Shorelines of the Alabama coastal region used in shoreline change analysis

Frequently anticipated questions:

• What does this data set describe?
  1. How might this data set be cited?
  2. What geographic area does the data set cover?
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
  5. What is the general form of this data set?
  6. How does the data set represent geographic features?
  7. How does the data set describe geographic features?
• Who produced the data set?
  1. Who are the originators of the data set?
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
• Why was the data set created?
• How was the data set created?
  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
  3. What similar or related data should the user be aware of?
• How reliable are the data; what problems remain in the data set?
  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
  5. How consistent are the relationships among the data, including topology?
• How can someone get a copy of the data set?
  1. Are there legal restrictions on access or use of the data?
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  3. What's the catalog number I need to order this data set?
  4. What legal disclaimers am I supposed to read?
  5. How can I download or order the data?
• Who wrote the metadata?

What does this data set describe?

1. How might this data set be cited?
   U.S. Geological Survey, 2017, Shorelines of the Alabama coastal region used in shoreline change analysis: data release DOI:10.5066/F78P5XNK, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

   Online Links:

   • https://doi.org/10.5066/F78P5XNK
   • https://www.sciencebase.gov/catalog/item/582ca4a5e4b04d580bd378ec
   • https://www.sciencebase.gov/catalog/file/get/582ca4a5e4b04d580bd378ec

   This is part of the following larger work.
   Himmelstoss, E.A., Kratzmann, M.G., and Thieler, E.R., 2017, National Assessment of Shoreline Change— A GIS compilation of Updated Vector Shorelines and Associated Shoreline Change Data for the Gulf of Mexico Coast: data release DOI:10.5066/F78P5XNK, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.5066/F78P5XNK
   • https://www.sciencebase.gov/catalog/item/58055db4e4b0824b2d1c1ee2

2. What geographic area does the data set cover?

   West_Bounding_Coordinate: -88.337457
   East_Bounding_Coordinate: -87.515428
   North_Bounding_Coordinate: 30.282130
   South_Bounding_Coordinate: 30.220700
   

3. What does it look like?
4. Does the data set describe conditions during a particular time period?

   Beginning_Date:

   Ending_Date: 2001

   Currentness_Reference:

   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 (460)
   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?

   AL_shorelines

   Vector shorelines (Source: U.S. Geological Survey)

   FID

   Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.

   Shape

   Feature geometry. (Source: Esri) Coordinates defining the features.

   RouteID

   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 (AL_shorelines_uncertainty.dbf). (Source: U.S. Geological Survey)

   Value	Definition
   0	Short integer field where zeros are "no data" and automatically filled in for the remaining shoreline polylines not derived from lidar.

   Date_

   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

   Uncy

   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 AL_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)

   Value	Definition
   0	Zeros 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.

   Source

   Agency that provided shoreline feature or the data source used (e.g. T-sheet) to digitize shoreline feature. (Source: U.S. Geological Survey)

   Value	Definition
   USGS	U.S. Geological Survey
   NOAA	National Oceanic and Atmospheric Administration
   FLDEP	Florida Department of Environmental Protection
   MOG	Mississippi Department of Environmental Quality, Office of Geology
   USA	University of South Alabama (USA)

   Source_b

   Type of data used to create shoreline. (Source: U.S. Geological Survey)

   Value	Definition
   lidar	Light detection and ranging (lidar).
   aerial photographs	aerial photographs
   T or TP with number	NOAA/NOS topographic survey sheet (T- or TP-sheet) with associated registry number
   CSC data	NOAA Coastal Services Center vector digital shoreline data
   [data source]; [region source file]	[data source] is NOAA/NOS topographic survey sheet (T- or TP-sheet) with associated registry number, or other data source; [region source] identifies file name downloaded from Florida Department of Environmental Protection (FLDEP)
   MOG data	Mississippi Department of Environmental Quality, Office of Geology
   USA data	University of South Alabama (USA)

   Year_

   Four digit year of shoreline (Source: U.S. Geological Survey)

   Range of values
   Minimum:	1849
   Maximum:	2001

   Default_D

   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)

   Value	Definition
   0	Shoreline month and day are known.
   1	Shoreline month and day are unknown and default value of 07/01 was used.

