Shorelines of the Georgia (GA) coastal region used in shoreline change analysis

Metadata also available as - [Outline] - [Parseable text] - [XML]

Frequently anticipated questions:

What does this data set describe?

Shorelines of the Georgia (GA) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.
Cross-referenced citations are applicable to the dataset as a whole. Additional citations are located within individual process steps that pertain specifically to the method described in that step.
  1. How might this data set be cited?
    U.S. Geological Survey, 2017, Shorelines of the Georgia (GA) coastal region used in shoreline change analysis: data release DOI:10.5066/F74X55X7, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Kratzmann, M.G., Himmelstoss, E.A., and Thieler, E.R., 2017, National Assessment of Shoreline Change— A GIS compilation of Updated Vector Shorelines and Associated Shoreline Change Data for the Southeast Atlantic Coast: data release DOI:10.5066/F74X55X7, U.S. Geological Survey, Reston, VA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.478244
    East_Bounding_Coordinate: -80.836787
    North_Bounding_Coordinate: 32.030977
    South_Bounding_Coordinate: 30.713862
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Ending_Date: 1999
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: vector digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set. It contains the following vector data types (SDTS terminology):
      • String (335)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000001. Longitudes are given to the nearest 0.000001. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.000000.
      The flattening of the ellipsoid used is 1/298.257224.
  7. How does the data set describe geographic features?
    Vector shorelines (Source: U.S. Geological Survey)
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Feature geometry. (Source: Esri) Coordinates defining the features.
    Route identification value assigned to individual lidar shoreline line segments. A unique cross-shore profile identification value is stored at each vertex of the lidar route and serves as a common attribute to the shoreline uncertainty table (GA_shorelines_uncertainty.dbf). (Source: U.S. Geological Survey)
    0Short integer field where zeros are "no data" and automatically filled in for the remaining shoreline polylines not derived from lidar.
    Date of shoreline position; date of survey as indicated on source material. A default date of 07/01 was assigned to shorelines where only the year was known (month and day unknown). Using July, the mid-point month of the calendar year, minimizes the potential offset to the actual shoreline date by a maximum of six months. (Source: U.S. Geological Survey) Character string of length 10
    Estimate of shoreline position uncertainty. Actual shoreline position is within the range of this value (plus or minus, meters). The historic shoreline uncertainty values incorporate measurement uncertainties associated with mapping methods and materials for historical shorelines, the geographic registration of shoreline position, and shoreline digitizing. The uncertainty of the High Water Line (HWL) at an individual transect was determined by using positional uncertainty information stored along the shoreline route. The HWL uncertainty was added to the historic shoreline uncertainty stored in the shoreline attribute table during the DSAS rate calculation process to better account for uncertainty at individual transects alongshore as opposed to using a regionally averaged value. This was done using linear referencing that interpolates a value based on data stored in the GA_shorelines_uncertainty dBase file (.dbf) at a specific transect/shoreline intersect alongshore. The lidar shoreline position uncertainty is also stored in the associated shorelines_uncertainty.dbf file and values are interpolated at specific transect/shoreline intersections using the same linear referencing method. The lidar uncertainty attribute field was filled with null values while residing in the geodatabase. Upon exporting the shoreline feature class to a shapefile for publication, the null values in the lidar uncertainty field were automatically converted to zero values. (Source: U.S. Geological Survey)
    0Zeros are "no data" and serve as place fillers for the lidar shorelines. Actual uncertainty values for lidar are stored in shoreline uncertainty dBase (.dbf) file.
    Agency that provided shoreline feature or the data source used (e.g. T-sheet) to digitize shoreline feature. (Source: U.S. Geological Survey)
    USGSU.S. Geological Survey
    NOAANational Oceanic and Atmospheric Administration
    Type of data used to create shoreline. (Source: U.S. Geological Survey)
    lidarLight detection and ranging (lidar).
    T or TP with numberNOAA/NOS topographic survey sheet (T- or TP-sheet) with associated registry number
    Four digit year of shoreline (Source: U.S. Geological Survey)
    Range of values
    Differentiates between shorelines that have known month and day attributes and those that use the default value of 07/01 when only the year is known. (Source: U.S. Geological Survey)
    0Shoreline month and day are known.
    1Shoreline month and day are unknown and default value of 07/01 was used.
    Location of shoreline with respect to wave energy exposure. An open ocean coast is directly exposed to ocean waves and is typically characterized by higher wave energy. A sheltered coast is not directly exposed to ocean waves and is characterized by lower wave energy. This shoreline dataset only includes open ocean locations. (Source: U.S. Geological Survey)
    open oceanShoreline on a coast with open ocean wave exposure.
    Length of feature in meter units (UTM zone 17N, WGS 84) (Source: Esri)
    Range of values
    The entity and attribute information provided here describes the tabular data associated with the dataset. Please review the individual attribute descriptions for detailed information.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • U.S. Geological Survey
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: E.A. Himmelstoss
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 (voice)
    508-547-2310 (FAX)

Why was the data set created?

