Sediment Texture and Geomorphology of the Sea Floor from Fenwick Island, Maryland to Fisherman's Island, Virginia

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Frequently anticipated questions:


What does this data set describe?

Title:
Sediment Texture and Geomorphology of the Sea Floor from Fenwick Island, Maryland to Fisherman's Island, Virginia
Abstract:
These data are a qualitatively derived interpretive polygon shapefile defining surficial sediment type and distribution, and geomorphology, for nearly 1,400 square kilometers of sea floor on the inner-continental shelf from Fenwick Island, Maryland to Fisherman’s Island, Virginia, USA. These data are classified according to Barnhardt and others (1998) bottom-type classification system, which was modified to highlight changes in secondary sediment-types such as mud and gravel across this primarily sandy shelf. Most of the geophysical and sample data used to create this interpretive layer were collected as part of the Linking Coastal Processes and Vulnerability: Assateague Island Regional Study project (GS2-2C), supported by the U.S. Department of the Interior Hurricane Sandy Recovery program. Additional sample data were provided by the Maryland Geological Survey and the Virginia Division of Geology and Mineral Resources. Additional hydrographic data were available through the National Oceanographic and Atmospheric Administration’s National Ocean Service surveys collected between 2006 and 2014. The primary objective of the Hurricane Sandy Recovery program is to provide science for coastal resilience, and these interpretive data support the program goal by supplying regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of seafloor geology are important first steps toward protecting fish habitat, delineating marine resources on the inner-shelf, understanding sediment transport pathways, and assessing environmental changes because of natural or human effects. The Assateague Island Regional Study project is focused on the inner-continental shelf of Maryland and Virginia, north of Chesapeake Bay entrance. Data collected during the mapping portion of this study have been released in a series of USGS data releases (https://woodshole.er.usgs.gov/project-pages/delmarva/). A combination of geophysical and sample data including high resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and sediment samples are used to create this seafloor interpretation.
  1. How might this data set be cited?
    Pendleton, E.A., Brothers, L.L., Sweeney, E.M., Thieler, E.R., and Foster, D.S., 2017, Sediment Texture and Geomorphology of the Sea Floor from Fenwick Island, Maryland to Fisherman's Island, Virginia: data release DOI:10.5066/F78K779J, U.S. Geological Survey, Reston, VA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -75.909217
    East_Bounding_Coordinate: -74.91305
    North_Bounding_Coordinate: 38.431125
    South_Bounding_Coordinate: 36.99978
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/58de56a5e4b02ff32c699fcf/?name=SedTexture.png (PNG)
    Image of the sediment texture and distribution shapefile for the Delmarva Peninsula
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 2006
    Ending_Date: 2015
    Currentness_Reference:
    ground condition of the source data that this interpretation is based on
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: vector digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set. It contains the following vector data types (SDTS terminology):
      • G-polygon (867)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000001. Longitudes are given to the nearest 0.000001. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257224.
  7. How does the data set describe geographic features?
    USGS_Delmarva_SedTexture_Geomorph
    Sediment texture shapefile for the inner continental shelf of Maryland and Virginia, north of Chesapeake Bay (Source: U.S. Geological Survey)
    FID
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Shape
    Feature geometry. (Source: Esri) Coordinates defining the features.
    Texture
    Bottom-type classification based on twelve composite units that represent combinations of four end-member units (R= rock (not found in this study area); G= gravel; S= sand; M= mud). (Source: Barnhardt and others (1998))
    ValueDefinition
    S99The nearly total texture (~99 %) Sand (S) is given the upper case letter and a 99 to indicate that the sediment texture is nearly 99 percent sand and indicative of sand ridge material
    MThe end-member texture (> 90%) Mud (M) is the primary texture.
    MsThe dominant texture (> 50%) Mud (M) is given the upper case letter and the subordinate texture (< 50%) sand (s) is given a lower case letter.
    SThe end-member texture (> 92%) Sand (S) is the primary texture.
    SgThe dominant texture (> 50%) Sand (S) is given the upper case letter and the subordinate texture (< 50%) gravel (g) is given a lower case letter.
    SmThe dominant texture (> 50%) Sand (S) is given the upper case letter and the subordinate texture (< 50%) mud (m) is given a lower case letter.
    GsThe dominant texture (> 50%) Gravel (G) is given the upper case letter and the subordinate texture (< 50%) sand (s) is given a lower case letter.
    Data_quali
    Each interpreted polygon was assigned a data interpretation confidence value of 1, 2, or 3 based on the number of input data sources. (Source: U.S. Geological Survey)
    ValueDefinition
    3Sediment texture regions that were defined based on sidescan sonar-mosaics, bathymetric derivatives, and bottom photos and sediment samples, were given the highest data interpretation confidence value of 3 (highest confidence).
