Transects with linear regression rates of change for GPS, Worldview, and aerial image shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020

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What does this data set describe?

Title:
Transects with linear regression rates of change for GPS, Worldview, and aerial image shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Abstract:
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021)
  1. How might this data set be cited?
    Terrano, Joseph F., and Smith, Kathryn E.L., 20210723, Transects with linear regression rates of change for GPS, Worldview, and aerial image shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020:.

    This is part of the following larger work.

    Terrano, Joseph F., Smith, Kathryn E.L., Pitchford, Jonathan L., Archer, Michael, and Brochard, Michael, 20210723, Shorelines from High-resolution WorldView Satellite Imagery, Real-time Kinematic Global Positioning Data, and Aerial Imagery for 2013 to 2020 for Study Sites Within Grand Bay National Estuarine Research Reserve, Mississippi: U.S. Geological Survey data release doi:10.5066/P9W8TNQM, 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: -88.466102
    East_Bounding_Coordinate: -88.391268
    North_Bounding_Coordinate: 30.387934
    South_Bounding_Coordinate: 30.324785
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 17-Dec-2013
    Ending_Date: 17-Nov-2020
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Vector Digital Data Set (Polyline)
  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 (358)
    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?
    GPS_WV_AI_LRR_2013_2020.shp
    Attribute table of the shapefile containing information associated with the 2013 to 2020 Grand Bay MS transects with linear regression rate calculations. (Source: USGS)
    Transect
    Number automatically generated by AMBUR as a unique identifier for each transect. (Source: USGS)
    Range of values
    Minimum:360
    Maximum:2448
    Site
    Site the data was collected at. (Source: GBNERR)
    ValueDefinition
    BHMSite at Bayou Heron Mouth.
    MBNSite at Middle Bay North.
    MBSSite at Middle Bay South.
    MBWSite at Middle Bay West.
    GBESite at Grand Battures East.
    BSISite at Bird Island.
    SPALSite at North Jose Bay.
    METSite at meterological station island.
    PACNSite at Point aux Chenes North.
    PACMSite at Point aux Chenes Middle.
    PACSSite at Point aux Chenes South.
    NDate_X
    Number of dates present. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) A text string describing the number of dates present.
    Min_Date_X
    Youngest date present. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Youngest date present in the mm/dd/yyyy HH:MM:SS, with 12:00:00 AM being used as a placeholder time for all dates.
    Max_Date_X
    Oldest date present. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Oldest date present in the mm/dd/yyyy HH:MM:SS, with 12:00:00 AM being used as a placeholder time for all dates.
    Elp_Yrs_X
    Elapsed years between oldest and youngest dates. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Elapsed years between oldest and youngest dates with the number after the decimal place being the fraction of the year.
    LRR_X
    Linear regression shoreline change rate. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Linear regression shoreline change rate in meters per year (m/yr).
    RSq_X
    R‐squared of the linear regression. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) R‐squared of the linear regression.
    Int_X
    Intercept of the linear regression. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Intercept of the linear regression.
    SEcoe_X
    Standard error of the coefficients of the linear regression. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Standard error of the coefficients of the linear regression.
    SEres_X
    Standard error of the residuals of the linear regression. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Standard error of the residuals of the linear regression.
    Pval_X
    P value of the linear regression. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) P value of the linear regression.
    CI_L_X
    Lower confidence interval of the linear regression shoreline change rate. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Lower confidence interval of the linear regression shoreline change rate.
    CI_U_X
    Upper confidence interval of the linear regression shoreline change rate. "_X" is a placeholder for "_g" (GPS data), "_w" (WorldView data), or "_a" (aerial image data). (Source: USGS) Upper confidence interval of the linear regression shoreline change rate.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Joseph F. Terrano
    • Kathryn E.L. Smith
  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. For access and pricing of imagery please contact Maxar Technologies.
  3. To whom should users address questions about the data?
    U.S. Geological Survey St Petersburg Coastal and Marine Science Center
    Attn: Joseph F. Terrano
    Researcher III
    600 4th Street South
    St. Petersburg, FL

    727-502-8047 (voice)
    727-502-8182 (FAX)
    jterrano@contractor.usgs.gov

Why was the data set created?

