention. The simulation and prediction of an urban green space system can enhance the foresight of urban planning. In this study, several land use change scenarios of the main urban area of Xuchang City were simulated from 2014 to 2030 based on high-resolution land use data. The layout of each scenario was evaluated using landscape indexes. A Cellular Automata–based method (i.e., future land use simulation, FLUS) was applied to develop the urban green space system, which we combined with urban land use evolution. Using recent data, the FLUS model effectively dealt with the uncertainty and complexity of various land use types under natural and human effects and solved the dependence and error transmission of multiperiod data in the traditional land use simulation process. The root mean square error (RMSE) of probability of the suitability occurrence module and the Kappa coefficient of the overall model simulation accuracy verification index both met accuracy requirements. It was feasible to combine the evolution of the urban green space system with urban land development. Moreover, under the Baseline Scenario, the urban land use layout was relatively scattered, and the urban green space system showed a disordered development trend. The Master Plan Scenario had a compact urban land use layout, and the green space system was characterized by networking and systematization, but it did not consider the service capacity of the green space. The Planning Guidance Scenario introduced constraint conditions (i.e., a spatial development strategy, green space accessibility, and ecological sensitivity), which provided a more intensive and efficient urban space and improved the service function of the green space system layout. Managers and planners can evaluate the urban future land use development mode under different constraints. Moreover, they would be able to adjust the urban planning in the implementation process. This work has transformed the technical nature of the planning work from “static results” to a “dynamic process”.

Suggested Citation

  • Jie Liu & Lang Zhang & Qingping Zhang, 2019. "The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
  • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:326-:d:303740
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    1. Wickramasuriya, Rohan Chandralal & Bregt, Arnold K. & van Delden, Hedwig & Hagen-Zanker, Alex, 2009. "The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model," Ecological Modelling, Elsevier, vol. 220(18), pages 2302-2309.
    2. Tian, Guangjin & Ouyang, Yun & Quan, Quan & Wu, Jianguo, 2011. "Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA," Ecological Modelling, Elsevier, vol. 222(5), pages 1129-1138.
    3. Guo, Xudong & Chang, Qing & Liu, Xiao & Bao, Huimin & Zhang, Yuepeng & Tu, Xueying & Zhu, Chunxia & Lv, Chunyan & Zhang, Yanyu, 2018. "Multi-dimensional eco-land classification and management for implementing the ecological redline policy in China," Land Use Policy, Elsevier, vol. 74(C), pages 15-31.
    4. Wei-Ning Xiang & Keith C Clarke, 2003. "The Use of Scenarios in Land-Use Planning," Environment and Planning B, , vol. 30(6), pages 885-909, December.
    5. 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.
    6. Patrícia Abrantes & Jorge Rocha & Eduarda Marques da Costa & Eduardo Gomes & Paulo Morgado & Nuno Costa, 2019. "Modelling urban form: A multidimensional typology of urban occupation for spatial analysis," Environment and Planning B, , vol. 46(1), pages 47-65, January.
    7. R White & G Engelen & I Uljee, 1997. "The Use of Constrained Cellular Automata for High-Resolution Modelling of Urban Land-Use Dynamics," Environment and Planning B, , vol. 24(3), pages 323-343, June.
    8. Tiba, C. & Candeias, A.L.B. & Fraidenraich, N. & Barbosa, E.M. de S. & de Carvalho Neto, P.B. & de Melo Filho, J.B., 2010. "A GIS-based decision support tool for renewable energy management and planning in semi-arid rural environments of northeast of Brazil," Renewable Energy, Elsevier, vol. 35(12), pages 2921-2932.
    9. Huang, Daquan & Huang, Jing & Liu, Tao, 2019. "Delimiting urban growth boundaries using the CLUE-S model with village administrative boundaries," Land Use Policy, Elsevier, vol. 82(C), pages 422-435.
    10. M Batty & Y Xie, 1994. "From Cells to Cities," Environment and Planning B, , vol. 21(7), pages 31-48, December.
    11. S Openshaw, 1998. "Neural Network, Genetic, and Fuzzy Logic Models of Spatial Interaction," Environment and Planning A, , vol. 30(10), pages 1857-1872, October.
    12. Yang, Xin & Zheng, Xin-Qi & Chen, Rui, 2014. "A land use change model: Integrating landscape pattern indexes and Markov-CA," Ecological Modelling, Elsevier, vol. 283(C), pages 1-7.
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    2. Sai Hu & Longqian Chen & Long Li & Ting Zhang & Lina Yuan & Liang Cheng & Jia Wang & Mingxin Wen, 2020. "Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China," IJERPH, MDPI, vol. 17(12), pages 1-21, June.
    3. Liu, Jie & Zhang, Lang & Zhang, Qingping & Li, Chao & Zhang, Guilian & Wang, Yuncai, 2022. "Spatiotemporal evolution differences of urban green space: A comparative case study of Shanghai and Xuchang in China," Land Use Policy, Elsevier, vol. 112(C).

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