Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground...Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This 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 scales.Methods 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 AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At 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 measurements.Important 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 ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The 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 models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.Therefore,ground-based hyperspectral measurements are useful for estimating AGB,which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.展开更多
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.
文摘Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This 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 scales.Methods 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 AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At 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 measurements.Important 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 ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The 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 models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.Therefore,ground-based hyperspectral measurements are useful for estimating AGB,which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.
