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Increasing Integrity in Sea-Level Rise Impact Assessment on Florida’s Coastal Everglades

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Date Issued:
2018
Abstract/Description:
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).
Title: Increasing Integrity in Sea-Level Rise Impact Assessment on Florida’s Coastal Everglades.
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Name(s): Cooper, Hannah M., author
Zhang, Caiyun, Thesis advisor
Florida Atlantic University, Degree grantor
Charles E. Schmidt College of Science
Department of Geosciences
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2018
Date Issued: 2018
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 146 p.
Language(s): English
Abstract/Description: 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).
Identifier: FA00005991 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2018.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Everglades (Fla.)
Sea level rise
Coastal ecology--Florida
Everglades (Fla)--Environmental conditions
Impact assessment
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00005991
Use and Reproduction: 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.
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Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.