Bathymetric data and grid representing single-beam data offshore Marconi Beach, Wellfleet, MA on March 16, 2022

The single-beam bathymetry data were collected by launching the Autonomous Surface Vehicle (ASV) Yellowfin into the water from the surf zone. Yellowfin was equipped with a Cerulean s500 echosounder and collected position information with an EMLID Reach M2 GPS. The boat was controlled near the shore by Peter Traykovski and put on auto-pilot using predetermined tracklines for longer transects offshore. The raw bathymetry data was processed with the following steps: 1. Parsed the binary data (BU353s4 GPS Puck, Time, Position and Echosounder range) into MATLAB (v. 2020b) from the echosounder using MATLAB Scripts developed by Peter Traykovski at WHOI - see contact below 2. Post Processed Kinematic (PPK) GNSS (Novatel OEM-V2_L1L2) data from the vessel were corrected using base station (Java Triumph-1) data in Novatel Inertial Explorer (Version 8.70.8722) using the datum NAD83(2011) and with GEOID 12B for the vertical datum NAVD88. A projection for UTM Zone 19N was also specified to get the Easting and Northing information. An antenna height of 0.0 was used in this step, but note the actual antenna height was corrected for in Step 5. 3. Time alignment was refined to account for small time delays between GPS data and echosounder data collection. This was accomplished using the time-lagged cross-correlation of echosounder range and PPK GNSS altitude in locations with a relatively flat bottom. The resulting time shift was 18 seconds. The GPS measured vertical fluctuation of the boat due to waves and these fluctuations were also visible in echosounder data. The equation in Step 5 removed the vertical fluctuation due to waves from the echosounder data leaving only true bathymetry if the GPS and the echosounder were well-synced. The lagged cross-correlation processing ensured they were synced optimally. 4. Duplicate times were removed from the PPK GNSS positions, and then the GPS values (GPS_z) were interpolated to the time of the Echosounder samples (Echo_z) with the MATLAB function interp1 (linear interpolation) and 'pchip'. 5. Calculated sea-floor elevation with reference to the NAVD88 datum in meters using the following equation: Seafloor_z = GPS_z - Antenna_Z_Offset + Echo_z; Antenna_Z_Offset = 0.25 m was the offset from the GNSS antenna to the Echosounder transducer on the z-axis of the ASV. These xyz values of the GPS and Echosounder are provided in the CSV 2022014FA_Marconi_raw_bathymetry.csv 7. Gridded Seafloor_z values onto 1 m resolution UTM eastings, northings using MATLAB file exchange script regularizedata3d by Jamal (2020), a cubic interpolation with the optimum smoothness coefficient determined by a Monte Carlo optimization procedure for the data regularization. Regularizedata3d entailed removing one cross-shore line of data from the processing and adjusting the smoothness parameter so the overall surface fit best fits the removed data. The process was then repeated for all the lines of data. The optimized smoothing parameter was used with all the data for the final processing. 8. The MATLAB function 'roipoly' returned the mask as a binary image, which sets pixels inside the region of interest (ROI) to 1 and pixels outside the ROI to 0. The boundary was developed so that there were no extrapolated bathymetry data outside the tracklines. The mask’s extent was the convex hull of the tracklines positions (x,y) and a bounding z coordinate of 0.8 represented the surface in reference to NAVD88 in meters. 9. Exported gridded 1 m data in NAD83(2011)/UTM Zone 19N in NAVD88 meters as a GeoTIFF: 2022015FA_Truro_Bathymetry_1m.tif. Note that using NAVD88 as a height means that the depths are not true-depths but the elevation of the seafloor above or below the geoid.
Jamal (2020). RegularizeData3D (https://www.mathworks.com/matlabcentral/fileexchange/46223-regularizedata3d), MATLAB Central File Exchange. Retrieved September 23, 2020.

Bathymetry grid was turned into a cloud-optimized GeoTIFF (COG) using gdal_translate with the following command: for %i in (.\*.tif) do gdal_translate %i .\cog\%~ni_cog.tif -of COG -stats -co BLOCKSIZE=256 -co COMPRESS=DEFLATE -co PREDICTOR=YES -co NUM_THREADS=ALL_CPUS -co BIGTIFF=YES (v. 3.1.4 accessed October 20, 2020 https://gdal.org/), where i is the name of each GeoTIFF.