Orthoimagery of Big Pine Ledge, Florida, 2021

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Frequently anticipated questions:

What does this data set describe?

Title: Orthoimagery of Big Pine Ledge, Florida, 2021
Abstract:
A seabed orthoimage was developed from underwater images collected at Big Pine Ledge, Florida, in July 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 650x120 meters (0.078 square kilometers) in size. It was created using image-averaging methods and saved as a tiled GeoTIFF raster at 5-millimeter resolution.
Supplemental_Information:
Additional information about the field activities from which these data were derived is available online at: https://cmgds.marine.usgs.gov/fan_info.php?fan=2021-633-FA and https://cmgds.marine.usgs.gov/fan_info.php?fan=2021-301-FA Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  1. How might this data set be cited?
    Hatcher, Gerald A., Kranenburg, Christine J., and Warrick, Jonathan A., 20240426, Orthoimagery of Big Pine Ledge, Florida, 2021: data release DOI:10.5066/P9R47TWT, U.S. Geological Survey - Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    This is part of the following larger work.

    Hatcher, Gerald A., Kranenburg, Christine J., Warrick, Jonathan A., Bosse, Stephen T., Zawada, David G., Yates, Kimberly K., and Johnson, Selena A., 2024, Underwater photogrammetry products of Big Pine Ledge, Florida from images acquired using the SQUID-5 system in July 2021: data release DOI:10.5066/P9R47TWT, U.S. Geological Survey - Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    Other_Citation_Details:
    Suggested Citation: Hatcher, G.A., Kranenburg, C.J., Warrick, J.A., Bosse, S.T., Zawada, D.G., Yates, K.K., Johnson, S.A., 2024, Underwater photogrammetry products of Big Pine Ledge, Florida from images acquired using the SQUID-5 system in July 2021, http://doi.org/P9R47TWT.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.38464921
    East_Bounding_Coordinate: -81.37764248
    North_Bounding_Coordinate: 24.55393089
    South_Bounding_Coordinate: 24.55195337
  3. What does it look like?
    BPL21_orthoaverage_fullrez.jpg (JPEG)
    Full-resolution sample view of larger orthomosaic image.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 16-Jul-2021
    Ending_Date: 20-Jul-2021
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: raster digital data
  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, type Pixel
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 17N
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -81
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000.0
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.005
      Ordinates (y-coordinates) are specified to the nearest 0.005
      Planar coordinates are specified in Meters
      The horizontal datum used is North American Datum of 1983 (2011).
      The ellipsoid used is GRS 1980.
      The semi-major axis of the ellipsoid used is 6378137.000000.
      The flattening of the ellipsoid used is 1/298.257222101.
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    The orthoimage is presented as a 4-band (R, G, B plus Alpha) 8-bit unsigned integer GeoTIFF where pixels represent RGB color from the dehazed images and no-data is represented as 0 in the alpha band. The horizontal projection is NAD83(2011) UTM Zone 17N.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Gerald A. Hatcher
    • Christine J. Kranenburg
    • Jonathan A. Warrick
  2. Who also contributed to the data set?
    Data collection was funded by the U.S. Geological Survey Pacific Coastal Marine Science Center and the U.S. Geological Survey Saint Petersburg Coastal and Marine Science Center. The authors would like to thank Dr. Jason Spadaro, Assistant Professor, Marine Science and Technology, College of the Florida Keys for installing calibration targets on the reef, Lisa Symons, Regional Response Coordinator, and the staff of the Eastern Region, Office of National Marine Sanctuaries, NOAA Florida Keys National Marine Sanctuary, for coordination efforts.
  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

    831-460-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

The underwater images and associated location data were collected to provide high-resolution elevation data and precisely co-registered, full-color orthoimage base maps for use in environmental assessment and monitoring of the coral reef and surrounding seafloor habitat. Additionally, the data were collected to evaluate their potential to improve USGS scientific efforts including seafloor elevation and stability modeling, and small-scale hydrodynamic flow modeling.

How was the data set created?

  1. From what previous works were the data drawn?
    raw images (source 1 of 2)
    Kranenburg, Christine J., Hatcher, Gerald A., Warrick, Jonathan A., Zawada, David G., and Yates, Kimberly K., 20231130, Overlapping seabed images collected at Big Pine Ledge coral reef, Florida, 2021: U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, online.