   Location

   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)

   Value	Definition
   open ocean	Shoreline on a coast with open ocean wave exposure.

   Shape_Leng

   Length of feature in meter units (UTM zone 16N, WGS 84) (Source: Esri)

   Range of values
   Minimum:	2.037043
   Maximum:	28377.680497
   Units:	meters

   Entity_and_Attribute_Overview:

   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
   

   USA

   508-548-8700 (voice)

   508-547-2310 (FAX)

   ehimmelstoss@usgs.gov

Why was the data set created?

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: 18-Nov-2008 (process 1 of 12)

   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
   

   USA

   508-548-8700 (voice)

   508-457-2310 (FAX)

   afarris@usgs.gov

   Date: 14-Jul-2009 (process 2 of 12)

   The lidar data were collected in projected coordinates (WGS 84 UTM zone 16N). 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 (AL_shoreline.shp) to the uncertainty table (AL_shoreline_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. https://woodshole.er.usgs.gov/project-pages/DSAS/version4/images/pdf/DSASv4_3.pdf This process step and all subsequent process steps, unless otherwise noted, were performed by the same person - E.A. Himmelstoss. Person who carried out this activity:
   

   E.A. Himmelstoss

   U.S. Geological Survey

   384 Woods Hole Road

   Woods Hole, MA
   

   USA

   508-548-8700 x2262 (voice)

   508-457-2310 (FAX)

   ehimmelstoss@usgs.gov

   Date: 2013 (process 3 of 12)

   Data from the previously-published National Assessment of Shoreline Change study (USGS Open-File Reports 2004-1043 and 2004-1089) were used as the starting point for the project’s continued efforts to compile as many quality shorelines as possible for the region, this included data from the National Oceanographic and Atmospheric Association (NOAA), the Florida Department of Environmental Protection (FLDEP), the University of South Alabama (USA), and the Mississippi Department of Environmental Quality, Office of Geology (MOG). Historical shorelines from the original shoreline assessment publication were provided as individual shapefiles in the report. These data were merged in Esri's ArcToolbox (v.10), Data Management Tools > General > Merge. Then the merged file was projected in Esri's ArcToolbox (v.10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = geographic (NAD 83); output projection = UTM zone 16N (WGS 84); transformation = NAD_1983_To_WGS_1984_1.

   Date: 2013 (process 4 of 12)

   Additional digitized shorelines were downloaded in vector format from the NOAA Historical Shoreline Survey Viewer, currently available as a Google Earth KMZ (https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.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. Shoreline data from NOAA were used with preference at any location where the shoreline date had previously been sourced from FLDEP, USA or MOG. Although the various sources extracted the shoreline from the same T-sheet, the NOAA data have fewer process steps and therefore a reduced likelihood of additional error. NOAA topographic survey sheets (T-sheets) and shorelines were quality checked and edited where necessary. Edgematching was corrected when discovered.

   Date: 2013 (process 5 of 12)

