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Ginikanda Nayani Ilangakoon

February 20 @ 3:00 pm - 5:00 pm MST

Dissertation Information

Title: Complexity and Dynamics of Semi-Arid Vegetation Structure, Function, and DIiversity Across Spatial Scales

Program: Doctor of Philosophy in Geosciences

Advisor: Dr. Nancy Glenn, Geosciences

Committee Members: Dr. Shawn Benner, Geosciences, Dr. Dylan Mikesell, Geosciences, and Dr. Jodi Brandt, Geosciences


Semi-arid ecosystems cover approximately 40% of the earth’s terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Due to disturbances (drought, fire, invasion, grazing, land use etc.), semi-arid ecosystems experience a high degree of structure-function transformations that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess ecosystem demography, response strategies to disturbances, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem.

Plant functional types (PFTs) of an ecosystem are important indicators for monitoring the ecosystem state, as well as its resistance and resilience to climate and human driven disturbances. However, differentiating PFTs, especially low stature shrub and grass from bare ground, in these heterogeneous ecosystems makes the use of optical remote sensing data challenging. In this study, I developed a workflow to differentiate key plant functional types in a semi-arid ecosystem using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81–0.86% and 0.60–0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees).

Therefore, using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. A wealth of research has also shown that functional traits and their diversity have a greater effect on ecosystem processes rather than species diversity. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbances. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched GEDI system (a sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI’s potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m – 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area lead the ecosystem into a more even and less divergent state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem’s health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be further affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. This may be due to the potential geolocation errors of lidar footprints. Overall, tackling remote sensing challenges, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This enhances our understanding of the present state of an ecosystem and provides necessary steps forward to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. In addition, this study provides insights to future NASA missions such as NISAR that is aimed to measure global vegetation dynamics, especially under disturbance regimes. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the deployed remote sensing system and atmospheric-driven noises.