Publication Date




Embargo Period


Degree Type


Degree Name

Doctor of Philosophy (PHD)


Marine Geology and Geophysics (Marine)

Date of Defense


First Committee Member

Shimon Wdowinski

Second Committee Member

Matthew D. Potts

Third Committee Member

Timothy H. Dixon

Fourth Committee Member

Falk Amelung


Wetlands are regions that are covered permanently or seasonally with water and/or have saturated soils for long periods of time. They provide benefits to human society, including flow regulation, storm protection, aquifer recharge, sediment and nutrient retention, energy production, conservation of fauna and flora, recreation and tourism, and are a natural laboratory for research and education. Wetland ecosystems are under severe pressure due to anthropogenic activities and climate change. There is an urgent need to conserve, restore and monitor wetlands at all scales (local to global). Wetlands are difficult to monitor, due to their large area and limited accessibility. High-resolution remote sensing technology represents a useful tool to quantify forests structural parameters such as vegetation structure (canopy height) and above-ground biomass (AGB) from regional up to global scales and to establish a baseline for present and future ecosystem comparisons. Quantifying vegetation structure and AGB is important to establish a monitoring database. Recent advances in remote sensing present an enormous opportunity to characterize wetland vegetation cover and structure. Studies have successfully used optical satellite, data such as Landsat 7 Enhanced Thematic Mapper Plus (ETM+), to estimate and classify wetland vegetation cover. However, wetland forest characterization requires also the quantification of forest canopy height, which cannot be obtained from optical remote sensing observations. A large-scale characterization of forested wetland vertical structure is possible using active remote sensing sensors from (1) air- or spaceborne LiDAR/Laser Scanning or (2) spaceborne Synthetic Aperture Radar (SAR) systems such as the Shuttle Radar Topography Mission (SRTM) and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X). However, these techniques depend on plot-level AGB estimations for validation and calibration purposes. Emerging remote sensing techniques such as Terrestrial Laser/LiDAR Scanning (TLS) can be used in wetland environments for accurately estimating AGB at the tree- and plot-levels. The Everglades National Park (ENP) wetland ecosystem presents a useful study area as it is largely protected from development. However, historical changes in its water flow have stressed the system. The ENP is home to a vast amount of unique endangered and native species (fauna and flora) that are being threatened by (1) deprivation of the freshwater inflow into the park and (2) the dominance of exotic and invasive species. The main objective of this study is to provide quantitative canopy height and AGB estimates for four wetland forest ecosystems: mangrove, rockland pine, bald cypress and tropical hardwood hammock, all located within the boundaries of the ENP. I produce canopy height and AGB maps using three techniques, TLS/LiDAR Scanning, Airborne Laser/LiDAR Scanning, and single-pass Polarimetric-Interferometric Synthetic Aperture Radar (Pol-InSAR) TanDEM-X data. Furthermore, I provide uncertainty estimations for the calculated parameters. I was able to successfully use TLS to estimate vegetation volume and AGB in addition to its related uncertainty. This dissertation provides the first TLS study ever reported in a wetland environment. Airborne LiDAR and TanDEM-X data were successfully used to estimate canopy height in the mangrove forests with an R2 = 0.85 and RMSE = 1.96 m. An important conclusion of this dissertation reveals that the integration of remote sensing techniques at multiple scales is fundamental and necessary for wetland forestry studies.


Wetlands; Terrestrial Laser Scanning; Airborne LiDAR; TanDEM-X; Canopy Height; Above-Ground Biomass; Mangroves; Forest Structure; Everglades National Park; InSAR; Allometry