    2Areas where sediment texture was defined based on sidescan sonar-mosaics and bathymetric derivatives, but no bottom photos or sediment samples were available with in the polygon, were given a confidence value of 2 (moderate confidence).
    1Sediment texture regions that were defined based on bathymetric derivatives, but no sidescan-sonar data (with or without sample information), were given the lowest data interpretation confidence value of 1 (lowest confidence).
    Count_
    The number of sediment samples (with laboratory analyzed grain size statistics) that occur within each qualitatively-derived polygon. This field is automatically generated by Esri when point data (sample database) is joined to a polygon (sediment texture interpretation). (Source: Esri)
    Range of values
    Minimum:0
    Maximum:29
    Units:count
    Resolution:1.0
    Avg_GRAVEL
    Average percent weight (%) gravel (as determined from samples with laboratory analyzed grain size statistics) within each qualitatively derived polygon. This field was automatically generated by Esri as a summary of the numeric attributes of the points that fall inside a polygon when point data (sample database) is joined to a polygon (sediment texture interpretation). -999 is a no data value (Source: U.S. Geological Survey)
    Range of values
    Minimum:0
    Maximum:76.39
    Units:percent
    Resolution:0.1
    Avg_SAND_P
    Average percent weight (%) sand (as determined from samples with laboratory analyzed grain size statistics) within each qualitatively derived polygon. This field was automatically generated by Esri as a summary of the numeric attributes of the points that fall inside a polygon when point data (sample database) is joined to a polygon (sediment texture interpretation). -999 is a no data value (Source: U.S. Geological Survey)
    Range of values
    Minimum:8.85
    Maximum:99.96
    Units:percent
    Resolution:0.01
    Avg_MUD_P
    Average percent weight (%) mud (silt plus clay; as determined from samples with laboratory analyzed grain size statistics) within each qualitatively derived polygon. This field was automatically generated by Esri as a summary of the numeric attributes of the points that fall inside a polygon when point data (sample database) is joined to a polygon (sediment texture interpretation). -999 is a no data value (Source: U.S. Geological Survey)
    Range of values
    Minimum:0.04
    Maximum:91.06
    Units:percent
    Resolution:0.1
    Avg_C_PCF
    Average percent weight (%) of the coarse fraction (sand plus gravel) that is carbonate (as determined from samples with laboratory analyzed carbonate content statistics) within each qualitatively derived polygon. This field was automatically generated by Esri as a summary of the numeric attributes of the points that fall inside a polygon when point data (sample database) is joined to a polygon (sediment texture interpretation). -999 is a no data value (Source: U.S. Geological Survey)
    Range of values
    Minimum:0.18
    Maximum:92.91
    Units:percent
    Resolution:0.1
    Biogenic_c
    Description of carbonate sediment based on percent weight of carbonate in the coarse fraction (sand and gravel) (Source: U.S. Geological Survey)
    ValueDefinition
    traceThe weight percent of the coarse fraction (sand + gravel) that is carbonate is less than 2 percent
    presentThe weight percent of the coarse fraction (sand + gravel) that is carbonate is more than 2 percent but less than 10 percent
    abundantThe weight percent of the coarse fraction (sand + gravel) that is carbonate is more than 10 percent but less than 50 percent
    dominantThe weight percent of the coarse fraction (sand + gravel) that is carbonate is more than 50 percent
    geomorph_f
    Geomorphic feature description (Source: U.S. Geological Survey)
    ValueDefinition
    sand ridge or bedformthe seafloor geomorphology (as characterized from hillshaded relief, slope, and rugosity imagery) can be described as a series or part of a bedform or sand ridge. Mean slope values for sand ridges can vay greatly, but are typically high, especially along slipfaces and flanks. The mean slope of all polygons classified as 'sand ridge or bedform' is 0.6 degrees.
    pitsthe seafloor geomorphology (as characterized from hillshaded relief, slope, rugosity, and backscatter imagery) can be characterized as scour pits filled with mud. The mean slope of all polygons classified as 'pits' is 0.3 degrees.
    gently-sloping shelfthe seafloor geomorphology (as characterized from hillshaded relief, slope, and rugosity imagery) can be described as a seaward-sloping relatively-smooth surface with a low slope (on average less than 0.20 degrees for all polygons identified as 'gently-sloping shelf) and not associated with a sand ridge or bedform. Polygons classified as 'gently-sloping shelf' are mostly located in the 2015 survey area where there are fewer sand ridges and thus larger extents of relatively smooth and featureless inner continental shelf.
    moderately-sloping shelfthe seafloor geomorphology (as characterized from hillshaded relief, slope, and rugosity imagery) can be described as a seaward-sloping relatively-smooth surface with a moderate slope (on average about 0.45 degrees for all polygons identified as 'moderately-sloping shelf') and not associated with a sand ridge or bedform. Polygons classified as 'moderately-sloping shelf' are typically narrow and located mostly in the 2014 survey area between features identified as 'sand ridges or bedform'
    mean_slope
    mean seafloor slope (in degrees) within designated polygon derived from NOAA and USGS bathymetry data. (Source: U.S. Geological Survey)
    Range of values
    Minimum:0.068267
    Maximum:2.175318
    Units:degrees
    Resolution:0.0001

