Coastal Topography--Northeast Atlantic Coast, Post-Hurricane Sandy, 2012: Mean-high-water shoreline

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Frequently anticipated questions:

What does this data set describe?

Title:
Coastal Topography--Northeast Atlantic Coast, Post-Hurricane Sandy, 2012: Mean-high-water shoreline
Abstract:
Mean-high-water (MHW) shoreline for a portion of the New York, Delaware, Maryland, Virginia, and North Carolina coastlines were derived from lidar data collected following Hurricane Sandy (Sandy was an October 2012 hurricane that made landfall as an extratropical cyclone on the 29th). Data were produced by the U.S. Geological Survey (USGS) from remotely sensed, geographically-referenced elevation measurements collected by Photo Science, Inc. (Delaware, Maryland, Virginia, and North Carolina) and Woolpert, Inc. (Fire Island, New York) using airborne lidar sensors. Storms cause significant shoreline changes and this variation was not removed from these data, showing a highly variable MHW shoreline in many areas.
Supplemental_Information:
Processed data products are used by the USGS Coastal and Marine Geology Program's National Assessments of Coastal Change Hazards project to quantify the vulnerability of shorelines to coastal change hazards such as severe storms, sea-level rise, and shoreline erosion and retreat.
  1. How might this data set be cited?
    U.S. Geological Survey, 2013, Coastal Topography--Northeast Atlantic Coast, Post-Hurricane Sandy, 2012: Mean-high-water shoreline: U.S. Geological Survey Data Series 765, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -76.658190
    East_Bounding_Coordinate: -72.756420
    North_Bounding_Coordinate: 40.763929
    South_Bounding_Coordinate: 34.586382
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 05-Nov-2012
    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 (39)
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 18
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.999600
      Longitude_of_Central_Meridian: -75.000000
      Latitude_of_Projection_Origin: 0
      False_Easting: 500000.000000
      False_Northing: 0
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 1.00000
      Ordinates (y-coordinates) are specified to the nearest 1.00000
      Planar coordinates are specified in meters
      The horizontal datum used is North American Datum of 1983.
      The ellipsoid used is Geodetic Reference System 80.
      The semi-major axis of the ellipsoid used is 6378137.000000.
      The flattening of the ellipsoid used is 1/298.25722210100002.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum of 1988 (GEOID96)
      Altitude_Resolution: 0.15
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method:
      Explicit elevation coordinate included with horizontal coordinates
  7. How does the data set describe geographic features?
    Post_Sandy_shoreline.shp
    Post-Hurricane Sandy lidar-extracted mean-high-water shoreline (Source: USGS)
    FID
    Internal feature number (Source: ESRI) Sequential unique whole numbers that are automatically generated.
    State
    U.S. State Name (Source: Geographic Names Information System (GNIS)) A text string containing the U.S. state name.

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?
    Hilary Stockdon
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL
    Research Oceanographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727 803-8747 (x3074) (voice)
    727 803-2031 (FAX)
    hstockdon@usgs.gov
    Hours_of_Service: M-F, 9:00-5:00 ET

Why was the data set created?

The purpose of this project was to derive mean-high-water shoreline, dune crest (DHIGH) and dune toe (DLOW) elevation for a portion of the Eastern United States coastline following Hurricane Sandy, for use as a management tool and to make these data available to natural-resource managers and research scientists.

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: 05-Nov-2012 (process 1 of 10)
    Using an Optech Gemini lidar sensor, 11 flight lines of high-density data, at a nominal pulse spacing (NPS) of 1 meter, were collected by Woolpert along the southern shore of Long Island, New York (approximately 15 square miles). Data Acquisition Height = 3,500 feet Above Ground Level (AGL) - Aircraft Speed = 125 Knots. Multiple returns were recorded for each laser pulse along with an intensity value for each return. A total of one mission was flown on November 5th. Two airborne global positioning system (GPS) base stations were used in support of the lidar data acquisition. Eight ground control points were surveyed through static methods. The GEOID used to reduce satellite-derived elevations to orthometric heights was GEOID96. Data for the task order is referenced to the UTM Zone 18N, North American Datum of 1983 (NAD83), and North American Vertical Datum of 1988 (NAVD88), in meters. Airborne GPS data was differentially processed and integrated with the post-processed inertial measurement unit (IMU) data to derive a smoothed best estimate of trajectory (SBET). The SBET was used to reduce the lidar slant range measurements to a raw reflective surface for each flight line. The coverage was classified to extract a bare earth digital elevation model (DEM) and separate last returns. In addition to the LAS deliverables, one layer of coverage was delivered in the ERDAS Imagine (IMG) Format: bare earth. Person who carried out this activity:
    Woolpert, Inc.
    Geospatial Services
    4454 Idea Center Blvd.
    Dayton, OH
    USA

