Emily J. Sturdivant Sara L. Zeigler Benjamin T. Gutierrez Kathryn M. Weber 2019 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/5d0bc921e4b0941bde4fc5ff Emily J. Sturdivant Sara L. Zeigler Benjamin T. Gutierrez Kathryn M. Weber 2019 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 Fire Island, 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. Complete None planned -73.31377852 -72.72632694 40.77144484 40.62020054 USGS Metadata Identifier USGS:5d0bc921e4b0941bde4fc5ff 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 Fire Island Fire Island National Seashore USGS Thesaurus geospatial datasets geospatial analysis geomorphology transect sampling None New York NY Long Island Fire Island Fire Island National Seashore 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/5d0bc921e4b0941bde4fc5ff/?name=fiis_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 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 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 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 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 (fiis10_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 (fiis10_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 (fiis10_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 (fiis10_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 (fiis10_DCpts.shp) feature and dune toe (fiis10_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 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 Fire Island (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. Process performed on 20200810. The metadata title was modified to move the location name to the beginning of the title to account for the alphabetical listing of the individual records when ScienceBase migrates the data release to a new platform and "flattens" the data release structure (20260227). 20260227 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 fiis10_DCpts Attribute values of 3,310 dune crest positions recorded in the shapefile fiis10_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 40 44 integer profile Grid row number corresponding to a cross-shore profile location within the given segment. Producer defined 1 3195 integer lon Longitude in WGS 84 Producer defined -73.311261 -72.709854 decimal degrees lat Latitude in WGS 84 Producer defined 40.621063 40.776752 decimal degrees easting Easting in NAD 83 UTM Zone 18N Producer defined 642828.460497 693260.875694 meters northing Northing in NAD 83 UTM Zone 18N Producer defined 4498074.040422 4516498.150009 meters dhigh_x Cross-shore feature location relative to the generalized reference line, only relevant during processing. Producer defined -1016.662145 72.597035 meters dhigh_z Feature elevation (NAVD88). Producer defined 2.065092 10.772426 meters z_error Root mean squared vertical error of feature elevation (NAVD88). Producer defined 0.015696 1.931439 meters fiis10_DTpts Attributes for 3264 dune toe positions recorded in the shapefile fiis10_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 40 44 integer profile Grid row number corresponding to a cross-shore profile location within the given segment. Producer defined 1 3194 integer lon Longitude in WGS 84 Producer defined -73.311451 -72.709807 decimal degrees lat Latitude in WGS 84 Producer defined 40.620966 40.776415 decimal degrees easting Easting in NAD 83 UTM Zone 18N Producer defined 642812.678824 693265.830133 meters northing Northing in NAD 83 UTM Zone 18N Producer defined 4498062.709856 4516460.873796 meters dlow_x Cross-shore feature location relative to the generalized reference line, only relevant during processing. Producer defined -1000.412145 91.977246 meters dlow_z Feature elevation (NAVD88). Producer defined 1.113445 6.324764 meters z_error Root mean squared vertical error of feature elevation (NAVD88). Producer defined 0.018858 2.126811 meters fiis10_SLpts Attribute values for 5035 shoreline positions recorded in the shapefile fiis10_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 40 44 integer profile Grid row number corresponding to a cross-shore profile location within the given segment. Producer defined 1 3044 integer sl_x Cross-shore feature location relative to the generalized reference line, only relevant during processing. Producer defined -949.896947 153.28207 meters ci95_slx Root mean squared vertical error of shoreline position (x) at the 95 percent CI. Producer defined 6.5e-05 4.965018 meters slope Mean beach slope calculated between dune toe and shoreline (NAVD88). Producer defined -0.214516 -0.0011 radians easting Easting in NAD 83 UTM Zone 18N Producer defined 642612.730393 691885.902867 meters northing Northing in NAD 83 UTM Zone 18N Producer defined 4497989.621717 4515872.821026 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 (fiis10_DCpts.shp and other shapefile components, fiis10_DCpts.csv), dune toe points (fiis10_DTpts.shp and other shapefile components, fiis10_DTpts.csv) and shoreline points (fiis10_SLpts.shp and other shapefile components, fiis10_SLpts.csv). Additionally, the CSDGM FGDC metadata (fiis10_DC_DT_SLpts_meta.xml) in XML format and the browse graphic (fiis_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. 2.1 https://www.sciencebase.gov/catalog/item/5d0bc921e4b0941bde4fc5ff https://www.sciencebase.gov/catalog/file/get/5d0bc921e4b0941bde4fc5ff 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. 20260227 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