Emily J. Sturdivant
Sara L. Zeigler
Benjamin T. Gutierrez
Kathryn M. Weber
2019
DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rockaway Peninsula, NY, 2010
1.0
vector digital data
data release
DOI:10.5066/P944FPA4
Woods Hole Coastal and Marine Science Center, Woods Hole, MA
U.S. Geological Survey, Coastal and Marine Geology Program
https://doi.org/10.5066/P944FPA4
https://www.sciencebase.gov/catalog/item/5d0bc94fe4b0941bde4fc638
Emily J. Sturdivant
Sara L. Zeigler
Benjamin T. Gutierrez
Kathryn M. Weber
2019
Barrier island geomorphology and shorebird habitat metrics—Four sites in New York, New Jersey, and Virginia, 2010–2014
1.0
data release
DOI:10.5066/P944FPA4
Reston, VA
U.S. Geological Survey
Suggested citation: Sturdivant, E.J., Zeigler, S.L., Gutierrez, B.T., and Weber, K.M., 2019, Barrier island geomorphology and shorebird habitat metrics—Four sites in New York, New Jersey, and Virginia, 2010–2014: U.S. Geological Survey data release, https://doi.org/10.5066/P944FPA4.
https://doi.org/10.5066/P944FPA4
https://www.sciencebase.gov/catalog/item/5be5c5bce4b0b3fc5cf8c7cb
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.
These datasets measure positions of geomorphic features, specifically dune crests, dune toes, and mean high water (MHW) shoreline pertaining to sandy beaches at Rockaway Peninsula, NY in 2010. They can be used to calculate beach width and height. The source data were created for the purpose of modeling coastal hazards related to dune overtopping.
Another version of these datasets is available at https://doi.org/10.5066/f7gf0s0z (Doran and others 2017). These data are scheduled to be released in an existing data publication. Because that release has not yet occurred and the data were made available to us prior to publication, we are publishing them here. As a result some of the processing is different from what is described in the associated methods report (Zeigler and others, 2019). For example, rather than being published in a stripped-down tabular format, we are publishing them as a projected shapefile.
20100819
20100827
Ground condition measured by source lidar data.
None planned
-73.94020716
-73.75210126
40.59111709
40.54253795
USGS Metadata Identifier
USGS:5d0bc94fe4b0941bde4fc638
ISO 19115 Topic Category
oceans
geoscientificInformation
None
Barrier Island
USGS
CMGP
Geographic Information Systems
GIS
U.S. Geological Survey
Coastal and Marine Geology Program
Woods Hole Coastal and Marine Science Center
St. Petersburg Coastal and Marine Science Center
MHW
Mean High Water
Coastal Hazards
Rockaway Peninsula
Gateway National Recreation Area
USGS Thesaurus
geospatial datasets
geospatial analysis
geomorphology
transect sampling
None
New York
NY
Long Island
Rockaway Peninsula
Gateway National Recreation Area
North America
United States
USA
Atlantic Ocean
none
Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information.
Emily J. Sturdivant
U.S. Geological Survey
Geographer
Mailing and Physical
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
(508) 548-8700 x2230
(508) 457-2310
esturdivant@usgs.gov
https://www.sciencebase.gov/catalog/file/get/5d0bc94fe4b0941bde4fc638/?name=rock_DC_DT_SLpts_browse.png
Example geomorphology points (mean high water shoreline, dune toe, and dune crest) overlain on the DEM from the source lidar dataset.
