Classification of Tropical Vegetation

• Author / Creator

  Harrison, Dominica, E

• The increasing anthropogenic pressure on biodiversity in the tropics is resulting in changes to ecosystem structure and increasing species extinction rates (Collen et al. 2013). These issues are heightened in the tropics where 50% of the world’s biodiversity is held in 7% of the world’s terrestrial landmass (Urquhart 2001). The biodiversity of vegetation has been used as an indicator of the overall health and processes occurring in a biological system. This thesis explores ways to quantify the biodiversity of vegetation using different avenues of vegetation classification. In the second chapter of this thesis the leaf economic spectra (LES), a vegetation classification that attempts to provide a general framework to assess plant functional diversity, is used to explore the relationships between the conventional plant functional traits with the newly applied anatomical traits (Wright et al. 2004). This system uses economic principles to help understand plant ecology trade-offs and performance strategies. The LES was used to discern two plant functional groups (PFG), lianas and tree species, at two different Panamanian tropical sites, a tropical wet forest and a tropical dry forest. This was accomplished by examining the interactions between commonly used whole-leaf traits from the LES and newly applied anatomical traits. The addition of anatomical traits to the LES is a new approach (Reich 2014). The findings of this investigation support the hypothesis that trees are resource conservative when compared to their liana counterpart, which are thought to be resource acquisitive (Schnitzer 2005; Asner and Martin 2012). Additionally, the interaction of whole-leaf and anatomical traits provide insight into the foliar mechanisms and cellular organization such as: the amount of palisade and nutrients dictates leaf growth rates, the amount of airspace in the spongy mesophyll and potassium regulate water uptake, and cell size and cell numbers affects the defences against herbivores. Furthermore, variations across sites demonstrated that more resource availability will result in conservative traits and fewer resources will result in acquisitive traits. Future research in the field should focus on the implementation of anatomical traits on different PFGs and at different sites in the tropics. The third chapter of this thesis takes a remote sensing perspective to vegetation classification by exploring a region of the electromagnetic spectrum that has not been exploited extensively for vegetation investigations, namely the long-wave infrared (LWIR; 8.0 µm-12.5 µm; Ribeiro da Luz and Crowley 2007). Focusing on species discrimination through spectral signature sampling, a Fourier Transform Infrared (FTIR) spectrometer was used to collect the LWIR spectra of 26 tropical dry forest tree species in Costa Rica. To begin, a spectral library of cuticle and cell wall compounds collected by Ribeiro da Luz and Crowley (2007) was used to identify spectral features in the spectra of each species. Then the species were divided into groups based on similarities between spectral features and main compound(s) driving their spectra. Linear discriminant analysis (LDAs) was applied to each group to discriminate species within a group with accuracy rates ranging from 0-67% after validation. A random forest boot-strapping analysis was then applied to entire suite of species to discriminate species resulting in an out-of-bag error rate of 11%. This investigation is a first step in the application of LWIR to environmental monitoring. Advances in technology and airborne sensors will facilitate future remote sensing investigations in species discrimination from imagery and at regional scales. These two studies demonstrate different approaches to vegetation classification, PFT`s and RS. Together they emphasize the need to classify vegetation to assess damage resulting from biodiversity loss. Future research should focus on a standardization of wide-scale classification techniques.

• Subjects / Keywords

  • Long-wave infrared
  • Remote sensing
  • Physiology

• Graduation date

  Fall 2016

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R33X83W9C

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.