Geology topics

Alexander C. Seymour

Publications and source records attributed to Alexander C. Seymour.

4 recordsLinked to original sources

USGS2026

Decadal-scale characteristics of natural and anthropogenic dune morphology along North Carolina Barrier Islands (SE Atlantic Coast)

Dunes offer protection to coastal communities from storms, yet quantitative research characterizing human influences on dune systems across decades and 10–100 km spatial scales constitutes a known data gap. This study analyzed a multidecadal archive of dune crest elevations and positions to map mesoscale spatiotemporal trends in foredune morphology across the barrier island coastline of North Carolina, SE Atlantic coast. Results are summarized within four categories of coastline representing a range of coastal management intensities, integrating beach width and dune lateral accommodation space to assess the comparative influence of differing intensities of anthropogenic intervention on dune morphology. These categories account for the presence of structures, roads, and alongshore connectivity to anthropogenic sediment sources (beach nourishment). These categories show distinct dune and beach system characteristics. Dunes are largest in the areas with the longest legacy of coastal management. As categories decrease in development intensity, dune and beach system characteristics generally depart from the large, static dunes and narrow beaches consistent with hold-the-line management. Where small or transgressive dunes exist on heavily developed coasts, they coincide with relatively strong hydrodynamic forcing or sediment-limiting geology. There is evidence of indirect coastal management influence on undeveloped barrier islands with alongshore connectivity to developed barriers. While this analysis was conducted at the mesoscale, the results provide researchers with the framework to make insights at smaller scales than those demonstrated in this study.

North Carolina↗

USGS2025

Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA

Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. Due to logistic, resource, and time constraints, wide area remotely sensed topographic surveys are not always accompanied by an in situ checkpoint network for validating and characterizing survey error. Here we describe and test a method for automatically generating synthetic elevation checkpoints in bulk across hundreds of kilometers using a publicly available lidar-derived DEM time-series, road vector network, and landcover classification map. Our method produced 6000–10,000 synthetic checkpoints across the developed barrier island coastline of North Carolina. These checkpoints characterized vertical error metrics in a statistically similar way as in situ checkpoints when assessing the vertical accuracy of a contemporary lidar-derived DEM and produced RMSEz metrics an average of 0.018 m from the RMSEz of historical lidar DEMs published with tested accuracy metrics. This new method has the potential to A) lower the cost and time required to validate new remotely sensed topographic surveys by reducing or eliminating the field work associated with in situ checkpoint surveys, B) provide a means of retroactively assessing the absolute vertical accuracy and systematic bias of historical topographic datasets that were not published with tested accuracy metrics, and C) generate reference networks to assess and correct spatially variable patterns of vertical bias in topographic datasets.

North Carolina↗

USGS2025

Modeling the impacts of sand placement strategies on barrier island evolution in a semi-enclosed bay system

This study assesses the impacts of five proposed restoration actions at Little Dauphin Island, a low-lying relic spit in a semi-enclosed bay system on the Alabama coast. A Delft3D model is developed to simulate annual scale (five-year) sediment transport and resulting bed level changes. The model is validated with observed water level and wave data, as well as sediment tracers that were deployed offshore of the island. An XBeach model is developed to simulate storm-driven morphologic change and is validated for hurricanes Ivan (2004), Katrina (2005)and Sally (2020). Together, the models are used to assess differences in the island's morphological response under a no-action (status quo) scenario representing a continuous island, tidal inlet realignment, a sand motor nourishment, beach and dune restoration and a dredged offshore borrow area. The no-action scenario revealed that the island breached at multiple locations including the location of the proposed inlet realignment during each storm. The realigned channel did not prevent breaching on the island, but reduced the magnitude of sand transported through the breaches. The sand motor provided some sheltering to leeward shorelines during storms but did not prevent breaching from occurring elsewhere. Fairweather waves and currents were not strong enough to transport sand outside of the vicinity of the feature to feed adjacent shorelines as intended. The beach and dune restoration reduced storm-driven overtopping along the nourished shoreline. For habitat purposes, strategically placed bayous provided low elevation points that allowed overwash depending on the direction of cross-barrier water level gradients.

Alabama↗

USGS2020

Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide

Coastal cliffs constitute 80 percent of the world’s coastline, with seacliffs fronting a large proportion of the U.S. West Coast shoreline, particularly in California. Erosion of coastal cliffs can threaten infrastructure and human life, yet the spatial and temporal scope of cliff studies have been limited by cumbersome traditional methods that rely on the manual interpretation of seacliff features—especially seacliff toes and top edges. The Cliff Feature Delineation Tool (CFDT) and the Baseline Builder Tool are designed to increase the efficiency of deriving seacliff features from remote sensing datasets by utilizing an automated, quantitative approach that eliminates traditional interpretive methods and ensures reproducibility. This document functions as a user guide for operating the Cliff Feature Delineation Tool and Baseline Builder Tool and includes a walkthrough of data-visualization and data-review workflows for the tools’ three-dimensional (3D) cliff feature outputs. Also included is a brief overview of cliff feature delineation at the U.S. Geological Survey (USGS) and a detailed description of the tools’ algorithmic logic.

Open-File Report↗