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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.

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A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results followed by the wavelet energy feature vector and a form of principal component analysis. For the same dataset our results indicate that none of the pattern recognition classifiers performed the best across all reduction techniques, and also that none of the parametric classifiers had the overall lowest training and testing errors. At the crown level, in addition to higher testing error rates (7%), it was found that there was no optimal data reduction technique. The significant wavebands were also found to be different between the leaf and crown levels. At the leaf level, the visible region of the spectrum was the most important for discriminating between lianas and trees whereas at the crown level the shortwave infrared was also important in addition to the visible and near infrared.

Current remote sensing technologies are effective tools for contributing to the estimation of terrestrial carbon stocks and carbon stock changes. This paper provides an overview of information requirements, sensor capabilities and limitations, and analysis approaches for the use of remotely sensed data in the generation of tropical carbon sequestration monitoring systems. While it is evident that remotely sensed data have tremendous utility for monitoring carbon stock changes, it is important to be aware of their limitations. Three critical limitations are: (1) the definition of methods and algorithms to accurately estimate forest age, (2) the provision of techniques that can yield accurate estimation of deforestation rates in both tropical dry and wet forest environments, and (3) the strong need to develop new approaches to link biophysical variables (e.g., leaf area index) to spectral reflectance to support spatially distributed carbon sequestration models. The validity of final estimates of carbon and carbon stock changes rests on complex issues at several levels, from the data themselves, to the analysis, interpretation, and validation of the data. Consideration of these issues, as well as the need for sound project planning and development within budget constraints, will be important in the development of carbon stock monitoring programs in the tropics.