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Impact of multiangular information on empirical models to estimate canopy nitrogen concentration in mixed forest


Huber, S; Koetz, B; Psomas, A; Kneubühler, M; Schopfer, J; Itten, K I; Zimmermann, N (2010). Impact of multiangular information on empirical models to estimate canopy nitrogen concentration in mixed forest. Journal of Applied Remote Sensing, 4:043530.

Abstract

Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (CN) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing CN on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the CN estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy CN with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring.

Abstract

Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (CN) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing CN on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the CN estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy CN with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2010
Deposited On:09 Nov 2010 10:11
Last Modified:05 Apr 2016 14:14
Publisher:SPIE - International Society for Optical Engineering
ISSN:1931-3195
Additional Information:Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Publisher DOI:https://doi.org/10.1117/1.3435334
Official URL:http://link.aip.org/link/?JRS/4/043530/1

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