Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables ( n=40). Ratio-based and soil-line-related broadband VI were cal...

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Vydáno v:	Remote sensing of environment Ročník 95; číslo 2; s. 177 - 194
Hlavní autoři:	Schlerf, Martin, Atzberger, Clement, Hill, Joachim
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY Elsevier Inc 30.03.2005 Elsevier Science
Témata:	Animal, plant and microbial ecology Applied geophysics Biological and medical sciences Biophysical forest variables Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Hyperspectral Imaging spectrometry Internal geophysics LAI Multispectral Teledetection and vegetation maps Vegetation indices Imaging spectrometry Multispectral LAI Biophysical forest variables Hyperspectral Vegetation indices Vegetation index vegetation Cross validation Forecast model imagery Procedure soils Forest stand Space remote sensing Landsat Prediction Biophysics Spectrometer forests interpolation channels Regression model techniques Reflectance Leaf area index
ISSN:	0034-4257, 1879-0704
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Abstract	This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables ( n=40). Ratio-based and soil-line-related broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) ( R 2=0.67, RMSE=0.69 m 2 m −2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2=0.77, RMSE=0.54 m 2 m −2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) ( R 2=0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.
AbstractList	Results are reported from a systematic assessment of linear predictive models between vegetation indices (VIs) obtained from radiometrically corrected airborne imaging spectrometer (HyMap) data and field analyses of biophysical forest stand variables. The project determined ratio-based and soil-line related broadband VI after the HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI considered in the effort included all possible two-band combinations of ratio VI and perpendicular VI, and the red edge inflection point determined using two methods, namely the inverted Gaussian Model and Lagrange Interpolation. The predictive power of the regression models was determined through the use of a cross-validation procedure. This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables ( n=40). Ratio-based and soil-line-related broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) ( R 2=0.67, RMSE=0.69 m 2 m −2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2=0.77, RMSE=0.54 m 2 m −2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) ( R 2=0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved. This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-line-related broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R=0.67, RMSE=0.69 m2 m-2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R=0.77, RMSE=0.54 m2 m-2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R=0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.
Author	Schlerf, Martin Atzberger, Clement Hill, Joachim
Author_xml	– sequence: 1 givenname: Martin surname: Schlerf fullname: Schlerf, Martin email: schlerf@uni-trier.de organization: University of Trier, Remote Sensing Department, Behringstrasse, D-54286 Trier, Germany – sequence: 2 givenname: Clement surname: Atzberger fullname: Atzberger, Clement organization: Institut National de la Recherche Agronomique, Site Agroparc, F-84914 Avignon, France – sequence: 3 givenname: Joachim surname: Hill fullname: Hill, Joachim organization: University of Trier, Remote Sensing Department, Behringstrasse, D-54286 Trier, Germany
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Keywords	Imaging spectrometry Multispectral LAI Biophysical forest variables Hyperspectral Vegetation indices Vegetation index vegetation Cross validation Forecast model imagery Procedure soils Forest stand Space remote sensing Landsat Prediction Biophysics Spectrometer forests interpolation channels Regression model techniques Reflectance Leaf area index
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Snippet	This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging... Results are reported from a systematic assessment of linear predictive models between vegetation indices (VIs) obtained from radiometrically corrected airborne...
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SubjectTerms	Animal, plant and microbial ecology Applied geophysics Biological and medical sciences Biophysical forest variables Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Hyperspectral Imaging spectrometry Internal geophysics LAI Multispectral Teledetection and vegetation maps Vegetation indices
Title	Remote sensing of forest biophysical variables using HyMap imaging spectrometer data
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