Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.

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Frequently anticipated questions:

What does this data set describe?

Title:
Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.
Abstract:
This shapefile consists of Dauphin Island, AL shorelines extracted from lidar data collected from November 1998 to January 2014. This dataset contains 14 Mean High Water (MHW) shorelines separated into 37 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open ocean, back-barrier, marsh shoreline.
Raw lidar point data was converted to a gridded surface, from which a contour of the operational MHW shoreline (0.24 m North American Vertical Datum of 1988 [NAVD 88]; Weber and others, 2005) was identified and extracted. This produced a continuous MHW shoreline for each of the lidar datasets from 1998 – 2014.
Shorelines for all 14 dates were compiled into a database for use with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009) to quantify rates of shoreline change over the 1998-2014 time period. The migration of shorelines through time is presented as the linear regression rate (LRR) in the associated transect files (https://coastal.er.usgs.gov/data-release/provisional/ip086178/).
Supplemental_Information:
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, 20170616, Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.: U.S. Geological Survey Data Release doi:10.5066/F7T43RB5, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -88.341683558
    East_Bounding_Coordinate: 88.07384796
    North_Bounding_Coordinate: 30.281611631
    South_Bounding_Coordinate: 30.216924258
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 02-Nov-1998
    Ending_Date: 21-Jan-2014
    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 (41)
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 16
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -87.0
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000.0
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.6096
      Ordinates (y-coordinates) are specified to the nearest 0.6096
      Planar coordinates are specified in Meter
      The horizontal datum used is D_North_American_1983.
      The ellipsoid used is GRS_1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222101.
  7. How does the data set describe geographic features?
    Lidar_MHW_Shorelines_1998_2014
    Vector shorelines (Source: U.S. Geological Survey)
    Shape
    Feature geometry. (Source: Esri) Coordinates defining the features.
    OBJECTID
    Internal feature number. (Source: Esri)
    Range of values
    Minimum:1
    Maximum:41
    DATE_
    Date of shoreline position; date of survey as indicated on source material using the MM/DD/YYYY format. (Source: USGS)
    Range of values
    Minimum:11/02/1998
    Maximum:01/21/2014
    NOTES
    Notes about each shoreline segment, according to 1) location along the island (Dauphin Island, Little Dauphin Island, Pelican Island) and shoreline type (open-ocean, back-barrier, marsh shoreline). (Source: USGS) Character string of length 150
    UNCERT
    Estimate of shoreline position uncertainty. Actual shoreline position is within the range of this value (plus or minus, meters). The uncertainty was determined by compiling the sources of uncertainty identified in:
    1) raw lidar data 2) conversion of points to grid surface 3) extraction/editing of horizontal line information from grid surface. (Source: USGS)
    Range of values
    Minimum:1.6
    Maximum:4.8
    Shape_Leng
    Length of feature in meters units (UTM zone 19N, WGS 84) (Source: Esri)
    Range of values
    Minimum:938.885502
    Maximum:41003.284693
    Entity_and_Attribute_Overview:
    This datasest contains MWH shoreline data extracted from lidar elevation datasets from 1998 to 2014 with associated attributes for use with the Digital Shoreline Analysis System.
    Entity_and_Attribute_Detail_Citation:
    The attributes required for use with DSAS include auto-generated fields - OBJECTID, Shape, Shape_Leng, and user-created fields - DATE_ and UNCERT.

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?
    Acknowledgment of the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, as a data source would be appreciated in products developed from these data, and such acknowledgment as is standard for citation and legal practices. Sharing of new data layers developed directly from these data would also be appreciated by the U.S. Geological Survey staff. Users should be aware that comparisons with other datasets for the same area from other time periods may be inaccurate due to inconsistencies resulting from changes in photointerpretation, mapping conventions, and digital processes over time. These data are not legal documents and are not to be used as such.
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov

Why was the data set created?

