Rachel E. Henderson

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.

The U.S. Geological Survey (USGS) maintains a database of historical shoreline positions for the United States coasts derived from historical sources, such as aerial photographs or topographic surveys, and contemporary sources, such as modern orthophotography, light detection and ranging (lidar) point clouds, and digital elevation models. These shorelines are compiled within a geographic information system and analyzed in the USGS Digital Shoreline Analysis System (version 5.1) software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling scientists and resource managers to identify areas that are historically most susceptible to erosion or accretion.The effort in this report represents an expansion of the USGS national-scale shoreline database to include Puerto Rico and the islands of the territory, Vieques and Culebra. The USGS, in cooperation with the Coastal Research and Planning Institute of Puerto Rico (part of the Graduate School of Planning at the University of Puerto Rico, Río Piedras Campus) has derived and compiled a database of historical shoreline positions for Puerto Rico from the early 1900s through 2018, with the goal of providing beneficial insight for coastal managers and communities vulnerable to coastal change.

Coastal resources are increasingly affected by erosion, extreme weather events, sea level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying effects on coastal landscapes because of the compounding of geologic, oceanographic, ecologic, and socioeconomic factors that exist at a given location. An assessment framework is introduced in this report that synthesizes existing datasets that cover the variability of the landscape, and hazards that may act on the landscape, to evaluate the likelihood of coastal change along the U.S. coastline on a decadal scale. The pilot study that aided in the development of the framework was run in the northeastern United States (from Maine to Virginia) and consists of datasets derived from a variety of Federal, State, and local sources.First, a decision-tree-based dataset was built that describes the resistance or integrity of the coastal landscape (called the fabric dataset for the purposes of this report) and includes land cover, elevation, slope, long-term (more than 50 years) shoreline change, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (for example, flooding, wave power, and probability of storm overwash) and persistent or perpetual hazards (for example, relative sea level rise rate, short-term [about 30-year] shoreline erosion rate, and storm recurrence interval). The fabric dataset was then merged with the coastal hazards databases, and a model training dataset made up of hundreds of polygons was generated from these combined data to support machine learning.The pilot study resulted in location-specific, 10-meter-resolution data classified into five raster datasets that include intrinsic characteristics of the coast used to determine the resistance of the landscape to change, the persistent and event hazards that act on the coast, the machine learning output (coastal change likelihood) based on the cumulative effects of the fabric and hazards datasets, and an estimate of the hazard type (event or persistent) that is the most likely to influence coastal change. Final outcomes are intended to be used as a first-order planning tool to determine which areas of the coast are more likely to change in response to future potential coastal hazards and to examine elements and drivers that make change in a location more likely.

The Digital Shoreline Analysis System version 5 software is an add-in to Esri ArcGIS Desktop version 10.4–10.7 that enables a user to calculate rate-of-change statistics from a time series of vector shoreline positions. The Digital Shoreline Analysis System provides an automated method for establishing measurement locations, performs rate calculations, provides the statistical data necessary to assess the reliability of the rates, and includes a beta model for forecasting shoreline position. The Digital Shoreline Analysis System version 5.1 includes updates to the interface and the application of proxy-datum bias. This in-depth user guide provides comprehensive instruction on the installation and use of the program, including how to create a reference baseline for measurements, steps needed to generate measurement transects and metadata, guidelines on how to manually add or edit existing transects, and an explanation of the visualization options to display calculated rates of shoreline change.

