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. Semi-arid ecosystems experience a high degree of disturbance
that can severely alter ecosystem services and processes. Understanding the
structure-function relationships across spatial extents are critical in order
to assess their demography, response to disturbance, 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.
Difficulty in differentiating low stature vegetation from bare ground is the key remote
sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to
differentiate key plant functional types (PFTs) 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).
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. I evaluated the trends and patterns of functional diversity
and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the
fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the
newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar 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 resulted in a more even and less divergent ecosystem 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 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.
Overall, 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 work enhances our understanding of the present
state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these
changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and
algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for
the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2
and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance.
A COMPARISON OF LIDAR REFLECTANCE AND RADIOMETRICALLY CALIBRATED HYPERSPECTRAL IMAGERY