To document the position of the Dauphin Island, AL shoreline, from November 1998 to January 2014 as observed from lidar datasets that were acquired by various agencies (USGS, National Aeronautics and Space Administration [NASA], U.S. Army Corps of Engineers [USACE]) using several lidar platforms (for example Airborne Topographic Mapper - ATM, Experimental Advanced Airborne Research Lidar - EAARL). Shorelines derived from these lidar data provide information about the position of the island through time and are used to quantify the rate of change during this time period. These data will aid in developing an understanding of the evolution of the barrier island position, size and shape as well as documenting spatially-variable patterns in erosion and accretion of different sections of the island.

How was the data set created?

  1. From what previous works were the data drawn?
    1998_LIDAR (source 1 of 14)
    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), United States Geological Survey (USGS), and National Aeronautics and Space Administration (NASA), 20000101, 1998 Fall Gulf Coast NOAA/USGS/NASA Airborne LiDAR Assessment of Coastal Erosion (ALACE) Project for the US Coastline: NOAA's Ocean Service, Office for Coastal Management (OCM), Charleston, SC.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing LIDAR point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2001_LIDAR (source 2 of 14)
    U.S. Geological Survey, 2009, ATM Coastal Topography-Alabama 2001: U.S. Geological Survey Data Series 418, U.S. Geological Survey, Saint Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    200405_LIDAR (source 3 of 14)
    Joint Airborne LiDAR Bathymetry Technical Center of Expertise (JALBTCX), 20060523, 2004 US Army Corps of Engineers (USACE) Topo/Bathy Lidar: Alabama, Florida, Mississippi and North Carolina: NOAA's Ocean Service, Office for Coastal Management (OCM), Charleston, SC.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    200409_LIDAR (source 4 of 14)
    U. S. Geological Survey, 20081027, EAARL Coastal Topography-Northern Gulf of Mexico: Data Series 384, U. S. Geological Survey, Saint Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2005_LIDAR (source 5 of 14)
    U.S. Geological Survey, 2016, EAARL Coastal Topography-Dauphin Island, Alabama, Post-Hurricane Katrina, 2005: U.S. Geological Survey Data Release doi:10.5066/F78G8HSG, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Other_Citation_Details: Dates of data collection 09/01 - 09/08/2005
    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    200603_LIDAR (source 6 of 14)
    Long, Joseph W., Karen L. M. Morgan, and Doran, Kara, 2016, EAARL Coastal Topography-Louisiana, Mississippi and Alabama, March 2006: U.S. Geological Survey Data Release doi:10.5066/F7BZ6443, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Other_Citation_Details: Date of collection 03/14/2006
    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    200609_LIDAR (source 7 of 14)
    Long, Joseph W., Karen L. M. Morgan, and Doran, Kara, 20160707, EAARL Coastal Topography-Louisiana, Mississippi and Alabama September 2006: U.S. Geological Survey Data Release doi:10.5066/F7765CF4, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Other_Citation_Details: Date of collection 09/20 - 09/22/2006
    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing LIDAR point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2007_LIDAR (source 8 of 14)
    U.S. Geological Survey, 2008, EAARL Coastal Topography-Northern Gulf of Mexico, 2007: Data Series 400, U.S. Geological Survey, Saint Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    200809_LIDAR (source 9 of 14)
    U.S. Geological Survey, 2016, EAARL Coastal Topography-Louisiana, Alabama, and Florida, June 2008: U.S. Geological Survey Data Release doi:10.5066/F7G15XZX, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    200806_LIDAR (source 10 of 14)
    U.S. Geological Survey, 2010, EAARL Coastal Topography-Mississippi and Alabama Barrier Islands, Post-Hurricane Gustav, 2008: U.S. Geological Survey Data Series 556, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2010_LIDAR (source 11 of 14)
    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), and Joint Airborne Lidar Bathymetry Technical Center of expertise (JALBTCX), 20160523, 2010 US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) Topobathy Lidar: Alabama Coast and Florida Gulf Coast: NOAA's Ocean Service, Office for Coastal Management (OCM), Charleston, SC.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing LIDAR point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2012_LIDAR (source 12 of 14)
    U.S. Geological Survey, 20140602, Topographic Lidar Survey of the Alabama, Mississippi, and Southeast Louisiana Barrier Islands, from September 5 to October 11, 2012: U.S. Geological Survey Data Series 839, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2013_LIDAR (source 13 of 14)
    U.S. Geological Survey, 20140602, Topographic Lidar Survey of Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana, July 12-14, 2013: U.S. Geological Survey Data Series 838, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
    2014_LIDAR (source 14 of 14)
    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), 20161215, 2014 Mobile County, AL Lidar: NOAA Office for Coastal Management (OCM), Charleston, SC.

