IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p15707-d984440.html
   My bibliography  Save this article

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

Author

Listed:
  • Changqing Sun

    (Department of Geography, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar 14200, Mongolia
    College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China)

  • Yulong Bao

    (College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
    Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Hohhot 010022, China)

  • Battsengel Vandansambuu

    (Department of Geography, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar 14200, Mongolia)

  • Yuhai Bao

    (College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
    Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Hohhot 010022, China)

Abstract

Modeling and predicting land use/cover change (LUCC) and identifying its drivers have been a focus of research over the past few decades. In order to solve the problem of land resource degradation in typical pastoral areas, reveal the temporal and spatial evolution characteristics of LUCC, and the contradiction between man and land in sustainable development, we analyze the Gurvanbulag area of Bulgan province, Mongolia, where grassland degradation is relatively serious. The LUCC data in 2000, 2010 and 2019 were obtained through interpreting human-computer interaction. On this basis, the same binary logistic regression (BLR) results were input into the multi-criteria evaluation analytic hierarchy process (MCE_AHP) of CLUE-S and CA_Markov models. The Current Trends (CT) and Ecological Protection (EP) development scenarios were used to predict the temporal and spatial evolution characteristics of LUCC in 2030 and 2040. The results show: (1) both models can effectively simulate the LUCC in 2019, and the CLUE-S model was significantly better than the CA_Markov model. (2) From 2000 to 2019, the LUCC in this region was dominated by a decrease in water and the growth of grassland and other land, indicating that the region is at the risk of land resource degradation. (3) In a multi-scenario development study, by 2030 and 2040, both models predicted that the EP development scenario is more effective in protecting the local ecological environment and it is easier to achieve the sustainability of land resources, than the CT development scenario. Combined with local policy demands and the prediction results of restraining land resource degradation, CLUE-S was significantly higher than the CA_Markov model, indicating that in typical pastoral areas, the former is more in line with the need for sustainable development of the local ecological environment than the latter.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15707-:d:984440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/15707/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/15707/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Siqi Liu & Qing Yu & Chen Wei, 2019. "Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective," Sustainability, MDPI, vol. 11(23), pages 1-20, November.
    3. Peter Verburg & Bas Eickhout & Hans Meijl, 2008. "A multi-scale, multi-model approach for analyzing the future dynamics of European land use," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 57-77, March.
    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. 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).
    6. 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.
    7. Ruci Wang & Ahmed Derdouri & Yuji Murayama, 2018. "Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area," Sustainability, MDPI, vol. 10(6), pages 1-18, June.
    8. Rui Zhou & Hao Zhang & Xin-Yue Ye & Xin-Jun Wang & Hai-Long Su, 2016. "The Delimitation of Urban Growth Boundaries Using the CLUE-S Land-Use Change Model: Study on Xinzhuang Town, Changshu City, China," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
    9. David Sneath, 2003. "Land use, the environment and development in post-socialist Mongolia," Oxford Development Studies, Taylor & Francis Journals, vol. 31(4), pages 441-459.
    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. 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.
    2. 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.
    3. Yongjiu Feng & Jiafeng Wang & Xiaohua Tong & Yang Liu & Zhenkun Lei & Chen Gao & Shurui Chen, 2018. "The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Yu Zhang & Pengcheng Wang & Tianwei Wang & Chongfa Cai & Zhaoxia Li & Mingjun Teng, 2018. "Scenarios Simulation of Spatio-Temporal Land Use Changes for Exploring Sustainable Management Strategies," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    9. Menglin Ou & Jingye Li & Xin Fan & Jian Gong, 2022. "Compound Optimization of Territorial Spatial Structure and Layout at the City Scale from “Production–Living–Ecological” Perspectives," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    10. 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.
    11. 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.
    12. 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.
    13. Xindong He & Xianmin Mai & Guoqiang Shen, 2019. "Delineation of Urban Growth Boundaries with SD and CLUE-s Models under Multi-Scenarios in Chengdu Metropolitan Area," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    14. Honglei Jiang & Xia Xu & Mengxi Guan & Lingfei Wang & Yongmei Huang & Yinghui Liu, 2019. "Simulation of Spatiotemporal Land Use Changes for Integrated Model of Socioeconomic and Ecological Processes in China," Sustainability, MDPI, vol. 11(13), pages 1-18, July.
    15. Nick Middleton, 2018. "Rangeland management and climate hazards in drylands: dust storms, desertification and the overgrazing debate," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 57-70, November.
    16. 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.
    17. 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.
    18. 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).
    19. Alvaro Calzadilla & Katrin Rehdanz & Richard Betts & Pete Falloon & Andy Wiltshire & Richard Tol, 2013. "Climate change impacts on global agriculture," Climatic Change, Springer, vol. 120(1), pages 357-374, September.
    20. 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.

    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:14:y:2022:i:23:p:15707-:d:984440. 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.