Point cloud data of Summerland Ledge, Florida, 2022

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Frequently anticipated questions:

What does this data set describe?

Title: Point cloud data of Summerland Ledge, Florida, 2022
Abstract:
A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Summerland Ledge (SL), Florida, in July 2022 using the SfM (Structure-from-Motion) Quantitative Underwater Imaging Device with 5 cameras (SQUID-5) system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, and RGB colors derived from the color-corrected imagery. LIDAR Aerial Survey files (LAS) - and its compressed form, LAZ - is an open format developed for the efficient use of point cloud data.
Supplemental_Information:
Each data collection is recorded in the USGS Coastal and Marine Hazards Resources Program (CMHRP) Coastal and Marine Geoscience Data System (CMGDS) field activity database and is assigned a Field Activity Number (FAN). 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=2022-314-FA
  1. How might this data set be cited?
    Kranenburg, Christine J., Hatcher, Gerald A., and Warrick, Jonathan A., 20240502, Point cloud data of Summerland Ledge, Florida, 2022: data release DOI:10.5066/P149NXTA, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL.

    Online Links:

    This is part of the following larger work.

    Kranenburg, Christine J., Zawada, David G., Hatcher, Gerald A., Warrick, Jonathan A., Yates, Kimberly K., and Johnson, Selena A., 20240426, Underwater Photogrammetry Products of Summerland Ledge, Florida From Images Acquired Using the SQUID-5 System in July 2022: data release DOI:10.5066/P149NXTA, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.44777897
    East_Bounding_Coordinate: -81.44164484
    North_Bounding_Coordinate: 24.54159046
    South_Bounding_Coordinate: 24.53902522
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 18-Jul-2022
    Ending_Date: 19-Jul-2022
    Currentness_Reference:
    ground condition
  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 Point data set. It contains the following vector data types (SDTS terminology):
      • Entity point
    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 0.001
      Ordinates (y-coordinates) are specified to the nearest 0.001
      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:
    Points represent three-dimensional locations of the mapped seabed with horizontal positions in meters projected in NAD83(2011) UTM Zone 17N and elevation in meters relative to NAVD88 (GEOID 12B). Points additionally have values for 8-bit RGB color derived from the color-corrected images and a classification of either low noise or 'unclassified' derived from Metashape confidence values. These data are available in the compressed LAZ format for eight blocks (also referred to as tiles) of the survey area. Each tile measures 150 meters on a side and are labeled SQUID5_SL_2022_PointCloud-col-row.laz where col represents the column name and can have a value of A-D, and row is the row number and can have a value of 0-1.
    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)
    • Christine J. Kranenburg
    • Gerald A. Hatcher
    • 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, St. Petersburg Coastal and Marine Science Center
    Attn: SPCMSC Data Management Group
    600 4th St South
    St. Petersburg, FL

    727-502-8000 (voice)
    gs-g-spcmsc_data_inquiries@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 U.S. Geological Survey (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., Zawada, David G., Hatcher, Gerald A., Warrick, Jonathan A., and Yates, Kimberly K., 20231222, Overlapping seabed images collected at Summerland Ledge coral reef, Florida, 2022: U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

    Type_of_Source_Media: TIFF
    Source_Contribution: Raw images to which SfM techniques were applied.
    GNSS antenna positions (source 2 of 2)
    Kranenburg, Christine J., Zawada, David G., Hatcher, Gerald A., Warrick, Jonathan A., and Yates, Kimberly K., 20231222, GNSS locations of seabed images collected at Summerland Ledge coral reef, Florida, 2022: U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