    Online Links:

    Type_of_Source_Media:
    LAZ (compressed LAS) format file containing lidar point cloud data
    Source_Contribution:
    Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted.
  2. How were the data generated, processed, and modified?
    Date: 18-Aug-2016 (process 1 of 8)
    Published X,Y,Z point data were converted from the originally published geoid to GEOID96 using files downloaded from NOAA's National Geodetic Survey https://www.ngs.noaa.gov/GEOID. After conversion, a survey specific elevation offset was then applied, uniformly, to each dataset according to the values published in Thompson and others (2017). Person who carried out this activity:
    Joseph Long
    USGS
    600 4th Street South
    St. Petersburg, FL
    USA

    772 502-8024 (voice)
    727 502-8182 (FAX)
    jwlong@usgs.gov
    Hours_of_Service: M-F, 8:00-4:00 ET
    Data sources used in this process:
    • 1998_LIDAR
    • 2001_LIDAR
    • 200405_LIDAR
    • 200409_LIDAR
    • 2005_LIDAR
    • 200603_LIDAR
    • 200609_LIDAR
    • 2007_LIDAR
    • 200806_LIDAR
    • 200809_LIDAR
    • 2010_LIDAR
    • 2012_LIDAR
    • 2013_LIDAR
    • 2014_LIDAR
    Data sources produced in this process:
    • dauphin_lidar_1998_11_raw.txt
    • dauphin_lidar_2001_09_raw.txt
    • dauphin_lidar_2004_05_raw.txt
    • dauphin_lidar_2004_09_raw.txt
    • dauphin_lidar_2005_0901_raw.txt
    • dauphin_lidar_2006_0314_raw.txt
    • dauphin_lidar_2006_0921_raw.txt
    • dauphin_lidar_2007_0627_raw.txt
    • dauphin_lidar_2008_0625_raw.txt
    • dauphin_lidar_2008_0908_raw.txt
    • dauphin_lidar_2010_01_raw.txt
    • dauphin_lidar_2012_09_raw.txt
    • dauphin_lidar_2013_07_raw.txt
    • dauphin_lidar_201401_raw.txt
    Date: 30-Aug-2016 (process 2 of 8)
    Creation of grid surface from raw data (fore example, file used : dauphin_lidar_DATE_raw.txt where DATE corresponds to the yyyy_mmdd of the lidar dataset).
    XYZ.txt files were imported in to ArcMap (v.10.0), and converted to a point shapefile using the following method: File >> Add data >> Add XY data (XYZ text file chosen). The resulting event layer feature was converted to a shapefile by right clicking the dataset in Table of Contents, and selecting Export Data.
    Features were then subset into 90/10% training/testing groups, using the following method: ArcToolbox>>GeoSatistical Analyst >> Utilities >> Subset Features(input XYZ point shapefile, output training feature class and test feature class, with size of training feature subset at 90%).
    A geodatabase (DATE_terrain) was created for each date and a new (empty) feature dataset created within this database.
    Training point features (90%) were imported into the feature dataset using the following method: ConversionTools >> To Geodatabase >> Feature Class to Feature Class (input training point features (90%) and output to new feature class within geodatabase (DATE_terrain).
    Right click in geodatabase select >> NEW terrain – use wizard to assign feature class points.
    Convert terrain to 2-meter grid using the following method: ArcToolbox >> Converstion >> From Terrain >> Terrain to Raster(ex. file created: DIDATEgrid, where DATE is the yyyymmdd of the original lidar data and DI refers to Dauphin Island)
    Process was repeated for each date. Person who carried out this activity:
    Rachel Henderson
    U.S. Geological Survey
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
    Data sources used in this process:
    • dauphin_lidar_1998_11_raw.txt
    • dauphin_lidar_2001_09_raw.txt
    • dauphin_lidar_2004_05_raw.txt
    • dauphin_lidar_2004_09_raw.txt
    • dauphin_lidar_2005_0901_raw.txt
    • dauphin_lidar_2006_0314_raw.txt
    • dauphin_lidar_2006_0921_raw.txt
    • dauphin_lidar_2007_0627_raw.txt
    • dauphin_lidar_2008_0625_raw.txt
    • dauphin_lidar_2008_0908_raw.txt
    • dauphin_lidar_2010_01_raw.txt
    • dauphin_lidar_2012_09_raw.txt
    • dauphin_lidar_2013_07_raw.txt
    • dauphin_lidar_201401_raw.txt