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Pendleton, E.A.
    • Brothers, L.L.
    • Sweeney, E.M.
    • Thieler, E.R.
    • Foster, D.S.
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    Elizabeth A. Pendleton
    U.S. Geological Survey
    U.S. Geological Survey
    Woods Hole, MA

    508-548-8700 x2259 (voice)
    ependleton@usgs.gov

Why was the data set created?

These sea-floor data were created from geophysical and sample data collected along the Delmarva Peninsula from Fenwick Island, Maryland to Fisherman's Island, Virginia. These data are an interpretation of the seafloor sediment type, distribution, and geomorphology, and are classified according to a modified Barnhardt bottom-type classification system in order to capture meaningful changes in seafloor texture along the inner continental shelf of this region. Seafloor interpretations are important data layers for marine resource managers charged with protecting fish habitat, delineating marine boundaries, and assessing environmental change due to natural or human impacts.

How was the data set created?

  1. From what previous works were the data drawn?
    Pendleton and others, 2014 (source 1 of 6)
    Pendleton, E.A., Ackerman, S.D., Baldwin, W.E., Danforth, W.W., Foster, D.S., and Thieler, E.R., 2014, High-resolution geophysical data collected along the Delmarva Peninsula 2014, U.S. Geological Survey Field Activity 2014-002-FA: data release DOI:10.5066/F7MW2F60, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: online
    Source_Contribution:
    This report provided the majority of the geophysical and sample data from the northern half (Fenwick Island to the southern end of Assateague Island) of the study area. The side-scan sonar mosaics formed the basis for digitizing polygons based on differences in backscatter intensity.
    Sweeney and others, 2015 (source 2 of 6)
    Sweeney, E.M., Pendleton, E.A., Ackerman, S.D., Andrews, B.D., Baldwin, W.E., and Danforth, W.W., 2015, High-resolution geophysical data collected along the Delmarva Peninsula in 2015, U.S. Geological Survey Field Activity 2015-001-FA: data release DOI:10.5066/F7P55KK3, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: online
    Source_Contribution:
    This report provided the majority of the geophysical and sample data from the southern half (Chincoteague Island to Fisherman's Island) of the study area. The side-scan sonar mosaics formed the basis for digitizing polygons based on differences in backscatter intensity.
    Toscano and others, 1989 (source 3 of 6)
    Toscano, M.A., Kerhin, R.T., York, L.L., Cronin, T.M., and Williams, S.J., 1989, Quaternary Stratigraphy of the inner continental shelf of Maryland: Report of Investigations 50, Maryland Geological Survey, Baltimore, MD.

    Online Links:

    Type_of_Source_Media: PDF
    Source_Contribution:
    This report provided additional sediment samples for the inner-shelf offshore of Assateague and Fenwick Islands. These data were visual textural descriptions in table form and were converted to digital vector data for use in this interpretation.
    VAGMR, 1985 (source 4 of 6)
    Berquist, C.R., and Hobbs, C.H., 1986, Assessment of economic heavy minerals of the Virginia inner continental shelf: Open-File Report 86-1, Virginia Division of Geology and Mineral Resources, Charlottesville, VA.

