SCIENTIA GEOGRAPHICA SINICA ›› 2012, Vol. 32 ›› Issue (12): 1488-1495.doi: 10.13249/j.cnki.sgs.2012.012.1488

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The Vegetation Classification Based on HJ-CCD Data and Decision Tree

Rui LIU1,2(), Min FENG2, Jiu-lin SUN2, Shun-bao LIAO2, Juan-le WANG2()   

  1. 1. College of Geographical Science, Chongqing Normal University, Chongqing 400047, China
    2. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2011-09-19 Revised:2012-04-05 Online:2012-12-20 Published:2012-12-20


Vegetation is critical for researches about global environmental change and regional sustainable development, and remote sensing is an important method for obtaining classification result. However, the Normalized Difference Vegetation Index (NDVI) time series method classification was limited by the coarse spatial resolution, and application of the medium high data such as Landsat TM was limited by the coverage and accessibility of remote sensing data. The Chinese environmental mitigation HJ satellite CCD sensors are capable of large area, all-time monitoring, and have a great advantage in coverage and frequency of repeated observations. A case study of Hulunbuir, Inner Mongolia was carried out in this paper. The NDVI time series curve of 7 vegetation types were extracted from both MODIS and HJ CCD data. Then, the curves and eigenvalue were analyzed. The result showed that between the 7 vegetation types, there was significant differences in the value range of early May NDVI, early August NDVI and the ratio of the two NDVI image. The vegetation classification rules were extracted based on these differences. The HJ-CCD was used as the main data sources in this paper. Three images including two NDVI and one ratio were extracted and the decision tree method was applied. Based on the result, 30 m spatial resolution vegetation classification result was carried out. By field verification, the result shows a 83.64% overall accuracy in the level one classification, and 70.91% in the level two classification. The cartographic accuracy of evergreen coniferous forest can achieve 100%, followed by cropland 82.61%, mixed forest 76.19% and desert steppe 75%. The accuracy of shrub is relatively low to 50%. This result proved a fast, simple and accurate method for vegetation classification, and provided the theory and data support for application of the Chinese HJ satellites.

Key words: vegetation classification, HJ-CCD data, NDVI time series curve, Hulunbuir

CLC Number: 

  • TP79