Julia L. Heslin
Rachel E. Henderson
Emily A. Himmelstoss
20211119
2015 Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
1
vector digital data
data release
DOI:10.5066/P9AZYW74
Woods Hole Coastal and Marine Science Center, Woods Hole, MA
U.S. Geological Survey, Coastal and Marine Geology Program
https://doi.org/10.5066/P9AZYW74
https://www.sciencebase.gov/catalog/item/613b7f28d34e40dd9c0f9671
Julia L. Heslin
Rachel E. Henderson
Emily A. Himmelstoss
2021
A GIS Compilation of Vector Shorelines for Puerto Rico from 2015 to 2018
1
vector digital data
data release
DOI:10.5066/P9AZYW74
Reston, VA
U.S. Geological Survey
Suggested citation: Heslin, J.L., Henderson, R.E., and Himmelstoss, E.A., 2021, A GIS compilation of vector shorelines for Puerto Rico from 2015 to 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9AZYW74.
https://doi.org/10.5066/P9AZYW74
https://www.sciencebase.gov/catalog/item/610abe37d34ef8d70568937a
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable.
This data release and other associated products represent an expansion of the USGS national-scale shoreline database to include Puerto Rico and its islands, Vieques and Culebra. The United States Geological Survey (USGS) in cooperation with the Coastal Research and Planning Institute of Puerto Rico (CoRePI, part of the Graduate School of Planning at the University of Puerto Rico, Rio Piedras Campus) has derived and compiled a database of historical shoreline positions using a variety of methods. These shorelines are used to measure the rate of shoreline change over time.
This shoreline dataset is a mean high water (MHW) datum-based shoreline extracted using a contour method (Farris and others 2018). These data are used in conjunction with other shoreline files to calculate rates of shoreline change.
Described in Farris and others (2018), the contour method extracts the elevation of average MHW value from DEM data using ArcMap tool Contour List with the MHW value chosen for the contour. The resulting shorelines are polyline shapefiles. 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.
20141116
20150125
ground condition
None planned
-67.2715
-65.5822
18.5160
17.9284
ISO 19115 Topic Category
geoscientificInformation
oceans
environment
None
USGS
U.S. Geological Survey
Coastal and Marine Hazards and Resources Program
Woods Hole Coastal and Marine Science Center
WHCMSC
NOAA
National Geodedic Survey
NGS
National Oceanic and Atmospheric Agency
Puerto Rico Shoreline Change
University of Puerto Rico
University of Puerto Rico Graduate School of Planning
Coastal Research and Planning Institute of Puerto Rico
DSAS
Digital Shoreline Analysis System
Shoreline
Shoreline Change
Mean High Water
MHW
USGS thesaurus
coastal processes
geospatial datasets
USGS Metadata Identifier
USGS:613b7f28d34e40dd9c0f9671
None
United States
Atlantic Coast
Caribbean
Puerto Rico
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 as the originator of the dataset.
U.S. Geological Survey
Rachel E. Henderson
mailing and physical address
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
508-548-8700
rehenderson@contractor.usgs.gov
https://www.sciencebase.gov/catalog/file/get/613b7f28d34e40dd9c0f9671?name=PR_2015_Lidar_Shoreline.jpg
Map of the NOAA/NGS 2015 MHW shoreline (shown in green) extracted from lidar data, with the extent of lidar coverage highlighted in red
JPEG
Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321
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
document
Open-File Report
2005-1027
Reston, VA
U.S. Geological Survey
https://doi.org/10.3133/ofr20051027
https://pubs.usgs.gov/of/2005/1027/
Emily A. Himmelstoss
Amy S. Farris
Rachel E. Henderson
Meredith G. Kratzmann
Ayhan Ergul
Ouya Zhang
Jessica L. Zichichi
2018
Digital Shoreline Analysis System (version 5.0): U.S. Geological Survey software
5
application/service
software release
version 5.0
Reston, VA
U.S. Geological Survey
https://doi.org/10.3133/ofr20181179
https://code.usgs.gov/cch/dsas
Amy S. Farris
Kathryn M. Weber
Kara S. Doran
Jeffrey H. List
2018
Comparing Methods Used by the U.S. Geological Survey Coastal and Marine Geology Program for Deriving Shoreline Position from Lidar Data
document
Open-File Report
2018-1121
Reston, VA
U.S. Geological Survey
https://doi.org/10.3133/ofr20181121
https://pubs.usgs.gov/of/2018/1121/
Julia L. Heslin
Rachel E. Henderson
Emily A. Himmelstoss
20211101
Historical Shorelines for Puerto Rico from 1901 to 1987
vector digital data
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/P9CLXCEG
https://www.sciencebase.gov/catalog/item/60cb7627d34e86b938a3a1b0
Loderay Bracero-Marrero
Maritza Barreto-Orta
Nias Hernández-Montcourt
Ruben Maldonado-González
Emily A. Himmelstoss
Julia L. Heslin
20211101
A GIS Compilation of Vector Shorelines and Shoreline Classification for Puerto Rico from 1970 and 2010
vector digital data
data release
https://doi.org/10.5066/P9SEUAHC
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/P9SEUAHC
https://www.sciencebase.gov/catalog/item/610ae914d34ef8d70568fba9
Julia Heslin
Rachel E. Henderson
Emily A. Himmelstoss
20211101
Puerto Rico Shoreline Change: A GIS Compilation of Shorelines, Baselines, Intersects, and Change Rates using the Digital Shoreline Analysis System version 5.1
vector digital data
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/P9FNRRN0
https://www.sciencebase.gov/catalog/item/61255b87d34e40dd9c03f390
The attributes are based on the requirements of the Digital Shoreline Analysis System (DSAS) software, (Himmelstoss and others, 2018).
