Estimating foliar biochemistry from hyperspectral data in mixed forest canopy

S. Huber, M. Kneubühler, A. Psomas, K.I. Itten, N.E. Zimmerman

Research output: Contribution to journalArticleAcademicpeer-review

53 Citations (Scopus)

Abstract

Estimating canopy biochemical composition in mixed forests at the level of tree species represents a critical tool for a better understanding and modeling of ecosystem functioning since many species exhibit differences in functional attributes or decomposition rates. We used airborne hyperspectral data to estimate the foliar concentration of nitrogen, carbon and water in three mixed forest canopies in Switzerland. With multiple linear regression models, continuum-removed and normalized HyMap spectra were related to foliar biochemistry on an individual tree level. The six spectral wavebands used in the regression models were selected using both an enumerative branch-and-bound (B&B) and a forward search algorithm. The models estimated foliar concentrations with adjusted R2 values between 0.47 and 0.63, based on the best-sampled study site. Regression models composed of wavebands selected by the B&B algorithm always performed better than those developed with forward search. When extrapolating nitrogen concentrations from one to another study site, regression models solely based on causal wavebands (known from literature) mostly outperformed models based on all wavebands. The study demonstrates the potential of both the use of causal wavebands and of the B&B algorithm.
Original languageEnglish
Pages (from-to)491-501
JournalForest Ecology and Management
Volume256
Issue number3
DOIs
Publication statusPublished - 2008

Keywords

  • infrared reflectance spectroscopy
  • imaging spectrometry data
  • band-depth analysis
  • absorption features
  • nitrogen concentration
  • ecosystem processes
  • continuum removal
  • pasture quality
  • national-park
  • aviris data

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