Orthoimagery of Big Pine Ledge, Florida, 2022

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.

DEHAZING First, local contrast and hue were adjusted in the Ruderman opponent color space by white-balancing chromatic components using the techniques of Bianco and others (2015). This was followed by color correction using the color balancing techniques of Ancuti and others (2017). 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 neighborhood Shadow/Highlight adjustment and Camera Raw filter that adjusts the tonal width and intensity of shadows, midtones, and highlights. Shadow/Highlight adjustment parameters were set as follows: Shadow Amount 30%, Shadow Tone Width 50%, Shadow Radius 50px, Shadow Highlight Amount_10%, Highlight Tone Width 50%, Highlight Radius 50px, Midtone Contrast 30, Midtone Color 20. Camera Raw filter parameters were set as follows: Contrast +25, Dehaze +10, Saturation Adjust Magenta -50, Saturation Adjust Blue -25. 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 simple.

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 1.8.5, build 15709, 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 easting and northing (in meters) coordinates were obtained in the North American Datum of 1983 (NAD83 [2011]) Universal Transverse Mercator (UTM) Zone 17 North (17N) projected coordinate system, and altitudes were obtained in 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 0.15 m in both the horizontal and 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 211 million tie points. The total positional errors for the cameras were reported to be 0.036 m, 0.034 m, and 0.039 m in the east, north and altitude directions, respectively. Thus, the total positional error was 0.064 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 74.6 million tie points, a reduction of roughly 64 percent of the original tie points. The camera positional errors were reported to be 0.027 m, 0.024 m, and 0.038 m in the east, north and altitude directions, respectively, and the total positional error was 0.053 m. The final computed arm offsets were found to be: Camera X(m) Y(m) Z(m) CAM01 -0.313 -0.209 0.864 CAM13 0.014 -0.145 0.775 CAM39 0.153 -0.273 0.866 CAM75 0.043 0.397 0.725 CAM82 -0.114 -0.694 0.706 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 2.8 billion points over the 0.12 square kilometer survey area, or roughly 24,000 points per square meter (2.4 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.

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 0.00742 meters.

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 orthomosaic image product was then made using the Metashape "Build Orthomosaic" workflow tool using the interpolated DEM surface as the base and "Average" blending mode. 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