Over drainage due to water management practices, abundance of native and rare
species, and low-lying topography makes the coastal Everglades especially vulnerable to
Sea-Level Rise (SLR). Water depths have shown to have a significant relationship to
vegetation community composition and organization while also playing a crucial role in
vegetation health throughout the Everglades. Modeling potential habitat change and loss
caused by increased water depths due to SLR requires better vertical Root Mean Square
Error (RMSE) and resolution Digital Elevation Models (DEMs) and Water Table
Elevation Models (WTEMs). In this study, an object-based machine learning approach
was developed to correct LiDAR elevation data by integrating LiDAR point data, aerial
imagery, Real Time Kinematic (RTK)-Global Positioning Systems (GPS) and total
station survey data. Four machine learning modeling techniques were compared with the
commonly used bias-corrected technique, including Random Forest (RF), Support Vector
Machine (SVM), k-Nearest Neighbor (k-NN), and Artificial Neural Network (ANN). The k-NN and RF models produced the best predictions for the Nine Mile and Flamingo study
areas (RMSE = 0.08 m and 0.10 m, respectively). This study also examined four
interpolation-based methods along with the RF, SVM and k-NN machine learning
techniques for generating WTEMs. The RF models achieved the best results for the dry
season (RMSE = 0.06 m) and the wet season (RMSE = 0.07 m) WTEMs. Previous
research in Water Depth Model (WDM) generation in the Everglades focused on a
conventional-based approach where a DEM is subtracted from a WTEM. This study
extends the conventional-based WDM approach to a rigorous-based WDM technique
where Monte Carlo simulation is used to propagate probability distributions through the
proposed SLR depth model using uncertainties in the RF-based LiDAR DEM and
WTEMs, vertical datums and transformations, regional SLR and soil accretion rates. It is
concluded that a more rigorous-based WDM technique increases the integrity of derived
products used to support and guide coastal restoration managers and planners concerned
with habitat change under the challenge of SLR. Future research will be dedicated to the
extension of this technique to model both increased water depths and saltwater intrusion
due to SLR (saltwater inundation). / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_40792 |
| Contributors | Cooper, Hannah M. (author), Zhang, Caiyun (Thesis advisor), Florida Atlantic University (Degree grantor), Charles E. Schmidt College of Science, Department of Geosciences |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 146 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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