2018 NOAA National Geodetic Survey Topobathy Lidar: Potomac River, Chesapeake Bay

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Data was acquired by Quantum Spatial (QSI) using a Riegl VQ-880G Topobathy LiDAR system. All delivered LiDAR data is referenced to:

Horizontal Datum-NAD83 (2011) epoch: 2010

Projection-UTM Zone 18N

Horizontal Units-meters

Vertical Datum-GRS 80 Ellipsoid

Vertical Units-meters

The dataset encompasses 5,974 500m x 500m tiles covering the states of Virginia and Maryland at the confluence of the Potomac River and the Chesapeake Bay. Green and NIR (for water surface model creation that is used during refraction of the green bathymetric data) LiDAR data were acquired with the Riegl sensor VQ-880G.

QSI reviewed all acquired flight lines to ensure complete coverage and positional accuracy of the laser points. QSI creates an initial product call Quick Look Coverage Maps. These Quick Look files are not fully processed data or final products. The collected LiDAR data is immediately processed in the field by QSI to a level that will allow QA\QC measures to determine if the sensor is functioning properly and assess the coverage of submerged topography. An initial SBET was created in POSPAC MMS 8.1 and used in RiProcess which applies pre-calibrated angular misalignment corrections of scanner position to extract the raw point cloud into geo-referenced LAS files. These files were inspected for sensor malfunctions and then passed through automated classification routines (TerraScan) to develop a rough topobathymetric ground model for an initial assessment of bathymetric coverage.

To correct the continuous onboard measurements of the aircraft position recorded throughout the missions, QSI concurrently conducted multiple static Global Navigation Satellite System (GNSS) ground surveys (1 Hz recording frequency) over established monuments located in or around the project area. After the airborne survey, the static GPS data were triangulated with nearby Continuously Operating Reference Stations (CORS) using the Online Positioning User Service (OPUS) for precise positioning. Multiple independent sessions over the same monument were processed to confirm antenna height measurements and to refine position accuracy. QSI then resolved kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. A final smoothed best estimate trajectory (SBET) was developed that blends post-processed aircraft position with attitude data. Sensor head position and attitude are calculated throughout the survey. The SBET data are used extensively for laser point processing. The software Trimble Business Center v.3.90, Blue Marble Geographic Calculator 2017, and PosPac MMS 8.1 SP3 are used for these processes.

QSI used RiProcess 1.8.5 to calculate laser point positioning of the Riegl VQ-880G data by associating SBET positions to each laser point return time, scan angle, intensity, etc. A raw laser point cloud is created in Riegl data format and erroneous points are filtered. Within RiProcess the RiHydro tool was used to classify water surface and create a water surface model. QSI used the green water surface points and as needed NIR water surface points to create water surface models. These models are used in the RiHydro refraction tool to determine angle of incidence and ranging time under water. They are created for a single swaths to ensure temporal differences and wave or water surface height variations between flight lines do not impact the refraction correction of the bathymetric data. All lidar data below water surface models were classified as water column and corrected for refraction. The green laser light travels at slower speed in water than air and its direction of travel is changed when entering water. The refraction tool corrects positioning of under water points by adjusting the ranging distance in water and horizontal position change due to the angle of refraction. Using raster-based QC methods, the output data is verified to ensure the refraction tool functioned properly. QSI used their proprietary LASMonkey refraction tool to correct some refraction in back bay/inland pond areas where RiHydro refraction was found to be inadequate.

Once all green data had been refracted by flight line all data was exported to LAS 1.2 format and are combined into 500 m x 500 m tiles. Data was then further calibrated using TerraScan, TerraModeler, and TerraMatch. QSI used custom algorithms in TerraScan to create the initial ground/submerged topography surface. Relative accuracy of the green swaths was compared to overlapping and adjacent swaths and verified through the use Delta-Z (DZ) orthos created using QSI's DZ Ortho creator. Absolute vertical accuracy of the calibrated data was assessed using ground RTK survey data and complete coverage was again verified.

QSI then performed manual editing to review all classification and improve the final topobathy surface. QSI's LasMonkey was used to update LAS header information, including all projection and coordinate reference system information. The final LiDAR data are in LAS format 1.2 and point data record format 3.

The classification scheme is as follows:

1-Unclassified

2-Ground

7-Noise

19-Overlap Default

20-Overlap Ground

21-Overlap Water Column

25-Water Column

26-Bathymetric Bottom or Submerged Topography

29-Submerged feature

30-Submerged Aquatic Vegetation*

31-Temporal Bathymetric Bottom

*Class 30 was submerged aquatic vegetation QSI identified within the bathymetric void shapes that precluded bathymetric bottom returns.

The National Geodetic Survey reclassied points in classes 19 (overlap default), 20 (overlap ground), and 21 (overlap water column) to 1(unclassified)

The final classification scheme is as follows:

1-Unclassified

2-Ground

7-Noise

25-Water Column

26-Bathymetric Bottom or Submerged Topography

29-Submerged feature

30-Submerged Aquatic Vegetation

31-Temporal Bathymetric Bottom

The NOAA Office for Coastal Management (OCM) received files in las format. The files contained lidar elevation and intensity measurements of the Potomac River, Chesapeake Bay area.. OCM performed the following processing on the data for Digital Coast storage and provisioning purposes: 1. Converted from UTM Zone 17 to geographic coordinates 2. Sorted by gps time 3. Compressed the data using laszip