Lidar points, elevation models, imagery, orthomosaic, and supporting GPS data collected during UAS operations at Marconi Beach, CACO, Wellfleet, MA on March 06, 2025

GROUND CONTROL: Ten AeroPoint2 GCPs were spaced out over the field site and left on for at least 30 minutes to collect GNSS data. After collection the AeroPoint2s data were uploaded via a Wi-Fi connection and run through a post-processing kinematic algorithm of the Propeller Corrections Network to get corrected positions. The data were exported in NAD83(2011) (EPSG:6318) to produce latitude, longitude, and ellipsoid heights, and then NAD83(2011)/UTM zone 19N (EPSG:6348) and NAVD88 (EPSP:5703) with GEOID 18 to produce easting and northing and orthometric heights. These were exported to a CSV file and named 2025005FA_Marconi_Mar_AeroPoints.csv. An Emlid RS2 was set up as a base station using a RTK fix on the highest section of boardwalk to record GNSS data for post processing the lidar data. An Emlid RS3 with tilt compensation was connected to the base to take RTK three-second averaged check shots of the bluff, beach, and AeroPoint2s; data were provided in 2025005FA_Marconi_Mar_GPS_Emlid_RS3.csv Step processed by J. Over and S. Brosnahan and assisted by Amit Millo.

UAS FLIGHTS: The lidar sensor was a 905 nm wavelength Livox Avia with a 70.4/4.5 degree horizontal and vertical field of view (FOV). A configuration text file that controls camera triggering height, the camera triggering interval, and the lidar scan pattern was pre-loaded onto a 256 GB USB thumb drive. The lidar scanner was set to a repetitive (linear back and forth) motion optimal for unvegetated surfaces. The camera module is a SONY UMC - 10RC, which takes a 20 megapixel (MP) photo. The system also consists of a Trimble AV18 GNSS antenna mounted to the top of the UAS and connected to the lidar system via GNSS cable. The lidar data was saved to the thumb drive in three different files: (1) the IMU/GPS data, decimated for quick post-processing, in binary *.ys format, (2) the scanner data in *.lvx format (~600 MB per minute of data collection), (3) and the complete IMU/GPS data in Applanix (Trimble) binary *.t04 format. The RGB images are saved to a 64 GB micro-SD card as *.JPG files. The YSMP was attached to a DJI Matrice 600 Pro UAS with approved government edition firmware. The YSMP lidar data were collected with the UAS flying at 10 m/s at 68 meters above ground level with north-south transect passes. The camera module was set to take images every 2 seconds. After the flight the lidar data were taken off the sensor. Note, the YSMP geotagged positions embedded in the imagery Exif information were in EPSG:4326, this is how the data were collected. The positions were converted to EPSG:6348 and EPSG:5703 in the processing software and the converted positions are reported in the imagery locations file (2025005FA_Marconi_Mar_YSMP_ImageLocations.csv) and represented in the final SfM products. Step processed by J. Over and S. Brosnahan.

RAW IMAGERY: The YSMP images were geotagged in Emlid Studio using the lidar *.T04 file and base station RINEX files. All images were processed to add additional information required by the USGS to the Exif headers using ExifTools (https://exiftool.org/, version: 12.06), and the files were renamed to a unique identifier using Namexif (http://www.digicamsoft.com/softnamexif.html, version 2.1) to avoid any possibility of duplicate names. These steps are described here. 1. ExifTools was used to tag each photo headers following the Imagery Data System EXIF Guidance. Attributes (e.g. Credit, Copyright, UsageTerms, ImageDescription, Artist, etc) were stored in a csv file and written to each image with the command:' exiftool -csv="C:\directory\name\EXIF.csv" C:\directory\name\of\photos *.JPG ' To read out the photo information to a csv when in the directory with the photos the command is: exiftool -csv *.JPG > directory/name/allheaders_out.csv 2. All the images were renamed with Namexif (https://us.digicamsoft.com/softnamexif.html v 2.2 accessed April 2020) to ensure unique filenames and compliance with the USGS Coastal and Marine Hazards and Resources Program's best practices for image naming convention. Images were renamed with the field survey ID prefix; flight number, and ID that distinguishes USGS cameras by make/camera number, the image acquisition date, coordinated universal time (UTC) in ISO8601 format, and a suffix with the original image name. For example, image name '2025005FA_f01YSMP_20250306T165822Z_IMG_####_#', 2025005FA is the field activity ID; f01 is the flight number; YSMP is the camera on the YellowScan Mapper Plus; 20250306 is the UTC date in the format YYYYMMDD, and a 'T' is used to separate UTC date from UTC time in format HHMMSS followed by a Z. The IMG_#### is the original raw photo name appended to the end of the new filename. 3. Images are validated and uploaded onto the Imagery Data System. 4. The 2025005FA_Marconi_Mar_YSMP_ImageLocations.csv was created with the exif command: exiftool –ImageName –GPSDateTime –csv *.JPG > .\2025005FA_Marconi_Mar_YSMP_ImageLocations.csv Step processed by J. Over.

