Lidar point cloud, elevation models, GPS data, imagery, and orthomosaic, collected during UAS operations at Town Neck Beach, Sandwich, MA on April 15th, 2024

GROUND CONTROL: Ten AeroPoint GCPs were spaced out over the field site and left on for at least two hours to collect Global Navigation Satellite System(GNSS) data, however four were not properly turned on. After collection and connected to Wi-Fi, the AeroPoint data were uploaded and run through a post-processing kinematic algorithm of the Continuously Operating Reference Station (CORS) network to get a global accuracy. The data were exported with xyz internal variances in the horizontal datum NAD83(2011) to produce latitude, longitude, and ellipsoid heights, and then UTM19N and NAVD88 using GEOID 18 was used to produce easting, northing, and orthometric heights. These were exported to a *.csv file and named 2024018FA_TNB_Apr_AeroPoints.csv. GPS DATA: A Spectra Precision 80 (SP80) was set up as a base station in the parking lot to collect RINEX data to correct the lidar data. A second SP80 was set up as a rover connected to the Massachusetts CORS network and used to take check shots across the study site. The check points are provided in 2024018FA_TNB_Apr_GPS_SP80.csv. Step completed by C. Sherwood, A. Farris, J. Cramer, and J. Over.

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). The LiDAR scanner was set to the repetitive scan pattern, which moves the sensor in a back-and-forth motion and is optimal for non-vegetated areas. The camera module was a SONY UMC - 10RC, a 20.4 megapixel (MP). 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 a 256 GB USB 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 were saved to a 64 GB micro SD card as *.jpeg files. A configuration text file (CONFIG.TXT) was pre-loaded onto the USB thumb drive and can be edited to control LiDAR and camera module settings, including the camera triggering height, the camera triggering interval, and the LiDAR scan pattern. 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 7 m/s at ~61 meters above ground level at a transect spacing of 45 m for a total of 42 minutes of flight time. The SONY camera triggered every 2 seconds. After each flight the lidar data were taken off the sensor. Note, the geotagged positions embedded in the Exif information are WGS84 and ellipsoid height, this is how the data are collected. The positions have been converted to NAD83(2011) and NAVD88 GEOID 18 in the imagery locations file (2024018FA_TNB_Apr_ImageryLocations.csv). Step completed by J. Cramer and J. Over.

RAW IMAGERY: YSMP 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 '2024018FA_f01r03_20240415T165822Z_DSC#', 2024018FA is the field activity ID, f01 is the flight number, r03 is the camera, 20240415 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 DSC# is the original raw photo name appended to the end of the new filename. Step completed by J. Cramer and J. Over.

PHOTOGRAMMETRY: The SfM products were created in Agisoft Metashape v. 2.0.1 using the following general steps (see Over and others, 2021 for a more detailed methodology explanation): 1. A project was created in Metashape and YSMP imagery was imported. 2. Photos were aligned at a low accuracy and then GCPs were automatically detected in the point cloud. GCP positions (2024018FA_TNB_Apr_AeroPoints.csv) were added to the project in the reference systems NAD83(2011)/UTM Zone 19N and NAVD88 (GEOID 18). The horizontal and vertical accuracies for the GCPs were set to 0.04/0.02 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 eight 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. At this point, multiple ‘chunks’ were created so that a high-quality dense cloud with a low-frequency filtering algorithm could be made from the images. The dense point cloud was then edited by visual inspection and Metashape’s confidence filter to remove points with a low confidence near the edges and near water bodies. 5. A DSM is built from the dense point cloud and then an orthomosaic is built from the DSM with refined seamlines. The DSM (2024018FA_TNB_Apr_YSMP_SfM_DSM_25cm.tif) and orthomosaic (2024018FA_TNB_Apr_YSMP_SfM_Ortho_5cm.tif) are exported in EPSG:6348 NAD83(2011)/UTM19N, the DSM has a vertical reference system in EPSG:5703 NAVD88 (GEOID 18). Step completed by J. Over.

LIDAR DATA: The YSMP lidar data were processed in YellowScan CloudStation software integrated with Trimble POSPac UAV 9.0. Base station GNSS data were downloaded from the SP80 collecting data on site for the time the UAS was flying. 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 is set. CloudStation flight trajectories are 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 lidar point cloud (LPC), along with a strip adjustment between transect swaths using CloudStation's "robust" setting. The LPC is then colorized using the images. The Cut Overlap function was applied to remove redundant points based on the closest parallel flight trajectory. The unclassified LPC model is exported from CloudStation as a 1.4 *.laz file. The LPC is brought into Global Mapper (v. 26.0) and manually cleaned of points interpreted to be noise or are above a reasonable elevation threshold based on the surrounding features. Then, the LPC is adjusted to the GCPs using a planar fit and exported as 2024018FA_TNB_Apr_YSMP_LPC.laz. The LPC is then gridded using maximum values and exported (2024018FA_TNB_Apr_YSMP_DSM_25cm.tif). All files were exported in EPSG:6348 NAD83(2011)/UTM zone 19N and the vertical datum in EPSG:5703 NAVD88 using GEOID 18. Step completed 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_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 section. Step completed by J. Over.