U.S. Geological Survey
2017
Shorelines of the Texas east (TXeast) coastal region used in shoreline change analysis
1
vector digital data
data release
DOI:10.5066/F78P5XNK
Woods Hole Coastal and Marine Science Center, Woods Hole, MA
U.S. Geological Survey, Coastal and Marine Geology Program
https://doi.org/10.5066/F78P5XNK
https://www.sciencebase.gov/catalog/item/582ca53be4b04d580bd37988
https://www.sciencebase.gov/catalog/file/get/582ca53be4b04d580bd37988
E.A. Himmelstoss
M.G. Kratzmann
E.R. Thieler
2017
National Assessment of Shoreline Change— A GIS compilation of Updated Vector Shorelines and Associated Shoreline Change Data for the Gulf of Mexico Coast
1
data release
DOI:10.5066/F78P5XNK
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/F78P5XNK
https://www.sciencebase.gov/catalog/item/58055db4e4b0824b2d1c1ee2
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 under 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 the National Assessment 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.
This dataset includes shorelines from 151 years ranging from 1850 to 2001 for the Texas east coastal region from Sabine Pass at the Louisiana border to Aransas Pass at the southern end of San Jose Island. Shorelines were compiled from topographic survey sheets, also known as T-sheets (National Oceanic and Atmospheric Administration (NOAA)), aerial photographs (Bureau of Economic Geology, The University of Texas (UT BEG) at Austin), and lidar data (United States Geological Survey/National Aeronautics & Space Administration and UT BEG).
Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's (USGS) National Assessment of Shoreline Change Project.
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. 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.
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.
1850
2001
ground condition
None planned
-97.047365
-93.695168
29.747312
27.838646
USGS Metadata Identifier
USGS:582ca53be4b04d580bd37988
None
Shoreline
Shoreline Change
Digital Shoreline Analysis System
DSAS
U.S. Geological Survey
USGS
Coastal and Marine Geology Program
CMGP
Woods Hole Coastal and Marine Science Center
WHCMSC
National Assessment of Shoreline Change Project
MHW
Mean High Water
HWL
High Water Line
National Oceanic and Atmospheric Administration
NOAA
National Ocean Service
NOS
Bureau of Economic Geology, The University of Texas at Austin
UT BEG
ISO 19115 Topic Category
oceans
environment
geoscientificInformation
Marine Realms Information Bank (MRIB) Keywords
effects of coastal change
coastal processes
shoreline accretion
shoreline erosion
USGS Thesaurus
coastal processes
erosion
shoreline accretion
None
Texas
TX
Sabine Pass
McFaddin Beach
High Island
Gilchrist
Capien
Crystal Beach
Bolivar Peninsula
Galveston Island
Bermuda Beach
Pirates Beach
Palm Beach
San Luis Island
Sunside Beach
Bryan Beach
Matagorda Peninsula
Matagorda Island
San Jose Island
Aransas Pass
Gulf of Mexico
United States
North America
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
E.A. Himmelstoss
mailing and physical address
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
508-548-8700
508-547-2310
ehimmelstoss@usgs.gov
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.0.4.4000
Robert A. Morton
Tara L. Miller
Laura J. Moore
2004
National Assessment of Shoreline Change: Part 1 Historical Shoreline Changes and Associated Coastal Land Loss along the U.S. Gulf of Mexico
Open-File Report
2004-1043
Reston, VA
U.S. Geological Survey
https://pubs.usgs.gov/of/2004/1043/
Tara L. Miller
Robert A. Morton
Asbury H. Sallenger
Laura J. Moore
2004
The National Assessment of Shoreline Change— A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for the U.S. Gulf of Mexico
Open-File Report
2004-1089
Reston, VA
U.S. Geological Survey
https://pubs.usgs.gov/of/2004/1089/
E.R. Thieler
E.A. Himmelstoss
J.L. Zichichi
A. Ergul
2009
Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change
Open-File Report
2008-1278
Reston, VA
U.S. Geological Survey
Current version of software at time of use was 4.3
https://woodshole.er.usgs.gov/project-pages/DSAS/version4/
https://woodshole.er.usgs.gov/project-pages/DSAS/
United States Geological Survey (USGS)
National Aeronautics & Space Administration (NASA)
Department of Commerce (DOC), National Oceanic & Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM)
20141114
2001 USGS/NASA Airborne Topographic Mapper (ATM) Lidar: Coastal Alabama, Florida, Louisiana, Mississippi, Texas
Charleston, SC
NOAA's Ocean Service, Office for Coastal Management (OCM)
Lidar data were obtained prior to the publication date listed in this citation.