    Online Links:

    Type_of_Source_Media: digital images
    Source_Contribution:
    raw images to which Structure-from-Motion (SfM) techniques were applied
    GNSS antenna positions (source 2 of 2)
    Kranenburg, Christine J., Hatcher, Gerald A., Warrick, Jonathan A., Zawada, David G., and Yates, Kimberly K., 2023, GNSS locations of seabed images collected at Big Pine Ledge coral reef, Florida, 2021: U.S. Geological Survey - Pacific Coastal and Marine Science Center, online.

    Online Links:

    Type_of_Source_Media: ASCII file
    Source_Contribution:
    Location data for the raw images to which Structure-from-Motion (SfM) techniques were applied
  2. How were the data generated, processed, and modified?
    Date: 01-Dec-2021 (process 1 of 6)
    IMAGERY COLOR CORRECTION Because of the strong color modifications caused by light adsorption and scattering in underwater imaging, a color correction process was conducted on the raw images. The color correction was a twofold process. First, images were corrected for the high adsorption (and low color values) in the red band using the color balancing techniques of Ancuti and others (2017). For this, the red channel was modified using the color compensation equations of Ancuti and others (2017, see equation 4 on page 383) that use both image-wide and pixel-by-pixel comparisons of red brightness with respect to green brightness. After compensation, the images were white balanced using the "greyworld" assumption that is summarized in Ancuti and others (2017). Combined, these techniques ensured that each color band histogram was centered on similar values and had similar spread of values. The remaining techniques of Ancuti and others (2017), which include sharpening techniques and a multi-product fusion, were not employed. The resulting images utilized only about a quarter to a half of the complete 0-255 dynamic range of the three-color bands. Thus, the brightness values of each band were stretched linearly over the complete range while allowing the brightest and darkest 0.05 percent of the original image pixels (that is, 2506 of the 5.013 million pixels) to be excluded from the histogram stretch. This final element was included to ensure that light or dark spots in the images, which often occurred from water column particles or image noise, did not exert undo control on the final brightness values. Color-corrected images were output with the same file names and file types as the originals to make replacement within the SfM photogrammetry project easy. As a courtesy, the script used to implement this procedure is provided as a supplemental support file (OrthoImage_Color_Correction_Procedure.m), included with this data release. Person who carried out this activity:
    Jonathan A. Warrick
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Research Geologist
    2885 Mission St.
    Santa Cruz, CA
    USA

