IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1396-d320362.html
   My bibliography  Save this article

Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Author

Listed:
  • Shufang Wang

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

  • Xiyun Jiao

    (College of Agricultural Engineering, Hohai University, Nanjing 210098, China
    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China)

  • Liping Wang

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

  • Aimin Gong

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

  • Honghui Sang

    (College of Agricultural Engineering, Hohai University, Nanjing 210098, China)

  • Mohamed Khaled Salahou

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China)

  • Liudong Zhang

    (College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China)

Abstract

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

Suggested Citation

  • Shufang Wang & Xiyun Jiao & Liping Wang & Aimin Gong & Honghui Sang & Mohamed Khaled Salahou & Liudong Zhang, 2020. "Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis," Sustainability, MDPI, vol. 12(4), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1396-:d:320362
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1396/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1396/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    2. Kritsana Kityuttachai & Nitin Kumar Tripathi & Taravudh Tipdecho & Rajendra Shrestha, 2013. "CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand," Sustainability, MDPI, vol. 5(4), pages 1-21, April.
    3. Guan, DongJie & Li, HaiFeng & Inohae, Takuro & Su, Weici & Nagaie, Tadashi & Hokao, Kazunori, 2011. "Modeling urban land use change by the integration of cellular automaton and Markov model," Ecological Modelling, Elsevier, vol. 222(20), pages 3761-3772.
    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. 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).
    2. Can Kara & Naciye Doratlı, 2021. "Predict and Simulate Sustainable Urban Growth by Using GIS and MCE Based CA. Case of Famagusta in Northern Cyprus," Sustainability, MDPI, vol. 13(8), pages 1-27, April.

    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. Michel Opelele Omeno & Ying Yu & Wenyi Fan & Tolerant Lubalega & Chen Chen & Claude Kachaka Sudi Kaiko, 2021. "Analysis of the Impact of Land-Use/Land-Cover Change on Land-Surface Temperature in the Villages within the Luki Biosphere Reserve," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    2. 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.
    3. Courage Kamusoko & Yukio Wada & Toru Furuya & Shunsuke Tomimura & Mitsuru Nasu & Khamma Homsysavath, 2013. "Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management," Land, MDPI, vol. 2(1), pages 1-19, January.
    4. 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.
    5. Eshetu Yirsaw & Wei Wu & Xiaoping Shi & Habtamu Temesgen & Belew Bekele, 2017. "Land Use/Land Cover Change Modeling and the Prediction of Subsequent Changes in Ecosystem Service Values in a Coastal Area of China, the Su-Xi-Chang Region," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    6. 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).
    7. 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.
    8. Shuqing Wang & Xinqi Zheng, 2023. "Dominant transition probability: combining CA-Markov model to simulate land use change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6829-6847, July.
    9. Thinh An Nguyen & Phuong Minh Thi Le & Tam Minh Pham & Huong Thi Thu Hoang & Minh Quang Nguyen & Hoa Quynh Ta & Hanh Thi My Phung & Ha Thi Thu Le & Luc Hens, 2019. "Toward a sustainable city of tomorrow: a hybrid Markov–Cellular Automata modeling for urban landscape evolution in the Hanoi city (Vietnam) during 1990–2030," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(1), pages 429-446, February.
    10. Cheechouyang Faichia & Zhijun Tong & Jiquan Zhang & Xingpeng Liu & Emmanuel Kazuva & Kashif Ullah & Bazel Al-Shaibah, 2020. "Using RS Data-Based CA–Markov Model for Dynamic Simulation of Historical and Future LUCC in Vientiane, Laos," Sustainability, MDPI, vol. 12(20), pages 1-20, October.
    11. Ruci Wang & Hao Hou & Yuji Murayama, 2018. "Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
    12. Auwalu Faisal Koko & Wu Yue & Ghali Abdullahi Abubakar & Roknisadeh Hamed & Akram Ahmed Noman Alabsi, 2020. "Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    13. Wang, Han & Tian, Fuan & Wu, Jianxian & Nie, Xin, 2023. "Is China forest landscape restoration (FLR) worth it? A cost-benefit analysis and non-equilibrium ecological view," World Development, Elsevier, vol. 161(C).
    14. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
    15. Katarzyna Kocur-Bera & Anna Lyjak, 2021. "Analysis of Changes in Agricultural Use of Land After Poland’s Accession to the EU," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 517-533.
    16. Meisam Jafari & Hamid Majedi & Seyed Masoud Monavari & Ali Asghar Alesheikh & Mirmasoud Kheirkhah Zarkesh, 2016. "Dynamic Simulation of Urban Expansion Based on Cellular Automata and Logistic Regression Model: Case Study of the Hyrcanian Region of Iran," Sustainability, MDPI, vol. 8(8), pages 1-18, August.
    17. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    18. Liu, Dongya & Zheng, Xinqi & Zhang, Chunxiao & Wang, Hongbin, 2017. "A new temporal–spatial dynamics method of simulating land-use change," Ecological Modelling, Elsevier, vol. 350(C), pages 1-10.
    19. Li, Sheng & Nadolnyak, Denis & Hartarska, Valentina, 2019. "Agricultural land conversion: Impacts of economic and natural risk factors in a coastal area," Land Use Policy, Elsevier, vol. 80(C), pages 380-390.
    20. 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.

    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:2020:i:4:p:1396-:d:320362. 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.