    Online Links:

    Type_of_Source_Media: digital vector
    Source_Contribution:
    This report provided additional sediment samples for the inner-shelf offshore of the Virginia barrier islands. These data were provided in digital vector format (point shapefile) by the Virginia Division of Geology and Mineral Resources, and aided the interpretation qualitatively, since the texture statistics were inconsistent with the 2014 and 2015 samples.
    NOS survey data report (source 5 of 6)
    Pendleton, E.A., Brothers, L.L., Thieler, E.R., Danforth, W.W., and Parker, C.E., 2014, National Oceanic and Atmospheric Administration Hydrographic Survey Data Used in a U.S. Geological Survey Regional Geologic Framework Study Along the Delmarva Peninsula: Open-File Report 2014-1262, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: digital raster
    Source_Contribution:
    This report provided high resolution multi-beam bathymetry collected by NOAA and combined and released by USGS to supplement the more widely-spaced interferometric bathymetry lines collected by USGS (FADD 2014-002-FA and FADD 2015-001-FA).
    MGS and NPS report (source 6 of 6)
    Wells, D. V., 2017, Inventory of Physical and Benthic Habitats and Ocean Resources within the Nearshore Boundary of Assateague Island National Seashore: Natural Resource Technical Report in press, Maryland Geological Survey and Maryland Department of Natural Resources, Baltimore, MD.

    Online Links:

    • available online following publication

    Type_of_Source_Media: digital vector
    Source_Contribution:
    This report provided additional sediment samples for the very nearshore along Assateague Island. This currently unpublished report is available by request to the Maryland Geological Survey and contatins digital vector layers with sediment sample statistics. However, the samples lacked carbonate analysis and were only incorporated into the interpretation qualitatively.
  2. How were the data generated, processed, and modified?
    Date: 2016 (process 1 of 5)
    The texture and spatial distribution of sea-floor sediment were qualitatively-analyzed in ArcGIS using several input data sources (listed in the source contribution), including acoustic backscatter intensity imagery, bathymetry, bottom photographs, and sediment samples. In order to create the interpretation, first a polygon shapefile of the study area was imported to a geodatabase as a feature class (with ArcMap version 10.2.2). Then sediment texture polygons were created using 'cut polygon' and 'auto-complete polygon' in an edit session. In general, polygon editing was done at scales between 1:8,000 and 1:20,000, depending on the size of the traced feature and the resolution of the source data. The following numbered steps outline the workflow of the data interpretation.
    1. Backscatter intensity data (available at 1 to 2 meter resolution) were the first input. Changes in backscatter amplitude were digitized to outline possible changes in seafloor texture based on acoustic return. Areas of high backscatter (light colors) have strong acoustic reflections and suggest coarse seafloor sediments like sand with gravel or shell for the Delmarva shelf. Low-backscatter areas (dark colors) have weak acoustic reflections and are generally characterized by finer grained material such as muds and fine sands.
    2. The polygons were then refined and edited based on gradient, rugosity (Jenness, 2013), and hillshaded relief images derived from interferometric and multibeam swath bathymetry (available at 0.5 to 25 m resolution). Areas of high rugosity are typically associated with coarser sediments, while smooth, low-rugosity regions tend to be blanketed by fine-grained sediment, and topographic undulations and steep slopes can often be associated with sand ridges and other large bedforms. These bathymetric derivatives helped to refine polygon boundaries where feature changes especially along sand ridge and shoal margins may not have been apparent in backscatter data, but could easily be identified in hillshaded relief and slope changes.
    3. After all the seafloor features were traced from the backscatter and bathymetry data, a new field was created called 'Texture'. Bottom photographs and sediment samples were then used to define and populate the sediment texture field for the polygons using a modified Barnhardt and others (1998) sea floor sediment classification. The Barnhardt and others (1998) system is based on four basic, easily recognized sediment units: gravel (G), mud (M), rock (R), and sand (S). The sea floor along the Delmarva Peninsula lacks rock, R, therefore this seafloor interpretation old contains 3 of the 4 Barnhardt primary units. Because the sea floor sediments along Delmarva are often a nonuniform mixture of S,G,and M, the Barnhardt classification is further divided into 12 composite units, which are 2-part combinations of the 3 basic units. The classification is defined such that the primary unit, representing more than 50 percent of an area's texture, is given an upper case letter, and the secondary texture, representing less than 50 percent of an area's texture, is given a lower case letter. If one of the basic sediment units represents more than 92 percent of the texture, only its upper case letter is used. The units defined under the modified Barnhardt and others (1998) classification within this study area include Sg, S, S99, Sm, M, and Ms. Some polygons had more than one sample, and some polygons lacked sample information. For multiple samples within a polygon, the dominant sediment texture was used to classify sediment type (often aided by the 'data join' sediment statistics described in a later processing step). Polygons that lacked sample information were texturally defined through extrapolation from adjacent or proximal polygons of similar acoustic character that did contain sediment samples. Samples with laboratory grain size analysis were preferred over visual descriptions when defining sediment texture throughout the study area.
    Typically, features below 5,000 square-meters or less than 50 m wide were not digitized due to positional uncertainty, lack of sample information, and the often ephemeral nature of small-scale sea floor features. Not all digitized sea floor features contained sample information, so often the sea floor texture is characterized by the nearest similar feature that contains a sample. Conversely, sometimes a digitized feature contains multiple samples and not all of the samples within the feature were in agreement (of the same texture). In these cases the dominant sediment texture was chosen to represent the primary texture for the polygon. Sea floor texture can change over small spatial scales, and many small-scale changes will not be detectable or mappable at a scale of 1:25,000. The boundaries of polygons are often inferred based on sediment samples, and even boundaries that are traced based on amplitude changes in geophysical data are subject to migration. Polygon boundaries should be considered an approximation of the location of a change in texture. Person who carried out this activity:
    Elizabeth A. Pendleton
    U.S. Geological Survey
    Geologist
    U.S. Geological Survey
    Woods Hole, MA

