Automating Geomorphological Modeling with GeoAI to Assess the Impact of Climate Change-Induced Tidal Surges on Freshwater Forested Wetlands in Coastal Georgia

Abstract Submission: Coastal flooding along the Georgia coast has intensified in recent years due to increased tidal surges and hurricanes, both driven by climate change. Tidal freshwater forested wetlands (TFFW), located in floodplains near coastal zones along freshwater rivers, are particularly vulnerable to these surges. The intrusion of brackish water into TFFWs is significantly damaging biodiversity. This study aims to: i) acquire 1-meter resolution LiDAR-based DEM data for the 10 coastal counties of Georgia; ii) model the potential impact of climate change-induced tidal surges by using soil properties to accurately delineate freshwater ecosystems at risk of saltwater intrusion; and iii) apply an Artificial Neural Network (ANN) Self-Organizing Map (SOM) image segmentation algorithm to classify and distinguish between saltwater and freshwater ecosystems in the study area. The 1-meter DEMs were mosaicked to create a comprehensive elevation raster for the study area, and a raster calculator was used to identify regions below 3 meters in elevation, representing areas at risk from tidal surges based on the assumption of 3-meter-high tides. The DEM, based on 2010 LiDAR data, provides an accurate depiction of the region's topography. To enhance the model, gSSURGO soil data were integrated, focusing on Soil Hydrologic Group (HG) and Available Water Storage/Capacity (AWS) characteristics, which helped identify areas vulnerable to saltwater percolation and the resulting stress on freshwater ecosystems. Finally, high-resolution (1-meter) NAIP imagery was used with SOM-based image segmentation to differentiate plant species and land use types in tidal-affected regions. The findings of this study provide valuable insights for coastal ecosystem managers, supporting proactive management and decision-making in response to climate change-driven tidal surges.

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