Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Titre : REMOTE SENSING TOOLS FOR DETECTING AND QUANTIFYING LIANAS AND TREES AT THE TROPICAL DRY FOREST

Auteur : Guzman Quesada, Jose A

Université de soutenance : University of Alberta

Grade : Doctor of Philosophy 2021

Résumé partiel
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.

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