Abstract
Dryland forests, which cover nearly one billion hectares worldwide, support biodiversity and provide essential ecosystem services. However, these water-limited ecosystems face escalating threats due to the increasing frequency and severity of drought periods, reduced precipitation amounts, and risingtemperatures. These changing conditions are reducing water availability, leading to changes in forest function (e.g. carbon assimilation as gross primary productivity—GPP, water consumption as evapotranspiration—ET, and water use efficiency WUE=GPP/ET), and structure (e.g. leaf surface area
index—LAI, and organization—LAO). These changes have far-reaching consequences for dryland forest carbon and water cycles. Given the critical ecological roles of dryland forests, the development of strategies for monitoring, managing, and conserving these ecosystems in the face of a changing
climate is imperative.
In this research, I studied dryland conifer forest function, structure, and dynamics across a climatic aridity gradient by investigating silvicultural thinning treatments to mitigate increasing water scarcity in these ecosystems. To assess forest function, I focused on GPP, ET, and WUE. To assess and monitor
forest structure, I focused on the ecosystem LAI (LAIEcosystem) and several derivatives of this parameter
(e.g. LAO). Climatic aridity was quantified by the aridity index (AI=Precipitation/Potential ET). Carbon, water, and energy exchanges between forest ecosystems and the atmosphere can be measured by eddy covariance on flux towers, considered a robust method for monitoring these fluxes. However,
flux towers are limited in their ability to represent large, heterogeneous landscapes (i.e. regional to global scales), on one hand, and isolate specific, small areas (e.g. experimental forest plots), on the other hand. Novel remote sensing (RS) technologies answer these limitations by enabling continuous
coverage of extensive areas over long periods and at high spatiotemporal resolution to assess and monitor forest function and structure. In my research, I combined RS tools (i.e. spectral data from novel satellite platforms and structural data from airborne LiDAR [Light Detection and Ranging]) to assess
and monitor the function and structure of dryland conifer forests. Specifically, I developed empirical prediction models for linking ground measurements of GPP, ET, and LAI with RS signals. I then used the RS signals to monitor ecosystem features across climatic aridity and silvicultural thinning levels.
My research is divided into three chapters as follows:
In the first chapter, I evaluated forest functional parameters in four dryland forest sites along an arid to dry subhumid climatic aridity gradient using newly developed earth observation systems. In dryland regions, forest ecosystem functions are influenced primarily by local water availability, which has the
potential to alter GPP, ET, and WUE. Additionally, I explored the ratio between GPP and LAI, known as leaf area efficiency (LAE = GPP/LAI), along the aridity gradient. Empirical prediction models were developed to estimate water and carbon fluxes at the ecosystem level. These models used multiple spectral data (as vegetation indices) derived from VENμS and Sentinel-2A satellites complemented by meteorological data and validated against local eddy covariance flux tower measurements. The redDryland forests, which cover nearly one billion hectares worldwide, support biodiversity and provide
essential ecosystem services. However, these water-limited ecosystems face escalating threats due to the increasing frequency and severity of drought periods, reduced precipitation amounts, and rising temperatures. These changing conditions are reducing water availability, leading to changes in forest
function (e.g. carbon assimilation as gross primary productivity—GPP, water consumption as evapotranspiration—ET, and water use efficiency WUE=GPP/ET), and structure (e.g. leaf surface area
index—LAI, and organization—LAO). These changes have far-reaching consequences for dryland forest carbon and water cycles. Given the critical ecological roles of dryland forests, the development of strategies for monitoring, managing, and conserving these ecosystems in the face of a changing
climate is imperative.
| Date of Award | 2024 |
|---|---|
| Original language | English |
| Awarding Institution |
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| Supervisor | Tarin Paz-Kagan (Supervisor), Tarin Paz-Kagan (Supervisor) & Tarin Paz-Kagan (Supervisor) |