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 20140717 - 20140809 in twenty two missions. The missions flown on 20140718, 20140719, 20140720, 20140721, 20140722, 20140723, 20140724, 20140725, 20140731, 20140806, and 20140807 represent the Low Water missions and the missions flown on 20140717, 20140718, 20140719, 20140722, 20140723, 20140725, 20140731, 20140801, 20140807, 20140808, and 20140809 represent the 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. The original project consisted of approximately 100 square miles along the Atlantic Coast of Rhode Island. The full project including buffered area and all flightline coverage is approximately 205 square miles. This dataset represents a contiguous area covering 2104 - 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

Phone: 301-713-2663

Process Steps:

• 2015-07-02 00:00:00 - Data for the NOAA Post Hurricane Sandy Topobathymetric lidar Mapping for Shoreline Mapping project was acquired by Quantum Spatial (QS) using a Riegl VQ-820G Topobathy lidar system. All delivered lidar data is referenced to: Horizontal Datum-NAD83 (2011) epoch: 2010 Projection-UTM Zone 19 Horizontal Units-meters Vertical Datum-NAD83 (2011) epoch: 2010 (ellipsoid heights) Vertical Units-meters This dataset encompasses 2104 500m x 500m tiles in Rhode Island. 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.
• 2015-07-02 00:00:00 - QS 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 using QS's DZ Ortho creator. QS 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. QS 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. 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 travelled) 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. QS used algorithms in TerraScan to create the initial ground/submerged topography surface. QS 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. All necessary edits were applied to the dataset. QS'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 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 QS then produced a final set of DZ orthos using the final ground (2) and submerged topography (26) classes. All data is then verified by an Independent QC department within QSI. The independent QC is performed by separate analysts who do not perform manual classification or editing. The independent QC involves quantitative and qualitative reviews.
• 2015-12-01 00:00:00 - Lidar data were received from NOAA NGS by NOAA OCM in LAS 1.2 format. Noise classes were dropped from the dataset (classes 7,18,22,23,24). Duplicate points were removed on a tile by tile basis using lasduplicate. Data were transformed from UTM zone 19 to geographic coordinates. Data points below -55 meters (approximately 25 meters below sea level) were considered noise and removed. The files were compressed to LAZ format with laszip and ingested into the Digital Coast Data Access Viewer system for distribution.

This data can be obtained on-line at the following

https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4976;