2014 NOAA NGS Topobathy Lidar: Connecticut

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This graphic shows the lidar footprint for the 2014 NOAA NGS topobathy lidar project in Connecticut and a small part of Rhode Island.

Date that the source FGDC record was last modified.

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Data for the CT Topobathy LiDAR project was acquired by NOAA using a Riegl VQ-820G Topobathy LiDAR systems. 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 637 500m x 500m tiles in Connecticut and Rhode Island. Both green LiDAR data and NIR LiDAR data (used for water surface model creation that is incorporated during refraction of the green bathymetric data) was acquired.

NOAA acquired and calibrated the LiDAR data.

Dewberry received the calibrated green and NIR data 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.

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

Dewberry then produced the final void layer and 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 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.

The NOAA Office for Coastal Management (OCM) received 637 files in las format from NGS. The files contained lidar elevation and intensity measurements. The data were in UTM Zone 19 projection coordinates and ellipsoid heights in meters. OCM performed the following processing for data storage and Digital Coast provisioning purposes: 1. LAS files were compressed to laz format using laszip. 2. Data were checked for elevation outliers and points with elevation values less than -100 m were dropped from the data set. 3. Class 18 points (refracted high water data landward of the MLLW land/water interface) were reclassified to class 19 and class 30 points (IHO S-57 objects) were reclassified to class 31. 4. Noise points classified as 22 (bathy noise), 23 (noise as defined by the sensor), and 24 (refracted sensor noise) were dropped from the data set. NOTE: Because three noise classes were dropped from the data set, 14 files which only contained noise points, were dropped from the original number of 637 files. Number of files in the Digital Coast are 623. 5. Data were converted from UTM Zone 19 coordinates to geographic coordinates.