KEYS2016_SM_z17_n88g12B_mosaic_metadata: Coastal Topography-Upper Florida Keys Reef Tract, Florida, 26-30 June 2016

Dewberry reports that data for the Florida Keys Reef Tract project was acquired by Leading Edge Geomatics using a Leica Chiroptera II Bathymetric and Topographic lidar sensor. LEG delivered raw calibrated lidar data to Dewberry referenced to: Florida Keys: Horizontal Datum-NAD83 (2011 Projection-UTM Zone 17 North Horizontal Units-meters Vertical Datum-NAD83 (2011) ellipsoid Vertical Units-meters

This dataset encompasses 184 1500 m x 1500 m tiles. Both green lidar data and Near Infrared (NIR) lidar data were acquired.
Leading Edge Geomatics acquired, calibrated and performed the refraction correction to the lidar data. Light travels at different speeds in air versus water and its direction of travel or angle is changed or refracted when entering the water column. The refraction correction process corrects for this difference by adjusting the depth (distance traveled) and horizontal position (change of angle/direction) of the lidar data acquired within water.
The calibration process considered all errors inherent with the equipment including errors in global positioning system (GPS), inertial measurement unit (IMU), and sensor specific parameters. Adjustments were made to achieve a flight line to flight line data match (relative calibration) and subsequently adjusted to control for absolute accuracy. Process steps to achieve this are as follows: Rigorous lidar calibration: all sources of error such as the sensor's ranging and torsion parameters, atmospheric variables, GPS conditions, and IMU offsets were analyzed and removed to the highest level possible. This method addresses all errors, both vertical and horizontal in nature. Ranging, atmospheric variables, and GPS conditions affect the vertical position of the surface, whereas IMU offsets and torsion parameters affect the data horizontally. The horizontal accuracy is proven through repeatability: when the position of features remains constant no matter what direction the plane was flying and no matter where the feature is positioned within the swath, relative horizontal accuracy is achieved. Absolute horizontal accuracy is achieved through the use of differential GPS with base lines shorter than 25 miles. The base station is set at a temporary monument that is 'tied-in' to the Continuously Operating Reference Station (CORS) network. The same position is used for every lift, ensuring that any errors in its position will affect all data equally and can therefore be removed equally.
Vertical accuracy is achieved through the adjustment to ground control survey points within the finished product. Although the base station has absolute vertical accuracy, adjustments to sensor parameters introduces vertical error that must be normalized in the final (mean) adjustment. The vertical accuracy of this lidar dataset was tested against sonar data. The result of the test was an 8-cm bias in the lidar data between the sonar and lidar. No adjustments to the data or derivative products were made based on these results.
The withheld and overlap bits are set and all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

Dewberry reports that they utilize a variety of software suites for inventory management, classification, and data processing. All lidar related processes begin by importing the data into the GeoCue task management software. The swath data is tiled according to project specifications (1500 m x 1500 m). The tiled data is then opened in Terrascan where Dewberry classifies problematic edge of flight line points that are geometrically unusable with the withheld bit. These points are separated from the main point cloud so they are not used in the ground algorithms; overage points are then identified with the overlap bit.
Dewberry used ArcGIS to create 2-D breaklines. These breaklines defined land/water interfaces and were used in conjunction with the ground algorithms to define topographic (class 2) and bathymetric bottom (40).
Dewberry then uses proprietary ground classification routines to remove any non-ground points and generate an accurate ground/bathymetric surface. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Water surface points are classified to class 41 and class 42 Derived Water Surface is set and flagged synthetic. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. A manual quality control (QC) routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a supervisor's manual inspection is completed on a percentage of the classified tiles based on the project size and variability of the terrain.
A final QC is performed on the data. All headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

The data were classified as follows: Class 1 = Unclassified. This class includes vegetation, buildings, noise etc. Class 2 = Ground (includes model key point bit for points identified as Model Key Point) Class 7 = Low Noise Class 17 = Bridge Decks Class 18 = High Noise Class 40 = Bathymetric Point (includes model key point bit for points identified as Model Key Point) Class 41 = Water Surface Class 42 = Derived Water Surface
The LAS header information was verified to contain the following: Class (Integer) Adjusted GPS Time (0.0001 seconds) Easting (0.003 m) Northing (0.003 m) Elevation (0.003 m) Echo Number (Integer) Echo (Integer) Intensity (16-bit integer) Flight Line (Integer) Scan Angle (degree) Dewberry used GeoCue software to convert the lidar to the North American Datum of 1983 (NAD 83) (2011) orthometric GEOID 12B. This dataset was used to create the other orthometric deliverables. Spatial reference information is updated in GeoCue software and then verified using proprietary Dewberry tools. Dewberry used GeoCue software to produce intensity imagery from the source lidar. The intensity imagery is georeferenced ortho-imagery that spatially aligns with the source lidar. The intensity imagery is created from the full point cloud first returns to show the full representation of the lidar dataset and was created with a 1-meter pixel resolution. The final format of the imagery is 8-bit, unsigned integer, grayscale GeoTIFF.

Dewberry reports that the void polygon layer was created in Global Mapper where every bathymetry bottom point was used to create a grid. The distance or threshold that sets how far Global Mapper can interpolate around each bathymetry bottom point was set as 2. The higher the interpolation threshold, the more bathymetry bottom points are connected to create a continuous surface in the Global Mapper grid with fewer areas of NoData. The NoData areas in the Global Mapper grids are exported to polygons. Void polygons greater than 9 square meters are imported into Arc Geodatabases where they are incorporated into the terrains as soft erase features. When the terrains are exported to raster, the void polygons used as an erase in the terrain remain as areas of NoData. The final void polygon layer was created using the final lidar dataset, after all editing, review, and corrections were performed.

Dewberry reports that class 2, ground, and class 40, bathymetric point, lidar points are exported from the LAS files into an Arc Geodatabase (GDB) in multipoint format. The final void polygons are imported into the same GDB. An Esri Terrain is generated from these inputs. The surface type of each input is as follows: Ground/Bathymetric Multipoint: Masspoints and Void Polygons: Soft Erase. The Esri Terrain is converted to a raster. The raster is created using linear interpolation with a 1-meter cell size. The DEM is reviewed with hillshades in both ArcGIS and Global Mapper. Hillshades allow the analyst to view the DEMs in 3D and to more efficiently locate and identify potential issues. Analysts review the DEM for missed lidar classification issues, anomalies, issues with the void polygons/NoData areas, and artifacts that are introduced during the raster creation process. The corrected and final DEM is clipped to individual tiles. Dewberry uses a proprietary tool that clips the DEM to each tile located within the final tile grid, names the clipped DEM to the tile grid cell name, and verifies that final extents are correct. All individual tiles are loaded into Global Mapper for the last review. During this last review, an analyst checks to ensure full, complete coverage, no issues along tile boundaries, tiles seamlessly edge-match, and that there are no remaining processing artifacts in the dataset.

The provided tiled DEM .tif files were mosaicked using Global Mapper 16.1 (20160309) and exported as one GeoTIFF.

Added keywords section with USGS persistent identifier as theme keyword.