Spatial technologies such as satellite remote sensing can be used to identify vegetation dynamics over space and time, which play a critical role in earth observations. Biophysical and biochemical features associated with vegetation cover can then be used to elucidate climate change impact such as floods and droughts on ecosystem that may in turn affect watershed-scale water resources management. Unlike single flood or drought event, intermittent extreme weather events may exert more physiological and biological pressures on the canopy vegetation. This study aims to investigate the climate change impacts on canopy vegetation, which occurred from March 2017 to October 2017 in the Santa Fe River Watershed, Florida, the United States of America. First, this study explores the effect of Hurricane Irma on vegetation dynamics via the pre and post landfall conditions in terms of biophysical and biochemical features. The environmental system analysis compares a suite of remote sensing indices: enhanced vegetation index (EVI), leaf area index (LAI), fraction of photosynthetically active radiation (FPAR), evapotranspiration (ET), land surface temperature (LST), gross primary productivity (GPP), and global vegetation moisture index (GVMI) for a holistic assessment. The satellite images from MODIS (Moderate Resolution Imaging Spectroradiometer) were projected from the MODIS Sinusoidal projection to WGS84 geographic coordination systems to conduct the essential spatial analysis. In addition, the evolution of features associated with the vegetation was analyzed in terms of a new indicator, the functional capacity of the different land uses for grassland, evergreen forested, deciduous forested, and agricultural land uses to elevate our understanding of the ecosystem's sustainability and possible recovery processes as a response to damage caused by the Hurricane Irma event. Urban land use and open water space showed a low level of EVI, LAI, FPAR, GVMI, whereas LST and ET were significantly higher compared to the forested and agricultural land uses. Coupling LAI and LST,EVI and GVMI, or EVI and LST confirms the hypotheses of the study, namely that biophysical features pre and post landfall of Hurricane Irma exhibit significant spatial and temporal variations, and integration of pairwise comparisons of biophysical and biochemical features can better portray the impacts driven by the landfall of Hurricane Irma than a single biophysical feature. The functional capacity of the ecosystem can be derived in terms of EVI, LAI, GVMI, and LST analysis over grassland, evergreen forested, deciduous forested, and agricultural land to quantitively reflect the ecosystem response due to landfall of Hurricane Irma. Secondly, emphasis was placed on determining the impacts of alternating adverse flood and drought events on four vegetative land use types via remote sensing and contrasting the vegetation canopy resilience, resistance, and elasticity in intermittent extreme weather events from March to Oct. 2017 in the same subtropical watershed. Nonlinear extreme weather events in sequence discriminated the marginal resilience, resistance, and thus elasticity of four land uses showing high resilience and elasticity in transitions of dry and wet events. It is indicative that thermodynamics driven LST served as the energy source that explains the forcing of variations of these vegetation indices and sustainability indicators.
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Master of Science (M.S.)
College of Engineering and Computer Science
Civil, Environmental, and Construction Engineering
Environmental Engineering; Environmental Engineering Sciences
Length of Campus-only Access
Masters Thesis (Open Access)
Bellanthudawage, Kushan, "Index-based Approach with Remote Sensing for the Assessment of Extreme Weather Impact on Watershed Vegetation Dynamics" (2021). Electronic Theses and Dissertations, 2020-. 472.