Cooper, Hannah M.

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).

Global sea-level rise SLR is projected to accelerate over the next century, with research
indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100.
Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify
assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming
from the use of Light Detection and Ranging LiDAR Digital Elevation Models DEMs in support
of SLR decision-making. A significant shortcoming in the methodology is the lack of
comprehensive standards for estimating LiDAR error, which causes inconsistent and sometimes
misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing
the difference between LiDAR error and the target SLR chosen for decision-making. The
practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a
normal distribution with zero bias, which is intermittently violated. Approaches to correcting
discrepancies between vertical reference systems for land and tidal datums may incorporate tidal
benchmarks and a vertical datum transformation tool provided by the National Ocean Service
VDatum. Mapping a minimum statistically significant SLR increment of 32 cm is difficult to
achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from ‘ground’
returns are essential, yet LiDAR providers may fail to remove returns over vegetated areas
successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are
vulnerable to direct marine inundation and groundwater inundation reduced drainage coupled
with higher water tables. Spatial analysis can identify potentially vulnerable ecosystems as well
as developed assets. A standardized mapping uncertainty needs to be developed given that SLR
vulnerability mapping requires absolute precision for use as a decision-making tool.