    Data sources produced in this process:
    • DI199811grid
    • DI200109grid
    • DI200405grid
    • DI200409grid
    • DI200509grid
    • DI200603grid
    • DI200609grid
    • DI200706grid
    • DI200806grid
    • DI200809grid
    • DI201001grid
    • DI201209grid
    • DI201307grid
    • DI201401grid
    Date: 15-Sep-2016 (process 3 of 8)
    Shoreline extracted from DEM.
    A Mean High Water (MHW) shoreline for Dauphin Island, AL was identified as 0.24 m NAVD88 (Weber and others., 2005) and extracted from each lidar survey within ArcMap, using a method similar to that described in Harris and others (2005). The contour shoreline extracted from ArcMap was produced using the following steps for each lidar date:
    1) Contour was extracted from each lidar grid using: ArcToolbox >> 3D Analyst Tools>>Raster Surface >> Contour List, where 0.24 was identified as the only contour, and saved to the geodatabase containing the original terrain and grid data.
    2) The contour was smoothed using: ArcToolbox >> Cartography Tools >> Generalization >> Smooth Line using Peak 10 m, accept all other defaults.
    3) Manual review/editing of MHW for large errors, or locations with multiple MHW values and ensure the proper location of the MHW shoreline, using a colorized elevation grid for reference.
    4)Merge all line segments. Editor>>Merge
    6)Add “DATE_” (text string 10 characters) and "UNCERT" (double) to the attribute table. Determine "DATE" from lidar data, "UNCERT" remains blank until completion of shoreline positional uncertainty (to follow). A field titled "NOTES" was also added to include an additional information about the shoreline. Person who carried out this activity:
    Rachel Henderson
    U.S. Geological Survey
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
    Data sources used in this process:
    • DI199811grid
    • DI200109grid
    • DI200405grid
    • DI200409grid
    • DI200509grid
    • DI200603grid
    • DI200609grid
    • DI200706grid
    • DI200806grid
    • DI200809grid
    • DI201001grid
    • DI201209grid
    • DI201307grid
    • DI201401grid
    Data sources produced in this process:
    • DI199811_MHW
    • DI200109_MHW
    • DI200405_MHW
    • DI200409_MHW
    • DI200509_MHW
    • DI200603_MHW
    • DI200609_MHW
    • DI200706_MHW
    • DI200806_MHW
    • DI200809_MHW
    • DI201001_MHW
    • DI201209_MHW
    • DI201307_MHW
    • DI201401_MHW
    Date: 10-Oct-2016 (process 4 of 8)
    All shorelines were appended into one feature class (in a personal geodatabase). ArcToolbox>>Data Management Tools>>General>>Append: select each DIDATE_MHW shoreline. Shorelines for the following dates were appended:
    11/02/1998 10/02/2001 05/05/2004 09/19/2004 09/01/2005 03/14/2006 09/21/2006 06/27/2007 06/25/2008 09/08/2008 01/01/2010 09/05/2012 07/12/2013 01/21/2014 Person who carried out this activity:
    U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center
    Attn: Rachel E. Henderson
    600 4th Street South
    St. Petersburg, FL
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
    Data sources used in this process:
    • DI199811_MHW
    • DI200109_MHW
    • DI200405_MHW
    • DI200409_MHW
    • DI200509_MHW
    • DI200603_MHW
    • DI200609_MHW
    • DI200706_MHW
    • DI200806_MHW
    • DI200809_MHW
    • DI201001_MHW
    • DI201209_MHW
    • DI201307_MHW
    • DI201401_MHW
    Data sources produced in this process:
    • DI_lidar_1998_2014
    Date: 01-Dec-2016 (process 5 of 8)
    Calculation of the grid surface error.
    Using the withheld 10% point "testing" dataset for each date(90% point data was used to create each lidar grid surface) the points were overlain on the corresponding raster; vertical RMSE on the interpolated surface was determined from comparisons between actual Z value and interpolated Z value using the following method:
    1) Surface information was pulled from the lidar grid and added to 10% point dataset (ArcToolbox>>3D Analyst Tools>>Functional Surface>>Add surface information, add 10% test points LS_date_10.shp as feature class, Input grid surface: date_grid, Output property check Z.