https://coast.noaa.gov/dataviewer/index.html?action=advsearch&qType=in&qFld=ID&qVal=525
ftp://coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid12a/data/525
https://coast.noaa.gov/dataviewer
https://www.coast.noaa.gov
National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS)
Unknown
Scanned National Ocean Service (NOS) Coastal Survey Maps (also known as Topographic Survey sheets, or T-sheets)
Washington, D.C.
National Oceanic and Atmospheric Administration
NOAA shoreline manuscripts (T-sheets)
https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.kmz
https://oceanservice.noaa.gov/news/features/feb13/historical-shoreline.html
https://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html
The University of Texas, Bureau of Economic Geology
20030806
Texas Shoreline Change Project
Austin, TX
University of Texas, Bureau of Economic Geology
Vector digital data
https://www.beg.utexas.edu/coastal/download.htm
https://www.beg.utexas.edu/coastal/zip_shoreline/zone14_up10.htm
Texas Bureau of Economic Geology
unknown
Texas Bureau of Economic Geology Coastal Survey, San Bernard NWR, TX to Sabine Pass, LA
Austin, TX
Bureau of Economic Geology, The University of Texas at Austin
https://www.beg.utexas.edu/coastal.safe/2001%20final%20report.pdf
E.A. Himmelstoss
M.G. Kratzmann
E.R. Thieler
2017
National Assessment of Shoreline Change: Summary Statistics for Updated Vector Shorelines and Associated Shoreline Change Data for the Gulf of Mexico and Southeast Atlantic Coasts
Open-File Report
2017-1015
Reston, VA
U.S. Geological Survey
https://doi.org/10.3133/ofr20171015
The data provided here are a compilation of shorelines from multiple sources, spanning 151 years. The attributes are based on the requirements of the Digital Shoreline Analysis System (DSAS) software and have gone through a series of quality assurance procedures.
Adjacent shoreline segments do not overlap and are not necessarily continuous. Shorelines were quality checked for accuracy. Any slight offsets between adjacent segments due to georeferencing and digitizing error are taken into account in the uncertainty of the shoreline position, as reported in the horizontal accuracy section of this metadata file.
This shoreline file is complete and contains all shoreline segments used to calculate shoreline change rates along sections of the Texas east coastal region where shoreline position data were available. These data adequately represented the shoreline position at the time of the survey. Remaining gaps in these data, if applicable, are a consequence of non-existing data or existing data that did not meet quality assurance standards.
The digitized shoreline vectors downloaded from NOAA included attributes defining the shoreline type (attribute field name varies by file). For the open-ocean facing shorelines, only Mean High Water shoreline features (Natural.Mean High Water; SPOR; 20) were retained. Other shoreline features (such as seawalls, bulkheads, manmade objects) were deleted.
NOAA has made non-georeferenced NOAA Shoreline Survey Scans available for download, and additional data for the region may be available (https://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html).
Shoreline data have been acquired from 1850 to 2001, the horizontal accuracy of which varies with respect to data source from which the shorelines were digitized, the lidar data from which the shorelines were extracted, and the time period. Shorelines prior to 1960 (T-sheets) have an estimated positional uncertainty of plus or minus 10.8 meters. Shorelines from the 1960s-1980s (T-sheets) have an estimated positional uncertainty of plus or minus 5.1 meters. The UT BEG air photos from the 1930s-1990s have an estimated positional uncertainty of plus or minus 4.2-5.1 meters with one shoreline from 1934 plus or minus 10.8 meters. The UT BEG ground GPS survey shorelines from 1996 and 1998 have an estimated positional uncertainty of plus or minus 3 meters. The UT BEG Digital Orthophoto (USGS) shorelines from 1995 have an estimated positional uncertainty of plus or minus 1.4 meters. The lidar shoreline from 2001 has an estimated positional uncertainty of plus or minus 3.2 meters.
Crowell, M., Leatherman, S.P., and Buckley, M.K., 1991. Historical Shoreline Change: Error Analysis and Mapping Accuracy. Journal of Coastal Research: v.7, n.3, pp.839-852.