    937 461-5660 (voice)
    937 461-0743 (FAX)
    Hours_of_Service: M-F, 8:00-5:00 ET
    Date: 2012 (process 2 of 10)
    The lidar calibration and system performance is verified on a periodic basis using Woolpert's calibration range. The calibration range consists of a large building and runway. The edges of the building and control points along the runway have been located using conventional survey methods. Inertial measurement unit (IMU) misalignment angles and horizontal accuracy are calculated by comparing the position of the building edges between opposing flight lines. The scanner scale factor and vertical accuracy is calculated through comparison of lidar data against control points along the runway. Field calibration is performed on all flight lines to refine the IMU misalignment angles. IMU misalignment angles are calculated from the relative displacement of features within the overlap region of adjacent (and opposing) flight lines. The raw lidar data are reduced using the refined misalignment angles. Person who carried out this activity:
    Woolpert, Inc.
    Geospatial Services
    4454 Idea Center Blvd.
    Dayton, OH
    USA

    937 461-5660 (voice)
    937 461-0743 (FAX)
    Hours_of_Service: M-F, 8:00-5:00 ET
    Date: 07-Nov-2012 (process 3 of 10)
    Once the data acquisition and GPS processing phases are complete, the lidar data were processed immediately by Woolpert to verify the coverage had no voids. The GPS and IMU data were post-processed using differential and Kalman filter algorithms to derive a best estimate of trajectory. The quality of the solution was verified to be consistent with the accuracy requirements of the project. Person who carried out this activity:
    Woolpert, Inc.
    Geospatial Services
    4454 Idea Center Blvd.
    Dayton, OH
    USA

    937 461-5660 (voice)
    937 461-0743 (FAX)
    Hours_of_Service: M-F, 8:00-5:00 ET
    Date: 07-Nov-2012 (process 4 of 10)
    The individual flight lines were inspected by Woolpert to ensure the systematic and residual errors have been identified and removed. Then, the flight lines were compared to adjacent flight lines for any mismatches to obtain a homogenous coverage throughout the project area. The point cloud underwent a classification process to determine bare-earth points and non-ground points utilizing "first and only" as well as "last of many" lidar returns. This process determined Default (Class 1), Ground (Class 2), Noise (Class 7), Water (Class 9), Ignored Ground (Class 10), Overlap Default (Class 17), and Overlap Ground (class 18) classifications. The bare-earth (Class 2 - Ground) lidar points underwent a manual QA/QC step to verify that artifacts have been removed from the bare-earth surface. The surveyed ground control points are used to perform the accuracy checks and statistical analysis of the lidar dataset. Person who carried out this activity:
    Woolpert, Inc.
    Geospatial Services
    4454 Idea Center Blvd.
    Dayton, OH
    USA