    2) Added new attribute field to 10% test point file – "Elev_diff" (float) and calculated the difference in original point height and grid surface "Z" height.
    3)Saved table to an Excel file and converted each elevation offset to an absolute value. The average of this offset was calculated, and applied as a term in the total shoreline positional uncertainty calculation. Person who carried out this activity:
    Rachel Henderson
    U.S. Geological Survey
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
    Date: 01-Mar-2017 (process 6 of 8)
    Calculation of lidar shoreline positional uncertainty.
    In order to determine the uncertainties associated with individual shorelines, a methodology following Morton and Miller (2005) and Hapke and others (2006) was used to estimate a positional uncertainty value for each shoreline. Total shoreline positional uncertainty is a function of the errors inherent in the source data (horizontal and vertical accuracy of the raw lidar data) the conversion of point data to a 3D surface (grid error) and those errors generated in the extraction of the vector shoreline (interpolation uncertainty).
    Four terms were identified to describe the uncertainty of the resulting lidar shoreline position. The first is the direct horizontal uncertainty from published lidar data. Following the methods described by Hapke and others (2010) the second term is derived from the vertical uncertainty from published lidar data, which is then converted to a horizontal uncertainty based on an averages slope around MHW for each lidar dataset, determined by pulling the slope data from the lidar data at the intersection of MHW and the existing alongshore DSAS transects. The third term is calculated as the "grid error" term. This is a measure of how well the surface (created from 90% of the raw data) captures the actual elevation of the remaining 10% of the data. A comparison of the grid elevation to the raw elevation is then converted to an RMS value describing the grid surface error. The initial calculation of grid surface error for the island-wide dataset was much higher than expected, due to the appearance of houses various areas of vegetation and water surfaces in the first return data. Thus, the calculation of grid error was constrained to a 20-meter buffer around the feature extracted (MHW) to provide a better estimate of the surface error from which the feature was derived. The fourth and final term used is the interpolation uncertainty, based on the grid cell size.
    The four terms were summed in quadrature and the resulting shoreline positional uncertainty was applied to each shoreline date in the "UNCERT" field of the attribute table. This value is used to determine the uncertainty of shoreline change rates when used with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009). Person who carried out this activity:
    Rachel Henderson
    U.S. Geological Survey
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
    Date: 10-Jun-2019 (process 7 of 8)
    Features exported from geodatabase to shapefile. Person who carried out this activity:
    Rachel Henderson
    U.S. Geological Survey
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
    Date: 13-Oct-2020 (process 8 of 8)
    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?
    Hapke, Cheryl J., Himmelstoss, Emily A., Kratzmann, Meredith G., List, Jeffrey, and Thieler, E. Robert, 20100101, National Assessment of Shoreline Change: Historical Shoreline Change along the New England and Mid-Atlantic Coasts: Open-File Report 2010-1118, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    Weber, K.M., List, J.H., and Morgan, K.L.M., 20050101, An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data: Open-File Report 2005-1027, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal & Marine Science Center, Woods Hole, MA.

