Gerald A. Hatcher Christine J. Kranenburg Jonathan A. Warrick 20240426 vector digital data data release DOI:10.5066/P9R47TWT Santa Cruz, California U.S. Geological Survey - Pacific Coastal and Marine Science Center https://doi.org/10.5066/P9R47TWT Gerald A. Hatcher Christine J. Kranenburg Jonathan A. Warrick Stephen T. Bosse David G. Zawada Kimberly K. Yates Selena A. Johnson 2024 data release DOI:10.5066/P9R47TWT Santa Cruz, California U.S. Geological Survey - Pacific Coastal and Marine Science Center 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 https://doi.org/10.5066/P9R47TWT A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Big Pine Ledge, Florida, in July 2021 using the SQUID-5 camera 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. LAS (and its compressed form, LAZ) is an open format developed for the efficient use of point cloud lidar data. 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. 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. 20210716 20210720 ground condition Complete None planned -81.38465324 -81.37764391 24.55393026 24.55194957 USGS Metadata Identifier USGS:4740a199-862e-46dc-9e66-a7de1752ebe7 Marine Realms Information Bank (MRIB) keywords seabed coral reefs Data Categories for Marine Planning Physical Habitats and Geomorphology ISO 19115 Topic Category oceans elevation USGS Thesaurus reef ecosystems geospatial datasets remote sensing visible light imaging structure from motion None U.S. Geological Survey USGS Coastal and Marine Hazards and Resources Program CMHRP Pacific Coastal and Marine Science Center PCMSC St. Petersburg Coastal and Marine Science Center SPCMSC Geographic Names Information System (GNIS) State of Florida None Big Pine Ledge None 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. U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

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Santa Cruz CA 95060 831-460-4747 pcmsc_data@usgs.gov BPL21_ptcloud_webview.jpg Full-resolution sample view of larger point cloud data set. JPEG 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. Microsoft Windows 10 Gerald A. Hatcher Jonathan A. Warrick Christine J. Kranenburg Andrew C. Ritchie 2023 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 https://doi.org/10.3390/rs15153727 Gerald A. Hatcher Jonathan A. Warrick Andrew C. Ritchie Evan T. Dailey David G. Zawada Christine Kranenburg Kimberly K. Yates 2020 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 https://doi.org/10.3389/fmars.2020.00525 Codruta O. Ancuti Cosmin Ancuti Christophe De Vleeschouwer Philippe Bekaert 2017 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 https://doi.org/10.1109/TIP.2017.2759252 Jin-Si R. Over Andrew C. Ritchie Christine J. Kranenburg Jenna A. Brown Daniel Buscombe Tom Noble Christopher R. Sherwood Jonathan A. Warrick Philippe A. Wernette 2021 U.S. Geological Survey Open-File Report 2021-1039 https://doi.org/10.3133/ofr20211039 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. All data fall within expected ranges. Dataset is considered complete for the information presented, as described in the abstract. No data were collected in 2 of the 10 potential tiles covered by the 5-by-2 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. 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. 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. Christine J. Kranenburg Gerald A. Hatcher Jonathan A. Warrick David G. Zawada Kimberly K. Yates 20231130 TIFF online U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center https://doi.org/10.5066/P9M3NYWI digital images 20210716 20210720 ground condition at time data were collected raw images raw images to which Structure-from-Motion (SfM) techniques were applied Christine J. Kranenburg Gerald A. Hatcher Jonathan A. Warrick David G. Zawada Kimberly K. Yates 20231130 comma-delimited text file online U.S. Geological Survey - Pacific Coastal and Marine Science Center https://doi.org/10.5066/P9M3NYWI ASCII file 20210716 20210720 ground condition at time data were collected GNSS antenna positions Location data for the raw images to which Structure-from-Motion (SfM) techniques were applied 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. raw images 20211201 color-corrected images Jonathan A. Warrick U.S. Geological Survey, Pacific Coastal and Marine Science Center Research Geologist Physical and Mailing

2885 Mission St.

Santa Cruz CA 95060 USA 831-460-7569 jwarrick@usgs.gov 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. raw images color-corrected images GNSS antenna positions 20221229 point cloud Christine J. Kranenburg U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center Cartographer Physical and Mailing

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St. Petersburg FL 33701 USA 727-502-8000 ckranenburg@usgs.gov Point Entity point Universal Transverse Mercator 17N 0.9996 -81 0.0 500000.0 0.0 coordinate pair 0.001 0.001 Meters North American Datum of 1983 (2011) GRS 1980 6378137.000000 298.257222101 North American Vertical Datum 1988 (NAVD88) 0.001 meters Attribute values 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. U.S. Geological Survey U.S. Geological Survey - CMGDS Mailing and Physical

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Santa Cruz CA 95060 1-831-427-4747 pcmsc_data@usgs.gov 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_BPL_2021_PointCloud-col-row.laz where col represents the column name and can have a value of A-E, and row is the row number and can have a value of 0-1. 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. LASer file 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 412 MB to 4.95 GB. LAZ 21712 https://doi.org/10.5066/P9R47TWT The LAZ data can be downloaded by going to the Network_Resource_Name link and scrolling down to the Location-Elevation Data section. File names correspond to the 5-by-2 tile index grid shown in the location map and are named according to the following convention: SQUID5_BPL_2021_PointCloud-col-row.laz, where col represents the column name and can have a value of A-E, and row represents the row number and can have a value of 0-1. Note that 2 of the possible 10 tiles are empty, resulting in 8 point cloud data files. None. 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 20240426 U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

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Santa Cruz CA 95060 831-460-4747 pcmsc_data@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998