Adjacent shoreline segments do not overlap and are not necessarily continuous. Visual QC was performed in ArcMap, displaying shorelines over imagery (Esri World Imagery in map view) to verify that data landward of the expected shoreline (such as low lying inland areas, or inland marsh areas) were removed. Shorelines located near any data-gaps or the edge of the elevation dataset were evaluated and removed as well.
Although not a continuous shoreline for Puerto Rico, this shoreline file is complete and contains all shoreline segments where shoreline position data could be extracted using this method. This file has a large data gap in the central north, and smaller gaps in the southwest, and eastern facing portions of the island due to incomplete coverage in the lidar source data. See source citations and process steps for a link to the lidar tile index, which describes the exact lidar data coverage. Additional data gaps in the DEM at or near the mean high water line are the consequence of non-existing data or existing data that did not meet quality assurance standards.
Contour Shoreline Positional Uncertainty: We accounted for three sources of uncertainty in the contour method: 1) The vertical uncertainty of the lidar data, which is stated as 0.098 m in the lidar metadata; 2) a mean high water (MHW) uncertainty of 0.06 m to account for our simplified use of a single MHW value when in reality MHW continuously varies around the islands; and 3) the horizontal uncertainty due to the 1 meter cell size of the DEM.
This 1 meter cell limits us to a plus or minus 0.5 m confidence in the location of the shoreline. Since the first two sources of uncertainty are vertical measurements, we converted both of them to a horizontal uncertainty using the beach slope. We divided both of the vertical uncertainty terms by the average beach slope of 0.12 (expressed as rise/run) in order to get the horizontal component of the two vertical uncertainty terms (see Heslin and others 2021). In order to estimate the total horizontal uncertainty, the three, now horizontal, components of uncertainty were added in quadrature. These are stored in the attribute table. The average uncertainty for the 2015 contour shoreline for Puero Rico is 1.38 meters.
NOAA
20201203
vertical Datum transformation (VDatum) version 4.2.1
raster digital data
Charleston, SC
NOAA
https://vdatum.noaa.gov/
online
20211201
ground condition
VDatum
used to define MHW elevation for Puerto Rico.
NOAA Office for Coastal Management (NOAA/OCM)
20190702
2015 NOAA NGS Topobathy Lidar DEM: Puerto Rico
raster digital data
Charleston, SC
NOAA Office for Coastal Management (OCM)
https://coast.noaa.gov/dataviewer/#/lidar/search/-7522626.5757139055,2004484.6297504622,-7254179.732376367,2138402.303306091/details/6211
https://www.fisheries.noaa.gov/inport/item/48380
https://coast.noaa.gov/htdata/raster2/elevation/PR_Puerto_Rico_NGS_DEM_2015_6211/
online
201411
20150501
ground condition
2015 DEM
The raster digital elevation model (in GeoTIFF format) was used to extract a polyline shoreline. The data were downloaded using the following parameters: projection in State Plane 1983; Zone 5200 Puerto Rico, NAD83 horizontal datum; PRVD02 vertical datum. The raster resolution is 1 meter.
