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Geology topics

Amy S. Farris

Publications and source records attributed to Amy S. Farris.

10 recordsLinked to original sources
USGS2023

Processes controlling coastal erosion along Cape Cod Bay, MA

Cape Cod Bay, MA, is a semi-enclosed embayment in the northeastern United States, open on the north to the Gulf of Maine. The coastline experiences impacts typically from strong Nor’easter storms that occur in the late fall or winter months, with some sections of this coastline being affected more severely than others. We investigate the processes that cause spatial variability in storm impacts by using geophysical surveys, shoreline-change analysis, and numerical modeling. We simulated the Gulf of Maine and Cape Cod Bay from Jan–April, 2021, using the COAWST modeling system, including ocean, wave, infragravity wave, and sediment transport models. Results identify bay-scale circulation of alongshore sediment fluxes and convergences at regional shoals. Nearshore modeling also revealed zones of increased wave heights that correlate with regions of increased erosion and coastal angle orientation. Modeled and computed shoreline-change have some correlation but the model does not capture all the variability. This overall approach can potentially be used for other coastal locations to identify regions of storm impacts and to manage coastal resources.

Massachusetts↗
USGS2023

Accuracy of shoreline forecasting using sparse data

Sandy beaches are important resources providing recreation, tourism, habitat, and coastal protection. They evolve over various time scales due to local winds, waves, storms, and changes in sea level. A common method used to monitor change in sandy beaches is to measure the movement of the shoreline over time. Typically, the rate of change is estimated by fitting a linear regression through a time series of shoreline positions. To best manage the valuable resources within a coastal environment, accurate forecasts of shoreline position are needed. A simple way to estimate future shoreline position is to extrapolate a linear regression into the future, this method is often used to establish management guidelines like construction setback lines. A more recently developed shoreline forecasting technique utilizes the Kalman filter to assimilate shoreline data and modify the linear regression. This paper calculates the uncertainty and accuracy of both the extrapolated linear regression and Kalman filter forecasting methods for 10- and 20-year hindcasts using data collected at five diverse study areas. These data are inherently sparse (8–10 measurements per location, collected over 150 years) and are representative of the observed historical data available for the continental United States for this timeframe. Both methods produced similar results and had regionally averaged forecast accuracies of 5–16 m. We determined that the inaccuracy of the forecasts is largely due to the effects of shorter time scale variability. This variability is roughly proportional to the standard error of the linear regression, which is a useful measure of forecast uncertainty.

Florida, Georgia, Massachusetts↗
USGS2021

Digital Shoreline Analysis System (DSAS) version 5.1 user guide

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. Note: As of May 2024, the current version of the Digital Shoreline Analysis System (DSAS), version 6.0, is a standalone desktop application for calculating shoreline or boundary change over time. The user guide for DSAS version 5.1 is applicable to many aspects of version 6.0. The user guide provides relevant information on the DSAS workflow, including how to define a reference baseline for measurements, attribute requirements for baselines and shorelines, and supporting information on rate calculations and statistics.

Open-File Report↗
USGS2019

Identifying salt marsh shorelines from remotely sensed elevation data and imagery

Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea-level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The Marsh Edge from Elevation Data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the “marsh scarp.” It is detected as the maximum slope along a cross-shore transect between Mean High Water and Mean Tide Level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the Marsh Edge by Image Processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The root-mean-square deviation between the two methods within the test area is 0.6 meter. The two methods were compared to each other using high resolution Unmanned Aircraft Systems (UAS) data and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods and less than 0.43 meters from the digitized shoreline. MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability

Massachusetts↗
USGS2018

Digital Shoreline Analysis System (DSAS) version 5.0 user guide

Overview 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.

Open-File Report↗
USGS2018

Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data

The U.S. Geological Survey Coastal and Marine Geology Program uses three methods to derive a datum-based, mean high water shoreline on open-ocean coasts from light detection and ranging (lidar) elevation surveys. This work compared the shorelines produced by the three methods for two different surveys: one survey with simple beach morphology, and one survey with complex beach morphology. For the survey with simple beach morphology, the three methods gave very similar results. The mean differences were less than 0.1 meter, and the root mean square differences were all less than 1.0 meter. For the survey of a beach with complex morphology, the quality control used in the Profile method and Smoothed Contour/Manual Hybrid method produced cleaner shorelines than the Grid method. Only the Profile method can extrapolate if there is no data around mean high water. The Grid and Profile methods produce a point by point estimate of uncertainty which is needed for some applications. Only the Contour method can be easily transferred to external users.

Open-File Report↗
USGS2017

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

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.

Massachusetts↗
USGS2007

Shoreline change as a proxy for subaerial beach volume change

It is difficult and expensive to calculate changes in sediment volume for large sections of sandy beaches. Shoreline change could be a useful proxy for volume change because it can be collected quickly and relatively easily over long distances. In this paper, we summarize several studies that find a high correlation between shoreline change and subaerial volume change. We also examine three new data sets. On Cape Cod, Massachusetts, the correlation coefficients between the time series of shoreline change and subaerial volume change at two locations are 0.73 and 0.96. On Assateague Island, the correlation coefficient between along-coast variations in shoreline change and subaerial volume change is 0.71. On the Outer Banks of North Carolina, the average correlation coefficient between temporal variations in shoreline change and subaerial volume change is 0.84. For spatial variations, the average correlation coefficient is 0.88. It is therefore concluded that shoreline change is a useful proxy for subaerial volume change.

Massachusetts, Maryland, North Carolina, Virginia↗