Author Type

Graduate Student

Date of Award

2018

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Zhang, Caiyun

Abstract

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).

Extent

146 p.

Comments

Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2018.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00005991

Format

pdf

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