PNG
Sara L. Zeigler
Emily J. Sturdivant
Benjamin T. Gutierrez
2019
Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic coast—Geospatial approaches and methodology
Open-File Report
2019–1071
Reston, VA
U.S. Geological Survey
Details the methods used to process these data for use in barrier island and piping plover habitat modeling.
https://doi.org/10.3133/ofr20191071
Kathryn M. Weber
Jeffrey H. List
Karen L. M. Morgan
2005
An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data
Open-File Report
2005-1027
Reston, VA
U.S. Geological Survey
Provides regional mean high water elevation used to determine shoreline position.
https://pubs.usgs.gov/of/2005/1027/
Kara J. Doran
Joseph W. Long
Justin J Birchler
Owen T. Brenner
Matthew W. Hardy
Karen L. M. Morgan
Hilary F. Stockdon
Miguel L. Torres
2017
Lidar-derived Beach Morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. Sandy Coastlines
tabular digital data
Reston, VA
U.S. Geological Survey
Affiliated datasets created using the same methods. A version of this geomorph points dataset may become available as 10CNT10_morphology.zip. These data were made available to us prior to publication. As a result some of the processing is different from what is described here.
https://doi.org/10.5066/F7GF0S0Z
Hilary F. Stockdon
Kara J. Doran
D. M. Thompson
K. L. Sopkin
Nathaniel G. Plant
Asbury H. Sallenger
2012
National assessment of hurricane-induced coastal erosion hazards: Gulf of Mexico
Open-File Report
2012–1084
Reston, VA
U.S. Geological Survey
Provides the methods for feature extraction (pp. 17–23).
https://pubs.usgs.gov/of/2012/1084/
Horizontal and vertical accuracy were estimated and recorded automatically during processing (see sections Positional Accuracy and Entity and Attribute Information). All output feature positions were manually verified against external aerial imagery and elevation data; positions deemed incorrect were manually deleted or relocated.
This dataset consists of three point shapefile datasets produced from elevation data and tidal datum values through an automated process described below. It was not explicitly checked for topological consistency, but feature locations were verified by displaying them with aerial imagery and digital elevation models. Incorrect feature positions were manually deleted or relocated.
These data include dune crest positions and elevations (rock10_DCpts.shp). In the absence of a dune, the seaward edge of the bluff or hard structure (e.g., seawall, road, parking lot) or the peak of the berm was chosen as the dune crest. Alongshore gaps of feature positions exist where the feature could not be accurately located along the profile.
These data include dune toe positions and elevations (rock10_DTpts.shp). Dune toe position is not determined for a given profile if any of the following conditions are met: dune crest elevation is less than the high water line (HWL) (the feature identified is interpreted as a beach berm); if the elevation of what would be considered the dune toe (the point of maximum slope change between the shoreline and the dune crest) is within 0.5 m of the dune crest elevation; if the curvature of the profile around the preliminary dune toe is negative.
For the shoreline points (rock10_SLpts.shp), alongshore gaps of feature positions exist where the feature could not be accurately located along the profile. Unlike the dune morphology positions, the shoreline positions do not include feature elevation. The shoreline elevation is considered the mean high water (MHW) elevation as documented by Weber and others (2005). Sandy shorelines are expected to be generally linear features. Consequently, an operator removed shoreline points where groups of three or fewer consecutive points (occupying 20 m or less alongshore) were separated from the shoreline trend in the surrounding areas (a general line suggested by the rest of the shoreline points and inferred by the operator).
Horizontal accuracy is dependent on the lidar point uncertainty and the density of and scatter in the lidar data points.
For the shoreline points (rock10_SLpts.shp) cross-shore positional accuracy is determined for each feature elevation based upon the scatter of data in the lidar grid cell. It is reported in the field 'ci95_slx'.
Vertical accuracy is determined for each dune crest (rock10_DCpts.shp) feature and dune toe (rock10_DTpts.shp) feature based upon the scatter of data in the lidar grid cell. It is reported in the field 'z_error'.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM)
JALBTCX (Joint Airborne Lidar Bathymetry Technical Center of eXpertise)
2018
2010 USACE NCMP Topobathy Lidar: Atlantic Coast (NY)
https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8606
https://coast.noaa.gov/htdata/lidar2_z/geoid12b/data/8606
https://coast.noaa.gov/
digital data
20100819
20100827
ground condition
Lidar point cloud
Lidar survey that was used to estimate dune morphology variables.