    Online Links:

    Harris, M., Brock, J., Nayegandhi, A., and Duffy, M., 2005, Extracting Shorelines from NASA Airborne Topographic Lidar-Derived Digital Elevation Models: U.S. Geological Survey Open-File report 2005-1427, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

    Other_Citation_Details: none.
    Morton, R. A., and Miller, T.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, Coastal and Marine Geology Program, Woods Hole Coastal & Marine Science Center, Woods Hole, MA.

    Online Links:

    Other_Citation_Details: none
    Hapke, C.J., Reid, D., Richmond, B.M., Ruggiero, P., and List, J., 2006, National Assessment of Shoreline Change: Part 3: Historical Shoreline Changes and Associated Coastal Land Loss along the Sandy Shorelines of the California Coast: Open-File Report 2006-1219, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal & Marine Science Center, Woods Hole, MA.

    Online Links:

    Other_Citation_Details: none
    M., Thompson D., Dalyander, P.S, Long, J.W., and Plant, N.G., 20170407, Correction of elevation offsets in multiple co-located lidar datasets: Open-File Report 2017-1031, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    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 - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

    Other_Citation_Details:
    Although the current citation is for v. 4.0, at the time of use the version number was 4.3.

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 lidar data from 1998 to 2014. 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?
    In order to determine the uncertainties associated with individual shorelines, a methodology following Morton and Miller (2005) and Hapke and others, (2006) was used to estimate a positional uncertainty value for each shoreline. Total shoreline positional uncertainty is a function of the errors inherent in the source data (horizontal and vertical accuracy of the raw lidar data) the conversion of point data to a 3D surface (grid error) and those errors generated in the extraction of the vector shoreline (interpolation uncertainty).
    Four terms were identified to describe the uncertainty of the resulting lidar shoreline position. The first is the direct horizontal uncertainty from published lidar data. Following the methods described by Hapke and others (2010) the second term is derived from the vertical uncertainty from published lidar data, which is then converted to a horizontal uncertainty based on an averages slope around MHW for each lidar dataset, determined by pulling the slope data from the lidar data at the intersection of MHW and the existing alongshore DSAS transects. The third term is calculated as the “grid error” term. This is a measure of how well the surface (created from 90% of the raw data) captures the actual elevation of the remaining 10% of the data. A comparison of the grid elevation to the raw elevation is then converted to an RMS value describing the grid surface error. The initial calculation of grid surface error for the island-wide dataset was much higher than expected, due to the appearance of houses, various areas of vegetation and water surfaces in the first return data. Thus, the calculation of grid error was constrained to a 20-meter buffer around the feature extracted (MHW) to provide a better estimate of the surface error from which the feature was derived. The fourth and final term used is the interpolation uncertainty, based on the grid cell size.
    The four terms were summed in quadrature and the resulting shoreline positional uncertainty was applied to each shoreline date in the "UNCERT" field of the attribute table. This value is used to determine the uncertainty of shoreline change rates, when used with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009).
  3. How accurate are the heights or depths?
  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 Dauphin Island, Alabama. 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.
  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.

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
    600 4th Street South
    St. Petersburg, Florida
    US

    (727)-502-8000 (voice)
    rehenderson@usgs.gov
  2. What's the catalog number I need to order this data set? Lidar_Shorelines_1998_2014.shp
  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?

Who wrote the metadata?

Dates:
Last modified: 13-Oct-2020
Metadata author:
U.S. Geological Survey
Attn: Rachel Henderson
600 4th Street South
St. Petersburg, Florida
US

(727)-502-8000 (voice)
rehenderson@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/Lidar_Shorelines_metadata.faq.html>
Generated by mp version 2.9.50 on Tue Sep 21 18:18:51 2021