The historic shorelines are proxy-based and estimate the HWL. The lidar shoreline is tidal-based and estimates MHW. While the proxy-based HWL shorelines are not shifted to the MHW datum, the distance measurements established by the transects and used by DSAS to compute rate-of-change statistics are adjusted to account for the offset between HWL and MHW. Therefore, all distance measurements along DSAS transects are referenced to the MHW datum before rates are calculated.
An operational Mean High Water (MHW) shoreline was extracted from the lidar surveys within MATLAB v7.6 using a method similar to the one developed by Stockdon et al. (2002). Shorelines were extracted from cross-shore profiles which consist of bands of lidar data 2 m wide in the alongshore direction and spaced every 20 m along the coast. For each profile, the seaward sloping foreshore points were identified and a linear regression was fit through them. The regression was evaluated at the operational MHW elevation to yield the cross-shore position of the MHW shoreline. 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 operational MHW elevation. A lidar positional uncertainty associated with this point was also computed. The horizontal offset between the datum-based lidar MHW shoreline and the proxy-based historical shorelines nearly always acts in one direction and the "bias" value was computed at each profile (Ruggiero and List, 2009). In addition an uncertainty associated with the bias was also computed, which can also be thought of as the uncertainty of the HWL shorelines due to water level fluctuations.
Repeating this procedure at successive profiles generated a series of X,Y points that contain a lidar positional uncertainty, a bias, and a bias uncertainty value.
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.
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.
20081003
Amy Farris
U.S. Geological Survey
mailing and physical address
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
508-548-8700
508-457-2310
afarris@usgs.gov
The lidar data were collected in projected coordinates (WGS 84 UTM zone 15N). The series of operational MHW points extracted from the cross-shore lidar profiles were converted into a calibrated route shapefile for use in ArcGIS 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 attribute field linking the shoreline route file (TXeast_shoreline.shp) to the uncertainty table (TXeast_shoreline_uncertainty.dbf) storing the lidar positional uncertainty, the bias correction value, and the uncertainty of the bias correction for each point of the original lidar data.
During the rate calculation process DSAS uses linear referencing to retrieve the uncertainty and bias values stored in the associated table. Visually identified HWL-type proxy shorelines are virtually never coincident with datum-based MHW-type shorelines. In fact, HWL shorelines are almost universally estimated to be higher (landward) on the beach profile than MHW shorelines. Not accounting for this offset will cause shoreline change rates to be biased toward slower shoreline retreat, progradation rather than retreat, or faster progradation than in reality (for the typical case where datum-based shorelines are more recent data than the proxy-based shoreline dates), depending on actual changes at a given site.
Ruggiero, Peter, and List, J.H., 2009, Improving accuracy and statistical reliability of shoreline position and change rate estimates: Journal of Coastal Research, v. 25, no. 5, p. 1069–1081.
Ruggiero, Peter, Kaminsky, G.M., and Gelfenbaum, Guy, 2003, Linking proxy-based and datum-based shorelines on high-energy coastlines—Implications for shoreline change analyses: Journal of Coastal Research, special issue 38, p. 57–82.
For a detailed explanation of the method used to convert the lidar shoreline to a route, please refer to "Appendix 2- A case study of complex shoreline data" in the DSAS user guide:
Himmelstoss, E.A. 2009. "DSAS 4.0 Installation Instructions and User Guide" in: Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan. 2009. Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. https://woodshole.er.usgs.gov/project-pages/DSAS/version4/images/pdf/DSASv4_3.pdf
This process step and all subsequent process steps, unless otherwise noted, were performed by the same person - E.A. Himmelstoss.
20090710
E.A. Himmelstoss
U.S. Geological Survey
mailing address
384 Woods Hole Road
Woods Hole
MA
02543
USA
508-548-8700 x2262
508-457-2310
ehimmelstoss@usgs.gov
Data from the previously-published National Assessment of Shoreline Change study (USGS Open-File Reports 2004-1043 and 2004-1089) were used as the starting point for the project’s continued efforts to compile as many quality shorelines as possible for the region, this included data from the National Oceanographic and Atmospheric Association (NOAA) and the University of Texas, Bureau of Economic Geology (UT BEG).
Historical shorelines from the original shoreline assessment publication were provided as individual shapefiles in the report. These data were merged in Esri's ArcToolbox (v.10), Data Management Tools > General > Merge. Then the merged file was projected in Esri's ArcToolbox (v.10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = geographic (NAD 83); output projection = NAD 1983 State Plane Texas South Central FIPS 4204 (Meters); transformation = none.
