Variability in leaf optical properties of Mesoamerican trees and the potential for species classification

Abstract

Leaf traits and physiological performance govern the amount of light reflected from leaves at visible and infrared wavebands. Information on leaf optical properties of tropical trees is scarce. Here, we examine leaf reflectance of Mesoamerican trees for three applications: (1) to compare the magnitude of within- and between-species variability in leaf reflectance, (2) to determine the potential for species identification based on leaf reflectance, and (3) to test the strength of relationships between leaf traits (chlorophyll content, mesophyll attributes, thickness) and leaf spectral reflectance. Within species, shape and amplitude differences between spectra were compared within single leaves, between leaves of a single tree, and between trees. We also investigated the variation in a species' leaf reflectance across sites and seasons. Using forward feature selection and pattern recognition tools, species classification within a single site and season was successful, while classification between sites or seasons was not. The implications of variability in leaf spectral reflectance were considered in light of potential tree crown classifications from remote airborne or satellite-borne sensors. Species classification is an emerging field with broad applications to tropical biologists and ecologists, including tree demographic studies and habitat diversity assessments.

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

“…A unique biochemistry and spectral signal is considered neither in the biological definition of a species, nor in the ecological classification of plant assemblages [6]. Further, species-specific biochemistry and spectral signal show a large spatio-temporal variation due to site conditions and other external influences [4]. This hampers a direct inversion of the relationship towards a quantification of the floristic composition and makes the direct mapping or classification of vegetation with remote-sensing approaches very challenging.…”

“…In particular at global and regional scales, where ground-based mapping is inefficient, remote sensing may be a practical alternative approach for vegetation mapping. Environmental conditions, such as climate, also affect the biophysical and biochemical properties of species and hence influence their spectral signal [4,7]. Environmental conditions, especially temperature and precipitation, are further major determinants of species distribution at macroscales [8,9].…”

“…A unique biochemistry of species along with phenological and structural characteristics may enable a discrimination of vegetation, but is inevitable for a reliable mapping result. Problems may arise if different species feature a similar spectral response, which is a common phenomenon in remote sensing [4,5]. A unique biochemistry and spectral signal is considered neither in the biological definition of a species, nor in the ecological classification of plant assemblages [6].…”

The effect of seasonal spectral variation on species classification in the Panamanian tropical forest

“…Investigating spectral variation within species, Castro-Esau et al (2006) found sufficient difference in the optical properties of species sampled across multiple sites that accurate automated classification was impossible.…”

Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery

“…Resasco et al [23] were able to map an invasive shrub in understorey vegetation based on comparisons of seasonal differences in vegetation indices. In several recent studies, phenological differences in leaf traits [24] and canopy characteristics [25] allowed discrimination of vegetation at the species level based on leaf spectral signatures. In the context of bush encroachment in Namibia, known differences in leaf phenology of the encroacher species could facilitate distinguishing between the two prevailing Acacia species based on multi-temporal remote sensing imagery.…”