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

doi:10.5814/j.issn.1674-764x.2017.01.006

Abstract

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

FU Gang, SUN Wei, LI Shaowei, ZHANG Jing, YU Chengqun, SHEN Zhenxi. Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau[J]. Journal of Resources and Ecology, 2017, 8(1): 42-49.

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