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Acta Prataculturae Sinica ›› 2013, Vol. 22 ›› Issue (1): 120-129.

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Estimation model of aboveground biomass in the Northern Tibet Plateau based on remote sensing date

ZHOU Yu-ting1,2, FU Gang1,2, SHEN Zhen-xi1, ZHANG Xian-zhou1, WU Jian-shuang1,2, LI Yun-long1,2, YANG Peng-wan1,2   

  1. 1.Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Science and Natural Resources Research, Beijing 100101, China;
    2.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2011-12-16 Online:2013-01-25 Published:2013-02-20

Abstract: Normalized difference vegetation indices (NDVI), enhanced vegetation indices (EVI) and land surface water indices (LSWI) were calculated based on MYD09A1 during 2010-2011 to estimate the aboveground biomass (ANPP) in the Northern Tibet Plateau using regression analysis. Air temperature, air moisture, soil temperature and soil moisture were compared in 2010 and 2011 when there was a great difference in aboveground biomass during the two years. Correlation analysis between aboveground biomass and vegetation indices showed that single factor regression model would be sufficient to reflect the relationship between aboveground biomass and vegetation indices. Regression analysis showed that both NDVI and EVI could be used to estimate aboveground biomass, however LSWI performed relatively poor. The simple linear regression model best reflected the relationship between vegetation indices and aboveground biomass while the power fitting was not quite as good as linear regression. The relationships between NDVI, EVI and aboveground biomass can be expressed by the linear regression equation: ANPP=115.478 NDVI (R2=0.956, P<0.001) and ANPP=174.225 EVI (R2=0.975, P<0.001). Results of verification showed that the power model had a better estimating capability than the linear model. When the power model was used to estimate ANPP, the relative error was 9.76% compared with 10.8% for the linear model. This difference demonstrated that the power model may be more robust in estimating the ANPP.

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