2013
Additional digitized shorelines were downloaded in vector format from two sources: 1) the NOAA Historical Shoreline Survey Viewer, currently available as a Google Earth KMZ (https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.kmz). The extracted shorelines are digitized vectors of the mean high water (MHW) position derived from the scanned and georeferenced T-sheet raster imagery that NOAA also manages, and 2) the Texas Shoreline Change Project (https://www.beg.utexas.edu/coastal/report.htm/). The extracted shorelines are digitized vectors of the shoreline position from georeferenced air photos, ground GPS surveys, Digital Orthophoto Quarter Quandrangle images.
Shoreline data from NOAA were used with preference at any location where the shoreline date had previously been sourced from UT BEG. Although NOAA labels historical shorelines as MHW, they are proxy-based and therefore considered HWL for the purposes of shoreline change rate calculation with the proxy-datum bias correction. The modern lidar shoreline used in the analysis is datum-based, not proxy-based, and designated as operational MHW which reflects this difference. Topographic survey sheets (T-sheets) and shorelines were quality checked and edited where necessary. Edgematching was corrected when discovered.
2013
The lidar shoreline file was projected in Esri's ArcToolbox (v.10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = WGS 84 UTM zone 15N; output projection = NAD 1983 State Plane Texas South Central FIPS 4204 (Meters); transformation = NAD_1983_To_WGS_1984_1.
2013
Historic shorelines were appended to the lidar route data in ArcToolbox (v.10) > Data Management Tools > General > Append to produce a single shoreline file for the region. The final shoreline dataset was coded with attribute fields (Date, Uncertainty (Uncy), Source, Source_b, Year_, Default_D, Location). These fields are required for the Digital Shoreline Analysis System (DSAS), which was used to calculate shoreline change rates.
2013
The appended shoreline file and the shoreline uncertainty table (.dbf) were imported into a personal geodatabase in ArcCatalog v.10 by right-clicking on the geodatabase > Import (feature class for shoreline file and table for uncertainty table) for use with the Digital Shoreline Analysis System (DSAS) v4.3 software to perform rate calculations.
2013
The shoreline feature class was exported from the personal geodatabase back to a shapefile in ArcCatalog v.10 by right-clicking on the shoreline file > Export > To Shapefile (single) for publication purposes.
2014
The data were projected in ArcToolbox v.10 > Data Management Tools > Projections and Transformations > Project. Parameters: input projection = NAD 1983 State Plane Texas South Central FIPS 4204 (Meters); output projection = geographic coordinates (WGS84); transformation = NAD_1983_To_WGS_1984_1.
2015
Keywords section of metadata optimized for discovery in USGS Coastal and Marine Geology Data Catalog.
20170825
U.S. Geological Survey
Alan O. Allwardt
Contractor -- Information Specialist
mailing and physical address
2885 Mission Street
Santa Cruz
CA
95060
831-460-7551
831-427-4748
aallwardt@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
Vector
String
856
0.000001
0.000001
Decimal degrees
D_WGS_1984
WGS_1984
6378137.000000
298.257224
TXeast_shorelines
Vector shorelines
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.
RouteID
Route identification value assigned to individual lidar shoreline line segments. A unique cross-shore profile identification value is stored at each vertex of the lidar route and serves as a common attribute to the shoreline uncertainty table (TXeast_shorelines_uncertainty.dbf).
U.S. Geological Survey
0
Short integer field where zeros are "no data" and automatically filled in for the remaining shoreline polylines not derived from lidar.
U.S. Geological Survey
Date_
Date of shoreline position; date of survey as indicated on source material. A default date of 07/01 was assigned to shorelines where only the year was known (month and day unknown). Using July, the mid-point month of the calendar year, minimizes the potential offset to the actual shoreline date by a maximum of six months.
