Greig Street digital elevation model (DEM) from low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Sodus Bay, New York in July 2017 (32-bit floating point GeoTIFF image)

Digital elevation models (DEM) for the Lake Ontario projects were constructed using photogrammetry software (Agisoft Photoscan Professional v. 1.3.2) and digital images taken approximately 80 and 100 m above the ground on July 10 - 14, 2017, with a Ricoh GRII digital camera mounted in a 3DR Solo quadcopter operated the U.S. Geological Survey. This DEM is the product from one of several photogrammetry projects from field activity 2017-042-FA. Four projects were used for flights in the vicinity of Sodus Bay on July 12, 13, and 14, 2017 (Lake Bluffs, Charles Point, Sodus North, and Greig Street). The projects combined images with ground control points to produce geolocated, colored (red-green-blue schema) point clouds. This DEM, for the Greig Street project, was created using a standard processing flow as described in the processing step below.

The following steps make up the processing flow for creating digital elevation models from UAS flights:
1. Geotagged images (see source citation) containing useful imagery from the survey area were imported into Agisoft Photoscan Professional v. 1.3.2 software using the “Add photos…” tool. Photos taken before and immediately after takeoff, photos taken immediately prior to and after landing, and photos with 100% water were not loaded into the project. Project coordinate system is set to EPSG::7660 WGS84 (G1150) geocentric coordinate system.
2. Using ”Convert”, the coordinate system of the images (called “cameras” in Photoscan) was converted from native latitude/longitude WGS84 (G1150) coordinate system to meters in NAD83/UTM zone 18N (EPSG::26918).
3. The photos were processed through an initial alignment and optimization procedure using the following settings: Alignment - Accuracy: “High”; Pair selection: “Reference”; Key point limit: 80,000; Tie point limit; 0 (unlimited). Adaptive camera model fitting option was selected. Optimization - Lens-calibration parameters f, cx, cy, k1, k2, k3, p1, and p2 were included; b1, b2, and higher-order parameters k4, p3, and p4 were not.
4. The sparse point cloud (also known as tie points; created as a result of photo alignment and optimization) was edited using an iterative error-reduction procedure to filter the data. This was done in several iterations of a process called "Gradual Selection" to first to reduce reconstruction uncertainty (to a unitless value of 10) and then projection accuracy (to a weighted value of 3).
5. Ground control points were created by first letting the software automatically detect markers where it finds all of the 4-ft square black/white targets deployed (false targets that are sometimes detected were manually deleted). The automatically-generated marker labels were manually changed to match the names in the survey notes and GCP location files.
6. "Import markers” was used to load the GCP location files, which assigned coordinates (northing, easting, and elevation in UTM Zone 18 North meters in NAD83 and NAVD88 coordinate systems) from the location file to the detected markers. The locations of auto-detected markers were retained.
7. Another round of "Gradual Selection" was done to reduce the reprojection error (to a value of 0.3 pixels, unless more than 10% of the points would be removed).
8. A dense point cloud was then created with the parameters "High" quality and “Aggressive” depth filtering.
9. A DEM was created with Global Mapper (v. 19.0) using average binning method with a resolution of 0.05. Elevation points below 72.0 meters were filter out before gridding, and interpolation was set to 'loose' at '75'.
10. The DEM was exported in GeoTIFF format (2017-07-10_GreigSt_DEM_5cm.tif) along with a world file (tfw), then both were renamed to the AIM data release filenaming standard (e.g. 2017042FA_GreigSt_DEM.tif).

Added keywords section with USGS persistent identifier as theme keyword.