A customized MATLAB routine was used to extract beach and dune morphology. The routine determines the position of the mean high water (MHW) shoreline and the position and elevation of dune toe and dune crest. The routine is summarized below. Refer to Stockdon and others (2012) for details and figures. A version of these data are released in Doran and others (2017).
Processing was performed in MATLAB version 2017a. Source lidar point clouds were adjusted to MHW. The NAVD88 elevation of MHW is 0.46 m for the region encompassing Rockaway Peninsula (Weber and others, 2005). The input lidar dataset was the lidar point cloud (see Source Information section). The automated routine (see below) was executed iteratively to optimize the parameters based on visual comparison with high-resolution imagery. Results were considered low quality and required modifications to the parameters if numerous feature positions appeared to be erroneous or if there were large alongshore gaps in the output features. Once optimal parameters were determined, the routine was run for a final time.
The shoreline position extraction selects the MHW shoreline grid cell within a dynamic distance of a prior shoreline. Stockdon and others (2012, p. 18) used a distance of 3 standard deviations of all shoreline positions within the grid segment. Starting with 3 standard deviations, the search distance was optimized to produce both quality and quantity of shoreline points as determined by the operator through trial and error. This usually resulted in a search distance between 0.5 and 3 standard deviations of the shoreline points within the grid segment. The prior shoreline was a first approximation and was selected as the most recent shoreline in a shoreline database.
The following summarizes the feature extraction routine:
First, lidar point cloud data are interpolated to 10 m (alongshore) by 2.5 m (cross-shore) grids oriented parallel to a roughly shore-parallel reference line that is used for all such feature extractions (Doran and others, 2017). Interpolation uses a Hanning window that is twice as wide as the grid resolution to minimize noise. Automated routines then identify the position of the three features (dune crest, shoreline, and dune toe) for each elevation profile. Each step is performed by a separate MATLAB program.
To determine the dune crest position, the algorithm selects the peak of the most seaward sand dune. It does so by classifying inflection points in cross-shore slope as morphologic crests and troughs and identifying the highest crest elevation as the primary dune. In the absence of a dune, the beach berm or seaward edge of the bluff or hard structure (e.g., road, parking lot, seawall) was extracted. The selection is limited to areas within 200 m of the shoreline, seaward of built structures, less than 0.5 m vertical error, and higher than the MHW elevation.
For each profile, the position of the MHW shoreline is determined. The cross-shore location of the MHW shoreline is automatically extracted with the incorporation of the RMS error surface. The probability that a grid cell elevation value is equal to MHW elevation is estimated from a normal distribution of beach elevation. The grid cell with the highest probability within a defined dynamic distance (standard deviation multiplier of all shoreline points within the grid segment) of a prior shoreline is selected as the most likely shoreline. The dynamic search distance varies based on the generalized beach width of the region. A more precise location of MHW is interpolated from a linear regression of the selected point and adjacent grid cells. A confidence interval for the estimate is defined from the regression error and the error in the lidar data.
Once the dune crest position is established, the dune toe position is determined. First, if the dune crest position is understood to represent a beach berm because the elevation is less than the high water line (HWL), dune toe position is not determined for the given profile. In the remainder of cases, dune toe is identified as the point of maximum slope change between the shoreline and the dune crest unless the elevation of that point is within 0.5 m of the dune crest elevation or the curvature of the profile around the dune toe was negative.
The feature positions resulting from the automated routine are manually verified by overlaying the horizontal positions on high-resolution coastal imagery. Profiles with identified inconsistencies are viewed in a customized MATLAB Graphical User Interface (GUI) that displays the features with the lidar data (interpolated and point cloud) and allows the operator to relocate or delete feature positions. High density of built structures and complex topography often cause errors in the feature positions that require manual editing.
