• DocumentCode
    2191885
  • Title

    Vegetation Biomass Estimation Based on CBERS Data: A Case Study of the Submerged Area of Angu Reservoir

  • Author

    Wang Hong-Zhi ; Liu Guiming ; Qiu Linqing

  • Author_Institution
    Sch. of City & Environ. Sci., Huazhong Normal Univ., Wuhan, China
  • fYear
    2010
  • fDate
    24-26 Aug. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The estimation and distribution of the vegetation biomass in submerged area of Angu Reservoir, Sichuan Province, based on CBERS data acquired in 2006 was researched. In order to select the optimum image characteristic data, correlative analysis were calculated among the biomass field sampling data, CBERS band 1- band 4 data and the NDVI data. The results show that field sampling data is best relative to band 4 data, and the correlation coefficients to aquatic vegetation and terrestrial vegetation were 0.703 and 0.678 respectively. Therefore, linear regression models were established between the field sampling data and band 4 data to estimate the total biomass of the Angu Reservoir area. The modeling results shows that the biomass of aquatic vegetation and terrestrial vegetation in the area were 1.15×108kg and 0.99×108kg respectively. And the biomass distribution map was drawn. The study shows that there are many advantages to apply CBERS image to biomass investigation over the traditional methods.
  • Keywords
    bioenergy conversion; regression analysis; reservoirs; vegetation mapping; Angu Reservoir; CBERS data; NDVI data; Sichuan Province; aquatic vegetation; biomass distribution map; correlative analysis; linear regression; optimum image characteristic data; submerged area; terrestrial vegetation; vegetation biomass estimation; Biological system modeling; Biomass; Estimation; Hydroelectric power generation; Pixel; Remote sensing; Vegetation mapping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management and Service Science (MASS), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5325-2
  • Electronic_ISBN
    978-1-4244-5326-9
  • Type

    conf

  • DOI
    10.1109/ICMSS.2010.5577963
  • Filename
    5577963