Journal of Resources and Ecology ›› 2017, Vol. 8 ›› Issue (1): 42-49.DOI: 10.5814/j.issn.1674-764x.2017.01.006

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Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

FU Gang1, SUN Wei1, LI Shaowei1, ZHANG Jing2, YU Chengqun1, SHEN Zhenxi1,*   

  1. 1. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. School of geography, Beijing Normal University, Beijing 100875, China
  • Received:2016-10-24 Online:2017-01-20 Published:2017-01-20
  • Contact: SHEN Zhenxi, E-mail: shenzx@igsnrr.ac.cn.
  • Supported by:
    National Natural Science Foundation of China (31600432), National Key Research Projects of China (2016YFC0502005; 2016YFC0502006), Chinese Academy of Science Western Light Talents Program (Response of livestock carrying capability to climatic change and grazing in the alpine meadow of Northern Tibetan Plateau), Science and Technology Plan Projects of Tibet Autonomous Region (Forage Grass Industry) and National Science and Technology Plan Project of China (2013BAC04B01, 2011BAC09B03, 2007BAC06B01).

Abstract: Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling. The monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mean air temperature (Ta), ≥5℃ accumulated air temperature (AccT), total precipitation (TP), and the ratio of TP to AccT (TP/AccT) were used to model aboveground biomass (AGB) in grasslands on the Tibetan Plateau. Three stepwise multiple regression methods, including stepwise multiple regression of AGB with NDVI and EVI, stepwise multiple regression of AGB with Ta, AccT, TP and TP/AccT, and stepwise multiple regression of AGB with NDVI, EVI, Ta, AccT, TP and TP/AccT were compared. The mean absolute error (MAE) and root mean squared error (RMSE) values between estimated AGB by the NDVI and measured AGB were 31.05 g m-2 and 44.12 g m-2, and 95.43 g m-2 and 131.58 g m-2 in the meadow and steppe, respectively. The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61g m-2 and 48.04 g m-2 in the steppe, respectively. The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m-2 and 42.71 g m-2, and 35.86 g m-2 and 47.94 g m-2, in the meadow and steppe, respectively. The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data. The accuracy of estimates varied depending on the type of grassland.

Key words: air temperature, alpine grassland, normalized difference vegetation index, precipitation, enhanced vegetation index