Lidar point cloud, elevation models, GPS data, and imagery and orthomosaic from true-color aerial imagery, collected during UAS operations at Marsh Island, New Bedford, MA on April 9th, 2024

GROUND CONTROL: Five AeroPoint GCPs on top of larger black and white reflective tarps were spaced out over the field site and left on for at least two hours to collect GNSS data. After collection and connected to Wifi, the AeroPoint data were uploaded and run through a post-processing kinematic algorithm of the CORS network to get a global accuracy. Internal accuracy is reported in xyz variance and with a baseline distance. The data were exported in the horizontal datum NAD83(2011) to produce latitude, longitude, and ellipsoid heights, and then UTM19N and vertical datum NAVD88 using Geoid 18 was used to produce easting and northing and orthometric heights. These were exported to a CSV file and named 2024004FA_MI_Apr_AeroPoints.csv.

UAS FLIGHTS: The lidar sensor is 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 non-repetitive scan pattern, which moves the sensor in spirograph (flower) motion and is optimal for vegetated areas as it can capture more angles by surveying in front and behind the sensor. The camera module is 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 is 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 are saved to a 64 GB micro SD card as *.jpeg files. A configuration text file (CONFIG.TXT) is 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 was collected with the UAS flying at 10 m/s at 80 meters above ground level with north-south and east-west transect passes. The scan method was set to repetitive. The camera module was set to take images every 2 seconds after reaching 45 meters altitude. After the 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 (2024004FA_MI_Apr_ImageryLocations.csv) and this is accounted for when transforming to NAD83(2011)/UTM19N and NAVD88 Geoid 18 in the products.

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 LPC, along with a strip adjustment between transect swaths using CloudStation's "robust" setting. The LPC was then classified in CloudStation using a bare-ground classification scheme (0-Created, never classified; 2-Ground) with the settings Object inner size = 55 m, Steepness = 0.05 m, minimal object height = 0.50 m, and a point cloud thickness of 0.15 m. The LPC was colored by the YSMP camera based on the photo timestamp. The classified LPC is used to export a DTM of the mean values of just the classified ground points (2024004FA_MI_Apr_YSMP_DTM_5cm.tif). The classification is then cleared in Global Mapper (v 26.0) so all points are unclassified. Points are also deleted in Global Mapper that are interpreted to be noise or are above a reasonable elevation threshold based on the surrounding features. The clean LPC is exported as a zipped laz file (v 1.4) (2024004FA_MI_Apr_YSMP_LPC.laz). A DSM using maximum values of all points is exported (2024004FA_MI_Apr_YSMP_DSM_5cm.tif). All files were exported in EPSG:6348 (NAD83(2011)/UTM zone 19N and the vertical datum in EPSG:5703 (NAVD88 height in meters).

RAW IMAGERY: The YSMP images were geotagged in Emlid Studio using the lidar .T04 file and MADA (https://geodesy.noaa.gov/CORS/ncn_station_pages/index.html?stationID=MADA) base rinex files from the time the UAS was in the air. The 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 '2024004FA_f01YSMP_20240409T165822Z_DSC####', 2024004FA is the field activity ID, f01 is the flight number, YSMP is the camera on the YellowScan Mapper Plus, 20240409 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.

PHOTOGRAMMETRY: The ortho product was created in Agisoft Metashape v. 2.0.1 using the following general steps (see Over and others, 2021 for a more detailed SfM methodology explanation): 1. A project was created in Metashape and imagery was imported. 2. Photos were aligned at a low accuracy and then GCPs were automatically detected in the point cloud. GCP positions (2024004FA_MI_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 0.01/0.02 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, which created a sparse point cloud and put the imagery into a relative spatial context using the GCPs. The resultant point cloud was filtered using one iteration of the 'Reconstruction uncertainty' filter at a level of 13, one iteration of the 'Projection accuracy' filter at a level of 3, and three iterations of the 'Reprojection accuracy' filter to get to a level of 1. 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. A high-quality dense cloud with a low-frequency filtering algorithm was made from the filtered spare point cloud. 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. An interpolated elevation model is built from the dense point cloud and then an orthomosaic is built from the DSM with refined seamlines. The YSMP orthomosaic is a 3-band RGB-averaged orthomosaic exported at 5 cm resolution (2024004FA_MI_Apr_RGBavg_5cm.tif).

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.