   Shorelines were acquired from the Florida Department of Environmental Protection that were not part of the previous USGS shoreline assessment and extend into Alabama, even though the data is primarily provided for Florida. Some of these were sourced from T-sheet data (not available from NOAA) while other shorelines were derived from georeferenced vertical aerial photographs. The shoreline data are organized by county and provided for download as CAD files (.dwg) (ftp://ftp.dep.state.fl.us/pub/water/beaches/HSSD/drawings/draw8390/). There are three State Plane FIPS zones for Florida (Florida East FIPS 0901, which covers the entire Atlantic coast along with Collier and Monroe counties on the southwestern coast of the peninsula; Florida West FIPS 0902, which covers the Gulf Coast section of the peninsula between Lee county to the south and Levy county to the north; Florida North FIPS 0903, which covers the panhandle section of the Florida Gulf Coast from Dixie county in the east to the FL/AL border in Escambia county to the west) and the appropriate zone was assigned to the data based on location. The .dwg files consist of several layers. After the individual county .dwg files were added to the ArcMap document, the polyline layer was selected from each county file and exported to a shapefile (right-click > export) from the ArcMap table of contents panel. The exported polyline shapefile for each county was added to the ArcMap document, an edit session was started, and all non-shoreline features were deleted from the file using the “Layer” attribute field. Non-shoreline features were determined to be any entry in the “Layer” attribute field that did not begin with a date. This removed most but not all of the annotation. The “Entity” field was then used to query non-shoreline features. In all files, features that had an Entity attribute field coded as “Circle” were deleted, in some files both “Circle” and “Line” were deleted (in some files the “Line” code contained shoreline position data). In all files the “Entity” field coded as “Arc” contained shoreline position data. After all annotation had been removed from the shapefile, a check for duplicate features was run using XTools (v7.1.0). All duplicate features were deleted. The Dissolve tool was used (ArcToolbox > Data Management > Generalization > Dissolve) to dissolve each shapefile based on the “Layer” field. The box next to “Unsplit lines (optional)” at the bottom was selected. A final visual check of the shorelines was performed. In many of the files it was concluded that edge matching was likely performed between adjacent T-sheets due to noticeable offsets in the shoreline. There is no way to differentiate these line segments from legitimate shoreline vectors using the attribute fields, but any that were observed during the visual inspection were deleted from the file. Close attention was paid to the overlapping areas between adjacent county shoreline files. There were several instances where inconsistent overlap of shoreline from the same date and source occurred, which required manual editing of the two county shapefiles including splitting of some shorelines at the location where the overlap began and deleting the duplicate/overlapping segments from one file. No edgematching was performed during this process. The DSAS-required attribute fields were added to the shapefiles and populated. Date or year and the source of shoreline data (T-sheet or aerial photograph) information was verified by reviewing the details summarized in the bibliography files provided by FLDEP for each county (ftp://ftp.dep.state.fl.us/pub/water/beaches/HSSD/sourcebibs/). All additional shoreline data compiled from FLDEP were projected in Esri's ArcToolbox (v.10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = NAD 1983 State Plane Florida North FIPS 0903 (Feet); output projection = UTM zone 16N (WGS 84); transformation = NAD_1983_To_WGS_1984_1.

   Date: 2013 (process 6 of 12)

   If no digital shoreline vectors were available from NOAA or FLDEP, 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. All shoreline data digitized from NOAA were projected in Esri's ArcToolbox (v.10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = geographic (NAD 83); output projection = UTM zone 16N (WGS 84); transformation = NAD_1983_To_WGS_1984_1.

   Date: 2013 (process 7 of 12)

   Historic shorelines were appended to the lidar route data in ArcToolbox (v.10) > 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 8 of 12)

   The appended shoreline file and the shoreline uncertainty table (.dbf) were imported into a personal geodatabase in ArcCatalog v.10 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: 2014 (process 9 of 12)

   The shoreline feature class was exported from the personal geodatabase back to a shapefile in ArcCatalog v.10 by right-clicking on the shoreline file > Export > To Shapefile (single) for publication purposes.

   Date: 2015 (process 10 of 12)

   The data were projected in ArcToolbox v.10 > Data Management Tools > Projections and Transformations > Project. Parameters: input projection = UTM zone 16N (WGS 84); output projection = geographic coordinates (WGS84); transformation = none.

   Date: 25-Aug-2017 (process 11 of 12)

   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)

   aallwardt@usgs.gov

   Date: 10-Aug-2020 (process 12 of 12)

   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)

   vatnipp@usgs.gov

3. What similar or related data should the user be aware of?
   Morton, Robert A., Miller, Tara L., and Moore, Laura J., 2004, National Assessment of Shoreline Change: Part 1 Historical Shoreline Changes and Associated Coastal Land Loss along the U.S. Gulf of Mexico: Open-File Report 2004-1043, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://pubs.usgs.gov/of/2004/1043/

   Miller, Tara L., Morton, Robert A., Sallenger, Asbury H., and Moore, Laura J., 2004, The National Assessment of Shoreline Change— A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for the U.S. Gulf of Mexico: Open-File Report 2004-1089, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://pubs.usgs.gov/of/2004/1089/

