Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Central California

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Frequently anticipated questions:

What does this data set describe?

Title:
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Central California
Abstract:
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 as part of the Coastal Change Hazards programmatic focus, formerly 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 this national scale 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.
In this release, three new tidal datum-based mean high water (MHW) shorelines extracted from 2009-2011, 2015, and 2016 lidar elevation data are included in the analysis (coverage not necessarily continuous statewide). The full range of shoreline data is 1852 to 2016. The proxy-datum bias correction has been applied on a transect-by-transect basis to reconcile offsets between the MHW shorelines and proxy-based HWL shorelines for the entire California coastal region which is divided into three subregions: Northern California (NorCal), Central California (CenCal), and Southern California (SoCal). In the previous report (Hapke et al., 2006), the proxy-datum bias correction was only applied to regional shoreline averages.
This shoreline change update for California reports proxy-datum bias corrected rates when that information was computed while extracting shoreline positions from lidar data. In areas where the methods for delineating shorelines did not include computing bias correction values, the rates are reported without that correction. The proxy-datum bias concept is explained further in Ruggiero and List (2009) and in the process steps of the metadata file associated with the transect rates.
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, 20240112, Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Central California: data release DOI:10.5066/P94J0K7Z, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Kratzmann, Meredith G., Farris, Amy S., and Himmelstoss, Emily A., 2024, National Shoreline Change—A GIS compilation of vector shorelines and associated shoreline change data from the 1800s to 2010s for the coast of California: data release DOI:10.5066/P94J0K7Z, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    suggested citation: Kratzmann, M.G., Farris, A.S., and Himmelstoss, E.A., 2024, National Shoreline Change—A GIS compilation of vector shorelines and associated shoreline change data from the 1800s to 2010s for the coast of California: U.S. Geological Survey data release, https://doi.org/10.5066/P94J0K7Z.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -123.024154
    East_Bounding_Coordinate: -120.000280
    North_Bounding_Coordinate: 38.240792
    South_Bounding_Coordinate: 34.442252
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2024
    Currentness_Reference:
    publication date
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: tabular digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      Indirect_Spatial_Reference:
      This dataset contains records that are related to the associated shoreline datasets by M-values in each polyline-M shapefile that is represented by the ID attribute in this dataset.
    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 0.0197422321. Longitudes are given to the nearest 0.0262000699. Latitude and longitude values are specified in Decimal seconds. The horizontal datum used is WGS_1984.
      The ellipsoid used is WGS_84.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    CenCal_2009_2010_lidar_uncertainty
    uncertainty table (Source: U.S. Geological Survey)
    OID
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    LID
    This field is case-sensitive and name specific. The field contains a cross-shore lidar profile ID stored as the M-value (measure value) at each vertex in the calibrated shoreline route. This serves as the link between the lidar shoreline and the uncertainty table and must be a unique number at each point. (Source: U.S. Geological Survey)
    Range of values
    Minimum:24950
    Maximum:58905
    UNCY
    This field heading is case-sensitive and name specific. The field contains the plus/minus horizontal uncertainty (meters) in the lidar shoreline position at each cross-shore beach profile. (Source: U.S. Geological Survey)
    Range of values
    Minimum:0.22
    Maximum:27.98
    Units:meters
    BIAS
    This field heading is case-sensitive and name specific. The field contains a proxy-datum bias value describing the unidirectional horizontal offset (in meters) between the MHW elevation of the lidar data and proxy-based historic HWL shoreline positions. The average value for CenCal is 18.97 meters. (Source: U.S. Geological Survey)
    Range of values
    Minimum:-1
    Maximum:36.4
    Units:meters
    UNCYB
    This field heading is case-sensitive and name specific. The field contains the uncertainty in the calculated proxy-datum bias value (BIAS) in meters. (Source: U.S. Geological Survey)
    Range of values
    Minimum:-1
    Maximum:18.119
    Units:meters
    ID
    This field is case-sensitive and name specific. The field contains a cross-shore lidar profile ID stored as the M-value (measure value) at each vertex in the calibrated shoreline route. This serves as the link between the lidar shoreline and the uncertainty table and must be a unique number at each point. It is the same value as LID and is present to be recogized by the DSAS software. (Source: U.S. Geological Survey)
    Range of values
    Minimum:24950
    Maximum:58905
    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: Meredith G. Kratzmann
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 (voice)
    508-457-2310 (FAX)
    mkratzmann@contractor.usgs.gov

Why was the data set created?

