Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data

Authors

M.A. Schull, Boston UniversityFollow
Y. Knyazikhin, Boston University
L. Xu, Boston University
A. Samanta, Boston University
P.L. Carmona, Universitat JaumeI
L. Lepine, University of New Hampshire
J.P. Jenkins, University of New Hampshire
S. Ganguly, NASA Ames Research Center
R.B. Myneni, Boston University

Date of this Version

2011

Citation

Journal of Quantitative Spectroscopy & Radiative Transfer 112 (2011) 736–750; doi:10.1016/j.jqsrt.2010.06.004

Abstract

Many studies have been conducted to demonstrate the ability of hyperspectral data to discriminate plant dominant species. Most of them have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing or unknown land cover. In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy reflectance in terms of the leaf spectral scattering and two spectrally invariant and structurally varying variables—recollision and directional escape probabilities. The methodology is based on the idea of retrieving spectrally invariant parameters from hyperspectral data first, and then relating their values to structural characteristics of three-dimensional canopy structure.