IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2019i1p326-d303740.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/1/326/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/1/326/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. S Openshaw, 1998. "Neural Network, Genetic, and Fuzzy Logic Models of Spatial Interaction," Environment and Planning A, , vol. 30(10), pages 1857-1872, October.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. M Batty & Y Xie, 1994. "From Cells to Cities," Environment and Planning B, , vol. 21(7), pages 31-48, December.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hemant Bherwani & Saima Anjum & Ankit Gupta & Anju Singh & Rakesh Kumar, 2021. "Establishing influence of morphological aspects on microclimatic conditions through GIS-assisted mathematical modeling and field observations," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 15857-15880, November.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han, Yu & Jia, Haifeng, 2017. "Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China," Ecological Modelling, Elsevier, vol. 353(C), pages 107-116.
    2. Changqing Sun & Yulong Bao & Battsengel Vandansambuu & Yuhai Bao, 2022. "Simulation and Prediction of Land Use/Cover Changes Based on CLUE-S and CA-Markov Models: A Case Study of a Typical Pastoral Area in Mongolia," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    3. Yusuyunjiang Mamitimin & Zibibula Simayi & Ayinuer Mamat & Bumairiyemu Maimaiti & Yunfei Ma, 2023. "FLUS Based Modeling of the Urban LULC in Arid and Semi-Arid Region of Northwest China: A Case Study of Urumqi City," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    4. Yanan Li & Linghua Duo & Ming Zhang & Zhenhua Wu & Yanjun Guan, 2021. "Assessment and Estimation of the Spatial and Temporal Evolution of Landscape Patterns and Their Impact on Habitat Quality in Nanchang, China," Land, MDPI, vol. 10(10), pages 1-19, October.
    5. Luoman Pu & Jiuchun Yang & Lingxue Yu & Changsheng Xiong & Fengqin Yan & Yubo Zhang & Shuwen Zhang, 2021. "Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050," IJERPH, MDPI, vol. 18(3), pages 1-21, January.
    6. Nij Tontisirin & Sutee Anantsuksomsri, 2021. "Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    7. Yangcheng Hu & Yi Liu & Changyan Li, 2022. "Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value in the Middle Reaches of Yangtze River Urban Agglomeration," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    8. Dan Yi & Xi Guo & Yi Han & Jie Guo & Minghao Ou & Xiaomin Zhao, 2022. "Coupling Ecological Security Pattern Establishment and Construction Land Expansion Simulation for Urban Growth Boundary Delineation: Framework and Application," Land, MDPI, vol. 11(3), pages 1-18, March.
    9. Nityaranjan Nath & Dhrubajyoti Sahariah & Gowhar Meraj & Jatan Debnath & Pankaj Kumar & Durlov Lahon & Kesar Chand & Majid Farooq & Pankaj Chandan & Suraj Kumar Singh & Shruti Kanga, 2023. "Land Use and Land Cover Change Monitoring and Prediction of a UNESCO World Heritage Site: Kaziranga Eco-Sensitive Zone Using Cellular Automata-Markov Model," Land, MDPI, vol. 12(1), pages 1-21, January.
    10. Cong Cao & Suzana Dragićević & Songnian Li, 2019. "Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    11. Sri Murniani Angelina Letsoin & David Herak & Fajar Rahmawan & Ratna Chrismiari Purwestri, 2020. "Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
    12. Cao, Jianjun & Wei, Chen & Adamowski, Jan F. & Zhou, Junju & Liu, Chunfang & Zhu, Guofeng & Dong, Xiaogang & Zhang, Xiaofang & Zhao, Huijun & Feng, Qi, 2020. "Could arid and semi-arid abandoned lands prove ecologically or economically valuable if they afford greater soil organic carbon storage than afforested lands in China’s Loess Plateau?," Land Use Policy, Elsevier, vol. 99(C).
    13. Giovanni A. Rabino & Lorenzo Papini & Silvana T. Lombardo & Antonio Colonna & Vittorio Di Stefano, 1998. "L.A.U.D.E: Learning Automata for Urban Development Exploration. The Case Study of Rome Urban System," ERSA conference papers ersa98p302, European Regional Science Association.
    14. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    15. Wafaa Majeed Mutashar Al-Hameedi & Jie Chen & Cheechouyang Faichia & Biswajit Nath & Bazel Al-Shaibah & Ali Al-Aizari, 2022. "Geospatial Analysis of Land Use/Cover Change and Land Surface Temperature for Landscape Risk Pattern Change Evaluation of Baghdad City, Iraq, Using CA–Markov and ANN Models," Sustainability, MDPI, vol. 14(14), pages 1-31, July.
    16. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    17. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    18. Elizabeth A Mack & Tony H Grubesic, 2010. "Sex Offenders and Residential Location: A Predictive–Analytical Framework," Environment and Planning A, , vol. 42(8), pages 1925-1942, August.
    19. Peng Tian & Luodan Cao & Jialin Li & Ruiliang Pu & Hongbo Gong & Changda Li, 2020. "Landscape Characteristics and Ecological Risk Assessment Based on Multi-Scenario Simulations: A Case Study of Yancheng Coastal Wetland, China," Sustainability, MDPI, vol. 13(1), pages 1-20, December.
    20. Javier Rubio-Herrero & Jesús Muñuzuri, 2023. "Sparse regression for data-driven deterrence functions in gravity models," Annals of Operations Research, Springer, vol. 323(1), pages 153-174, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:326-:d:303740. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.