The uncertainty table includes: measurement and positional errors associated with the 2009/2010/2011 lidar shoreline for California, a proxy-datum bias value that corrects for the unidirectional offset between the mean high water (MHW) elevation of the lidar and the high water line (HWL) shorelines, as well as a measurement uncertainty in the total water level.
The dataset contains a common attribute with the M-values stored for the lidar data within the shorelines shapefile. These tabular data are used in conjunction with the shorelines file to calculate rates of shoreline change for the U.S. Geological Survey's (USGS) national shoreline change effort. Please note there are some locations where multiple historical shoreline positions exist but there is no proxy-datum bias value.
Rates of long-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) v5.0. 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?
    2009-2011 lidar (source 1 of 1)
    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management, 201201, 2009 - 2011 CA Coastal Conservancy Coastal Lidar Project: NOAA's Ocean Service, Office for Coastal Management, Charleston, SC.

    Online Links:

    Type_of_Source_Media: digital data
    Source_Contribution: Lidar data that were used to extract a shoreline.
  2. How were the data generated, processed, and modified?
    Date: 2013 (process 1 of 4)
    The reference line that was used for the first National Assessments shoreline was used for this work (except for a couple of areas where there were gaps in the reference line which were filled). The reference line is made of straight segments and is roughly coast-following with points every 20 meters. At each point a profile line (or transect) is defined that is perpendicular to the reference line. A program written by Amy Farris using Matlab version 2012b loads the cloud of (x,y,z) lidar points and finds all the data points within 2 meters of each profile line. The shoreline was extrapolated from data on each of these profiles.
    The Matlab program described by Stockdon et al. (2002) was further developed by Laura Fauver, Jeff List and Kathy Weber at USGS. It was run on the profile data using Matlab version 2012b. This code works with one profile at a time. The code identified points located on the foreshore and fit a linear regression through them. The slope of the regression is an estimate of the slope of the foreshore. The intersection of the regression line with Mean High Water (MHW) is the calculated shoreline position. 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 MHW elevation.
    The MHW values used for the three regions of California are: Northern California: Oregon/California border to Cape Mendocino 1.81 meters, Central California: Cape Mendocino to Point Buchon 1.46 meters, and Southern California: Point Buchon to U.S./Mexico Border 1.33 meters (Weber et al., 2005 and Hapke et al., 2006).
    The shoreline code has a graphical user interface (GUI) which plots all the data for a given profile, indicates which points were determined to be on the foreshore, plots the regression line and the calculated shoreline position. The information was visually checked and then the solution was either accepted or rejected. This manual verification process was repeated for each profile.
    Each lidar shoreline point has an error associated with it. This error has three components: the error due to the linear regression, the error associated with the lidar data collection system, and the error due to extrapolation (if the shoreline point was determined by extrapolation). The error due to the linear regression is simply the 95% confidence interval about the regression estimate.
    Sallenger et al. (2003) determined that the vertical accuracy of NASA's Airborne Topographic Mapper lidar system is about 15 centimeters. This vertical error is converted to a horizontal error using the beach slope as determined by the linear regression. The final part of the total shoreline error is the error due to extrapolation. If the shoreline point was determined by extrapolation, this error term is calculated as the amount of uncertainty in horizontal shoreline position due to the variability of the beach slope between the last point on the linear regression and the MHW elevation. These three error terms are then added in quadrature, yielding a total error for each shoreline point.
    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.
    Sallenger, A.H., Krabill, W., Swift, R., Brock, J., List, J., Hansen, M., Holman, R. A., Manizade, S., Sonntag, J., Meredith, A., Morgan, K., Yunkel, J.K., Frederick, E., and Stockdon, H., 2003. Evaluation of airborne scanning lidar for coastal change applications: Journal of Coastal Research, v. 19, pp. 125-133.
