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 veget...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.展开更多
Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by m...Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate forest.The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected contemporaneously.The RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function.The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.展开更多
基金supported 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).
文摘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.
文摘Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate forest.The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected contemporaneously.The RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function.The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.
