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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
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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.
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    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. 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.
    5. 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.
    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.
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