Predicting community traits along an alpine grassland transect using field imaging spectroscopy  

作　　者：Feng Zhang Wenjuan Wu Lang Li Xiaodi Liu Guangsheng Zhou Zhenzhu Xu 

机构地区：[1]State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,the Chinese Academy of Sciences,Beijing,100093 China [2]China National Botanical Garden,Beijing,100093 China [3]University of Chinese Academy of Sciences,Beijing,100049 China [4]Chinese Academy of Meteorological Sciences,Beijing,100081 China

出　　处：《Journal of Integrative Plant Biology》2023年第12期2604-2618,共15页植物学报（英文版）

基　　金：This work wassupported by the Second Tibetan Plateau ScientificExpedition and Research(STEP)program(2019QZKK0106);the Strategic Priority Research Program of Chinese Academy of Sciences(XDA26020103);Fengyun Application Pioneering Project(FY-APP-2021.0401).

摘　　要：Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change.Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems,although it remains to be tested in areas with dwarf and sparse vegetation,such as the Tibetan Plateau.We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect.We estimated community structural and functional traits,as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices(VIs)derived from hyperspectral images.We quantified the contributions of environmental drivers,VIs,and community traits to community function by structural equation modelling(SEM).Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index,enhanced vegetation index,and simple ratio.Structural equation modelling showed that VIs and community traits positively affected community function,whereas environmental drivers and specific leaf area had the opposite effect.Additionally,VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits.This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes.Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

关 键 词：aboveground net primary productivity canopy chlorophyll content canopy leaf nitrogen concentration fractional vegetation cover hyperspectral remote sensing Tibetan Plateau 

分 类 号：S812[农业科学—草业科学]