Data Management Plan

These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using a Riegl VQ820G system. The data were acquired from 20140108 - 20140522 in four missions. The missions flown on 20140108 and 20140109 represent Low Water missions and the missions flown on 20140516 and 20140522 represent High Water (everything outside of MLLW tidal requirements) missions. The data includes topobathy data in an LAS 1.2 format file classified as unclassified (1), ground (2), topo noise (7), refracted High Water data landward of the MLLW land/water interface (18), bathy noise (22), noise as defined by the sensor (23), refracted sensor noise (24), water column (25), bathymetric bottom or submerged topography (26), water surface (27), International Hydrographic Organization (IHO) S-57 objects (30), and temporal bathy bottom (31) in accordance with project specifications. Several of the noise classes were filtered out prior to distribution on the Digital Coast. The full project consists of 2,775 square miles along the Atlantic Coast from New York to South Carolina. This dataset represents a contiguous area covering a portion of acquisition block 1 to 140 with 500 m x 500 m lidar tiles.

Original contact information:

Contact Org: National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division

Title: Chief, Remote Sensing Division

Phone: 301-713-2663

Process Steps:

• 2014-11-01 00:00:00 - Data for the NOAA Post Hurricane Sandy Topobathymetric LiDAR Mapping for Shoreline Mapping project was acquired by Quantum Spatial (QS) using three Riegl VQ-820G Topobathy LiDAR systems. All delivered LiDAR data were referenced to: Horizontal Datum-NAD83 (2011) epoch: 2010 Projection-UTM Zone 18 Horizontal Units-meters Vertical Datum-NAD83 (2011) epoch: 2010 (ellipsoid heights) Vertical Units-meters This dataset encompasses 46 500m x 500m tiles in North Carolina. Green LiDAR data was acquired with the Riegl sensor 9999609 and NIR LiDAR data (for water surface model creation that is used during refraction of the green bathymetric data) was acquired with the Leica ALS 50-II sensor 94. QS reviewed all acquired flight lines to ensure complete coverage and positional accuracy of the laser points. To correct the continuous onboard measurements of the aircraft position recorded throughout the missions, QS concurrently conducted multiple static Global Navigation Satellite System (GNSS) ground surveys (1 Hz recording frequency) over each monument. 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. QS then resolved kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. A 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.3.10, Blue Marble Geographic Calculator 2013, and PosPac MMS 6.2 SP2 are used for these processes. Next, QS used RiProcess 1.6 to calculate laser point positioning of the Riegl VQ-820G 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. Erroneous points are filtered and then automated line-to-line calibrations are performed for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calibrations are calculated on matching surfaces within and between each line and results are applied to all points in a flight line. Every flight line is used for relative accuracy calibration. This same process is performed on the NIR data using IPAS TC 3.1/Inertial Explorer 8.5 to generate the SBET and Leica ALSPP 2.75 to apply the SBET to the raw scan range files. Green data and NIR data are calibrated together using TerraScan, TerraModeler, and TerraMatch. Accuracy of the calibrated data is assessed using ground RTK survey data. All data are then exported to LAS 1.2 format and are ready for processing and editing. QS also creates an initial product call Quick Look Coverage Maps. These Quick Looks files are not fully processed data or final products. The collected LiDAR data is immediately processed in the field by QS 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 is created in POSPAC MMS 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 are inspected for sensor malfunctions and then passed through automated classification routines (TerraScan) to develop an initial topo-bathymetric ground model. The ground models are posted to the Sandy project portal where they are further inspected by NOAA to determine adequate coverage of submerged topography for each flight mission of collected LiDAR data.
• 2014-11-01 00:00:00 - Dewberry received the calibrated green and NIR tiles and verified complete coverage. Relative accuracy of the green swaths compared to overlapping and adjacent green swaths as well as the relative accuracy of green swaths compared to overlapping and adjacent NIR swaths was verified through the use Delta-Z (DZ) orthos created in GeoCue software. Dewberry used E-Cognition to create 2D breaklines representing land/water interfaces. These 2D breaklines were manually reviewed and adjusted where necessary to ensure all well-defined hydrographic features (at 1:1200-scale) were represented with breaklines. Using TerraScan, all green LiDAR data within breaklines are classified as water column and a sub-set of these points meeting specific criteria are classified as green water surface points. Using TerraScan, all NIR LiDAR data within breaklines are classified as water column and a sub-set of these points meeting specific criteria are classified as NIR water surface points. Dewberry used the green water surface points and NIR water surface points to create water surface models. These models are used in the refraction tool to determine the depth of bathymetric points and are created for single swaths to ensure temporal differences and wave or water surface height variations between flight lines do not impact the refraction of the bathymetric data.
• 2014-11-01 00:00:00 - Using the SBET data and the water surface models, all green LiDAR data classified as water column (data within the breaklines) is refracted using Dewberry's LiDAR Processor (DLP). 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 tool corrects for this difference by adjusting the depth (distance traveled) and horizontal position (change of angle/direction) of the green LiDAR data. Using statistics and limited manual review, the output data is verified to ensure the refraction tool functioned properly. Once all green data has been refracted by flight lines, all flight lines covering each tile are combined into a single 500 m x 500 m tile. As the various flight lines may include data collected at Mean Lower Low Water (MLLW) and higher water (HW), which includes everything that is outside the range of MLLW, any HW refracted data points landward of the MLLW land/water interface were classified to class 18 to ensure these HW bathymetric points were not used when MLLW exposed ground points exist in those locations. Dewberry used algorithms in TerraScan to create the initial ground/submerged topography surface. Dewberry then performed manual editing to review and improve the final topobathy surface. Locations of temporal differences were resolved using the Temporal Difference Decision Tree approved by NOAA. Polygons marking the locations of large temporal differences are provided as part of the deliverables. All LiDAR data was peer-reviewed. Dewberry's internal QC also included creating void polygons for use during review. All necessary edits were applied to the dataset. GeoCue software 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 final classification scheme is as follows: 1-Unclassified 2-Ground 7-Topo Noise 18-Refracted High Water data landward of the MLLW land/water interface 22-Bathy Noise 23-Sensor Noise (as defined by the sensor using Riegl's noise classifier) 24-Refracted Sensor Noise 25-Water Column 26-Bathymetric Bottom or Submerged Topography 27-Water Surface 30-International Hydrographic Organization (IHO) S-57 objects 31-Temporal Bathymetric Bottom All data is then verified by an Independent QC department within Dewberry. The independent QC is performed by separate analysts who do not perform manual classification or editing. The independent QC involves quantitative and qualitative reviews. Dewberry then produced the final void layer and final set of DZ orthos using the final ground (2) and submerged topography (26) classes.
• 2015-05-01 00:00:00 - Data were received by NOAA Office for Coastal Management from NOAA NGS. Data were reprojected from UTM 18 to geographic coordinates (NAD83(2011)). Points in classes 7, 18, 22, 23, and 24 were eliminated and class 29 (submerged object not otherwise specified) was moved to class 28 using las2las (version 150406) from LAStools. Data were compressed to LAZ format using laszip.