• DocumentCode
    2904384
  • Title

    Using Multi-spectral Remote Sensing Data to Extract and Analyze the Vegetation Information in Desert Areas

  • Author

    Zhao, Huai-Bao ; Liu, Tong ; Cui, Yao-Ping ; Lei, Jia-Qiang

  • Author_Institution
    Xinjiang Inst. of Ecology & Geogr., Chinese Acad. of Sci., Urumqi, China
  • Volume
    3
  • fYear
    2009
  • fDate
    4-5 July 2009
  • Firstpage
    697
  • Lastpage
    702
  • Abstract
    The coverage and spatial distribution of vegetation in desert areas are fundamental indexes to estimate the desertification severity, and acquiring vegetation information is very beneficial to carry out desertification monitoring and evaluation. Based on Landsat TM image of 2007, vegetation information of the western Gurbantunggut Desert was extracted with vegetation indexes and spectral mixture analysis. The results show that: the U-min method (one of the SMA methods) is the best one and this method was used for further analysis. Moreover, a significant linear relationship is found between vegetation coverage and vegetation fraction extracted from SMA, with a correlation coefficient of 0.858. This indicate that vegetation coverage in desert areas can be extracted through remote sensing images indirectly.
  • Keywords
    feature extraction; remote sensing; vegetation; Gurbantunggut Desert; Landsat TM image; SMA methods; U-min method; desert areas; desertification monitoring; desertification severity; remote sensing; spatial distribution; time 2007 year; vegetation information; Data mining; Environmental factors; Geography; Image analysis; Information analysis; Monitoring; Remote sensing; Satellites; Spectral analysis; Vegetation mapping; Gurbantunggut desert; NDVI; TM image; spectral mixture analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Environmental Science and Information Application Technology, 2009. ESIAT 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3682-8
  • Type

    conf

  • DOI
    10.1109/ESIAT.2009.522
  • Filename
    5199789