IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i5p3882-3902d49132.html
   My bibliography  Save this article

Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China

Author

Listed:
  • Yuanyuan Yang

    (College of Earth Science, Jilin University, 2199 Jianshe Street, Changchun 130061, China
    Northeast Institute of Geography and Agroecology, Chinese Academy Sciences, 4888 Shengbei Street, Changchun 130102, China
    Center for International Earth Science Information Network (CIESIN), Earth Institute, Columbia University, P.O. Box 1000 (61 Route 9W), Palisades, NY 10964, USA)

  • Shuwen Zhang

    (Northeast Institute of Geography and Agroecology, Chinese Academy Sciences, 4888 Shengbei Street, Changchun 130102, China)

  • Jiuchun Yang

    (Northeast Institute of Geography and Agroecology, Chinese Academy Sciences, 4888 Shengbei Street, Changchun 130102, China)

  • Xiaoshi Xing

    (Center for International Earth Science Information Network (CIESIN), Earth Institute, Columbia University, P.O. Box 1000 (61 Route 9W), Palisades, NY 10964, USA)

  • Dongyan Wang

    (College of Earth Science, Jilin University, 2199 Jianshe Street, Changchun 130061, China)

Abstract

Decadal to centennial land use and land cover change has been consistently singled out as a key element and an important driver of global environmental change, playing an essential role in balancing energy use. Understanding long-term human-environment interactions requires historical reconstruction of past land use and land cover changes. Most of the existing historical reconstructions have insufficient spatial and thematic detail and do not consider various land change types. In this context, this paper explored the possibility of using a cellular automata-Markov model in 90 m × 90 m spatial resolution to reconstruct historical land use in the 1930s in Zhenlai County, China. Then the three-map comparison methodology was employed to assess the predictive accuracy of the transition modeling. The model could produce backward projections by analyzing land use changes in recent decades, assuming that the present land use pattern is dynamically dependent on the historical one. The reconstruction results indicated that in the 1930s most of the study area was occupied by grasslands, followed by wetlands and arable land, while other land categories occupied relatively small areas. Analysis of the three-map comparison illustrated that the major differences among the three maps have less to do with the simulation model and more to do with the inconsistencies among the land categories during the study period. Different information provided by topographic maps and remote sensing images must be recognized.

Suggested Citation

  • Yuanyuan Yang & Shuwen Zhang & Jiuchun Yang & Xiaoshi Xing & Dongyan Wang, 2015. "Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China," Energies, MDPI, vol. 8(5), pages 1-21, May.
  • Handle: RePEc:gam:jeners:v:8:y:2015:i:5:p:3882-3902:d:49132
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/5/3882/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/8/5/3882/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Yuanyuan Yang & Shuwen Zhang & Dongyan Wang & Jiuchun Yang & Xiaoshi Xing, 2014. "Spatiotemporal Changes of Farming-Pastoral Ecotone in Northern China, 1954–2005: A Case Study in Zhenlai County, Jilin Province," Sustainability, MDPI, vol. 7(1), pages 1-22, December.
    3. Gary Jefferson & Bai Huamao & Guan Xiaojing & Yu Xiaoyun, 2006. "R&D Performance in Chinese industry," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 345-366.
    4. Xiangzheng Deng & Chunhong Zhao & Yingzhi Lin & Tao Zhang & Yi Qu & Fan Zhang & Zhan Wang & Feng Wu, 2014. "Downscaling the Impacts of Large-Scale LUCC on Surface Temperature along with IPCC RCPs: A Global Perspective," Energies, MDPI, vol. 7(4), pages 1-20, 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. Sourafel Girma & Yundan Gong & Holger Görg, 2016. "Foreign Direct Investment, Access to Finance, and Innovation Activity in Chinese Enterprises," World Scientific Book Chapters, in: MULTINATIONAL ENTERPRISES AND HOST COUNTRY DEVELOPMENT Volume 53: World Scientific Studies in International Economics, chapter 5, pages 79-94, World Scientific Publishing Co. Pte. Ltd..
    2. 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).
    3. 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.
    4. Dragan Tevdovski & Katerina Tosevska-Trpcevska & Elena Makrevska Disoska, 2017. "What is the role of innovation in productivity growth in Central and Eastern European countries?," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 25(3), pages 527-551, July.
    5. Woo Sung Kim & Kunsu Park & Sang Hoon Lee & Hongyoung Kim, 2018. "R&D Investments and Firm Value: Evidence from China," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    6. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    7. Huy-Cuong Vo-Thai & Trinh-Hoang Hong-Hue & My-Linh Tran, 2021. "Technological- and Non-Technological Innovation During the Growth Phase of Industry Life Cycle: An Evidence From Vietnamese Manufacturing Enterprises," SAGE Open, , vol. 11(3), pages 21582440211, July.
    8. Efstathios G. Parcharidis & Nikos C. Varsakelis, 2010. "R&D and Tobin's q in an emerging financial market: the case of the Athens Stock Exchange," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 31(5), pages 353-361.
    9. Aritta Suwarno & Meine van Noordwijk & Hans-Peter Weikard & Desi Suyamto, 2018. "Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(2), pages 211-229, February.
    10. Cassiman, Bruno & Golovko, Elena, 2007. "Innovation and the export-productivity link," IESE Research Papers D/688, IESE Business School.
    11. Hall, B.H., 2011. "Innovation and productivity," MERIT Working Papers 2011-028, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    12. Bonoua Faye & Guoming Du & Edmée Mbaye & Chang’an Liang & Tidiane Sané & Ruhao Xue, 2023. "Assessing the Spatial Agricultural Land Use Transition in Thiès Region, Senegal, and Its Potential Driving Factors," Land, MDPI, vol. 12(4), pages 1-20, March.
    13. Suyun Chen & Yu Ji, 2022. "Do Corporate Social Responsibility Categories Distinctly Influence Innovation? A Resource-Based Theory Perspective," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
    14. Xiaowei Yao & Zhanqi Wang & Hongwei Zhang, 2016. "Dynamic Changes of the Ecological Footprint and Its Component Analysis Response to Land Use in Wuhan, China," Sustainability, MDPI, vol. 8(4), pages 1-14, April.
    15. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    16. 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.
    17. Marco Capasso & Tania Treibich & Bart Verspagen, 2015. "The medium-term effect of R&D on firm growth," Small Business Economics, Springer, vol. 45(1), pages 39-62, June.
    18. 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.
    19. Gary H. Jefferson & Zhong Kaifeng, 2002. "An Investigation of Firm-Level R&D Capabilities in East Asia," William Davidson Institute Working Papers Series 583, William Davidson Institute at the University of Michigan.
    20. Bronwyn Hall & Francesca Lotti & Jacques Mairesse, 2009. "Innovation and productivity in SMEs: empirical evidence for Italy," Small Business Economics, Springer, vol. 33(1), pages 13-33, June.

    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:jeners:v:8:y:2015:i:5:p:3882-3902:d:49132. 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.