    Type_of_Source_Media: comma-delimited text file
    Source_Contribution:
    Location data for the raw images to which SfM techniques were applied.
  2. How were the data generated, processed, and modified?
    Date: 05-Dec-2022 (process 1 of 2)
    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: 02-Jan-2023 (process 2 of 2)
    SfM PHOTOGRAMMETRY Digital imagery and position data recorded by the SQUID-5 system were processed using 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 three 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 from each camera were 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 (CAM01, 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) CAM01 -0.320 -0.205 0.823 CAM13 0.033 0.036 0.739 CAM39 0.170 -0.280 0.838 CAM75 0.047 0.396 0.698 CAM82 -0.110 -0.690 0.675 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 4 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 images, the Metashape reference settings were assigned. The coordinate system was "NAD83(2011) / UTM zone 17N". The camera accuracy was set to 0.10 m in the horizontal and 0.15 m in vertical dimensions, 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 turned "ON" and 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 41 million tie points. The total positional errors for the cameras were reported to be 0.015 m, 0.016 m, and 0.008 m in the east, north and altitude directions, respectively. Thus, the total positional error was 0.023 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 under 14.6 million tie points, a reduction of roughly 65 percent of the original tie points. The camera positional errors were reported to be 0.006 m, 0.006 m, and 0.005 m in the east, north and altitude directions, respectively, and the total positional error was 0.010 m. The final computed arm offsets were found to be: Camera X(m) Y(m) Z(m) CAM01 -0.316 -0.206 0.854 CAM13 0.015 -0.141 0.773 CAM39 0.154 -0.272 0.867 CAM75 0.044 0.398 0.722 CAM82 -0.111 -0.692 0.709 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 2.4 billion points over the 0.085 square kilometer survey area, or roughly 28,000 points per square meter (2.8 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
  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., 20230726, Accurate Maps of Reef-scale Bathymetry with Synchronized Underwater Cameras and GNSS: Remote Sensing 15(15), 3727.

    Online Links:

    Hatcher, Gerald A., Warrick, Jonathan A., Ritchie, Andrew C., Dailey, Evan T., Zawada, David G., Kranenburg, Christine, and Yates, Kimberly K., 20200626, Accurate Bathymetric Maps From Underwater Digital Imagery Without Ground Control: Frontiers in Marine Science Volume 7, Article 525.

    Online Links:

    Ancuti, Codruta O., Ancuti, Cosmin, Vleeschouwer, Christophe De, and Bekaert, Philippe, 2017, Color Balance and Fusion for Underwater Image Enhancement: IEEE Transactions on Image Processing Volume 27, Number 1.

    Online Links:

    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 and precision of the Global Navigation Satellite System (GNSS) equipment and camera timing. The post-processed GNSS navigation data produced a 10-hertz (Hz) vehicle trajectory with an estimated 2-sigma accuracy of 10 centimeters (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 1 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 (Hatcher and others, 2020). No independent assessment of horizontal accuracy was possible from the Summerland 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 (Hatcher and others, 2020). No independent assessment of vertical accuracy was possible from the Summerland Ledge 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. 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 240 square meter void in the south-western corner of the dataset. Additionally, small gaps may be present at vertical or concave surfaces due to the lack of visibility of these surfaces when viewed from above. These high-relief areas are common around the perimeter of reef spurs.
  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, St. Petersburg Coastal and Marine Science Center
    600 4th St. South
    St. Petersburg, FL

    727-502-8000 (voice)
    gs-g-spcmsc_data_inquiries@usgs.gov
  2. What's the catalog number I need to order this data set? SQUID5_SL_2022_PointCloud-col-row.laz*
  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. 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?
    • Availability in digital form:
      Data format: LAS (and its compressed version, LAZ) is an open source, directly accessible, ready-to-use format for point cloud data (x,y,z) originating in surveys using lidar or other fine-scale elevation measurements. The individual LAZ files available for download in this data release range in size from 1.1 GB to 5.4 GB. in format LASer file Size: 24264
      Network links: https://doi.org/10.5066/P149NXTA
    • Cost to order the data: None

  5. What hardware or software do I need in order to use the data set?
    A description of the LAZ format and links to software tools for using LAZ files are provided at the USGS website: https://www.usgs.gov/news/3d-elevation-program-distributing-lidar-data-laz-format

Who wrote the metadata?

Dates:
Last modified: 09-Aug-2024
Metadata author:
U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
Attn: SPCMSC Data Management Group
600 4th St. South
St. Petersburg, FL

727-502-8000 (voice)
gs-g-spcmsc_data_inquiries@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/SQUID5_SL_2022_PointCloud_metadata.faq.html>
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