GEOGRAPHICAL RESEARCH ›› 2009, Vol. 28 ›› Issue (6): 1713-1721.doi: 10.11821/yj2009060027

• Geo-information Science • Previous Articles     Next Articles

Study on prediction models of wetland types in Yancheng

ZHANG Huai-qing1, TANG Xiao-xu2, LIU Rui3, ZHOU Jin-xing4, LING Cheng-xing1   

  1. 1. Research Institute of Forestry Resource Information Techniques, Chinese Academy of Forestry (CAF), Beijing 100091, China;
    2. Beijing Institute of Surveying and Mapping, Beijing 100038, China;
    3. School of Geography, Beijing Normal University, Beijing 100875, China;
    4. Research Institute of Forestry, CAF, Beijing 100091, China
  • Received:2009-02-18 Revised:2009-06-15 Online:2009-11-25 Published:2009-11-25
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This paper takes three main wetland distributing counties of Dongtai, Dafeng and Sheyang of Yancheng as study areas. With the processing of four-period (every six years from 1988 to 2006) remote sensing (RS) images, a dynamic change analysis of the Yancheng wetland types was illuminated at first. Then, according to the results of the RS images interpretation, the change prediction of the wetland types was analyzed by using cellular automata (CA) model based on extension matter-element model and Markov model. The results are shown as follows: (1) It is a feasible method in wetland types prediction according to the comparability (70%) after the comparison between the results calculated by CA model based on extension matter-element model and the remote sensing classification. (2) The results of CA model based on extension matter-element model is greatly consistent with the results of the results of Markov model, that is, the aquaculture farm is the main wetland type covering areas of 750.06 km2 and 740.20 km2 in 2006, respectively. Generally, the area of cropland, residential land, aquaculture farm, Spartina patens and seepweed has an increasing tendency, while the area of mudflat, reed and brine pan tends to decrease sharply. Spartina patens will become the dominant species gradually due to its evolution trend. The most important reason for these changes is the current policy of large-scale coastal exploitation.

Key words: remote sensing monitoring, wetland types, prediction model, Yancheng