This dataset contains transects with linear regression rates of change for GPS, WorldView, and aerial image (AI) shorelines. The purpose of these data is for the calculation of shoreline change rates for marsh shorelines of the GBNERR from 2013-2020.

How was the data set created?

  1. From what previous works were the data drawn?
    GPS_WV_AI_LRR_2013_2020.shp (source 1 of 1)
    Joseph F. Terrano and Kathryn E.L. Smith, 20210723, Transects with linear regression rates derived from shorelines of the Grand Bay National Estuarine Research Reserve from 2013 to 2020.: U.S. Geological Survey, St Petersburg, Florida.

    Online Links:

    Other_Citation_Details:
    Shorelines derived from WorldView 2 or 3, GPS data, and aerial imagery were used to generate transects. Not all dates have complete study area coverage.
    Type_of_Source_Media: digital data
    Source_Contribution:
    Transects with net shoreline change calculations derived from GPS and WorldView shorelines.
  2. How were the data generated, processed, and modified?
    Date: 2021 (process 1 of 2)
    For methods used to derive shorelines please refer to the GPS, AI, or WorldView shoreline metadata records in this data release. Transects were generated by buffering the available shoreline datasets to create inner (land based) and outer (water based) baselines. The baselines form the start and end point for shoreline-perpendicular transects. Baselines were input into the AMBUR statistical package for R (version 4.0.4). AMBUR has several tools, which utilize shoreline parallel baselines to generate transects that are generally perpendicular to the shoreline. Transects were constructed at a sampling distance of 10 meters. Length varied depending on location, and so a length longer than the largest shoreline distance to ensure transects covered all dated shorelines. Once transects were created, the Filter Transects tool was used to adjust and even out the spread of transects. Where shorelines experience sharp bends, such as in small bays and narrow spits, transects may fall at a non-perpendicular angle. Please see the shoreline datasets to determine the estuarine shorelines analyzed. Final transects were checked and edited, if necessary, in ArcGIS (version 10.5.1) in order to improve analysis results and improve shoreline coverage. Transects were manually edited to reduce errors in the analyses or to provide a more perpendicular intersection with the shorelines. Shoreline points and final statistical analyses were completed in AMBUR to generate the shoreline change rates. The following analysis parameters were used: first intersection (if transect intersects the same shoreline more than once, then by default it selected the first intersection), confidence level 95 (confidence level for the linear regression statistics), unit label meters (the units of measure for the map is for Universal Transverse Mercator; in meters), analysis type is advanced (advanced includes additional statistics for a robust linear regression), and time unit for rates is year (utilizes years for calculating rates of shoreline change). More information on the AMBUR program can be obtained from Jackson (2010). AMBUR was run three separate times (once using WV shorelines, once using AI shorelines, and once using GPS shorelines) to calculate the rates of change for each shoreline type. Transects not covering study area shorelines or transects with less than three dates were removed. All fields except the transect number, min and max date, number of dates, elapsed years, and LRR fields were deleted. Person who carried out this activity:
    Joseph F. Terrano
    Researcher III
    600 4th Street South
    St.Petersburg, FL
    USA

    727-502-8047 (voice)
    jterrano@contractor.usgs.gov
    Data sources used in this process:
    • Transects with net shoreline change calculations derived from GPS and WorldView shorelines.
    Date: 2021 (process 2 of 2)
    Within ArcMap, the WV, AI, and GPS attribute tables were merged together. Fields were renamed so each data type had a unique identifier ("_w" for WV shorelines, "_g" for GPS shorelines, "_a" for AI shorelines). Lastly, a "site" field was added. The table was checked for errors or null values. Person who carried out this activity:
    Joseph Terrano
    Researcher III
    600 4th Street South
    St.Petersburg, FL
    USA

    727-502-8047 (voice)
    jterrano@contractor.usgs.gov
  3. What similar or related data should the user be aware of?
    Smith, K.E.L., Terrano, J.F., Pitchford, J.L., Archer, M., 2021, Coastal wetland shoreline change monitoring: A comparison of shorelines from high-resolution WorldView satellite imagery, aerial imagery, and field surveys: Special Issue; New Insights into Ecosystem Monitoring Using Geospatial Techniques Unknown, MDPI; Remote Sensing, Basel, Switzerland.