Historical cross-shore positions of the shoreline and dune base were used as inputs for a Kalman filter algorithm to forecast the positions of these features in the year 2028. The beach width was also computed as the cross-shore distance between the forecasted 2028 shoreline and dune-base positions. While it does not evaluate the suitability of a nesting beach or identify optimal nesting habitat, the beach width can be used as a proxy for habitat availability. An analysis was conducted along the Florida Atlantic coast with an initial goal of demonstrating a method that combines available data for shoreline and dune positions with a Kalman Filter algorithm developed to predict decadal-scale shoreline evolution and then uses these features to define future beach width. This section of the southeastern United States hosts the largest assemblage of nesting loggerhead sea turtles (Caretta caretta) in the world, in addition to other species, and critical habitat is designated as part of the species’ listing package under the Endangered Species Act of 1973 (16 U.S.C. ch. 35 § 1531 et seq) for most of the nesting beaches within the study area. This work introduces an approach to inform ecosystem services assessments using data typically derived for shoreline change and storm vulnerability models.

The Digital Shoreline Analysis System (DSAS) is a freely available software application that works within the Esri Geographic Information System (ArcGIS) software. DSAS computes rate-of-change statistics for a time series of shoreline vector data. DSAS version 5.0 (v5.0) was released in December 2018 and has been tested for compatibility with ArcGIS versions 10.4 and 10.5. It is supported on Windows 7 and Windows 10 operating systems. If you use it, please cite it as follows and make note of the current version:Himmelstoss, E.A., Farris, A.S., Henderson, R.E., Kratzmann, M.G., Ergul, Ayhan, Zhang, Ouya, Zichichi, J.L., Thieler, E. R., 2018, Digital Shoreline Analysis System (version 5.0): U.S. Geological Survey software release, https://code.usgs.gov/cch/dsas.This user guide describes the system requirements, installation procedures, and necessary inputs to establish measurement locations with DSAS-generated transects and compute rate-of-change calculations. Although the nomenclature for this software utility is based on use in a coastal environment, the DSAS application could be used to compute rates of change for any boundary-change problem that incorporates a clearly identified feature position at discrete times, such as glacier limits, river banks, or land use/cover boundaries.

Dauphin Island and Little Dauphin Island, collectively, make up a geomorphically complex barrier island system located along Alabama’s southern coast, separating Mississippi Sound from the Gulf of Mexico and Mobile Bay. The barrier island system provides numerous economical (tourism, fisheries) and natural (habitat for migratory birds, natural protection of inland and coastal areas from storms) benefits to the State of Alabama. The complex geomorphology of Dauphin Island is partly a response to temporal variations in the direction and magnitude of sediment transport along and across the barrier island system. In this report, we present open-ocean and back-barrier shoreline change rates at different time scales to evaluate the island’s dominant behavior (expansion or widening and contraction or narrowing) over the last 75 years. The spatial and temporal variability of barrier island width provides baseline and historical context for potential restoration alternatives being considered as part of the Alabama Barrier Island Restoration Feasibility Study. Open-ocean shorelines have eroded continuously over the last 75 years, with rates ranging from 1.5 to 4 meters per year. Back-barrier shorelines are less uniform than open-ocean shorelines, but are, on average, also eroding over the same period. Periods of back-barrier progradation are observed but generally occur during discrete, large altering events like hurricanes that overwash or breach narrow sections of the barrier island. Because both shorelines are eroding, the width of the island has decreased during the last 75 years. The section to the west of a breach that opened during Hurricanes Ivan and Katrina (known as Katrina Cut) exhibits a steady, rapid decrease in width while the section to the east of the breach has gone through periods of expansion and contraction and has only recently begun slowly narrowing. Although the recent trends indicate declining widths, the back-barrier progradation rates in this area were the highest compared to other time periods, which abated extreme narrowing caused by increased open-ocean shoreline erosion. These data and the interpreted results indicate that both short-term (annual) and long-term (decadal) cross-barrier sediment exchange is a key component of sustaining barrier island width. Therefore, any mechanisms that influence this exchange, whether from natural processes (overwash, breaching, or inlet dynamics) or human activities (development, post-storm recovery, restoration), should be considered when evaluating the long-term sustainability of barrier island systems.

