Topographic point cloud for the Liberty Island Conservation Bank Wildlands restoration site, Sacramento-San Joaquin Delta, California, 2018-10-23

Aerial imagery was collected using two Department of Interior owned 3DR Solo quadcopters fitted with Ricoh GR II digital cameras featuring global shutters. The cameras were mounted using a fixed mount on the bottom of the UAS and oriented in a roughly nadir orientation. The UAS were flown on pre-programmed autonomous flight lines at an approximate altitude of 120 meters above-ground-level. The flight lines were oriented roughly east-west and were spaced to provide approximately 66 percent overlap between images from adjacent lines. The cameras were triggered at 1 Hz using a built in intervalometer, and were programmed to simultaneously acquire imagery in both JPG and camera raw (Adobe DNG) formats. Due to the limited UAS battery life, a total of 8 flights were required to achieve full coverage of the study area. The flights were conducted on 2018-10-23 between 18:21 and 20:35 Universal Coordinated Time (UTC) (11:21 and 13:35 Pacific Daylight Time (PDT)). Before each flight, the camera digital ISO, aperture and shutter speed were manually set to adjust for ambient light conditions. Although these settings were changed between flights, they were not permitted to change during a flight; thus, the images from each individual flight were acquired with consistent camera settings.

Ground control was established using twenty-four ground control points (GCPs) consisting of small square tarps with black-and-white cross patterns placed on the ground surface throughout the survey area. The GCP positions were measured using survey-grade GPS receivers operating in real-time-kinematic (RTK) mode. The GPS receivers were placed on short fixed-height tripods and set to occupy each GCP for a minimum occupation time of one-minute. The RTK corrections were referenced to a static GPS base station operating on a benchmark approximately 3 kilometers south of the survey area. The position of the benchmark was previously established using the average of three static GPS occupations (2017-06-26 to 2017-06-28) with durations between 4 and 8 hours, processed using the National Geodetic Survey (NGS) Online Positioning User Service (OPUS).

The image files were renamed using a custom python script. The file names were formed using the following pattern Fx-YYYYMMDDThhmmssZ_Ryz.*, where: - Fx = Flight number - YYYYMMDDThhmmssZ = date and time in the ISO 8601 standard, where 'T' separates the date from the time, and 'Z' denotes UTC ('Zulu') time. - Ry = RA or RB to distinguish camera 'RicohA' from 'RicohB' - z = original image name assigned by camera during acquisition - * = file extension (JPG or DNG)
The approximate image acquisition coordinates were added to the image metadata (EXIF) ('geotagged') using the image timestamp and the telemetry logs from the UAS onboard single-frequency 1-Hz autonomous GPS. The geotagging process was done using GeoSetter software. To improve timestamp accuracy, the image acquisition times were adjusted to true ('corrected') UTC time by comparing the image timestamps with several images taken of a smartphone app ('Emerald Time') showing accurate time from Network Time Protocol (NTP) servers. For RicohA no image time adjustment was needed; for RicohB, +00:00:02 (2 seconds) were added to the image time to synchronize with corrected UTC time. The positions stored in the EXIF are in geographic coordinates referenced to the WGS84(G1150) coordinate reference system (EPSG:7660), with elevation in meters relative to the WGS84 ellipsoid.
Additional information was added to the EXIF using the command-line 'exiftool' software with the following command: exiftool ^ -P ^ -Copyright="Public Domain. Please credit U.S. Geological Survey." ^ -CopyrightNotice="Public Domain. Please credit U.S. Geological Survey." ^ -ImageDescription="Low-altitude aerial image of the Liberty Island Conservation Bank Wildlands restoration site, Cache Slough Complex, Sacramento-San Joaquin Delta, California, USA, from USGS survey 2018-676-FA" ^ -Caption-Abstract="Liberty Island Conservation Bank Wildlands restoration site, Cache Slough Complex, Sacramento-San Joaquin Delta, California, USA, from USGS survey 2018-676-FA" ^ -Caption="Liberty Island Conservation Bank Wildlands restoration site, Cache Slough Complex, Sacramento-San Joaquin Delta, California, USA, from survey 2018-676-FA" ^ -sep ", " ^ -keywords="Liberty Island, Liberty Island Conservation Bank, Wildlands restoration site, Cache Slough Complex, Sacramento-San Joaquin Delta, California, 2018-676-FA, Unmanned Aircraft System, UAS, aerial imagery, USGS, Pacific Coastal and Marine Science Center" ^ -comment="Low-altitude aerial image from USGS Unmanned Aircraft System (UAS) survey 2018-676-FA" ^ -Credit="U.S. Geological Survey" ^ -Contact="pcmsc_data@usgs.gov" ^ -Artist="Pacific Coastal and Marine Science Center"

Structure-from-motion (SfM) processing techniques were used to create the topographic point cloud in the Agisoft Photoscan/Metashape software package using the following workflow: 1. Initial image alignment was performed with the following parameters - Accuracy: 'high'; Pair selection: 'reference', 'generic'; Key point limit: 0 (unlimited); Tie point limit: 0 (unlimited). 2. Sparse point cloud error reduction was performed using an iterative gradual selection and camera optimization process with the following parameters: Reconstruction Uncertainty: 10; Projection Accuracy: 3. Lens calibration parameters f, cx, cy, k1, k2, k3, p1, and p2 were included in the optimization. Additional sparse points obviously above or below the true surface were manually deleted after the last error reduction iteration. 3. Ground control points (GCPs) were automatically detected using the 'Cross (non-coded)' option. False matches were manually removed, and all markers were visually checked and manually placed or adjusted if needed. 4. Additional sparse point cloud error reduction was performed using an iterative gradual selection and camera optimization process with the following parameters: Reconstruction Error: 0.3. Lens calibration parameters f, cx, cy, k1, k2, k3, p1, and p2 were initially included in the optimization, but additional parameters k4, b1, b2, p3, and p4 were included once Reconstruction Error was reduced below 1 pixel. Additional sparse points obviously above or below the true surface were manually deleted after the last error reduction iteration, and a final optimization was performed. 5. A dense point cloud was created using the 'high' accuracy setting, with 'aggressive' depth filtering. 6. Low-noise points were identified using the 'Classify Ground Points' tool in Agisoft with the following parameters: Max. Angle: 15 degrees; Max. Distance: 0.5 meters; Cell Size: 5 meters. Due to the prevalence of water, vegetation and tree cover some areas, it is not expected that this step identified all noise. 7. The point cloud was exported in 500x500-meter tiles using the LAZ format to reduce file sizes. 8. An exterior boundary was digitized and used as a clipping mask to exclude obvious edge artifacts and large areas of interpolation. 9. An initial Digital Surface Model (DSM) with a native resolution of 6.3 centimeters per-pixel was created using all points in the dense point cloud, except those classified as 'low-noise'. The DSM was exported to a GeoTIFF format with a 10-centimeter pixel resolution. 10. A water clipping mask was developed in ArcGIS by filtering elevation values below 1.6 meters and combining those with large areas of interpolation in the DSM (representing large areas of the original point cloud which had been classified as noise). The water clipping mask was converted to a polygon shapefile. 11. LAStools 'las2las' was used to reset the 'ground' classification to 'unclassified' due to the high likelihood of misclassification resulting from the prevalence of dense vegetation in the survey area. The horizontal and vertical coordinate reference system were also defined during this process. 12. LAStools 'lasclip' was used to set the classification of all points falling within the horizontal bounds of the water clipping mask as Class 9 ('water').

Edited metadata to add keywords section with USGS persistent identifier as theme keyword. No data were changed.