    831-460-7569 (voice)
    jwarrick@usgs.gov
    Data sources used in this process:
    • raw images
    Data sources produced in this process:
    • color-corrected images
    Date: 01-Aug-2022 (process 2 of 6)
    DEHAZING A final step to reduce haze and shadows in the images was performed in Adobe Photoshop. This was achieved by creating a custom action with a Camera Raw filter that adjusts the tonal width and intensity of shadows, midtones, and highlights. Camera Raw filter parameters were set as follows: Contrast +35, Highlights -100, Shadows, +100, Clarity +10, Dehaze +15, Saturation Adjust Red -100, Shadow Luminance +25, Midtone Luminance +35, Highlight Luminance -20. The resulting dehazed images were not used for point cloud or DEM creation--they were used solely for creating a sharper, more color-rich orthoimage. Dehazed images were output with the same file names and file types as the originals to make replacement within the SfM photogrammetry project easy. Person who carried out this activity:
    Selena A. Johnson
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Research Physical Scientist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    selenajohnson@usgs.gov
    Data sources used in this process:
    • color-corrected images
    Data sources produced in this process:
    • dehazed images
    Date: 21-Feb-2022 (process 3 of 6)
    SARGASSUM REMOVAL During data collection large mats of floating Sargassum (seaweed) were present in the study area. As the SQUID-5 was towed through these patches, it became tangled in the branches, some of which entered the field of view of the cameras. Images with Sargassum were identified using the Dash Doodler and Segmentation Zoo Machine Learning software packages by Buscombe and others, 2022. The names of 797 images almost completely covered with sargassum were saved to a text file and used to disable the images in the Metashape project prior to orthoimage creation. Person who carried out this activity:
    Stephen T. Bosse
    Cherokee Nation System Solutions, contracted to U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Researcher I
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    sbosse@contractor.usgs.gov
    Data sources used in this process:
    • raw images
    Data sources produced in this process:
    • Image exclude file list
    Date: 29-Dec-2022 (process 4 of 6)
    SfM PHOTOGRAMMETRY Digital imagery and position data recorded by the SQUID-5 system were processed using Structure-from-Motion (SfM) photogrammetry techniques that generally follow the workflow outlined by Hatcher and others (2020 and 2023). These techniques are detailed here and include specific references to parameter settings and processing workflow. The primary software used for SfM processing was Agisoft Metashape Professional, version 1.6.6, build 11715, which will be referred to as "Metashape" in the discussion herein. First, the raw images collected during the five mission days were added to a new project in Metashape. Raw images were used over the color-corrected images, owing to their larger dynamic range, which generally resulted in more SfM tie points. The images were derived from only four of the five cameras on the SQUID-5 system due to a camera focusing problem (see metadata for the raw image files for more explanation), so each camera was assigned a unique camera calibration group in the Camera Calibration settings. Within the Camera Calibration settings, the camera parameters were also entered as 0.00345 x 0.00345 mm pixel sizes for all camera sensors, 8 mm focal length for the central camera (CAM13), and 6 mm focal lengths for the remaining cameras (CAM39, CAM75, CAM82). These different focal lengths represented different lenses chosen for each camera. Additionally, the cameras required offsets to transform the GNSS positions to each camera's entrance pupil (that is, optical center). Initial measurements of these offsets were obtained using a separate SfM technique, outlined in Hatcher and others (2020), which found the offsets to be: Camera X(m) Y(m) Z(m) CAM13 0.034 0.011 0.840 CAM39 0.273 -0.109 0.916 CAM75 0.131 0.559 0.754 CAM82 -0.010 -0.594 0.762 Where X and Y are the camera sensor parallel offsets, and Z is the sensor normal offset. The accuracy settings were chosen to be 0.01 m for CAM13 and 0.025 m for the other 3 cameras. Lastly, these offsets were allowed to be adjusted using the "Adjust GPS/INS offset" option, because slight camera shifts may occur with each rebuild and use of the SQUID-5 system. The SQUID-5 GNSS antenna positions were then imported into the project and matched with each image by time. The coordinates were converted in Metashape to the North American Datum of 1983 (NAD83 [2011]) Universal Transverse Mercator (UTM) Zone 17 North (17N) projected coordinate system, and altitudes were converted to the North American Vertical Datum of 1988 (NAVD88) orthometric heights (in meters). Prior to aligning the data, the Metashape reference settings were assigned. The coordinate system was "NAD83(2011) / UTM zone 17N" The camera accuracy was 0.05 m in the horizontal dimensions and 0.10 m in the vertical, following an examination of the source GNSS data. Tie point accuracy was set at 1.0 pixels. The remaining reference settings were not relevant, because there were no camera orientation measurements, marker points, or scale bars in the SfM project. The data were then aligned in Metashape using the "Align Photos" workflow tool. Settings for the alignment included "High" accuracy, Generic preselection turned OFF and "Reference" preselection using the "Source" information. This last setting allowed the camera position information to assist with the alignment process. Additionally, the key point limit was set to 50,000 and the tie point limit was assigned a value of zero, which