Gerald A. Hatcher Christine J. Kranenburg Jonathan A. Warrick 20240426 raster 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 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. 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 -81.38464921 -81.37764248 24.55393089 24.55195337 USGS Metadata Identifier USGS:d9ecf55a-0354-4c02-baa1-38b0c4cf939f 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_orthoaverage_fullrez.jpg Full-resolution sample view of larger orthomosaic image. 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 D. Buscombe E. B. Goldstein C. R. Sherwood C. Bodine J. A. Brown J. Favela S. Fitzpatrick C. J. Kranenburg J. R. Over A. C. Ritchie J. A. Warrick P. Wernette 2022 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 https://doi.org/10.1029/2021EA002085 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. 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. 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 2023 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

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Santa Cruz CA 95060 USA 831-460-7569 jwarrick@usgs.gov 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. color-corrected images 20220801 dehazed images Selena A. Johnson U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center Research Physical Scientist Physical and Mailing

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St. Petersburg FL 33701 USA 727-502-8000 selenajohnson@usgs.gov 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. raw images 20220221 Image exclude file list Stephen T. Bosse Cherokee Nation System Solutions, contracted to U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center Researcher I Physical and Mailing

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St. Petersburg FL 33701 USA 727-502-8000 sbosse@contractor.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 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. point cloud 20230507 DEM 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 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 DEM dehazed images 20230623 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 Raster Pixel Universal Transverse Mercator 17N 0.9996 -81 0.0 500000.0 0.0 coordinate pair 0.005 0.005 Meters North American Datum of 1983 (2011) GRS 1980 6378137.000000 298.257222101 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. 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 GeoTIFF format for the entire surveyed area. 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. TIFF One compressed GeoTIFF file is available for download totaling 6.19 GB. Adobe Deflate 6190 https://doi.org/10.5066/P9R47TWT The orthoimage GeoTIFF file can be downloaded by going to the Network_Resource_Name link and scrolling down to the Imagery Data section. none 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