Seafloor character offshore of the Eel River, California

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Frequently anticipated questions:


What does this data set describe?

Title: Seafloor character offshore of the Eel River, California
Abstract:
Seafloor character, a combination of seafloor induration (surface hardness) and rugosity, was derived from multibeam echosounder (MBES) and annotated underwater video data collected offshore of the Eel River, California. The MBES and underwater video data were collected in support of the U.S. Geological Survey (USGS) California Seafloor Mapping Program, under a collaboration with the California State University Monterey Bay Seafloor Mapping Lab, the California Ocean Protection Council, and the National Oceanic and Atmospheric Administration (NOAA). Substrate observations from the underwater video were translated into Coastal and Marine Ecological Classification Standard (CMECS; Federal Geographic Data Committee, 2012) induration classes to use as training for a supervised numerical classification of the MBES data. The seafloor character raster is provided as a 2-meter resolution GeoTIFF.
Supplemental_Information:
Additional information about the USGS field activity from which some of these data were derived is available online at:
https://cmgds.marine.usgs.gov/fan_info.php?fan=C109NC https://cmgds.marine.usgs.gov/fan_info.php?fan=C210NC
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in Esri format, this metadata file may include some Esri-specific terminology.
  1. How might this data set be cited?
    Cochrane, Guy R., 20230930, Seafloor character offshore of the Eel River, California: data release DOI:10.5066/P902YIF5, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    This is part of the following larger work.

    Cochrane, Guy R., 2023, Bathymetry, backscatter intensity, and benthic habitat offshore of the Eel River, California: data release DOI:10.5066/P902YIF5, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested Citation: Cochrane, G.R., 2023, Bathymetry, backscatter intensity, and benthic habitat offshore of the Eel River, California: U.S. Geological Survey data release, https://doi.org/10.5066/P902YIF5.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -124.460418
    East_Bounding_Coordinate: -124.245087
    North_Bounding_Coordinate: 40.69593
    South_Bounding_Coordinate: 40.536667
  3. What does it look like?
    SeafloorCharacter_OffshoreEelRiver.jpg (JPEG)
    Preview image of Seafloor Character data offshore of the Eel River
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2010
    Currentness_Reference:
    ground condition at time MBES and video data were collected
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: GeoTIFF
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 8981 x 8972 x 1, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 10
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -123.00000
      Latitude_of_Projection_Origin: 0.00000
      False_Easting: 500000.0
      False_Northing: 0.00
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 10.0
      Ordinates (y-coordinates) are specified to the nearest 10.0
      Planar coordinates are specified in Meters
      The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_844.
      The semi-major axis of the ellipsoid used is 6378137.00.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    value
    seafloor character class integer value (Source: Producer defined)
    Value
    seafloor character class (Source: Cochrane (2008))
    ValueDefinition
    1soft and flat fine grain sedimented seafloor
    2hard and flat coarse grain sediment and bedrock seafloor
    3hard and rugose boulder, megaclast, and bedrock seafloor
    count
    number of pixels assigned to class (Source: none)
    Range of values
    Minimum:20504
    Maximum:24553056
    Units:none
    Entity_and_Attribute_Overview:
    The complete 2-m resolution Offshore the Eel River seafloor character grid is a GeoTIFF image with integer values representing induration and rugosity classes: 1 for soft and flat bottom, 2 for hard and flat bottom, and 3 for hard and rugose bottom.
    Entity_and_Attribute_Detail_Citation: Cochrane, 2008

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Guy R. Cochrane
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

This raster is used for resource management as a first cut substrate model as described by Cochrane (2008). The raster was also used as a GIS layer in the analysis and classification of the study area into CMECS habitat polygons. These data are intended to provide regional surficial geology and benthic habitat information in an area of interest for offshore wind energy development, as well as ecosystem management. These data are also intended for science researchers, students, policy makers, and the public. These data can be used with geographic information systems or other software to help identify geomorphologic features and surficial lithology. These data are not intended to be used for navigation.

How was the data set created?