    After the shoreline code was run, the resultant shoreline was fed into another GUI for further quality checking. This GUI created a map view plot of the profile data color-coded by z values with a color jump at z = MHW so the approximate location of MHW is easily visible. The shoreline solutions were added to this plot. The shoreline data were visually scanned to look for incorrect solutions. For example the shoreline point was occasionally on the back of a barrier island or on a groin. These solutions were flagged and later removed. Small gaps on straight beaches were identified and later filled using linear interpolation but gaps at inlets or large gaps on curved beaches were not interpolated over. A visual quality check using imagery was conducted at a later stage.
    This datum-based shoreline is often used in conjunction with proxy-based shoreline positions. There is a recognized offset between datum-based and proxy-based shorelines, therefore the proxy-datum bias as defined by Ruggiero and List (2009) was calculated for these shorelines. The formula for the bias is based on an equation for wave run-up which depends on beach slope and the recent wave climate (specifically, wavelength and wave height). The beach slope was calculated by the shoreline code (described in a previous process step). It was averaged alongshore in 1-kilometer non-overlapping blocks. The wave climate was estimated from averages of historical data. Historical wave lengths were obtained from offshore buoys. The buoy data were downloaded from the National Buoy Data Center (https://www.ndbc.noaa.gov/). Buoys were chosen that had at least 10 years of data in at least 100 meters of water. Historical wave heights were obtained from wave information studies (WIS) stations (http://wis.usace.army.mil/). At least 10 years of data were averaged. The formula for the proxy-datum bias also needs MHW and Mean Higher High Water, which were taken from the OFR mentioned in a previous Process Step (USGS OFR 2005-1027).
    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.
    The output file from the shoreline code, the map-view checker and the bias code were merged and saved as an American Standard Code for Information Interchange (ASCII) text file to be loaded by DSAS. Person who carried out this activity:
    Amy S. Farris
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 x2344 (voice)
    508-457-2310 (FAX)
    afarris@usgs.gov
    Data sources used in this process:
    • 2009-2011 lidar
    Data sources produced in this process:
    • ASCII text file
    Date: 2014 (process 2 of 4)
    The ASCII file was converted into a calibrated route shapefile for use in ArcGIS by 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 field linking the two files. The lidar data were collected in projected coordinates (WGS 84 UTM zone 10N). During the rate calculation process DSAS uses linear referencing to retrieve the uncertainty values stored in the associated table.
    For a detailed explanation of the method used to convert the lidar shoreline to a route, please refer to the DSAS user guide:
    Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2018, Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey Open-File Report 2018–1179, 110 p., https://doi.org/10.3133/ofr20181179 Person who carried out this activity:
    Emily 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
    Data sources used in this process:
    • ASCII text file from 2009-2011 lidar
    Data sources produced in this process:
    • calibrated route shapefile
    • accessory dBase (.dbf) file
    Date: 2020 (process 3 of 4)
    The shoreline uncertainty table (.dbf) was imported into a personal geodatabase in ArcCatalog v10.7 by right-clicking on the geodatabase > Import > Table (single). The uncertainty table was used with the Digital Shoreline Analysis System (DSAS) v5.0 software to perform rate calculations. Person who carried out this activity:
    Meredith G. Kratzmann
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 (voice)
    508-457-2310 (FAX)
    mkratzmann@contractor.usgs.gov
    Date: 2020 (process 4 of 4)
    The shoreline uncertainty table was exported from the personal geodatabase back to a stand-alone dBase file using ArcCatalog v10.7 by right-clicking on the database file > Export > To dBase file (single) for publication purposes. Person who carried out this activity:
    Meredith G. Kratzmann
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 (voice)
    508-457-2310 (FAX)
    mkratzmann@contractor.usgs.gov
  3. What similar or related data should the user be aware of?
    Kratzmann, Meredith G., 2024, National Shoreline Change—Summary Statistics of Shoreline Change From the 1800s To the 2010s for the Coast of California: data report 1187, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details: Data report associated with data release DOI:10.5066/P94J0K7Z.
    Himmelstoss, Emily A., Farris, Amy S., Henderson, Rachel E., Kratzmann, Meredith G., Ergul, Ayhan, Zhang, Ouya, and Zichichi, Jessica L., 2018, Digital Shoreline Analysis System (version 5.0): U.S. Geological Survey Software: software release version 5.0, U.S. Geological Survey, Reston, VA.

