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Application of continuous wavelet analysis to hyperspectral data for the characterization of vegetation Open Access

Descriptions

Other title
Subject/Keyword
Green attack
Leaf water content
Hyperspectral
Feature selection
Leaf reflectance
Mountain pine beetle (Dendroctonus ponderosae Hopkins)
Wavelet
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Cheng, Tao
Supervisor and department
Rivard, Benoit (Earth and Atmospheric Sciences)
Examining committee member and department
Jacquemoud, Stéphane (Université Paris Diderot / Institut de Physique du Globe de Paris)
Sánchez-Azofeifa, Arturo (Earth and Atmospheric Sciences)
Yang, Herb (Computing Science)
Croitoru, Arie (Earth and Atmospheric Sciences)
Department
Department of Earth and Atmospheric Sciences
Specialization

Date accepted
2010-10-01T02:51:08Z
Graduation date
2010-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This thesis explores the application of continuous wavelet analysis (CWA) to hyperspectral data for the characterization of vegetation at the leaf level. The first study dealt with the spectral detection of green attack damage (pre-visual stress) due to mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation that occurs on lodgepole pines at an early stage, in contrast to considerable research on the remote detection of red attack damage. A new methodology was developed to separate healthy pine trees from beetle infested trees, based on the CWA of hyperspectral measurements for pine needles. This pilot study showed that a decline in water content occurred for the pine trees at the green attack stage and the spectral response to that physiological change could be detected using a few features in the wavelet domain. The second topic addressed the application of CWA to the determination of leaf water content from remotely sensed reflectance. Unlike most previous studies involving a limited number of species, this study examined a wide range of tropical forest species with the aim to determine reliable and effective wavelet features (coefficients) sensitive to changes in leaf gravimetric water content (GWC). Of those significant wavelet features extracted, some related to the absorption of leaf water while more related to the absorption of dry matter. An evaluation of the wavelet features as compared with published water indices indicated their great potential for the estimation of leaf GWC. Lastly, the third study tested the wavelet-based methodology developed in the second study using a leaf spectral database generated by the PROSPECT radiative transfer model. The ability of PROSPECT to simulate leaf reflectance measured for the tropical data set was first assessed. Then the performance of the aforementioned methodology was evaluated in terms of the consistency of wavelet features extracted across data sets. This work demonstrated the effectiveness of the wavelet-based methodology and the robustness and reliability of recurrent wavelet features for the estimation of leaf GWC across a wide range of species.
Language
English
DOI
doi:10.7939/R3X020
Rights
License granted by Tao Cheng (tao.cheng@ualberta.ca) on 2010-09-29T16:14:16Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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