TY - JOUR
T1 - Multi-method ensemble selection of spectral bands related to leaf biochemistry
AU - Feilhauer, Hannes
AU - Asner, Gregory P.
AU - Martin, Roberta E.
N1 - Funding Information:
HF's contribution to this study was financially supported by the German Research Foundation (DFG) through research grant FE 1331/2-1 . Funding for the Spectranomics project is provided by the John D. and Catherine T. MacArthur Foundation and the Carnegie Institution for Science . The authors thank the Accelerated Canopy Chemistry Program for making available the spectral data sets used in this study as well as Jennifer Dungan and two anonymous reviewers for their helpful comments on this manuscript.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - Multi-method ensembles are generally believed to return more reliable results than the application of one method alone. Here, we test if for the quantification of leaf traits an ensemble of regression models, consisting of Partial Least Squares (PLSR), Random Forest (RFR), and Support Vector Machine regression (SVMR) models, is able to improve the robustness of the spectral band selection process compared to the outcome of a single technique alone. The ensemble approach was tested using one artificial and five measured data sets of leaf level spectra and corresponding information on leaf chlorophyll, dry matter, and water content. PLSR models optimized for the goodness of fit, an established approach for band selection, were used to evaluate the performance of the ensemble. Although the fits of the models within the ensemble were poorer than the fits achieved with the reference approach, the ensemble was able to provide a band selection with higher consistency across all data sets. Due to the selection characteristics of the methods within the ensemble, the ensemble selection is moderately narrow and restrictive but in good agreement with known absorption features published in literature. We conclude that analyzing the range of agreement of different model types is an efficient way to select a robust set of spectral bands related to the foliar properties under investigation. This may help to deepen our understanding of the spectral response of biochemical and biophysical traits in foliage and canopies.
AB - Multi-method ensembles are generally believed to return more reliable results than the application of one method alone. Here, we test if for the quantification of leaf traits an ensemble of regression models, consisting of Partial Least Squares (PLSR), Random Forest (RFR), and Support Vector Machine regression (SVMR) models, is able to improve the robustness of the spectral band selection process compared to the outcome of a single technique alone. The ensemble approach was tested using one artificial and five measured data sets of leaf level spectra and corresponding information on leaf chlorophyll, dry matter, and water content. PLSR models optimized for the goodness of fit, an established approach for band selection, were used to evaluate the performance of the ensemble. Although the fits of the models within the ensemble were poorer than the fits achieved with the reference approach, the ensemble was able to provide a band selection with higher consistency across all data sets. Due to the selection characteristics of the methods within the ensemble, the ensemble selection is moderately narrow and restrictive but in good agreement with known absorption features published in literature. We conclude that analyzing the range of agreement of different model types is an efficient way to select a robust set of spectral bands related to the foliar properties under investigation. This may help to deepen our understanding of the spectral response of biochemical and biophysical traits in foliage and canopies.
KW - Hyperspectral
KW - Imaging spectroscopy
KW - Partial least squares regression
KW - Random forest regression
KW - Remote sensing
KW - Support vector machine regression
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U2 - 10.1016/j.rse.2015.03.033
DO - 10.1016/j.rse.2015.03.033
M3 - Article
AN - SCOPUS:84928672637
SN - 0034-4257
VL - 164
SP - 57
EP - 65
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -