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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
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