Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery

Abstract

Hyperspectral remote sensing provides great potential to monitor and study biodiversity of tropical forests through species identification and mapping. In this study, five species were selected to examine crown-level spectral variation within and between species using HYperspectral Digital Collection Experiment (HYDICE) data collected over La Selva, Costa Rica. Spectral angle was used to evaluate the spectral variation in reflectance, first derivative and wavelet-transformed spectral domains. Results indicated that intra-crown spectral variation does not always follow a normal distribution and can vary from crown to crown, therefore presenting challenges to statistically define the spectral variation within species using conventional classification approaches that assume normal distributions. Although derivative analysis has been used extensively in hyperspectral remote sensing of vegetation, our results suggest that it might not be optimal for species identification in tropical forestry using airborne hyperspectral data. The wavelet-transformed spectra, however, were useful for the identification of tree species. The wavelet coefficients at coarse spectral scales and the wavelet energy feature are more capable of reducing variation within crowns/species and capturing spectral differences between species. The implications of this examination of intra- and inter-specific variability at crown-level were: (1) the wavelet transform is a robust tool for the identification of tree species using hyperspectral data because it can provide a systematic view of the spectra at multiple scales; and (2) it may be impractical to identify every species using only hyperspectral data, given that spectral similarity may exist between species and that within-crown/species variability may be influenced by many factors.