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Volume 2012 (2012), Article ID 839584, 14 pages
Research Article

Leaf Spectra and Weight of Species in Canopy, Subcanopy, and Understory Layers in a Venezuelan Andean Cloud Forest

1Department of Electrical Engineering, University of North Texas, Denton, TX 76203, USA
2Department of Geography and Environmental Science Program, University of North Texas, Denton, TX 76203, USA
3Centro de Simulación y Modelos (CESIMO), Universidad de Los Andes, Mérida 5101, Venezuela
4Instituto de Ciencias Ambientales y Ecológicas (ICAE), Facultad de Ciencias, Universidad de Los Andes, Mérida 5101, Venezuela

Received 18 March 2012; Accepted 22 April 2012

Academic Editors: F. Ayuga, I. Cannayen, and S. Hayat

Copyright © 2012 Miguel F. Acevedo and Michele Ataroff. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We characterize the leaf spectra of tree species of an Andean cloud forest in Venezuela, grouped according to position in canopy, subcanopy and understory. We measured leaf reflectance and transmittance spectra in the 400–750 nm range using a high-resolution spectrometer. Both signals were subtracted from unity to calculate the absorbance signal. Nine spectral variables were calculated for each signal, three based on wide-bands and six based on features. We measured leaf mass per unit area of all species, and calculated efficiency of absorbance, as ratio of absorbance in photosynthetic range over leaf mass. Differences among groups were significant for several absorbance and transmittance variables, leaf mass, and efficiency of absorbance. The clearest differences are between canopy and understory species. There is strong correlation for at least one pair of band variables for each signal, and each band variable is strongly correlated with at least one feature variable for most signals. High canonical correlations are obtained between pairs of the three canonical axes for bands and the first three canonical axes for features. Absorbance variables produce species clusters having the closest correspondence to the species groups. Linear discriminant analysis shows that species groups can be sorted by all signals, particularly absorbance.