This dataset includes shorelines from 144 years ranging from 1855 to 1999 in Georgia's coastal region. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)) and lidar (U.S. Geological Survey (USGS)/NOAA/National Aeronautics and Space Administration (NASA)). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 06-Oct-2008 (process 1 of 11)
    An operational Mean High Water (MHW) shoreline was extracted from the lidar surveys within MATLAB v7.6 using a method similar to the one developed by Stockdon et al. (2002). Shorelines were extracted from cross-shore profiles which consist of bands of lidar data 2 m wide in the alongshore direction and spaced every 20 m along the coast. For each profile, the seaward sloping foreshore points were identified and a linear regression was fit through them. The regression was evaluated at the operational MHW elevation to yield the cross-shore position of the MHW shoreline. If the MHW elevation was obscured by water points, or if a data gap was present at MHW, the linear regression was simply extrapolated to the operational MHW elevation. A lidar positional uncertainty associated with this point was also computed. The horizontal offset between the datum-based lidar MHW shoreline and the proxy-based historical shorelines nearly always acts in one direction and the "bias" value was computed at each profile (Ruggiero and List, 2009). In addition an uncertainty associated with the bias was also computed, which can also be thought of as the uncertainty of the HWL shorelines due to water level fluctuations. Repeating this procedure at successive profiles generated a series of X,Y points that contain a lidar positional uncertainty, a bias, and a bias uncertainty value. Ruggiero, P. and List, J.H., 2009. Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates. Journal of Coastal Research: v.25, n.5, pp.1069-1081. Stockdon, H.F., Sallenger, A.H., List, J.H., and Holman, R.A., 2002. Estimation of Shoreline Position and Change using Airborne Topographic Lidar Data: Journal of Coastal Research, v.18, n.3, pp.502-513. Person who carried out this activity:
    Amy Farris
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 (voice)
    508-457-2310 (FAX)
    Date: 23-Mar-2010 (process 2 of 11)
    The lidar data were collected in projected coordinates (WGS 84 UTM zone 17N). The series of operational MHW points extracted from the cross-shore lidar profiles were converted into a calibrated route shapefile for use in ArcGIS using a Python script. The script generates a point shapefile, converts it to a polyline-M file, saves the uncertainty information in an accessory dBase (.dbf) file and finally generates a calibrated route for the newly-created polyline-M file. Calibration is based on the unique and sequential profile ID value provided with the point data and stored as the M-value. This value is also stored as an attribute in the uncertainty .dbf file and is used as the common attribute field linking the shoreline route file (shoreline.shp) to the uncertainty table (shorelines_uncertainty.dbf) storing the lidar positional uncertainty, the bias correction value, and the uncertainty of the bias correction for each point of the original lidar data. During the rate calculation process DSAS uses linear referencing to retrieve the uncertainty and bias values stored in the associated table. Visually identified HWL-type proxy shorelines are virtually never coincident with datum-based MHW-type shorelines. In fact, HWL shorelines are almost universally estimated to be higher (landward) on the beach profile than MHW shorelines. Not accounting for this offset will cause shoreline change rates to be biased toward slower shoreline retreat, progradation rather than retreat, or faster progradation than in reality (for the typical case where datum-based shorelines are more recent data than the proxy-based shoreline dates), depending on actual changes at a given site. Ruggiero, Peter, and List, J.H., 2009, Improving accuracy and statistical reliability of shoreline position and change rate estimates: Journal of Coastal Research, v. 25, no. 5, p. 1069–1081. Ruggiero, Peter, Kaminsky, G.M., and Gelfenbaum, Guy, 2003, Linking proxy-based and datum-based shorelines on high-energy coastlines—Implications for shoreline change analyses: Journal of Coastal Research, special issue 38, p. 57–82. For a detailed explanation of the method used to convert the lidar shoreline to a route, please refer to "Appendix 2- A case study of complex shoreline data" in the DSAS user guide: Himmelstoss, E.A. 2009. "DSAS 4.0 Installation Instructions and User Guide" in: Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan. 2009. Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. Person who carried out this activity:
    E.A. Himmelstoss
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2262 (voice)
    508-457-2310 (FAX)
    Date: 2012 (process 3 of 11)
    Data from the previously-published National Assessment of Shoreline Change study for the Southeast Atlantic (USGS Open-File Report 2005-1401 and USGS Open-File Report 2005-1326) were used as the starting point for the project's continued efforts to compile as many quality shorelines as possible for the region. Digital shorelines, if available from another agency, were acquired. This process step and all subsequent process steps were performed by the same person - M.G. Kratzmann. Person who carried out this activity:
    M.G. Kratzmann
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 (voice)
    508-457-2310 (FAX)
    Date: 2013 (process 4 of 11)
    Digitized shorelines were downloaded in vector format from the NOAA Historical Shoreline Survey Viewer, currently available as a Google Earth KMZ ( The extracted shorelines are digitized vectors of the mean high water (MHW) position derived from the scanned and georeferenced T-sheet raster imagery that NOAA also manages. Although NOAA labels historical shorelines as MHW, they are proxy-based and therefore considered HWL for the purposes of shoreline change rate calculation with the proxy-datum bias correction. The modern lidar shoreline used in the analysis is datum-based, not proxy-based, and designated as operational MHW which reflects this difference. NOAA topographic survey sheets (T-sheets) and shorelines were quality checked and edited where necessary. Edgematching was corrected when discovered. If no digital shoreline vectors were available, the original T-sheet scan and world file were downloaded from the NOAA Google Earth viewer. Vector shorelines were digitized from the georeferenced T-sheets using standard editing tools in ArcMap v10. Quality assessments were performed and shorelines were edited to remove any overlap between adjacent t-sheets from the same time period. No edge-matching between neighboring shorelines was attempted. The DSAS-required attribute fields were added to the shapefiles and populated.
    Date: 2013 (process 5 of 11)
    Historical shorelines were merged in Esri's ArcToolbox (v10), Data Management Tools > General > Merge. Then the merged file was projected in ArcToolbox (v10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = geographic (NAD 83); output projection = UTM zone 17N (WGS 84); transformation = NAD_1983_To_WGS_1984_1.
    Date: 2013 (process 6 of 11)
    Historical shorelines were appended to the lidar route data in ArcToolbox (v10) > Data Management Tools > General > Append to produce a single shoreline file for the region. The final shoreline dataset was coded with attribute fields (Date, Uncertainty (Uncy), Source, Source_b, Year_, Default_D, Location). These fields are required for the Digital Shoreline Analysis System (DSAS), which was used to calculate shoreline change rates.
    Date: 2013 (process 7 of 11)
    The appended shoreline file and the shoreline uncertainty table (.dbf) were imported into a personal geodatabase in ArcCatalog v10 by right-clicking on the geodatabase > Import (feature class for shoreline file and table for uncertainty table) for use with the Digital Shoreline Analysis System (DSAS) v4.3 software to perform rate calculations.
    Date: 2013 (process 8 of 11)
    The shoreline feature class was exported from the personal geodatabase back to a shapefile in ArcCatalog v10 by right-clicking on the shoreline file > Export > To Shapefile (single) for publication purposes.
    Date: 2015 (process 9 of 11)
    The data were projected in ArcToolbox v10.2 > Data Management Tools > Projections and Transformations > Project. Parameters: input projection = UTM zone 17N (WGS84); output projection = geographic coordinates (WGS84); transformation = none.
    Date: 25-Aug-2017 (process 10 of 11)
    Keywords section of metadata optimized for discovery in USGS Coastal and Marine Geology Data Catalog. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Alan O. Allwardt
    Contractor -- Information Specialist
    2885 Mission Street
    Santa Cruz, CA