    937 461-5660 (voice)
    937 461-0743 (FAX)
    Hours_of_Service: M-F, 8:00-5:00 ET
    Date: 2012 (process 5 of 10)
    Photo Science, Inc. located a total of 29 calibration control points used in the post processing of the lidar data. The points were located on relatively flat terrain on surfaces that generally consisted of grass, gravel, or bare earth. Applanix software (PosPAC MMS) was used in the post processing of the airborne GPS and inertial data, which are critical to the positioning and orientation of the sensor during all flights. POSPac MMS provides the smoothed best estimate of trajectory (SBET) that is necessary for the post processor to develop the point cloud from the lidar missions. The point cloud is the mathematical three-dimensional collection of all returns from all laser pulses as determined from the aerial mission. The GEOID used to reduce satellite derived elevations to orthometric heights was GEOID96. Data for the task order is referenced to the UTM Zone 18N, NAD83, and NAVD88, in meters. At this point the data are ready for analysis, classification, and filtering to generate a bare-earth surface model in which the above ground features are removed from the data set. The point cloud was manipulated by the Optech or Leica software; GeoCue, TerraScan, and TerraModeler software were used for the automated data classification, manual cleanup, and bare-earth generation from the data. Project specific macros were used to classify the ground and to remove the side overlap between parallel flight lines. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. All ground (ASPRS Class 2) lidar data inside of the Lake Pond and Double Line Drain hydro flattening breaklines were then classified to water (ASPRS Class 9) using TerraScan macro functionality. All Lake Pond and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) were reclassified to the correct classification after the automated classification was completed. All overlap data were processed through automated functionality provided by TerraScan to classify the overlapping flight line data to approved classes by USGS. The overlap data were classified to Class 17 (USGS Overlap Default) and Class 18 (USGS Overlap Ground). These classes were created through automated processes only and were not verified for classification accuracy. Data were then run through additional macros to ensure deliverable classification levels matching the ASPRS LAS Version 1.2 Classification structure. GeoCue functionality was then used to ensure correct LAS Versioning. In-house software was used as a final QA/QC check to provide LAS Analysis of the delivered tiles. QA/QC checks were performed on a per tile level to verify final classification metrics and full LAS header information. Person who carried out this activity:
    Photo Science, Inc.
    Geospatial Services
    4454 Idea Center Blvd.
    Lexington, KY
    USA

    859 277-8700 (voice)
    Hours_of_Service: M-F, 9:00-5:00 ET
    Date: 27-Feb-2013 (process 6 of 10)
    All Photo Science elevation points classified as ground (Class 2), water (Class 9), and unclassified (Class 1) were extracted from the LAS files and converted to ASCII xyz point files using LASTools las2las.exe. The ASCII point files were then written to NetCDF (Network Common Data Form) using MATLAB 8.0.0.783. Person who carried out this activity:
    Kristin Sopkin
    Cherokee Nation Technology Solutions, U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL
    Data Modeler/Analyst
    600 4th Street South
    St. Petersburg, FL
    USA

    727 803-8747 (x3141) (voice)
    ksopkin@usgs.gov
    Hours_of_Service: M-F, 8:00-4:00 ET
    Date: 27-Feb-2013 (process 7 of 10)
    All Woolpert, Inc. LAZ files were extracted to LAS and converted to ASCII xyz point files using LASTools las2las.exe. The ASCII point files were then written to netcdf format using MATLAB 8.0.0.783. Person who carried out this activity:
    Kristin Sopkin
    Cherokee Nation Technology Solutions, U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL
    Data Modeler/Analyst
    600 4th Street South
    St. Petersburg, FL
    USA

    727 803-8747 (x3141) (voice)
    ksopkin@usgs.gov
    Hours_of_Service: M-F, 8:00-4:00 ET
    Date: 09-Apr-2013 (process 8 of 10)
    Elevation data from lidar surveys were interpolated in MATLAB (version 2012a) to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 meters in the longshore direction and 2.5 meters in the cross-shore direction. The interpolation method applies spatial filtering with a Hanning window that is twice as wide as the grid resolution. The shoreline position was determined as the intersection of the 20-year mean high water contour line with the cross-shore profiles of the gridded elevations at 10-meter intervals along the coast using MATLAB 8.0.0.783. Reference for 20-year mean high water line: Weber, K.M., List, J.H., and Morgan, K.L.M., 2005, An operational mean high water datum for determination of shoreline position from topographic lidar data: U.S. Geological Survey Open-File Report 2005-1027, available at http://pubs.usgs.gov/of/2005/1027/. Dune crest and toe were interpolated in Matlab (version 2012a) and smoothed to a 1-kilometer alongshore spacing using a Hanning window filter. Sections with greater than 75 percent of data missing are flagged with the invalid number of 999. The 1-kilometer smoothed dune crest, toe and RMSE for each were written to line shapefiles using Matlab's shapewrite.m script. Shoreline position was exported from Matlab into point shapefiles using Matlab's shapewrite.m script and converted to lines using XTools Pro (version 9.1) in ArcGIS Desktop (10.1). The resulting line connected points across inlets and channels, so these areas were hand-edited in ArcMap (version 10.1). Person who carried out this activity:
    Kristin Sopkin
    Cherokee Nation Technology Solutions, U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL
    Data Modeler/Analyst
    600 4th Street South
    St. Petersburg, FL
    USA