The positions are exported from Matlab with geographic coordinate values (WGS84) in a table of comma-separated values and converted to an Esri shapefile in NAD83 UTM Zone 18N (Make XY Event Layer tool in ArcGIS 10.5). Coordinates were transformed from WGS84 to NAD83 using the transformation WGS_1984_(ITRF00)_To_NAD_1983 (WKID: 108190, accuracy: 0.1 m).
Lidar point cloud
2017
Kathryn M Weber
U.S. Geological Survey
mailing address
384 Woods Hole Road
Woods Hole
MA
02543
United States
508-457-8700 x2351
508-457-2310
kweber@usgs.gov
Positions were converted from a table of comma-separated values to a shapefile using the function functions_warcpy.MorphologyCSV_to_FCsByFeature in the python package bi-transect-extractor (Sturdivant, 2018; version 1.0).
The dune crest, dune toe, and shoreline positions were overlaid on the DEM in ArcGIS 10.5 to check for incongruences between the morphology points and the elevation. Features that displayed logical inconsistency relative to the other features were flagged. For example, dune toe and crest points located seaward of shoreline points would be flagged and evaluated with reference to the DEM to determine which of the features were the more accurate.
Finally, the points were converted from shapefile format to comma-separated text using the ArcGIS tool Export Table (version 10.5). Both the shapefile and the CSV files are included in this data release.
2018
Emily J Sturdivant
U.S. Geological Survey
mailing address
384 Woods Hole Road
Woods Hole
MA
02543
US
508-457-8700 x2230
508-457-2310
esturdivant@usgs.gov
Added keywords section with USGS persistent identifier as theme keyword.
20200810
U.S. Geological Survey
VeeAnn A. Cross
Marine Geologist
Mailing and Physical
384 Woods Hole Road
Woods Hole
MA
02543-1598
508-548-8700 x2251
508-457-2310
vatnipp@usgs.gov
Universal Transverse Mercator
18
0.9996
-75.0
0
500000.0
0
coordinate pair
0.6096
0.6096
meters
North_American_Datum_1983
GRS_1980
6378137.0
298.257222101
NAVD88
0.000001
meter
Attribute values
rock10_DCpts
Attribute values of 529 dune crest positions recorded in the shapefile rock10_DCpts.shp.
USGS
FID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
Esri
Point
Point geometry
Esri
state
State segment file identification (ID) number.
Producer defined
16
New York
Producer defined
seg
Segment ID number used in processing.
Producer defined
8
9
integer
profile
Grid row number corresponding to a cross-shore profile location within the given segment.
Producer defined
1
511
integer
lon
Longitude in WGS 84
Producer defined
-73.935717
-73.76356
decimal degrees
lat
Latitude in WGS 84
Producer defined
40.545273
40.59178
decimal degrees
easting
Easting in NAD 83 UTM Zone 18N
Producer defined
590120.981335
604626.654173
meters
northing
Northing in NAD 83 UTM Zone 18N
Producer defined
4488824.202783
4494176.980622
meters
dhigh_x
Cross-shore feature location relative to the generalized reference line, only relevant during processing.
Producer defined
-569.814147
-110.527515
meters
dhigh_z
Feature elevation (NAVD88).
Producer defined
2.0037
6.59128
meters
z_error
Root mean squared vertical error of feature elevation (NAVD88).
Producer defined
0.015722
0.781671
meters
rock10_DTpts
Attributes for 523 dune toe positions recorded in the shapefile rock10_DTpts.shp.
USGS
FID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
Esri
Point
Point geometry
Esri
state
State segment file identification (ID) number.
Producer defined
16
New York
Producer defined
seg
Segment ID number used in processing.
Producer defined
8
9
integer
profile
Grid row number corresponding to a cross-shore profile location within the given segment.
Producer defined
1
511
integer
lon
Longitude in WGS 84
Producer defined
-73.935665
-73.763532
decimal degrees
lat
Latitude in WGS 84
Producer defined
40.54518
40.591681
decimal degrees
easting
Easting in NAD 83 UTM Zone 18N
Producer defined
590125.480399
604629.176881
meters
northing
Northing in NAD 83 UTM Zone 18N
Producer defined
4488813.891579
4494166.017117
meters
dlow_x
Cross-shore feature location relative to the generalized reference line, only relevant during processing.