allows for the generation of the maximum number of points for each image. Lastly, neither the "Guided image matching" nor the "Adaptive camera model fitting" options were used. This process resulted in over 101 million tie points. The total positional errors for the cameras were reported to be 0.025 m, 0.027 m, and 0.041 m in the east, north and altitude directions, respectively. Thus, the total positional error was 0.055 m. To improve upon the camera calibration parameters and computed camera positions, an optimization process was conducted that was consistent with the techniques of Hatcher and others (2020), which are based on the general principles provided in Over and others (2021). First, a duplicate of the aligned data was created in case the optimization process eliminated too much data using the "Duplicate Chunk" tool. Within the new chunk, the least valid tie points were removed using the "Gradual Selection" tools. As noted in Hatcher and others (2020), these tools are used less aggressively for the underwater imagery of SQUID-5 than commonly used for aerial imagery owing to the differences in image quality. First, all points with a "Reconstruction Uncertainty" greater than 20 were selected and deleted. Then, all points with a "Projection Accuracy" greater than 8 were selected and deleted. The camera parameters were then recalibrated with the "Optimize Cameras" tool. Throughout this process the only camera parameters that were adjusted were f, k1, k2, k3, cx, cy, p1, and p2. Once the camera parameters were adjusted, all points with "Reprojection Errors" greater than 0.4 were deleted, and the "Optimize Cameras" tool was used one final time. This optimization process resulted in slightly over 46.4 million tie points, a reduction of roughly 54 percent of the original tie points. The camera positional errors were reported to be 0.018 m, 0.021 m, and 0.038 m in the east, north and altitude directions, respectively, and the total positional error was 0.047 m. The final computed arm offsets were found to be: Camera X(m) Y(m) Z(m) CAM13 0.030 0.009 0.825 CAM39 0.274 -0.111 0.911 CAM75 0.128 0.556 0.745 CAM82 -0.012 -0.563 0.777 Following the alignment and optimization of the SQUID-5 data, mapped SfM products were generated in Metashape. For these steps, the original raw images were replaced with color-corrected images. This replacement was conducted by resetting each image path from the raw image to the color-corrected image. First, a three-dimensional dense point cloud was generated using the "Build Dense Cloud" workflow tool. This was run with the "High" quality setting and the "Mild" depth filtering, and the tool was set to calculate both point colors and confidence. The resulting dense cloud was over 2 billion points over the 0.08 square kilometer survey area, or roughly 33,000 points per square meter (3.3 points per square centimeter). The dense points were classified by thresholding Metashape-computed confidence values, which are equivalent to the number of image depth maps that were integrated to make each point. Values of one were assigned "low noise", and values of two and greater were assigned "unclassified". The final Dense cloud was partitioned into blocks (also referred to as tiles) measuring 150 meters on a side, and exported with point colors and classification as a LAZ file type. Person who carried out this activity:
    Christine J. Kranenburg
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Cartographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    ckranenburg@usgs.gov
    Data sources used in this process:
    • raw images
    • color-corrected images
    • GNSS antenna positions
    Data sources produced in this process:
    • point cloud
    Date: 07-May-2023 (process 5 of 6)
    GENERATION OF DIGITAL ELEVATION MODEL (DEM) A digital elevation model (DEM), which is a x,y raster of elevation values, was generated from the point cloud using the Metashape "Build DEM" workflow tool using a geographic projection, dense cloud source data, interpolation disabled, and the recommended output resolution of 0.00682 meters. Person who carried out this activity:
    Christine J. Kranenburg
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Cartographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    ckranenburg@usgs.gov
    Data sources used in this process:
    • point cloud
    Data sources produced in this process:
    • DEM
    Date: 23-Jun-2023 (process 6 of 6)
    ORTHOIMAGERY GENERATION Before building the orthoimage, the color-corrected images were replaced with dehazed images. This replacement was conducted by resetting each image path from the color-corrected images to the dehazed images. An orthoimage product was then made using the Metashape "Build Orthomosaic" workflow tool using the DEM surface as the base. In order to remove remaining sargassum from the resulting orthoimage the "Average" blending mode was selected, hole-filling was disabled and seamlines were not refined. An output pixel size of 0.005 m was used in generating the orthoimage product. The orthoimage was then converted to a Cloud Optimized GeoTIFF (COG) format using the Geospatial Data Abstraction Library (GDAL). For more information on COGs see the USGS website: https://www.usgs.gov/faqs/what-are-cloud-optimized-geotiffs-cogs Person who carried out this activity:
    Christine J. Kranenburg
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Cartographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    ckranenburg@usgs.gov
    Data sources used in this process:
    • DEM
    • dehazed images
  3. What similar or related data should the user be aware of?
    Hatcher, Gerald A., Warrick, Jonathan A., Kranenburg, Christine J., and Ritchie, Andrew C., 2023, Accurate Maps of Reef-scale Bathymetry with Synchronized Underwater Cameras and GNSS.

