IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i1p189-d1027556.html
   My bibliography  Save this article

A Loosely Coupled Model for Simulating and Predicting Land Use Changes

Author

Listed:
  • Jing Liu

    (School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China)

  • Chunchun Hu

    (School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China)

  • Xionghua Kang

    (School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China)

  • Fei Chen

    (Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510120, China)

Abstract

The analysis and modeling of spatial and temporal changes in land use can reveal changing urban spatial patterns and trends. In this paper, we introduce a linear transformation optimization Markov (LTOM) model that can be exploited to estimate the state transition probability matrix of land use, building a loosely coupled ANN-CA-LTOM model for simulating and predicting land use changes. The advantages of this model are that it is flexible and high expansibility; it can maintain semantic coupling between the Artificial Neural Networks (ANN), Cellular Automata (CA), and LTOM model and enhance their functions; and it can break the limitation of requiring two periods of land use data when calculating the transition probability matrix. We also construct a suitability atlas of land use as the transition rules into the CA-LTOM model, taking into account the regional natural and socioeconomic driver factors, by exploiting the ANN model. The ANN-CA-LTOM model is employed to simulate the distribution of the three major types of land use, i.e., construction land, agricultural land, and unused land, in the Nansha District, China, in 2018 and 2020. The results show that the model performs well and the overall accuracy of the land use simulation was 97.72%, with a kappa coefficient of 0.962761. Furthermore, the simulated and predicted results of land use changes from 2021 to 2023 in Nansha District show changing trends in construction, agricultural, and unused land use. This study provides an approach for estimating a Markov transition probability matrix and a coupled mode of the models for simulating and predicting land use changes.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:1:p:189-:d:1027556
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/1/189/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/1/189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Pakawan Chotchaiwong & Saowanee Wijitkosum, 2019. "Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios," Land, MDPI, vol. 8(9), pages 1-16, September.
    3. 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.
    4. 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.
    5. Muhammad Fahad Baqa & Fang Chen & Linlin Lu & Salman Qureshi & Aqil Tariq & Siyuan Wang & Linhai Jing & Salma Hamza & Qingting Li, 2021. "Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan," Land, MDPI, vol. 10(7), pages 1-17, July.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. Wang, Quan & Wang, Haijun & Chang, Ruihan & Zeng, Haoran & Bai, Xuepiao, 2022. "Dynamic simulation patterns and spatiotemporal analysis of land-use/land-cover changes in the Wuhan metropolitan area, China," Ecological Modelling, Elsevier, vol. 464(C).
    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. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    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. Yuxiang Zhang & Dongjie Guan & Xiujuan He & Boling Yin, 2022. "Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    9. Qihao Wang & Dongya Liu & Feiyao Gao & Xinqi Zheng & Yiqun Shang, 2023. "A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model," Land, MDPI, vol. 12(2), pages 1-20, February.
    10. Shuangqing Sheng & Wei Song & Hua Lian & Lei Ning, 2022. "Review of Urban Land Management Based on Bibliometrics," Land, MDPI, vol. 11(11), pages 1-25, November.
    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. Qing Liu & Dongdong Yang & Lei Cao & Bruce Anderson, 2022. "Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China," Land, MDPI, vol. 11(2), pages 1-24, February.
    13. Ge Wang & Ziqi Zhou & Jianguo Xia & Dinghua Ou & Jianbo Fei & Shunya Gong & Yuxiao Xiang, 2023. "Optimal Allocation of Territorial Space in the Minjiang River Basin Based on a Double Optimization Simulation Model," Land, MDPI, vol. 12(11), pages 1-26, October.
    14. Zhang, Zuo & Li, Jiaming, 2022. "Spatial suitability and multi-scenarios for land use: Simulation and policy insights from the production-living-ecological perspective," Land Use Policy, Elsevier, vol. 119(C).
    15. Abdulaziz I. Almulhim & Simon Elias Bibri & Ayyoob Sharifi & Shakil Ahmad & Khalid Mohammed Almatar, 2022. "Emerging Trends and Knowledge Structures of Urbanization and Environmental Sustainability: A Regional Perspective," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    16. Yangyang Yuan & Yuchen Yang & Ruijun Wang & Yuning Cheng, 2022. "Predicting Rural Ecological Space Boundaries in the Urban Fringe Area Based on Bayesian Network: A Case Study in Nanjing, China," Land, MDPI, vol. 11(11), pages 1-24, October.
    17. Xinbei Huang & Chengming Ye & Hongyu Tao & Junjie Zou & Yuzhan Zhou & Shufan Zheng, 2024. "Integrating Future Multi-Scenarios to Evaluate the Effectiveness of Ecological Restoration: A Case Study of the Yellow River Basin," Land, MDPI, vol. 13(7), pages 1-19, July.
    18. 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.
    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. Qing Lu & Jing Ning & Hong You & Liyan Xu, 2023. "Urban Intensity in Theory and Practice: Empirical Determining Mechanism of Floor Area Ratio and Its Deviation from the Classic Location Theories in Beijing," Land, MDPI, vol. 12(2), pages 1-16, February.

    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:jlands:v:12:y:2023:i:1:p:189-:d:1027556. 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.