Producer defined
-536.532322
-89.277515
meters
dlow_z
Feature elevation (NAVD88).
Producer defined
1.350452
4.278564
meters
z_error
Root mean squared vertical error of feature elevation (NAVD88).
Producer defined
0.019258
1.16033
meters
rock10_SLpts
Attribute values for 1486 shoreline positions recorded in the shapefile rock10_SLpts.shp.
USGS
FID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
Esri
Point
Point geometry
Esri
state
State segment file identification (ID) number.
Producer defined
16
New York
Producer defined
seg
Segment ID number used in processing.
Producer defined
8
9
integer
profile
Grid row number corresponding to a cross-shore profile location within the given segment.
Producer defined
1
1114
integer
sl_x
Cross-shore feature location relative to the generalized reference line, only relevant during processing.
Producer defined
-285.060313
-6.511715
meters
ci95_slx
Root mean squared vertical error of shoreline position (x) at the 95 percent CI.
Producer defined
1.8e-05
0.356246
meters
slope
Mean beach slope calculated between dune toe and shoreline (NAVD88).
Producer defined
-0.11718
-0.002355
radians
easting
Easting in NAD 83 UTM Zone 18N
Producer defined
590157.207808
604644.033273
meters
northing
Northing in NAD 83 UTM Zone 18N
Producer defined
4488741.176958
4494107.770575
meters
X
Cross-shore feature location relative to the generalized reference line, only relevant during processing.
Producer defined
-73.9353009035
-73.7633674763
meters
This section provides a separate detailed entity and attribute information sections for each dataset described in these metadata. Each shapefile has a companion CSV file that contains the same information. The first row of the CSV is a header line and corresponds to the attributes described in the detailed entity and attribute section (except for the Shape attribute) for that particular dataset. Fields in the CSV may be reordered and the FID field may be renamed to OBJECTID.
Methods Open-File Report by Zeigler and others, 2019
U.S. Geological Survey - ScienceBase
mailing address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
United States
1-888-275-8747
sciencebase@usgs.gov
These datasets contain three individual shapefile point and corresponding CSV datasets: Dune crest points (rock10_DCpts.shp and other shapefile components, rock10_DCpts.csv), dune toe points (rock10_DTpts.shp and other shapefile components, rock10_DTpts.csv) and shoreline points (rock10_SLpts.shp and other shapefile components, rock10_SLpts.csv). Additionally, the CSDGM FGDC metadata (rock10_DC_DT_SLpts_meta.xml) in XML format and the browse graphic (rock_DC_DT_SLpts_browse.png) in PNG format are included. These datasets can be downloaded individually or packaged on-demand in a zip file (see the Digital Transfer Option section).
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. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), and 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 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. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Shapefile
ArcGIS 10.5
Esri point shapefile and corresponding comma-separated values files
This dataset contains the point shapefiles and corresponding CSV files of the dune crest, dune toe, and shoreline positions. Also included is the CSDGM metadata and browse graphic.
0.229
https://www.sciencebase.gov/catalog/item/5d0bc94fe4b0941bde4fc638
https://www.sciencebase.gov/catalog/file/get/5d0bc94fe4b0941bde4fc638
https://doi.org/10.5066/P944FPA4
The first link is to the page containing the data. The second is a direct link to download all data available from the page as a zip file. The final link is to the publication landing page. The data page (first link) may have additional data access options, including web services.
None
To utilize these data, the user must have software capable of reading shapefile or CSV format.
20211119
Emily Sturdivant
U.S. Geological Survey
Geographer
Mailing and Physical
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
(508) 548-8700 x2230
(508) 457-2310
whsc_data_contact@usgs.gov
The metadata contact email address is a generic address in the event the metadata contact is no longer with the USGS or the email is otherwise invalid.
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998