NOAA Office for Coastal Management (NOAA/OCM)
20190702
Tile index for the 2015 NOAA NGS Topobathy Lidar: Puerto Rico Point Cloud files with Orthometric Vertical Datum Puerto Rico Vertical Datum of 2002 (PRVD02) using GEOID18
vector digital data
Charleston, SC
NOAA Office for Coastal Management (OCM)
https://chs.coast.noaa.gov/htdata/lidar4_z/geoid18/data/5193/tileindex.zip
https://chs.coast.noaa.gov/htdata/lidar4_z/geoid18/data/5193/
online
20141101
20150501
ground condition
2015 Tile Index
The tile index shapefile for the lidar point cloud data shares the same footprints as he DEM data used in this analysis. The DATE the lidar was flown is embedded in the name of the .laz file which is stored in the tile index. The shoreline date was extracted from the tile index.
Overview of the methods used to extract shoreline features in Puerto Rico and the islands of Vieques and Culebra: This data release uses the contour method of shoreline extraction. Described in Farris and others (2018), the contour method extracts the elevation of average MHW value from DEM data using ArcMap tool “Contour List” with the MHW value chosen for the contour. The resulting shorelines are polyline shapefiles.
The average MHW for Puerto Rico was calculated using NOAA’s vdatum tool (v4.0.1; https://vdatum.noaa.gov/) to model the regional surface using local MHW values based on the PRVD02 vertical datum.
20200101
Rachel E. Henderson
U.S. Geological Survey
Researcher VII
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-548-8700
rehenderson@contractor.usgs.gov
Definition of average MHW for Puerto Rico: The height of MHW was calculated using NOAA's VDatum (v4.0.1) tool (https://vdatum.noaa.gov/). We continued the practice set out by Weber and others (2005) of using one MHW value for a continuous section of coast (as opposed to using a continuously varying value). This value is always within 6 cm of the value returned by vdatum at any point along the coast. The MHW value ranges from 0.09 m to 0.19 m from all tide observation stations in Puerto Rico, Vieques, and Culebra. Taking the mean from each of those MHW observations results in 0.13 m. This is the contour value used to delineate the shoreline and is recorded as the contour attribute in this shapefile.
VDatum
20200201
Rachel E. Henderson
U.S. Geological Survey
Researcher VII
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-548-8700
rehenderson@contractor.usgs.gov
Contour Shoreline extraction: A suite of ArcGIS tools (ArcGIS version 10.7.1) were compiled into a series of in-house models with ArcGIS ModelBuilder and used to help automate the contour method of shoreline extraction. First, the MHW shoreline elevation (0.13 m NAV88) was extracted from the DEMs using the ArcGIS Contour List tool (ArcToolbox > Spatial Analyst > Surface > Contour List). This step was iterated for each individual DEM in the study area. As a result, individual shapefiles were created for each DEM.
2015 DEM
20200401
PR MHW Contours
Julia L. Heslin
U.S. Geological Survey
Geographer
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-457-2262
jheslin@usgs.gov
Contour Shoreline smoothing: Each contour shoreline was generalized using the Smooth Line tool (ArcToolbox > Cartography > Generalization > Smooth Line), applying the PAEK algorithm with a 5 m tolerance. The shoreline features were then merged (ArcToolbox > Data Management > General > Merge) into one feature class.
PR MHW Contours
20200501
PR Smoothed Contour
Julia L. Heslin
U.S. Geological Survey
Geographer
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-457-2262
jheslin@usgs.gov
Contour Shoreline date calculation: The date for each shoreline is required to identify the dataset, and is used for shoreline change analysis with DSAS (Himmelstoss and others 2018). The date for each contour shoreline was calculated using the corresponding lidar point cloud tile index shapefile.
The tile index shapefile was downloaded from the 2015 NOAA NGS Topobathy Lidar bulk download page (https://chs.coast.noaa.gov/htdata/lidar4_z/geoid18/data/5193/). The dates associated with the tile index in the attribute table were used to apply the date to the contour shoreline features. In some instances, however, there were multiple tiles to one shoreline feature as some tiles were overlapping. In this case, the average date was calculated and applied to those shoreline features.
To do this, the date field from the tile index was converted to the Julian day (in this case, Julian day refers to the day of the year number).
In order to spatially join the tiles to the contour shorelines and calculate the average date, some modifications to the tile index shapefile was required. A “Count” field was added and calculated with a value of 1 in the tile index shapefile. Then, the contour shorelines were split where they intersected with the tile boundaries. This was done by creating a separate point shapefile where the contours intersected with the tile boundaries (ArcToolbox > Analysis > Overlay > Intersect) and then split the contours at these intersection points (ArcToolbox > Data Management > Features > Split Line at Point).
Each of these split contours were then spatially joined to the corresponding tile index or indices within which they fell (ArcToolbox > Analysis > Overlay > Spatial Join). The sum of the Julian day and Count fields was designated as the merge rule when spatially joining the tiles to the contours.
