Bathymetric digital elevation model (DEM) of Eastern Dry Rocks coral reef, Florida, 2021

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Frequently anticipated questions:


What does this data set describe?

Title:
Bathymetric digital elevation model (DEM) of Eastern Dry Rocks coral reef, Florida, 2021
Abstract:
A digital elevation model (DEM) was created from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was derived in Metashape (ver. 1.6.5) from the point cloud, but it excludes the 'low noise' class. The DEM covers a rectangular area of seafloor approximately 800x160 meters (0.12 square kilometers) in size and was saved as a tiled GeoTIFF raster at 1-cm resolution.
Supplemental_Information:
Additional information about the field activity from which these data were derived is available online at: https://cmgds.marine.usgs.gov/fan_info.php?fan=2021-620-FA and https://cmgds.marine.usgs.gov/fan_info.php?fan=2021-313-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., 20220611, Bathymetric digital elevation model (DEM) of Eastern Dry Rocks coral reef, Florida, 2021: data release DOI:10.5066/P93RIIG9, 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., 2022, Overlapping seabed images and location data acquired using the SQUID-5 system at Eastern Dry Rocks coral reef, Florida, in May 2021, with derived point cloud, digital elevation model and orthomosaic of submerged topography: data release DOI:10.5066/P93RIIG9, U.S. Geological Survey - Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.84593563
    East_Bounding_Coordinate: -81.83777265
    North_Bounding_Coordinate: 24.46369150
    South_Bounding_Coordinate: 24.45766816
  3. What does it look like?
    DEM_example.jpg (JPEG)
    Full-resolution sample view of the DEM.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 07-May-2021
    Ending_Date: 13-May-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 0.01
      Ordinates (y-coordinates) are specified to the nearest 0.01
      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.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum 1988 (NAVD88)
      Altitude_Resolution: 0.001
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method: Attribute values
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    The DEM is presented as a 32-bit floating point GeoTIFF where pixels represent elevation in meters relative to NAVD88 and the no-data value is -32767. 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 Captain Chad Stolka, Operations Specialist for the NOAA Florida Keys National Marine Sanctuary, for his local knowledge and maritime expertise during data acquisition; 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 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 orthomosaic 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)
    Hatcher, Gerald A., Kranenburg, Christine J., Warrick, Jonathan A., Bosse, Stephen T., Zawada, David G., Yates, Kimberly K., and Johnson, Selena A., 2022, Overlapping seabed images collected at Eastern Dry Rocks coral reef, Florida, 2021: U.S. Geological Survey - Pacific 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)
    Hatcher, Gerald A., Kranenburg, Christine J., Warrick, Jonathan A., Bosse, Stephen T., Zawada, David G., Yates, Kimberly K., and Johnson, Selena A., 2022, GNSS locations of seabed images collected at Eastern Dry Rocks 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 3)
    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. 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: 10-Dec-2021 (process 2 of 3)
    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). 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 1.6.5, build 11249, which will be referred to as "Metashape" in the discussion herein. Because of the large number of images in this dataset, processing was conducted on a 792-CPU-core linux-based High-Performance Computing (HPC) cluster at the USGS Advanced Research Computing (ARC) group (https://doi.org/10.5066/P9XE7ROJ). First, the raw images collected during the six 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 five cameras on the SQUID-5 system, 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 (CAM30, 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 CAM30 -0.311 -0.029 0.911 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 0.05 m for all 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 images by time. The easting and northing (in meters) were obtained from the NAD83 UTM Zone 17N data, and altitudes were obtained from the 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 0.06 m in the horizontal dimensions and 0.12 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 and "Reference" preselection using the "Source" information. This latter 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 253 million tie points. The total positional errors for the cameras were reported to be 0.025 m, 0.021 m, and 0.037 m in the east, north and altitude directions, respectively. Thus, the total positional error was 0.049 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 147.4 million tie points, a reduction of roughly 40 percent of the original tie points. The camera positional errors were reported to be 0.024 m, 0.020 m, and 0.037 m in the east, north and altitude directions, respectively, and the total positional error was 0.049 m. The final computed arm offsets were found to be: Camera X(m) Y(m) Z(m) CAM13 0.033 0.010 0.829 CAM30 -0.310 -0.031 0.908 CAM39 0.274 -0.114 0.912 CAM75 0.132 0.557 0.749 CAM82 -0.012 -0.596 0.756 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 images to the color-corrected images. 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 "Moderate" depth filtering, and the tool was set to calculate both point colors and confidence. The resulting dense cloud was over 5.3 billion points over the 0.12 square kilometer survey area, or roughly 44,600 points per square meter (4.46 points per square centimeter). The dense points were classified with the 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, confidence, 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: 04-Mar-2022 (process 3 of 3)
    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, disabled interpolation, and the recommended output resolution of 0.00685 meters. Additionally, the DEM was generated using only the "unclassified" dense points, which excluded the "low noise" dense points. The DEM was partitioned into 150-meter blocks and resampled to a 1-cm resolution pixel size during export. DEM tile boundaries are coincident with those of the point cloud. 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
  3. What similar or related data should the user be aware of?
    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:


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 of the post-processed GNSS navigation data, which 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 4 cm in the vertical.
  2. How accurate are the geographic locations?
    Previous SfM-based measurements of the field-based Sediment Elevation Table (SET) stations from 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 Eastern Dry Rocks 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 Eastern Dry Rocks field site.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented, as described in the abstract. No data were collected in 15 of the 30 potential tiles covered by the 6-by-5 index map. Data in those tiles were either too deep to be captured with an optical system or too shallow to access with the vessel used during data collection. Users are advised to read the rest of the metadata record carefully for additional details.
  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? The DEM GeoTIFF is available in 1-cm resolution.
  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: Fifteen individual compressed GeoTIFF files available for download range in size from 63 MB to 917 MB. in format GeoTIFF Size: 5630
      Network links: https://doi.org/10.5066/P93RIIG9
    • Cost to order the data: none


Who wrote the metadata?

Dates:
Last modified: 11-Jun-2022
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)

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