Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr⁻¹). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices.

Characterizing bathymetric change in coastal environments is an important component in understanding shoreline evolution, especially along barrier island platforms. Bathymetric change is a function of the regional sediment budget, long-term wave and current patterns, and episodic impact from high-energy events such as storms. Human modifications may also cause changes in seafloor elevation. This study, conducted by the U.S. Geological Survey in collaboration with the U.S. Fish and Wildlife Service, evaluates bathymetric and volumetric change and sediment characteristics around Breton Island and Gosier Shoals located offshore of the Mississippi River Delta in Louisiana. This area has been affected by significant storm events such as Hurricane Katrina in 2005. Sedimentation patterns at Breton Island and offshore have also been modified by the excavation of a shipping channel north of the island. Four time periods are considered that encompass these episodes and include long-term change and short-term storm recovery: 1869–2014, 1869–1920, 1920–2014, and 2007–2014. Finally, sediment characteristics are reported in the context of seafloor elevation.

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.