2019 NOAA NGS Topobathy Lidar DEM: U.S. Virgin Islands

Data only covers coastal area.

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Topobathymetric Lidar Data Report

Data for the Puerto Rico and the US Virgin Islands project was acquired by Leading Edge Geomatics using a Riegl VQ-880-G II sensor. All delivered US Virgin Islands lidar data is referenced to:

Horizontal Datum-NAD83 (2011) epoch: 2010

Projection-UTM Zone 20 North

Horizontal Units-meters

Vertical Datum-NAD83 (2011)

Vertical Units-meters

Both green lidar data and NIR lidar data were acquired.

Leading Edge Geomatics acquired, calibrated, and performed refraction correction for 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 of Delta-Z (DZ) orthos created in GeoCue software. Intraswath relative accuracy was verified using Quick Terrain Modeler. Profiles of elevated planar features, such as roofs, were used to verify horizontal alignment between overlapping swaths. Dewberry then verified absolute vertical accuracy of the swath data prior to full-scale production.

Dewberry used algoritms in TerraScan to create the intial ground/submerged topography surface.

Dewberry used rasterized aggregate extents of refracted points to create automated 2-D breaklines with LAStools and ArcGIS. Light travels at different speeds in air versus water and its speed and direction of travel change when it enters the water column. The refraction correction process accounts for this difference by adjusting the depth (distance traveled) and horizontal position (change of angle/direction) of the lidar points acquired within water. These breaklines delineate areas where the refraction correction was applied to the lidar data by Riegl's automated refraction correction software based on the software's detection of water. Where the automated process missed discrete water bodies that did not contain valid bathymetry (submerged topography) data, breaklines were manually drawn and added to a separate feature class to ensure the correct classification of the point cloud.

Dewberry used the 2-D refraction extents and additional bathy features to classify the bathymetric bottom and ground points properly in TerraScan.

All lidar data was peer-reviewed. Dewberry's QAQC 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 1.4 and point data record format 6.

The final classificaton scheme is as follows:

0-Created, never classified

1-Unclassified

2-Ground

7-Noise

40-Bathymetric bottom

41-Water surface

42-Derived water surface

43-Submerged object, not otherwise specified

44-International Hydrograpic Organization (IHO) S-57 objects

45-No bottom found (bathymetric lidar point for which no detectable bottom return was received)

46-Bathymetric bottom temporal changes

All data was then verified by an Independent QC department within Dewberry. The independent QC was performed by separate analysts who did not perform manual classification or editing. The independent QC involved quantitative and qualitative reviews.

Lidar data classified as ground (2) and submerged topography (40) were converted to Esri multipoint format. These multipoints were then used to generate a terrain and the terrain was converted to a raster in IMG format with 1 meter pixel resolution. The terrain and output raster were created over the full project area to reduce edge-matching issues and improve seamlessness. The raster was clipped to the tile grid and named according to project specifications to result in tiled topobathymetric DEMs.

All tiled DEMs incorporate the use of the void polygons. The void polygons represent bathymetric areas with no bathymetric bottom returns and are set as NoData in the DEMs. Void polygon creation is described in the final project report and the void polygon metadata.

A point density layer has been created and provided to NOAA as part of the deliverables. The point density layer is a raster product in IMG format with 1 meter square pixels. The density grid identifies the number of ground and/or bathy bottom points located within each pixel. The pixels in the point density layer align with the pixels in the topobathy DEMs so that the point density layer shows the density of ground/submerged topography points located in each cell that were used to determine elevations for each cell in the topobathy DEMs. Higher density lends itself to higher confidence. The point density layer can be displayed by unique values or classified into desired bins/ranges for analysis over larger areas.

A confidence layer has been created and provided to NOAA as part of the deliverables. The confidence layer is a raster product in IMG format with 1 meter square pixels. The confidence layer provides a standard deviation value for every pixel by calculating the standard deviation of all ground and/or submerged topography lidar points that are located within a single pixel. The confidence layer pixels align to the pixels in the topobathy DEMs. The confidence layer can be displayed by unique values or classified into desired bins/ranges for analysis over larger areas.

The NOAA Office for Coastal Management (OCM) received files in IMG format. OCM performed the following processing on the data for Digital Coast storage and provisioning purposes: 1. Converted from IMG to TIFF