Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

By Margaret Kalacska, Stephanie A. Bohlman, G. Arturo Sanchez-Azofeifa, K. Castro-Esau, Terry M. Caelli. 2007

123 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/1671920bc12387beb57987049f65d17d7d1cef64

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Citations in the corpus

Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data (2011, background): “Cellular structure and water content are the main determinants in the infrared region (Kalacska et al. 2007), and our data may be an indicator of water stress suffered by the infected oil-palm leaves.”
Review of optical-based remote sensing for plant trait mapping (2013, background): “Kalacska et al. (2007) discriminated between forest 322 lianas and their host trees in tropical forest.”
Differentiating plant species within and across diverse ecosystems with imaging spectroscopy (2015, background): “Furthermore, most studies that explicitly evaluate the effects of dimension reduction (Dópido et al., 2012; Kalacska et al., 2007; Pu & Gong, 2000) or classification method (Clark et al.”
Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments (2009, methodology): “This work expands previous studies by Castro-Esau et al. (2004) and Kalácska et al. (2007) who explored the use of advanced parametric and non-parametric classification approaches to evaluate the spectral separability between lianas and trees at the same sites, but without exploring in-depth the…”