2015 NOAA NGS Topobathy Lidar DEM: Puerto Rico

Create custom data files by choosing data area, product type, map projection, file format, datum, etc.

Simple download of data files.

Date that the source FGDC record was last modified.

Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details.

Partial upload of Positional Accuracy fields only.

Partial upload to move data access links to Distribution Info.

Data for the Puerto Rico 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 5,721 500m x 500m tiles in Puerto Rico 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

Dewberry made a copy of the final lidar data and transformed the ellipsoid heights into orthometric heights referenced to NAVD88 using Geoid 12B. lidar data classified as ground (2) and submerged topography (26) were then 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 DEM deliverables will also include tiled DEMs that incorporate the use of a void layer.

Void DEM dataset- The void layer was created in Global Mapper where every bathy bottom point was used to create a grid. The distance or threshold that sets how far Global Mapper can interpolate around each bathy bottom point was set as 2. The higher the interpolation threshold, the more bathy bottom points are connected to create a continuous surface in the Global Mapper grid with fewer areas of NoData. The NoData areas in the Global Mapper grids are exported to polygons. Void polygons greater than 9 square meters are imported into Arc 10.1 Geodatabases where they are incorporated into the terrains as erase features. When the terrains are exported to raster, the void polygons used as an erase in the terrain remain as areas of NoData.

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.