Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

作     者：Miaogen Shen Yanhong Tang Julia Klein Pengcheng Zhang Song Gu Ayako Shimono Jin Chen 

作者机构：State Key Laboratory of Earth Surface Processes and Resource EcologyAcademy of Disaster Reduction and Emergency ManagementBeijing Normal UniversityBeijing 100875China National Institute for Environmental StudiesTsukubaIbaraki 305-8569Japan Colorado State UniversityFort CollinsCO 80523USA Terrestrial Ecology LabGraduate School of Life and Environment ScienceUniversity of TsukubaTennodai 1-1-1TsukubaIbaraki 305-8577Japan Northwest Plateau Institute of BiologyChinese Academy of ScienceXining 810001China 

出 版 物：《Journal of Plant Ecology》 (植物生态学报（英文版）)

年 卷 期：2008年第1卷第4期

页      面：247-257页

核心收录：

学科分类：0907[农学-林学] 08[工学] 0829[工学-林业工程] 09[农学] 

基　　金：The field investigation was partly supported by a program on long-term monitoring of alpine ecosystems on the Tibetan Plateau from the Ministry of Environment,Japan to T.Y Program for New Century Excellent Talents in University to C.J Director-encouragement fund from National Institute for Environmental Studies to S.A 

主　　题：biomass estimation dummy variable hyperspectral remote sensing Tibetan Plateau regression analysis vegetation index VIUPD 

摘      要：Aims There are numerous grassland ecosystem types on the Tibetan *** include the alpine meadow and steppe and degraded alpine meadow and *** study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger *** We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate ***,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem ***,we estimated AGB for the five pooled ecosystem types by developing models containing dummy *** last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral *** findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different *** AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation *** regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression ***,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with d