IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i8p2878-d163584.html
   My bibliography  Save this article

Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models

Author

Listed:
  • Xiaoli Hu

    (Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 320 West Donggang Road, Lanzhou 730000, China)

  • Xin Li

    (Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
    CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100049, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Ling Lu

    (Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 320 West Donggang Road, Lanzhou 730000, China)

Abstract

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

Suggested Citation

  • Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2878-:d:163584
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/8/2878/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/8/2878/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. K C Clarke & S Hoppen & L Gaydos, 1997. "A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area," Environment and Planning B, , vol. 24(2), pages 247-261, April.
    2. F Wu & C J Webster, 1998. "Simulation of Land Development through the Integration of Cellular Automata and Multicriteria Evaluation," Environment and Planning B, , vol. 25(1), pages 103-126, February.
    3. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    4. Huiran Han & Chengfeng Yang & Jinping Song, 2015. "Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    5. F Wang, 1994. "The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment," Environment and Planning A, , vol. 26(2), pages 265-284, February.
    6. 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.
    7. Yassemi, S. & Dragićević, S. & Schmidt, M., 2008. "Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour," Ecological Modelling, Elsevier, vol. 210(1), pages 71-84.
    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. Fengqiang Wu & Caijian Mo & Xiaojun Dai, 2022. "Analysis of the Driving Force of Land Use Change Based on Geographic Detection and Simulation of Future Land Use Scenarios," Sustainability, MDPI, vol. 14(9), pages 1-17, 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. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    2. 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.
    3. 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.
    4. Youjung Kim & Galen Newman & Burak Güneralp, 2020. "A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models," Land, MDPI, vol. 9(8), pages 1-22, July.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. Sarah Hasan & Wenzhong Shi & Xiaolin Zhu & Sawaid Abbas & Hafiz Usman Ahmed Khan, 2020. "Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data," Sustainability, MDPI, vol. 12(11), pages 1-24, May.
    10. Brian Pickard & Joshua Gray & Ross Meentemeyer, 2017. "Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models," Land, MDPI, vol. 6(3), pages 1-21, August.
    11. Harik, G. & Alameddine, I. & Zurayk, R. & El-Fadel, M., 2023. "Uncertainty in forecasting land cover land use at a watershed scale: Towards enhanced sustainable land management," Ecological Modelling, Elsevier, vol. 486(C).
    12. Jaekyung Lee & Galen Newman & Yunmi Park, 2018. "A Comparison of Vacancy Dynamics between Growing and Shrinking Cities Using the Land Transformation Model," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    13. Cláudia M. Viana & Jorge Rocha, 2020. "Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method," Sustainability, MDPI, vol. 12(10), pages 1-28, May.
    14. 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.
    15. 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.
    16. Meryem Qacami & Abdellatif Khattabi & Said Lahssini & Nabil Rifai & Modeste Meliho, 2023. "Land-cover/land-use change dynamics modeling based on land change modeler," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(1), pages 237-258, February.
    17. Markos Mathewos & Semaria Moga Lencha & Misgena Tsegaye, 2022. "Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation," Land, MDPI, vol. 11(10), pages 1-28, September.
    18. Yishao Shi & Jie Wu & Shouzheng Shi, 2017. "Study of the Simulated Expansion Boundary of Construction Land in Shanghai Based on a SLEUTH Model," Sustainability, MDPI, vol. 9(6), pages 1-15, May.
    19. Xuesong Gao & Yu Liu & Lun Liu & Qiquan Li & Ouping Deng & Yali Wei & Jing Ling & Min Zeng, 2018. "Is Big Good or Bad?: Testing the Performance of Urban Growth Cellular Automata Simulation at Different Spatial Extents," Sustainability, MDPI, vol. 10(12), pages 1-10, December.
    20. Siddique Ullah & Adnan Ahmad Tahir & Tahir Ali Akbar & Quazi K. Hassan & Ashraf Dewan & Asim Jahangir Khan & Mudassir Khan, 2019. "Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region," Sustainability, MDPI, vol. 11(19), pages 1-16, 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:10:y:2018:i:8:p:2878-:d:163584. 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.