Chinese Geographical Science ›› 2015, Vol. 25 ›› Issue (5): 629-643.doi: 10.1007/s11769-015-0753-2

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Reconstructing Spatial Distribution of Historical Cropland in China's Traditional Cultivated Region: Methods and Case Study

YANG Xuhong1, GUO Beibei1, JIN Xiaobin1, LONG Ying1,2, ZHOU Yinkang1   

  1. 1. College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;
    2. Beijing Institute of City Planning, Beijing 100045, China
  • Received:2014-01-17 Revised:2014-05-12 Online:2015-05-27 Published:2015-07-30
  • Contact: JIN
  • Supported by:

    Under the auspices of National Basic Research Program of China (No. 2011CB952001), National Natural Science Foundation of China (No. 41340016, 412013860)


As an important part of land use/cover change (LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ‘top-down’ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets (SAGE datasets) and History Database of the Global Environment datasets (HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton (CA) of ‘bottom-up’. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ‘SAGE datasets’ and ‘HYDE datasets’, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.

Key words: traditional cultivated region, historical cropland, reconstruction, constrained Cellular Automaton (CA), Shandong Province