U.S. Geological Survey
Character string of length 10
Uncy
Estimate of shoreline position uncertainty. Actual shoreline position is within the range of this value (plus or minus, meters). The historic shoreline uncertainty values incorporate measurement uncertainties associated with mapping methods and materials for historical shorelines, the geographic registration of shoreline position, and shoreline digitizing. The uncertainty of the High Water Line (HWL) at an individual transect was determined by using positional uncertainty information stored along the shoreline route. The HWL uncertainty was added to the historic shoreline uncertainty stored in the shoreline attribute table during the DSAS rate calculation process to better account for uncertainty at individual transects alongshore as opposed to using a regionally averaged value. This was done using linear referencing that interpolates a value based on data stored in the TXeast_shorelines_uncertainty dBase file (.dbf) at a specific transect/shoreline intersect alongshore. The lidar shoreline position uncertainty is also stored in the associated shorelines_uncertainty.dbf file and values are interpolated at specific transect/shoreline intersections using the same linear referencing method. The lidar uncertainty attribute field was filled with null values while residing in the geodatabase. Upon exporting the shoreline feature class to a shapefile for publication, the null values in the lidar uncertainty field were automatically converted to zero values.
U.S. Geological Survey
0
Zeros are "no data" and serve as place fillers for the lidar shorelines. Actual uncertainty values for lidar are stored in shoreline uncertainty dBase (.dbf) file.
U.S. Geological Survey
Source
Agency that provided shoreline feature or the data source used (e.g. T-sheet) to digitize shoreline feature.
U.S. Geological Survey
USGS
U.S. Geological Survey
U.S. Geological Survey
NOAA
National Oceanic and Atmospheric Administration
U.S. Geological Survey
UT BEG
University of Texas Bureau of Economic Geology
U.S. Geological Survey
Source_b
Type of data used to create shoreline.
U.S. Geological Survey
lidar
Light detection and ranging (lidar).
U.S. Geological Survey
T or TP with number
NOAA/NOS topographic survey sheet (T- or TP-sheet) with associated registry number
U.S. Geological Survey
USGS Digital Orthophoto
Shoreline derived from a USGS Digital Orthophoto Quad (DOQ).
U.S. Geological Survey
air photo
Shoreline derived from aerial photographs that have had their shorelines transferred to a topo sheet using optical transfer device.
U.S. Geological Survey
ground GPS survey
Shoreline derived from ground GPS survey.
U.S. Geological Survey
rectified air photo
Shoreline derived from digitally rectified aerial photographs.
U.S. Geological Survey
Year_
Four digit year of shoreline
U.S. Geological Survey
1850
2001
Default_D
Differentiates between shorelines that have known month and day attributes and those that use the default value of 07/01 when only the year is known.
U.S. Geological Survey
0
Shoreline month and day are known.
U.S. Geological Survey
1
Shoreline month and day are unknown and default value of 07/01 was used.
U.S. Geological Survey
Location
Location of shoreline with respect to wave energy exposure. An open ocean coast is directly exposed to ocean waves and is typically characterized by higher wave energy. A sheltered coast is not directly exposed to ocean waves and is characterized by lower wave energy. This shoreline dataset only includes open ocean locations.
U.S. Geological Survey
open ocean
Shoreline on a coast with open ocean wave exposure.
U.S. Geological Survey
Shape_Leng
Length of feature in meter units (NAD 1983 State Plane Texas South Central FIPS 4204).
Esri
3.134974
74360.783119
meters
The entity and attribute information provided here describes the tabular data associated with the dataset. Please review the individual attribute descriptions for detailed information.
U.S. Geological Survey
U.S. Geological Survey
mailing and physical address
Denver Federal Center
Building 810
MS 302
Denver
CO
80225
USA
1-888-275-8747
sciencebase@usgs.gov
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 10.0
Esri polyline shapefile
These files (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, and .shx) are a collection of files with a common filename prefix and must be downloaded and stored in the same directory. Together they are the components of the shapefile and include FGDC compliant metadata.
no compression applied
2.05
https://www.sciencebase.gov/catalog/file/get/582ca53be4b04d580bd37988
https://www.sciencebase.gov/catalog/item/582ca53be4b04d580bd37988
https://www.sciencebase.gov/catalog/item/58055db4e4b0824b2d1c1ee2
https://doi.org/10.5066/F78P5XNK
The first link downloads the contents of the data page as a zip file, the second link is to the landing page of the data, the third and fourth links are to the main landing page of the data release.
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.
20200810
E.A. Himmelstoss
U.S. Geological Survey
mailing address
384 Woods Hole Road
Woods Hole
MA
02543-1598
USA
508-548-8700 x2262
508-547-2310
ehimmelstoss@usgs.gov
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
local time