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Citation:

Study on Highland Wetlands Remote Sensing Classification Based on Decision Tree Algorithm

  • Received Date: 2010-11-11
  • Suojia-Qumahe Nature Reserve, which locates in the source region of Three Rivers (Yangtze River, Yellow River and Lancang River), was taken as the research field to discuss the proper method for remote sensing classification of highland wetlands. The TM images, DEM, NDWI (Normalized Difference Water Indices) and the brightness, greenness and humidity after the tasseled cap transformation were used as the indicators to establish the decision tree model to distinguish the different wetlands and other land cover types. The authors compared the results with the traditional maximum likelihood supervised classification, it showed that the decision tree method based on the indices can improve the overall accuracy by 12.05%, and the overall kappa coefficient by 0.140 7. For rivers, lakes, swamps and floodplains, the producer’s accuracy and user’s accuracy increased by 6.06%, 6.25%; 0.12%, 3.13%; 6.99%, 25.00% and 6.12%, 28.13% respectively. The results of this study suggest that the decision tree method based on indices is an effective tool for wetlands remote sensing classification in highland area.
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Study on Highland Wetlands Remote Sensing Classification Based on Decision Tree Algorithm

  • 1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Abstract: Suojia-Qumahe Nature Reserve, which locates in the source region of Three Rivers (Yangtze River, Yellow River and Lancang River), was taken as the research field to discuss the proper method for remote sensing classification of highland wetlands. The TM images, DEM, NDWI (Normalized Difference Water Indices) and the brightness, greenness and humidity after the tasseled cap transformation were used as the indicators to establish the decision tree model to distinguish the different wetlands and other land cover types. The authors compared the results with the traditional maximum likelihood supervised classification, it showed that the decision tree method based on the indices can improve the overall accuracy by 12.05%, and the overall kappa coefficient by 0.140 7. For rivers, lakes, swamps and floodplains, the producer’s accuracy and user’s accuracy increased by 6.06%, 6.25%; 0.12%, 3.13%; 6.99%, 25.00% and 6.12%, 28.13% respectively. The results of this study suggest that the decision tree method based on indices is an effective tool for wetlands remote sensing classification in highland area.

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