    Online Links:

    Other_Citation_Details:
    Hatcher, G.A., Warrick, J.A., Kranenburg, C.J., and Ritchie, A.C., Accurate Maps of Reef-scale Bathymetry with Synchronized Underwater Cameras and GNSS: Remote Sensing, 2023, 15, 3727. doi:10.3390/rs15153727
    Hatcher, Gerald A., Warrick, Jonathan A., Ritchie, Andrew C., Dailey, Evan T., Zawada, David G., Kranenburg, Christine, and Yates, Kimberly K., 2020, Accurate bathymetric maps from underwater digital imagery without ground control.

    Online Links:

    Other_Citation_Details:
    Hatcher, G.A., Warrick, J.A., Ritchie, A.C., Dailey, E.T., Zawada, D.G., Kranenburg, C., and Yates, K.K., 2020, Accurate bathymetric maps from underwater digital imagery without ground control: Frontiers in Marine Science, v. 7, art. 525, doi:10.3389/fmars.2020.00525
    Ancuti, Codruta O., Ancuti, Cosmin, Vleeschouwer, Christophe De, and Bekaert, Philippe, 2017, Color balance and fusion for underwater image enhancement.

    Online Links:

    Other_Citation_Details:
    Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., and Bekaert, P., 2017, Color balance and fusion for underwater image enhancement: IEEE Transactions on Image Processing, v. 27, p. 379-393, doi:10.1109/TIP.2017.2759252
    Over, Jin-Si R., Ritchie, Andrew C., Kranenburg, Christine J., Brown, Jenna A., Buscombe, Daniel, Noble, Tom, Sherwood, Christopher R., Warrick, Jonathan A., and Wernette, Philippe A., 2021, Processing Coastal Imagery with Agisoft Metashape Professional Edition, Version 1.6--Structure from Motion Workflow Documentation: U.S. Geological Survey Open-File Report 2021-1039.

    Online Links:

    Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., Fitzpatrick, S., Kranenburg, C. J., Over, J. R., Ritchie, A. C., Warrick, J. A., and Wernette, P., 2022, Human-in-the-loop segmentation of Earth surface imagery.

    Online Links:

    Other_Citation_Details:
    Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C., Warrick, J.A., and Wernette, P., 2022, Human-in-the-loop segmentation of Earth surface imagery: Earth and Space Science, v. 9, no. 3, doi:10.1029/2021EA002085

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

  1. How well have the observations been checked?
    The accuracy of the position data used for SfM data processing is based on the accuracy and precision of the GNSS equipment and camera timing. The post-processed GNSS navigation data produced a 10-Hz vehicle trajectory with an estimated 2-sigma accuracy of 10 cm horizontal and 15 cm vertical. The horizontal and vertical accuracies of the surface models generated by SfM were assessed with positional error assessments of the cameras and found to be less than 3 cm in the horizontal dimensions and less than 5 cm in the vertical.
  2. How accurate are the geographic locations?
    Previous SfM-based measurements of the field-based Sediment Elevation Table (SET) stations at USGS field sites in the Florida Keys were within 3 cm of the total uncertainty of the field-based GPS measurements. Additionally, the average horizontal scaling of the models was found to be between 0.016 percent and 0.024 percent of water depth. No independent assessment of horizontal accuracy was possible from the Big Pine Ledge field site.
  3. How accurate are the heights or depths?
    Previous SfM-based measurements of the field-based Sediment Elevation Table (SET) stations at USGS field sites in the Florida Keys were within 3 cm of the total uncertainty of the field-based GPS measurements. The average vertical scaling of the models is between 0.016 percent and 0.024 percent of water depth. No independent assessment of vertical accuracy was possible from the Big Pine Ledge field site.
  4. Where are the gaps in the data? What is missing?
    Gaps in the data coverage are coded with a NoData value of -32,767 in the DEM. Gaps primarily occur either because the line spacing briefly widened such that there was insuffient sidelap for reconstruction, or over sand patches which have moving sandwaves and / or lack texture, which is necessary for image correlation. The largest of these data voids is an approximately 1,600 square meter void over a sandpatch in the western quarter of the dataset.
  5. How consistent are the relationships among the observations, including topology?
    All data fall within expected ranges.

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

    1-831-427-4747 (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 for the entire surveyed area.
  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?
    • Availability in digital form:
      Data format: One compressed GeoTIFF file is available for download totaling 6.19 GB. in format TIFF Size: 6190
      Network links: https://doi.org/10.5066/P9R47TWT
    • Cost to order the data: none

Who wrote the metadata?

Dates:
Last modified: 26-Apr-2024
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-460-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/DataReleases/CMGDS_DR_tool/DR_P9R47TWT/SQUID5_BPL_2021_Orthoaverage_metadata.faq.html>
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