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

Hurricane Sandy, which made landfall on October 29, 2012, near Atlantic City, New Jersey, had a significant impact on the coastal system along the south shore of Long Island, New York. A record significant wave height of 9.6 meters (m) was measured at wave buoy 44025, approximately 48 kilometers offshore of Fire Island, New York. Surge and runup during the storm resulted in extensive beach and dune erosion and breaching of the Fire Island barrier island system at two locations, including a breach that formed within the Otis Pike Fire Island High Dune Wilderness area on the eastern side of Fire Island.The U.S. Geological Survey (USGS) has a long history of conducting morphologic change and processes research at Fire Island. One of the primary objectives of the current research effort is to understand the morphologic evolution of the barrier system on a variety of time scales (from storm scale to decade(s) to century). A number of studies that support the project objectives have been published. Prior to Hurricane Sandy, however, little information was available on specific storm-driven change in this region. The USGS received Hurricane Sandy supplemental funding (project GS2–2B: Linking Coastal Processes and Vulnerability, Fire Island, New York, Regional Study) to enhance existing research efforts at Fire Island. The existing research was greatly expanded to include inner continental shelf mapping and investigations of processes of inner shelf sediment transport; beach and dune response and recovery; and observation, analysis, and modeling of the newly formed breach in the Otis Pike High Dune Wilderness area, herein referred to as the wilderness breach. The breach formed at the site of Old Inlet, which was open from 1763 to 1825. The location of the initial island breaching does not directly correspond with topographic lows of the dunes, but instead the breach formed in the location of a cross-island boardwalk that was destroyed during Hurricane Sandy.From 2013 to November 2015, bathymetric data were collected by the USGS St. Petersburg Coastal and Marine Science Center during three surveys of the breach channel and tidal shoals, and shoreline positions on each side of the breach (also collected by the National Park Service). Additionally, pre-storm topography/bathymetry EAARL–B light detection and ranging (lidar) data were collected by the USGS the day prior to Hurricane Sandy’s landfall. These data serve as a baseline for change analyses during four subsequent periods: June 2013, June 2014, October 2014, and May 2015. The June 2013 single-beam bathymetry data were collected in collaboration with the U.S. Army Corps of Engineers (USACE), using the Lighter Amphibious Resupply Cargo (LARC) vessel, and included the ebb shoal and breach channel. The USGS collected and processed the three additional bathymetric datasets using personal watercraft equipped with single-beam echo sounders and backpack Global Positioning System (GPS) over shallow flood shoals.Eastern and western breach shorelines were surveyed weekly to monthly beginning on November 6, 2012 (by the National Park Service [NPS], and USGS St. Petersburg Coastal and Marine Science Center), with measurements made every few weeks for the first year and every few months after October 2013. The NPS and researchers from Stony Brook University monitored the breach by collecting field data of the breach channel bathymetry, conducting aerial photographic overflights, and performing water-quality analyses (see http://po.msrc.sunysb.edu/GSB/). The aerial photography collected and rectified by Stony Brook University is used extensively in our morphologic change description to examine changes to breach shorelines (supplementing shoreline data collected in the field), channel width, and orientation. Due to the uncertainties and the variation in survey methods, a rigorous quantitative analysis was not performed. However, average calculations of various breach metrics allow a qualitative analysis of breach development and evolution.This report presents an overview of the data collected and a summary discussion of the observed changes to the breach system and the seasonal wave climatology associated with the breach morphodynamic response.

Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida, conducted a bathymetric survey of Fire Island from May 6-20, 2015. The USGS is involved in a post-Hurricane Sandy effort to map and monitor the morphologic evolution of the wilderness breach as a part of the Hurricane Sandy Supplemental Project GS2-2B. During this study, bathymetry data were collected with single-beam echo sounders and Global Positioning Systems, which were mounted to personal watercraft, along the Fire Island shoreface and within the wilderness breach. Additional bathymetry and elevation data were collected using backpack Global Positioning Systems on flood shoals and in shallow channels within the wilderness breach.