The summed Julian day field was divided by the summed Count field to derive the average Julian day for each of the contour shorelines. The average Julian day was then converted back to a date as a text field, compatible with DSAS.
PR Smoothed Contour
2015 Tile Index
20200601
PR Contour with DATE
Julia L. Heslin
U.S. Geological Survey
Geographer
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-457-2262
jheslin@usgs.gov
Contour Shoreline attributes: Additional attributes are added UNCY (see Horizontal Positional Accuracy Report), PROXY (MHW), AGENCY, TYPE (lidar), METHOD (contour).
PR Contour with DATE
20200701
PR Contour with ATTRIBUTES
Julia L. Heslin
U.S. Geological Survey
Geographer
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-457-2262
jheslin@usgs.gov
Contour Shoreline edits: The shoreline was then manually edited with the lidar data displayed with categorized elevation values to highlight the MHW values and a modern base map image for reference. Segments of the contour that did not fall near the open ocean MHW were removed.
PR Contour with ATTRIBUTES
20200801
PR Contour Shoreline
Rachel E. Henderson
U.S. Geological Survey
Researcher VII
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-548-8700
rehenderson@contractor.usgs.gov
Final coordinate transformation from "NAD_1983_NSRS2007_StatePlane_Puerto_Rico_Virgin_Isls_FIPS_5200" to "GCS_WGS_1984".
PR Contour Shoreline
20201101
PR_2015_NOAA_NGS_MHW_Shoreline
Rachel E. Henderson
U.S. Geological Survey
Researcher VII
mailing and physical
384 Woods Hole Road
Woods Hole
MA
02540
508-548-8700
rehenderson@contractor.usgs.gov
Vector
String
732
0.0198254314
0.0207101782
Decimal seconds
WGS_1984
WGS_84
6378137.0
298.257223563
PR_2015_NOAA_NGS_MHW_Shoreline.shp
The mean high water (MHW) shoreline for the Puerto Rican coast used in shoreline change analysis.
U.S. Geological Survey
FID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
Esri
Coordinates defining the features.
CONTOUR
Elevation of the feature in meters using Mean High Water tidal datum.
U.S. Geological Survey
0.13
0.13
DSAS_DATE
Date of shoreline position in mm/dd/yyyy; date of survey as indicated on source material.
U.S. Geological Survey
Date of the shoreline
UNCY
Estimate of shoreline position uncertainty derived by adding in quadrature the identified components of uncertainty described in the horizontal positional accuracy section of this metadata file. Actual shoreline position is assumed to be within the range of this value (plus or minus, meters).
U.S. Geological Survey
1.38
1.38
PROXY
Method of deriving shoreline feature. These shorelines were all extracted from lidar using a MHW elevation. When combined with other data (high water line, wet-dry line) for shoreline analysis this field preserves the method used to derive this shoreline.
U.S. Geological Survey
MHW
The average Mean high water (MHW) for Puerto Rico, as described in process steps
Producer defined
AGENCY
The agency that provided the source data to create the shoreline
U.S. Geological Survey
NOAA NGS
National Oceanic and Atmospheric Administration National Geodetic Survey
U.S. Geological Survey
TYPE
The type of source data used to create the shoreline
U.S. Geological Survey
Lidar
Light detection and ranging elevation data
Producer defined
METHOD
Method of shoreline extraction from lidar - for this dataset, it is contour. See process steps for details.
U.S. Geological Survey
Contour
Shorelines were extracted using the contour method (Farris and others, 2018)
U.S. Geological Survey
U.S. Geological Survey - ScienceBase
mailing and physical address
Federal Center, Building 810, MS 302
Denver
CO
80225
USA
1-888-275-8747
sciencebase@usgs.gov
The dataset contains the polyline shapefile of shoreline data for Puerto Rico derived from 2015 NOAA NGS lidar using contour (DEM) shoreline extraction methods, and the FGDC CSDGM metadata in XML format
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.
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Shapefile
ArcGIS 10.6
Esri polyline shapefile
Dataset contains the shapefile, browse graphic, and CSDGM metadata.
11
https://doi.org/10.5066/P9AZYW74
https://www.sciencebase.gov/catalog/file/get/613b7f28d34e40dd9c0f9671
https://www.sciencebase.gov/catalog/item/613b7f28d34e40dd9c0f9671
The first link is to the USGS publication page, the second link downloads all the data on the landing page, and the third link is to the dataset landing page.
None
These data are available in a polyline shapefile format. The user must have software to read and process the data components of a shapefile.
20211119
Rachel E. Henderson
U.S. Geological Survey
mailing and physical address
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
508-548-8700
rehenderson@contractor.usgs.gov
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
local time