PHOTOGRAMMETRY: The ortho product was created in Agisoft Metashape v. 2.1.4 using the following general steps (see Over and others, 2021 for a more detailed SfM methodology explanation): 1. A project was created and imagery was imported and assigned the WGS84 (EPSG:4326) reference system and then were immediately converted to EPSG: 6318 and then EPSG:6348 and EPSG:5703. These converted positions were added to the 2025005FA_Marconi_Mar_YSMP_ImageLocations.csv. 2. Photos were aligned at a low accuracy and then GCPs were automatically detected in the point cloud. GCP positions (2025005FA_Marconi_Mar_AeroPoints.csv) were added to the project in the reference systems EPSG:6348 and EPSG:5703 using GEOID 18. The horizontal and vertical accuracies for the GCPs were set to 0.01/0.02 m, respectively and the camera positions for the images were turned off. The photos were then re-aligned with high accuracy (the pixels were not subsampled) using a keypoint limit of 60,000 and unlimited tie points. 3. The alignment process matched pixels between images to create point clouds and put the imagery into a relative spatial context using the GCPs. The resultant point clouds were filtered using one iteration of the 'Reconstruction uncertainty' filter at a level of 10, one iteration of the 'Projection accuracy' filter at a level of 3, and multiple iterations of the 'Reprojection accuracy' filter to get to a level of 0.3. With each filter, iteration points are selected, deleted, and then the camera model was optimized to refine the focal length, cx, cy, k1, k2, k3, p1, and p2 camera model coefficients. 4. In a new chunk a high quality dense cloud with a low-frequency filtering algorithm was made. The dense point cloud was then edited by visual inspection to remove points with a low confidence near the edges and near water bodies. 5. An interpolated DSM was built from the dense point cloud and then an orthomosaic was built from the DSM with refined seamlines. The orthomosaic is a 3-band orthomosaic exported at 5 cm resolution (2025005FA_Marconi_Mar_YSMP_SfM_Ortho_5cm.tif). Step processed by J. Over.

LIDAR DATA: The YSMP lidar data were processed in YellowScan CloudStation software integrated with Trimble POSPac UAV 9.0. Raw scanner data (*.ys file) was imported into YellowScan CloudStation, the sensor LiDAR (*.profile) and Camera (*.camera) profiles, provided by the vendor, were selected for the project and the project coordinate reference system was set to EPSG:6318. CloudStation flight trajectories were adjusted manually to select the desired data to be processed. The *.T04 file and base station GNSS RINEX file were used to correct and optimize the sensor position trajectories and produce a Smoothed Best Estimate of Trajectory (SBET) file in *.txt ASCII format, which represents the Post Processing Kinematic (PPK) Solution. The lever arm offsets and boresight angle corrections were applied to the LPC, along with a strip adjustment between transect swaths using CloudStation's "robust" setting. The lidar point cloud (LPC) was colored by the YSMP camera based on the photo timestamp. Finally, the Cut Overlap function was applied to remove redundant points based on the smaller scan angle. The LPC was then exported in *.laz 1.4 in EPSG:6348. The LPC was brought into Global Mapper and manually cleaned of points interpreted to be noise or are above a reasonable elevation threshold based on the surrounding features before the data were QA/QC’d with 3DEP, GCPs, and GPS check points. After assessing the agreement with the colorization and intensity it was deemed that re-colorizing the LPC with the SfM orthomosaic would be more accurate. Then, the LPC was gridded at 0.25 m into a DSM using maximum values binning grid method and the setting 'Grid "No Data" Distance' set to 5; the resulting file was exported as 2025005FA_Marconi_Mar_YSMP_Lidar_DSM_25cm.tif. The LPC was exported as 2025005FA_Marconi_Mar_YSMP_LPC.laz. All files were exported in EPSG:6348 and EPSG:5703 using GEOID 18. Step processed by J. Over.

CLOUD OPTIMIZATION: All GeoTIFF products were Deflate compressed and turned into a cloud-optimized GeoTIFFs (COG) using gdal_translate with the following command: for %i in (.\*.tif) do gdal_translate %i .\cog\%~ni.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 section. Step processed by J. Over.