    831-460-7551 (voice)
    831-427-4748 (FAX)
    Date: 10-Aug-2020 (process 11 of 11)
    Added keywords section with USGS persistent identifier as theme keyword. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
  3. What similar or related data should the user be aware of?
    Morton, Robert A., and Miller, Tara L., 2005, National Assessment of Shoreline Change: Part 2 Historical Shoreline Changes and Associated Coastal Land Loss along the U.S. Southeast Atlantic Coast: Open-File Report 2005-1401, U.S. Geological Survey, Reston, VA.

    Online Links:

    Miller, Tara L., Morton, Robert A., and Sallenger, Asbury H., 2005, The National Assessment of Shoreline Change— A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for the U.S. Southeast Atlantic Coast: Open-File Report 2005-1326, U.S. Geological Survey, Reston, VA.

    Online Links:

    Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, A., 2009, Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change: Open-File Report 2008-1278, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details: Current version of software at time of use was 4.3
    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management, United States Geological Survey (USGS), and National Aeronautics and Space Administration (NASA), 20140121, Fall 1999 East Coast NOAA/USGS/NASA Airborne LiDAR Assessment of Coastal Erosion (ALACE) Project for the US Coastline: NOAA's Ocean Service, Office for Coastal Management, Charleston, SC.

    Online Links:

    Lidar data were obtained prior to the publication date listed in this citation.
    National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Unknown, Scanned National Ocean Service (NOS) Coastal Survey Maps (also known as Topographic Survey sheets, or T-sheets): National Oceanic and Atmospheric Administration, Washington, D.C..

    Online Links:

    Other_Citation_Details: NOAA shoreline manuscripts (T-sheets)
    Himmelstoss, E.A., Kratzmann, M.G., and Thieler, E.R., 2017, National Assessment of Shoreline Change: Summary Statistics for Updated Vector Shorelines and Associated Shoreline Change Data for the Gulf of Mexico and Southeast Atlantic Coasts: Open-File Report 2017-1015, U.S. Geological Survey, Reston, VA.

    Online Links:

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    The data provided here are a compilation of shorelines from multiple sources, spanning 144 years. The attributes are based on the requirements of the Digital Shoreline Analysis System (DSAS) software and have gone through a series of quality assurance procedures.
  2. How accurate are the geographic locations?
    Shoreline data have been acquired from 1855 to 1999, the horizontal accuracy of which varies with respect to data source from which the shorelines were digitized, the lidar data from which the shorelines were extracted, and the time period. Shorelines prior to 1960 (T-sheets) have an estimated positional uncertainty of plus or minus 10.8 meters. Shorelines from the 1960s-1980s (T-sheets) have an estimated positional uncertainty of plus or minus 5.1 meters. The lidar shoreline from 1999 has an estimated positional uncertainty of plus or minus 4.4 meters. Crowell, M., Leatherman, S.P., and Buckley, M.K., 1991. Historical Shoreline Change: Error Analysis and Mapping Accuracy. Journal of Coastal Research: v.7, n.3, pp.839-852.
  3. How accurate are the heights or depths?
    The historic shorelines are proxy-based and estimate the HWL. The lidar shoreline is tidal-based and estimates MHW. While the proxy-based HWL shorelines are not shifted to the MHW datum, the distance measurements established by the transects and used by DSAS to compute rate-of-change statistics are adjusted to account for the offset between HWL and MHW. Therefore, all distance measurements along DSAS transects are referenced to the MHW datum before rates are calculated.
  4. Where are the gaps in the data? What is missing?
    This shoreline file is complete and contains all shoreline segments used to calculate shoreline change rates along sections of the Georgia coastal region where shoreline position data were available. These data adequately represented the shoreline position at the time of the survey. Remaining gaps in these data, if applicable, are a consequence of non-existing data or existing data that did not meet quality assurance standards. The digitized shoreline vectors downloaded from NOAA included attributes defining the shoreline type (attribute field name varies by file). For the open-ocean facing shorelines, only Mean High Water shoreline features (Natural.Mean High Water; SPOR; 20) were retained. Other shoreline features (such as seawalls, bulkheads, manmade objects) were deleted. NOAA has made non-georeferenced NOAA Shoreline Survey Scans available for download, and additional data for the region may be available (
  5. How consistent are the relationships among the observations, including topology?
    Adjacent shoreline segments do not overlap and are not necessarily continuous. Shorelines were quality checked for accuracy. Any slight offsets between adjacent segments due to georeferencing and digitizing error are taken into account in the uncertainty of the shoreline position, as reported in the horizontal accuracy section of this metadata file.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None
Use_Constraints Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the originator of the dataset.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey
    Denver Federal Center
    Denver, CO

    1-888-275-8747 (voice)
  2. What's the catalog number I need to order this data set?
  3. What legal disclaimers am I supposed to read?
    Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    These data are available in a polyline shapefile format. The user must have software to read and process the data components of a shapefile.

Who wrote the metadata?

Last modified: 19-Mar-2024
Metadata author:
M.G. Kratzmann
U.S. Geological Survey
384 Woods Hole Road
Woods Hole, MA

508-548-8700 (voice)
508-547-2310 (FAX)
The metadata contact email address is a generic address in the event the person is no longer with USGS. (updated on 20240319)
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <>
Generated by mp version 2.9.51 on Wed Jun 26 15:25:00 2024