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Permanent link (DOI): https://doi.org/10.7939/R33J3973R

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Temporal dynamics and leaf trait variability in Neotropical dry forests Open Access

Descriptions

Other title
Subject/Keyword
remote sensing
temporal variation
tropical dry forests
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Hesketh, Michael S
Supervisor and department
Arturo Sanchez-Azofeifa (Earth and Atmospheric Sciences)
Examining committee member and department
Neil Sims (CSIRO)
Murray Gingras (Earth and Atmospheric Sciences)
Mike MacGregor (Computing Sciences)
Benoit Rivard (Earth and Atmospheric Sciences)
Department
Department of Earth and Atmospheric Sciences
Specialization

Date accepted
2014-01-28T10:13:46Z
Graduation date
2014-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This thesis explores the variability of leaf traits resulting from changes in season, ecosystem successional stage, and site characteristics. In chapter two, I present a review of the use of remote sensing analysis for the evaluation of Neotropical dry forests. Here, I stress the conclusion, drawn from studies on land cover characterization, biodiversity assessment, and evaluation of forest structural characteristics, that addressing temporal variability in spectral properties is an essential element in the monitoring of these ecosystems. Chapter three describes the effect of wet-dry seasonality on spectral classification of tree and liana species. Highly accurate classification (> 80%) was possible using data from either the wet or dry season. However, this accuracy decreased by a factor of ten when data from the wet season was classified using an algorithm trained on the dry, or vice versa. I also address the potential creation of a spectral taxonomy of species, but found that any clustering based on spectral properties resulted in markedly different arrangements in the wet and dry seasons. In chapter 4, I address the variation present in both physical and spectral leaf traits according to changes in forest successional stage at dry forest sites in Mexico and Costa Rica. I found significant differences in leaf traits between successional stages, but more strongly so in Costa Rica. This variability deceased the accuracy of spectral classification of tree species by a factor of four when classifying data using an algorithm trained on a different successional stage. Chapter 5 shows the influence of seasonality and succession on trait variability in Mexico. Differences in leaf traits between successional stages were found to be greater during the dry season, but were sufficient in both seasons to negatively influence spectral classification of tree species. Throughout this thesis, I show clear and unambiguous evidence of the variability of key physical and spectral leaf properties over various temporal scales, with the conclusion that an understanding of this variability must play a central role in the establishment of monitoring techniques for dry forests.
Language
English
DOI
doi:10.7939/R33J3973R
Rights
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 these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before 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.
Citation for previous publication
Hesketh, M., & Sánchez-Azofeifa, A. (2013). A Review of Remote Sensing of Tropical Dry Forests. In A. Sánchez-Azofeifa, J.S. Powers, G.W. Fernandes, & M. Quesada (Eds.), Tropical Dry Forests in the Americas: Ecology, Conservation, and Management (pp. 83-100): CRC PressHesketh, M., & Sánchez-Azofeifa, G.A. (2012). The effect of seasonal spectral variation on species classification in the Panamanian tropical forest. Remote Sensing of Environment, 118, 73-82.

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