    508-548-8700 x2259 (voice)
    508-457-2310 (FAX)
    ependleton@usgs.gov
    Data sources used in this process:
    • Pendleton and others, 2014; Sweeney and others, 2015; NOS survey data report
    Date: 2016 (process 2 of 5)
    A new field was added called 'Data_quali' as a data interpretation confidence assessment, which describes how confident we are in the interpretation based on the number of input data sources (see the entity and attribute sections for more information on these classes). The following 5 fields contain sediment texture statistics information and were created and populated using a data join function within ArcMap (version 10.2.2). The fields beginning with "Avg_" and the "Count_" field were automatically generated by computing a data join where the 2014 and 2015 USGS sediment sample database with laboratory analysis for clastic and carbonate components (vector points) were joined to the qualitatively-derived polygons. Each polygon was given an average of the numeric attributes of the points (with laboratory grain size analysis) that fell inside it, and the count field shows how many laboratory analyzed points fall inside it. Several fields that were not wanted were deleted after the join. Polygons with a zero count value, meaning there were no sediment samples from USGS surveys in 2014 or 2015 within the polygon, were given a no data value of -999 for the 'Avg_' fields. Sediment samples from the Toscano and others (1989), Virginia Division of Geology and Mineral Resources (1985), and NPS and MGS (2011) report lacked the same laboratory analysis methods and could not be joined with the 2014 an 2015 USGS sample data, or were based on visual textural descriptions. These data were used separately and qualitatively to aid texture interpretation in the absence of USGS samples and were not included in the data join described above. Three more fields were added. One called 'Biogenic_c' is based on the 'Avg_C_PCF' field and is a description of the amount of carbonate present in the interpreted polygon. Field 'geomorph_f' is a description of the geomorphic region of the interpreted polygon, based on slope measures, and geophysical data properties. The field 'mean_slope' was calculated by zonal statistics, such that the mean slope from the NOAA multibeam bathymetry dataset was included for each interpeted polygon. Person who carried out this activity:
    Elizabeth A. Pendleton
    U.S. Geological Survey
    Geologist
    U.S. Geological Survey
    Woods Hole, MA

    508-548-8700 x2259 (voice)
    508-457-2310 (FAX)
    ependleton@usgs.gov
    Data sources used in this process:
    • Pendleton and others, 2014; Sweeney and others, 2015; Toscano and others (1989); VAGMR (1985); MGS and NPS report (2011)
    Date: 2016 (process 3 of 5)
    Finally, topology rules were designated within the geodatabase (ArcCatalog version 10.2.2), to make sure that there were no overlapping polygons or accidental gaps between adjacent polygons. The topology error inspector (ArcMap version 10.2.2) was used to find topology errors and fix them. Overlapping polygon and gap errors were fixed. Length and Area fields were generated automatically for all polygons in the geodatabase. The data were then exported to a shapefile and the polygons were reprojected from UTM Zone 18N, WGS84 to GCS WGS84, and the length and area fields were deleted. Person who carried out this activity:
    Elizabeth A. Pendleton
    U.S. Geological Survey
    Geologist
    384 Woods Hole Rd.
    Woods Hole, MA