    Online Links:

    • Unknown

    Other_Citation_Details:
    Associated journal article with additional methodology, data analysis, and results.
    Jackson, C.W., Jr., 2010, Basic User Guide for the AMBUR package for R, version 1.0a: Unknown, Unknown.

    Online Links:

    Other_Citation_Details:
    User guide includes requirements for shorelines that are to be run through AMBUR

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

  1. How well have the observations been checked?
    Shoreline change rates are influenced by availability and accuracy of shoreline data. Analyses of highly dynamic areas are particularly challenging, including 1) areas near inlets, where there are excessively dynamic depositional/erosional sand bars that may appear/disappear rapidly, and 2) tidal creeks, where the land/water line is hard to distinguish due to sporadic vegetation lines.
  2. How accurate are the geographic locations?
    A formal accuracy assessment of the horizontal positional information in the dataset has not been conducted. Elevation and water levels were not used to correct the data but may be presented in the attribute as supporting information. For additional information on data collection techniques (such as shorelines) see the source metadata.
  3. How accurate are the heights or depths?
    Several factors may influence the accuracy and uncertainty of shoreline position for vegetated shorelines, such as water level. Water level at the time of imagery collection was not taken into account when deriving shorelines. Published general uncertainty estimates for shorelines were used for all shorelines in this study based on the data source (satellite derived, aerial imagery, GPS). WV imagery classifications were checked along study area shorelines to ensure the land/water classification was correct. WV shorelines cover extensive amounts of Grand Bay and not all shorelines beyond the transects were checked for detailed accuracy. The classification also has potential variations in shoreline positions from other published data due to image interpretation differences.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented. GPS shorelines were only collected at specific sites and do not cover the entire study area. WV data was subject to image availability and not all dates cover the entire study area. Users are advised to read the rest of the metadata record carefully for additional details. This dataset contains transects with shoreline change rates calculated using AMBUR. To view shorelines and shoreline metadata please see the main data release page (https://doi.org/10.5066/P9W8TNQM).
  5. How consistent are the relationships among the observations, including topology?
    Vector features and attributes were checked for completeness and accuracy within ArcGIS. Linework is generated by the "Construct transects" and "Filter transects" algorithm in the AMBUR program and are generally perpendicular to the shorelines. However, where shorelines experience sharp bends, such as in small bays and narrow spits, the filter algorithm can create transects that are not perpendicular to the shoreline. Transects not covering a study area shoreline were removed as they would not calculate rates of change in AMBUR.

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 requests to be acknowledged as originator of the data in future products or derivative research. This metadata record should be reviewed in its entirety to ensure specific data are suitable for other studies. WorldView imagery remains the property of Maxar Technologies and is not published in this data release. Aerial imagery was downloaded from the USGS Earth Explorer website and is not published in this data release. Shoreline change rates are influenced by availability and accuracy of shoreline data. Analyses of highly dynamic areas are particularly challenging, including 1) areas near inlets, where there are excessively dynamic depositional/erosional sand bars that may appear/disappear rapidly, and 2) tidal creek shorelines where the vegetation/water line is not easily distinguished. Transects were modified so they crossed the shorelines in a perpendicular direction, but some small peninsulas were difficult to determine a perpendicular direction. Users of this data should examine the variables and values closely to determine if the analyses are appropriate for their intended purpose.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey St Petersburg Coastal and Marine Science Center
    Attn: Joseph F. Terrano
    Researcher III
    600 4th Street South
    St. Petersburg, FL

    727-502-8047 (voice)
    727-502-8182 (FAX)
    jterrano@contractor.usgs.gov
  2. What's the catalog number I need to order this data set? GPS_WV_AI_LRR_2013_2020.shp
  3. What legal disclaimers am I supposed to read?
    This digital publication was prepared by an agency of the United States Government. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, nor shall the act of distribution imply any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and (or) contained herein. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. Imagery remains the property of Maxar Technologies. For image pricing and downloads please contact Maxar.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 21-Jul-2021
Metadata author:
U.S. Geological Survey St Petersburg Coastal and Marine Science Center
Attn: Joseph F. Terrano
Researcher III
600 4th Street South
St. Petersburg, FL

727-502-8047 (voice)
727-502-8182 (FAX)
jterrano@contractor.usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/GPS_WV_AI_LRR_2013_2020_metadata.faq.html>
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