    Online Links:

    Himmelstoss, Emily A., Henderson, Rachel E., Kratzmann, Meredith G., and Farris, Amy S., 2018, Digital Shoreline Analysis System (DSAS) Version 5.0 User Guide: Open-File Report 20181179, U.S. Geological Survey, Reston, VA.

    Online Links:

    Weber, Kathryn M., List, Jeffrey H., and Morgan, Karen L.M., 2005, An operational mean high water datum for determination of shoreline position from topographic lidar data: Open-File Report 2005-1027, U.S. Geological Survey, Reston, VA.

    Online Links:

    Hapke, Cheryl J., Reid, David, Richmond, Bruce M., Ruggiero, Peter, and List, Jeff, 2006, National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coastal Land Loss Along Sandy Shorelines of the California Coast: Open-File Report 2006-1219, U.S. Geological Survey, Reston, VA.

    Online Links:

    Hapke, Cheryl J., and Reid, David, 2006, National Assessment of Shoreline Change: A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for the Sandy Shorelines of the California Coast: Open-File Report 2006-1251, U.S. Geological Survey, Reston, VA.

    Online Links:

    Ruggiero, Peter, and List, Jeffrey H., 200909, Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates: Journal of Coastal Research vol. 255, Coastal Education and Research Foundation, n/a.

    Online Links:

    Other_Citation_Details: pp. 1069-1081

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

  1. How well have the observations been checked?
    The attributes in this table record positional and measurement uncertainties and datum offsets calculated during the process of extracting an operational mean high water shoreline from the lidar data as described in the process steps. The field names are based on the requirements for use within the Digital Shoreline Analysis System (DSAS) software.
  2. How accurate are the geographic locations?
    Each MHW shoreline point extracted using the profile method has an uncertainty associated with it. This uncertainty includes three components:
    1) the 95% confidence interval on the linear regression estimate of the shoreline position;
    2) the uncertainty associated with the elevation of the raw lidar data which is stated as 0.1 m RMS in the lidar metadata;
    3) the uncertainty due to extrapolation if the shoreline point was determined by extrapolation.
    These three components of uncertainty were then added in quadrature, yielding a total error for each shoreline point which is stored in the shoreline uncertainty DBF file associated with these data.
    The lidar shoreline from 2009/2010/2011 has an estimated positional uncertainty of plus or minus 1.9 meters (NorCal), plus or minus 2.2 meters (CenCal), plus or minus 2.3 meters (SoCal).
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    The table only contains data where a mean high water shoreline point could be extrapolated from the lidar.
  5. How consistent are the relationships among the observations, including topology?
    Each row contains data associated with an individual vertex point along the lidar shoreline in the California shorelines shapefiles for each region.

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 - ScienceBase
    MS 302
    Denver, CO
    USA

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? The dataset contains the uncertainty data in DBF format and the FGDC CSDGM metadata.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Although these data have been processed successfully on a computer system at the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein.
  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 dBase file format. The user must have software capable of reading or importing the dBase formatted data file.

Who wrote the metadata?

Dates:
Last modified: 12-Jan-2024
Metadata author:
Meredith G. Kratzmann
U.S. Geological Survey
384 Woods Hole Road
Woods Hole, MA
USA

508-548-8700 (voice)
508-457-2310 (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.
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P94J0K7Z/Cencal_uncertainty_metadata.faq.html>
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