    727 803-8747 (x3141) (voice)
    ksopkin@usgs.gov
    Hours_of_Service: M-F, 8:00-4:00 ET
    Date: 24-Jan-2017 (process 9 of 10)
    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: 13-Oct-2020 (process 10 of 10)
    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?

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

  1. How well have the observations been checked?
    The attribute (State) was visually inspected and manually-assigned within ArcGIS software. No other accuracy checks were conducted on attributes.
  2. How accurate are the geographic locations?
    Horizontal position of the mean-high-water shoreline is defined by the intersection of the mean-high water (MHW) contour with the beach profile. Horizontal error is an estimate of both the positional error of the lidar data (which has a horizontal accuracy of +/- 0.194 meters and vertical accuracy of 0.147 meters at the 95% confidence level) and the probabilistic selection procedure used to identify the MHW point on the beach slope. MHW shoreline position is also influenced by the highly-variable, post-storm beach profile, which includes offshore sand bars and beach scarps. Root mean square (RMS) error estimates for the post-Sandy MHW shoreline were between 0.001 to 10 meters, depending on the variability of the lidar data and the beach slope. For example, highly variable lidar point-cloud and low beach slope could produce higher RMS errors. For individual states, RMS error estimates are as follows: New York: 0.0013 to 10.64 meters, Delaware: 1.30 x 10^-5 to 6.67 meters, Maryland: 2.33 x 10^-5 to 0.83 meters, Virginia: 5.42 x 10^-7 to 0.84 meters, North Carolina: 7.29 x 10^-7 to 0.83 meters.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    These data span from Long Island, New York to Cape Hatteras, North Carolina. The state of New Jersey was collected using the EAARL-B sensor and that data will be published separately.
  5. How consistent are the relationships among the observations, including topology?
    The data were visually checked in ArcGIS for consistency and hand-edited where MHW points were connected across inlets and water features.

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:
The U.S. Geological Survey and National Park Service request to be acknowledged as originators of these data in future products or derivative research.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL
    Attn: Hilary Stockdon
    Research Oceanographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727 803-8747 (x3074) (voice)
    Hours_of_Service: M-F, 9:00-5:00 ET
  2. What's the catalog number I need to order this data set? DS 765
  3. What legal disclaimers am I supposed to read?
    Although these data have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The USGS shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any use of trade, firm, or product 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: SHP; ZIP (version 1) The mean-high-water shoreline (Post_Sandy_shoreline.shp) is an ArcGIS shapefile polyline dataset compressed into a zip file with metadata.
      Network links: https://pubs.usgs.gov/ds/765/pubs765/data/dune/
    • Cost to order the data: None, if obtained online; otherwise, prices vary.

    • Special instructions:
      Publications are available from USGS Information Services, Box 25286, Federal Center, Denver, CO 80225-0046 (telephone: 1-888-ASK-USGS, e-mail: infoservices@usgs.gov).

Who wrote the metadata?

Dates:
Last modified: 13-Oct-2020
Metadata author:
Xan Fredericks
Cherokee Nation Technology Solutions, U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL
Lidar Validation and Processing Analyst
600 4th Street South
St. Petersburg, FL
USA

727 803-8747 (x3086) (voice)
afredericks@usgs.gov
Hours_of_Service: M-F, 8:00-4:00 ET
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/ds765_Shoreline_metadata.faq.html>
Generated by mp version 2.9.50 on Tue Sep 21 18:18:54 2021