    (508)-548-8700 x2259 (voice)
    (508)-457-2310 (FAX)
    ependleton@usgs.gov
    Date: 29-Jan-2018 (process 4 of 5)
    An error was fixed in one of the originator names. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
    Date: 07-Aug-2020 (process 5 of 5)
    Added keywords section with USGS persistent identifier as theme keyword. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
  3. What similar or related data should the user be aware of?
    Shepard, F.P., 1954, Nomenclature Based on Sand-Silt-Clay Ratios: Journal Sedimentary Petrology volume 24, Society for Sedimentary Geology, Tulsa, OK.

    Online Links:

    Barnhardt, W.A., Kelley, J.T., Dickson, S.M., and Belknap, D.F., 1998, Mapping the Gulf of Maine with Side-scan Sonar: a New Bottom-type Classification for Complex Seafloors: Journal of Coastal Research volume 14(2), Coastal Education and Research Foundation, Inc., Royal Palm Beach, FL.

    Online Links:

    Wentworth, C.K., 1922, A Scale of Grade and Class Terms for Clastic Sediments: Journal of Geology vol. 30, University of Chicago Press, Chicago, IL.

    Online Links:

    Poppe, L.J., McMullen, K.Y., Williams, S.J., and Paskevich, V.F., 2014, USGS East-coast Sediment Analysis: Procedures, Database, and GIS Data: Open File Report 2005-1001, U.S. Geological Survey, Reston, VA.

    Online Links:

    Blackwood, D.S., 2000, "SeaBOSS": an elegantly simple image and sample collecting system: Proceedings, 6th Annual International Conference on Remote Sensing for Marine Environments Volume 1, Veridian ERIM International, Charleston, SC.

    Jenness, J, 2013, DEM surface tools.

    Online Links:


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

  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
    These data were produced qualitatively from acoustic and sample data with varying resolutions. Horizontal uncertainty associated with sample collection especially, can be quite high (100's of meters), much higher than positional uncertainty associated with acoustic data (usually less than 10's of meters). The date of sample collection and ship station positioning all contribute to sample position uncertainty. These qualitatively derived polygons outlining sea floor features are estimated to be within 50 meters, horizontally, but locally may be higher when sediment texture delineation is based on sample information alone.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    These sediment cover data are defined for areas where source data exists. In general, gaps in the coverage coincide with gaps in the source data. However, some data gaps were interpreted through extrapolation or by using hydrographic survey data only from NOS. Areas with fewer input data sources, lower data quality, and incomplete coverage are noted in a data quality attribute field.
  5. How consistent are the relationships among the observations, including topology?
    These data were drawn and vetted for accuracy using the source input rasters and point sample data described in the processing steps and source contributions. Overlapping features and unintentional gaps within the survey area were identified using the topology checker in ArcMap (version 10.2.2) and corrected or removed.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints: None
Use_Constraints:
Not to be used for navigation. 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. Additionally, there are limitations associated with qualitative sediment mapping interpretations. Because of the scale of the source geophysical data and the spacing of samples, not all changes in sea floor texture are captured. The data were mapped between 1:8,000 and 1:25,000, but the recommended scale for application of these data is 1:25,000.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO

    1-888- 275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? This data release from the Delmarva Peninsula area is comprised of the shapefile USGS_Delmarva_SedTexture_Geomorph.shp (and the additional files that make up a shapefile), a browse graphic (SedTexture.png) and FGDC CSDGM metadata (USGS_Delmarva_SedTexture_Geomorph.xml).
  3. What legal disclaimers am I supposed to read?
    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 U.S. Geological Survey 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.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    These data are available in Esri shapefile format. The user must have software capable of importing and processing this data type.

Who wrote the metadata?

Dates:
Last modified: 07-Aug-2020
Metadata author:
Elizabeth A. Pendleton
U.S. Geological Survey
Geologist
U.S. Geological Survey
Woods Hole, MA

508-548-8700 x2259 (voice)
508-457-2310 (FAX)
ependleton@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

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