IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2015i1p9-d61068.html
   My bibliography  Save this article

An Efficient Graph-based Method for Long-term Land-use Change Statistics

Author

Listed:
  • Yipeng Zhang

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Yunbing Gao

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    College of Information and Electrical Engineering, China Agriculture University, Beijing 100083, China)

  • Bingbo Gao

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Yuchun Pan

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
    Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China)

  • Mingyang Yan

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China)

Abstract

Statistical analysis of land-use change plays an important role in sustainable land management and has received increasing attention from scholars and administrative departments. However, the statistical process involving spatial overlay analysis remains difficult and needs improvement to deal with mass land-use data. In this paper, we introduce a spatio-temporal flow network model to reveal the hidden relational information among spatio-temporal entities. Based on graph theory, the constant condition of saturated multi-commodity flow is derived. A new method based on a network partition technique of spatio-temporal flow network are proposed to optimize the transition statistical process. The effectiveness and efficiency of the proposed method is verified through experiments using land-use data in Hunan from 2009 to 2014. In the comparison among three different land-use change statistical methods, the proposed method exhibits remarkable superiority in efficiency.

Suggested Citation

  • Yipeng Zhang & Yunbing Gao & Bingbo Gao & Yuchun Pan & Mingyang Yan, 2015. "An Efficient Graph-based Method for Long-term Land-use Change Statistics," Sustainability, MDPI, vol. 8(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2015:i:1:p:9-:d:61068
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/1/9/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/1/9/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rutherford, Gillian N. & Bebi, Peter & Edwards, Peter J. & Zimmermann, Niklaus E., 2008. "Assessing land-use statistics to model land cover change in a mountainous landscape in the European Alps," Ecological Modelling, Elsevier, vol. 212(3), pages 460-471.
    2. 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.
    3. Richard D. McBride, 1998. "Advances in Solving the Multicommodity-Flow Problem," Interfaces, INFORMS, vol. 28(2), pages 32-41, April.
    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. Chunliu Gao & Deqiang Cheng & Javed Iqbal & Shunyu Yao, 2023. "Spatiotemporal Change Analysis and Prediction of the Great Yellow River Region (GYRR) Land Cover and the Relationship Analysis with Mountain Hazards," Land, MDPI, vol. 12(2), pages 1-24, January.
    2. Yunfeng Hu & Batu Nacun, 2018. "An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015," Sustainability, MDPI, vol. 10(11), pages 1-22, November.
    3. Bernard Fosu Frimpong & Frank Molkenthin, 2021. "Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana," Land, MDPI, vol. 10(1), pages 1-21, January.
    4. Andrew Allan & Ali Soltani & Mohammad Hamed Abdi & Melika Zarei, 2022. "Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review," Land, MDPI, vol. 11(8), pages 1-20, August.
    5. 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.
    6. Hashem Dadashpoor & Hossein Panahi, 2021. "Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13628-13649, September.
    7. 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.
    8. Jeffery L. Kennington & Karen R. Lewis, 2004. "Generalized Networks: The Theory of Preprocessing and an Empirical Analysis," INFORMS Journal on Computing, INFORMS, vol. 16(2), pages 162-173, May.
    9. Ying Li & Suiliang Huang, 2015. "Landscape Ecological Risk Responses to Land Use Change in the Luanhe River Basin, China," Sustainability, MDPI, vol. 7(12), pages 1-22, December.
    10. Vitus Tankpa & Li Wang & Alfred Awotwi & Leelamber Singh & Samit Thapa & Raphael Ane Atanga & Xiaomeng Guo, 2021. "Modeling the effects of historical and future land use/land cover change dynamics on the hydrological response of Ashi watershed, northeastern China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7883-7912, May.
    11. Liping Zhang & Shiwen Zhang & Yajie Huang & Meng Cao & Yuanfang Huang & Hongyan Zhang, 2016. "Exploring an Ecologically Sustainable Scheme for Landscape Restoration of Abandoned Mine Land: Scenario-Based Simulation Integrated Linear Programming and CLUE-S Model," IJERPH, MDPI, vol. 13(4), pages 1-20, March.
    12. Fondevilla, Cristian & Àngels Colomer, M. & Fillat, Federico & Tappeiner, Ulrike, 2016. "Using a new PDP modelling approach for land-use and land-cover change predictions: A case study in the Stubai Valley (Central Alps)," Ecological Modelling, Elsevier, vol. 322(C), pages 101-114.
    13. 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.
    14. Garg, Manish & Smith, J. Cole, 2008. "Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios," Omega, Elsevier, vol. 36(6), pages 1057-1071, December.
    15. 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.
    16. 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.
    17. Pingtao Yi & Weiwei Li & Lingyu Li, 2018. "Evaluation and Prediction of City Sustainability Using MCDM and Stochastic Simulation Methods," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    18. van Vliet, Jasper & Bregt, Arnold K. & Hagen-Zanker, Alex, 2011. "Revisiting Kappa to account for change in the accuracy assessment of land-use change models," Ecological Modelling, Elsevier, vol. 222(8), pages 1367-1375.
    19. 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.
    20. 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.

    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:8:y:2015:i:1:p:9-:d:61068. 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.