Spectroscopic classification of tropical forest species using radiative transfer modeling

Abstract

Leaf spectroscopy may be useful for tropical species discrimination, but few studies have provided an understanding of the spectral separability of species or how leaf spectroscopy scales to the canopy level relevant to mapping. Here we report on a study to classify humid tropical forest canopy species using field-measured leaf optical properties with leaf and canopy radiative transfer models. The experimental dataset included 188 canopy species collected in humid tropical forests of Hawaii. The leaf optical model PROSPECT-5 was used to simulate the leaf spectra of each species, which was used to train a classifier based on Linear Discriminant Analysis, and a canopy radiative transfer model 4SAIL2 to scale leaf measurements to the canopy level. The relationship linking classification accuracy at the leaf level to biodiversity showed an asymptotic trend reaching a maximum error of 47% when applied to the entire 188 species experimental dataset, and 56% when a simulated dataset showing amplified within-species spectral variability was used, suggesting uniqueness of the spectral signature for a significant proportion of species under study. The maximum error in canopy-level species classification was higher than leaf-level classification: 55% when canopy structure was held constant, and 64% with varying and unknown canopy structure. However, when classifying fewer species at a time, errors dropped considerably; for example, 20 species can be classified to 82–88% accuracy. These results highlight the potential of imaging spectroscopy to provide species discrimination in high-diversity, humid tropical forests.