   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:

   • https://woodshole.er.usgs.gov/project-pages/DSAS/version4/
   • https://woodshole.er.usgs.gov/project-pages/DSAS/

   Other_Citation_Details: Current version of software at time of use was 4.3
   

   United States Geological Survey (USGS), National Aeronautics & Space Administration (NASA), and Department of Commerce (DOC), National Oceanic & Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), 20141114, 2001 USGS/NASA Airborne Topographic Mapper (ATM) Lidar: Coastal Alabama, Florida, Louisiana, Mississippi, Texas: NOAA's Ocean Service, Office for Coastal Management (OCM), Charleston, SC.

   Online Links:

   • https://coast.noaa.gov/dataviewer/index.html?action=advsearch&qType=in&qFld=ID&qVal=525
   • ftp://coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid12a/data/525
   • https://coast.noaa.gov/dataviewer
   • https://www.coast.noaa.gov

   Other_Citation_Details:

   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:

   • https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.kmz
   • https://oceanservice.noaa.gov/news/features/feb13/historical-shoreline.html
   • https://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html

   Other_Citation_Details: NOAA shoreline manuscripts (T-sheets)
   

   Florida Department of Environmental Protection (FLDEP), Unknown, Historic Shoreline Drawings: FL Department of Environmental Protection, Tallahassee, FL.

   Online Links:

   • https://www.dep.state.fl.us/beaches/data/his-shore.htm

   Other_Citation_Details: Historic Shoreline Database
   

   Mississippi Department of Environmental Quality, Office of Geology (MOG), Unknown, Historic Shorelines: Mississippi Office of Geology, Energy and Coastal Geology Division, Jackson, MS.

   Online Links:

   • https://geology.deq.state.ms.us/coastal/Shorelines-Historic.htm

   Other_Citation_Details:

   Data were digitized from NOS U.S. Coast and Geodectic T-Sheets by Louisiana State University. These shorelines represent reliable positions for use in analyzing rates of change and documenting the location of the shoreline. In general the shoreline positions provide an accurate location to +/- 10 meters root mean square. The T-Sheet data was gathered using survey techniques of the time. Map scales of base data range from 1:10,000 to 1:20,000. MOG Contact Person - Barbara Yassin - (601) 961-5571

   University of South Alabama (USA), Unknown, Historic Shoreline Database: University of South Alabama, Mobile, AL.

   Other_Citation_Details: USA Contact Person - Scott Douglass - (251) 460-6174
   

   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:

   • https://doi.org/10.3133/ofr20171015

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 152 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 1849 to 2001, 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. Shorelines from the 1960s-1970s (FLDEP, air photos) have an estimated positional uncertainty of plus or minus 3.2 meters. The lidar shoreline from 2001 has an estimated positional uncertainty of plus or minus 1.6 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 Alabama 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 (https://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html).
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?

1. Who distributes the data set? (Distributor 1 of 1)
   
   U.S. Geological Survey

   Denver Federal Center

   Denver, CO
   

   USA

   1-888-275-8747 (voice)

   sciencebase@usgs.gov
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?
   • Availability in digital form:

     Data format:	These files (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, and .shx) are a collection of files with a common filename prefix and must be downloaded and stored in the same directory. Together they are the components of the shapefile and include FGDC compliant metadata. in format Shapefile (version ArcGIS 10.0) Esri polyline shapefile Size: 0.52
     Network links:	https://www.sciencebase.gov/catalog/file/get/582ca4a5e4b04d580bd378ec https://www.sciencebase.gov/catalog/item/582ca4a5e4b04d580bd378ec https://www.sciencebase.gov/catalog/item/58055db4e4b0824b2d1c1ee2 https://doi.org/10.5066/F78P5XNK

   • Cost to order the data: None

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?

Dates:

Last modified: 19-Mar-2024

Metadata author:

E.A. Himmelstoss

U.S. Geological Survey

384 Woods Hole Road

Woods Hole, MA

USA

508-548-8700 x2262 (voice)

508-547-2310 (FAX)

whsc_data_contact@usgs.gov

Contact_Instructions:

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)