  1. From what previous works were the data drawn?
    CSUMB Seafloor mapping lab library (source 1 of 2)
    California State University, Monterey Bay, Seafloor Mapping Lab, 2008, Southern California 2008 CSMP surveys: California State University, Monterey Bay, Seafloor Mapping Lab Data Library: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database and viewer
    Source_Contribution: MBES data is classified into seafloor character data
    USGS Marine video portal (source 2 of 2)
    Golden, Nadine E., Ackerman, Seth D., and Dailey, Evan T., 2015, Coastal and Marine Geology Program video and photograph portal: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database and viewer
    Source_Contribution:
    underwater video was used to help train and classify habitat maps
  2. How were the data generated, processed, and modified?
    Date: 2010 (process 1 of 3)
    Bathymetry and backscatter data were collected by Fugro Pelagos in 2008, using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounders. An Applanix POS-MV (Position and Orientation System for Marine Vessels) was used to accurately position the vessels during data collection, and it also accounted for vessel motion such as heave, pitch, and roll, with navigational input from GPS receivers. Smoothed Best Estimated Trajectory (SBET) files were postprocessed from logged POS-MV files. Sound-velocity profiles were collected with an Applied Microsystems (AM) SVPlus sound velocimeter. Soundings were corrected for vessel motion using the Applanix POS-MV data, for variations in water-column sound velocity using the AM SVPlus data, and for variations in water height (tides) and heave using the postprocessed SBET data (California State University, Monterey Bay, Seafloor Mapping Lab, 2016). The Reson backscatter data were postprocessed using Geocoder software. The backscatter intensities were radiometrically corrected (including despeckling and angle-varying gain adjustments), and the position of each acoustic sample was geometrically corrected for slant range on a line-by-line basis. After the lines were corrected, they were mosaicked into 0.5-m resolution images (California State University, Monterey Bay, Seafloor Mapping Lab, 2016). Data sources used in this process:
    • CSUMB Seafloor mapping lab library
    Date: 2012 (process 2 of 3)
    Substrate observations used to supervise the classification of the MBES data were derived from underwater video collected by the USGS in 2012. A USGS camera sled was used that housed two standard-definition video cameras (one forward looking and one downward looking), as well as a high-definition (1,080 x 1,920-pixel resolution) video camera and an 8-megapixel digital still camera. The camera sled was towed 1 to 2 m over the seafloor at speeds of between 1 and 2 nautical miles/hour. While the camera is deployed, several different observations are recorded for a 10-second period once every minute, using the protocol of Anderson and others (2007). Observations of primary substrate, secondary substrate, slope, abiotic complexity, biotic complexity, and biotic cover are mandatory observations every minute. Observations of key geologic features and the presence of key species also are made when observed. A digital still photograph was captured once every 30 seconds. Additional observations of substrate were obtained from usSEABED (Reid and others, 2009).
    Date: 2023 (process 3 of 3)
    The CSUMB 0.5-m-resolution backscatter images were mosaiced and clipped to the study area boundary. The backscatter mosaics were decimated to 2-m resolution georeferenced TIFF images using the 2m bathymetry as a snap grid. A vector ruggedness raster derived from the bathymetry data and a backscatter-intensity raster were used in the seafloor characterization in an ArcGIS Geographic Information Systems (GIS) project. Seafloor character was classified in ArcGIS using the Maximum Likelihood Classification tool. Observations of substrate from the seafloor video were used to supervise the classification as described in Cochrane (2008). The seafloor character grids are available in a GeoTIFF file.
  3. What similar or related data should the user be aware of?
    Reid, Jane A., Reid, Jamey M., Jenkins, Chris J., Zimmermann, Mark, Williams, S. Jeffress, and Field, Michael E., 2019, usSEABED: Pacific Coast (California, Oregon, Washington) Offshore Surficial-Sediment Data Release, version 1: Data Series DS 182, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Reid, Jane.A., Jamey M. Reid, Chris J. Jenkins, Mark Zimmermann, S. Jeffress Williams, Michael E. Field, 2006, U.S. Geological Survey Data Series, number 182.
    Golden, Nadine, 2019, California State Waters Map Series Data Catalog: Data Series DS 781, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Golden, N.E., compiler, 2019, California State Waters Map Series Data Catalog, https://doi.org/10.3133/ds781
    Cochrane, Guy R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat.

    Online Links:

    Other_Citation_Details:
    Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194.
    Federal Geographic Data Committee, 2012, Coastal and Marine Ecological Classification Standard.

    Online Links:

    Other_Citation_Details:
    Federal Geographic Data Committee, 2012, Coastal and Marine Ecological Classification Standard, 343 p.
    Anderson, Tara J., Cochrane, Guy R., Roberts, Dale A., Chezar, Hank, and Hatcher, Gerry, 2007, A rapid method to characterize seabed habitats and associated macro-organisms.

    Other_Citation_Details:
    Anderson, T.J., Cochrane, G.R., Roberts, D.A., Chezar, H., and Hatcher, G., 2007, A rapid method to characterize seabed habitats and associated macro-organisms, in Todd, B.J., and Greene, H.G., eds., Mapping the seafloor for habitat characterization: Geological Association of Canada Special Paper 47, p. 71-79.

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

  1. How well have the observations been checked?
    A small number of observations of seafloor character from the video data were used to supervise the classification of the MBES data. All the video observations were subsequently used to assess the accuracy of the predicted character values. Additional observations of substrate were obtained from usSEABED (Reid and others, 2009). Two types of accuracy were calculated for the raster using a 10-meter radius circular buffer around each observation point (shown on the overview figure) in the study area. Presence accuracy is the number of observations where the buffer around the observation had at least one predicted value that matched the observed value. Majority accuracy is the percentage of observations where the largest number of predicted pixels in the buffer matched the observed value. There were 29 observations in the study area. All the observations were in the soft-flat class so there is no accuracy value for the other two classes. For soft-flat class the presence accuracy was 100 percent and the majority accuracy was 100 percent.
  2. How accurate are the geographic locations?
    The POS MV position and motion compensation system used when collecting the MBES data has a horizontal positional accuracy of about 0.5 m with DGPS corrections and roll and pitch accuracies of about 0.02 degrees (http://www.measutronics.com/wp-content/uploads/2011/01/posmv320_specifications.pdf). Accuracies of final products may be lower due to water depth and total propagated uncertainties of the mapping systems, which include sonar system, position and motion compensation system, and navigation, as well as data processing that includes sounding cleaning, gridding, and datum transformations.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the area mapped with MBES data.
  5. How consistent are the relationships among the observations, including topology?
    No formal logical accuracy tests were conducted.

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 USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey as the originator of the dataset and in products derived from these data. This information is not intended for navigation purposes.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - CMGDS
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov
  2. What's the catalog number I need to order this data set? These data are available in GeoTIFF format (SeafloorCharacter_OffshoreEelRiver.tif), along with a tif world file (.tfw).
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    The downloadable data file has been compressed with the Windows 10 "zip" command and can be unzipped using Windows File Explorer, or with Winzip (or other compression tools). To utilize these data, the user must have software capable of viewing a GeoTIFF file.

Who wrote the metadata?

Dates:
Last modified: 30-Sep-2023
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

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