• Author / Creator
    Guzman Quesada, Jose A
  • Lianas are woody thick-stemmed climbers that use host trees to reach the forest canopy. Studies have shown a remarkable increase in liana abundance in the last two decades, while others have shown that liana abundance is associated with detrimental effects on forest dynamics. Liana abundance presents peaks in highly seasonal forests such as the Tropical Dry Forest (TDF); regions that are under threat for frequent droughts, fires, and anthropogenic pressures. Despite their abundance and relevance in these fragile ecosystems, there are no clear research priorities that help to conduct an efficient detection and monitoring of lianas. This dissertation aims to integrate new remote sensing perspectives to detect and quantify lianas and trees at the TDF. This was addressed using passive (Chapters 2 ‒ 4) and active remote sensing (Chapter 5). Using thermography, Chapters 2 explored the temporal variability of leaf temperature of lianas and trees at the canopy. Temperature observations were conducted in different seasons and ENSO years on lianas and trees infested and non-infested by lianas. The findings revealed that the presence of lianas on trees does not affect the temperature of exposed tree leaves; however, liana leaves tended to be warmer than tree leaves at noon. The results emphasize that lianas are an important biotic factor that can influence canopy temperature, and perhaps, its productivity. Chapter 3 assessed the discrimination of liana and tree leaves using visible-near infrared (VIS-NIR) and longwave infrared (LWIR) spectra. This chapter compared the former contrasting spectral regions, four representations of leaf spectra, twenty-one algorithms of classification, and two contrasting life forms in the context of machine learning to explore the question of whether it is possible to discriminate between liana and tree leaves. The results revealed that both life forms are more accurately discriminated using LWIR spectra (accuracy between 66% and 96%) compared with VIS-NIR spectra (accuracy between 50% and 69%). However, such accuracies of discrimination were achieved depending on the kind of spectral pre-processing and machine learning algorithm. The chapter’s outcomes suggest the possibility to extend the discrimination between lianas and trees to airborne or satellite LWIR observations. The prediction of leaf traits of lianas and trees using Partial Least-Square Regression (PLSR) models based on leaf reflectance or wavelet spectra is addressed in Chapter 4. This chapter revealed that the model performance differs between life forms or between reflectance/wavelet spectra models. Differences in model performance between life forms seemed to be the product of the intraspecific variability of leaf traits within these life forms. Likewise, it was shown that PLSR models based on wavelet spectra help to overcome current limitations of PLSR models based on reflectance spectra. The results showed that the variability of leaf traits between life forms influences predictive models. Thus, the variability of traits between plant groups may have an essential role in estimated errors associated with the mapping of leaf traits. Using Terrestrial Laser Scanning, Chapter 5 evaluated the relationship between fractal geometry and tree-stands metrics on point clouds of trees. The chapter’s results suggested that the intercept extracted from fractal geometry is an accurate and fast parameter that helps predict plant volume, crown coverage, or plant basal area at the tree or stand level. The fractal geometry also revealed that the fractal dimension is strongly associated with the presence/absence of leaves in the point cloud or the number of trees in the stands. Since this method is not susceptible to irregular structures, this method may potentially contribute to quantifying the volume of lianas or buttress roots of trees. Chapter 6 provides future research directions that may help explain the drivers that lead the observed findings or the potential applicability of the results. Overall, this thesis highlighted the need for new efficient and fast approaches that help assess the role and extent of lianas in the tropics. In the absence of a solid understanding of the presence and the effect of lianas in forest dynamics, future predictions of tropical forest productivity will remain speculative.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • 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.