2019 - 2020 USGS Lidar: Hurricane Florence, NC

NC_HurricaneFlorence_1 (Work Unit 186825) covering approximately 2543 total square miles in 13 counties: Bladen, Brunswick, Columbus, Dillon, Duplin, Hoke, Horry, Marlboro, New Hanover, Pender, Robeson, Sampson, Scotland

Dates of collection for NC_HurricaneFlorence_1 (Work Unit 186825)

NC_HurricaneFlorence_2 (Work Unit 196896) covering approximately 1717 total square miles in 8 counties: Brunswick, Carteret, Craven, Duplin, Jones, New Hanover, Onslow, Pender

Dates of collection for NC_HurricaneFlorence_2 (Work Unit 196896)

NC_HurricaneFlorence_3 (Work Unit 198494) covering approximately 1124 total square miles in 9 counties: Craven, Duplin, Greene, Jones, Lenoir, Onslow, Pitt, Wayne, Wilson

Dates of collection for NC_HurricaneFlorence_3 (Work Unit 198494)

NC_HurricaneFlorence_4 (Work Unit 208939) covering approximately 516 total square miles in 5 counties: Franklin, Granville, Mecklenburg, Vance, Warren

Dates of collection for NC_HurricaneFlorence_4 (Work Unit 208939)

NC_HurricaneFlorence_5 (Work Unit 210877) covering approximately 2152 total square miles in 7 counties: Cumberland, Duplin, Harnett, Johnston, Sampson, Wake, Wayne

NC_HurricaneFlorence_5 (Work Unit 210877)

NC_HurricaneFlorence_6 (Work Unit 224678) covering approximately 2239 total square miles in 9 counties: Beaufort, Bertie, Dare, Edgecombe, Hyde, Martin, Pitt, Tyrrell, Washington

Dates of collection for NC_HurricaneFlorence_6 (Work Unit 224678)

NC_HurricaneFlorence_7 (Work Unit 217033) covering approximately 1834 total square miles in 8 counties: Bladen, Columbus, Cumberland, Dillon, Hoke, Horry, Robeson, Sampson

NC_HurricaneFlorence_7 (Work Unit 217033)

NC_HurricaneFlorence_8 (Work Unit 218544) covering approximately 1181 total square miles in 11 counties: Brunswick, Franklin, Granville, Halifax, Johnston, Mecklenburg, Nash, Wake, Warren, Wayne, Wilson

Dates of collection for NC_HurricaneFlorence_8 (Work Unit 218544)

NC_HurricaneFlorence_9 (Work Unit 220804) covering approximately 2546 total square miles in 17 counties: Bertie, Camden, Chesapeake, Chowan, Currituck, Edgecombe, Gates, Greensville, Halifax, Hertford, Martin, Northampton, Pasquotank, Perquimans, Southampton, Suffolk, Virginia Beach

Dates of collection for NC_HurricaneFlorence_9 (Work Unit 220804)

NC_HurricaneFlorence_10 (Work Unit 225700) covering approximately 1833 total square miles in 11 counties: Beaufort, Carteret, Craven, Duplin, Greene, Jones, Lenoir, Pamlico, Pitt, Wayne, Wilson

Dates of collection for NC_HurricaneFlorence_10 (Work Unit 225700)

NC_HurricaneFlorence_11 (Work Unit 225793) covering approximately 2120 total square miles in 11 counties: Brunswick, Edgecombe, Franklin, Greene, Greensville, Halifax, Nash, Northampton, Pitt, Warren, Wilson

Dates of collection for NC_HurricaneFlorence_11 (Work Unit 225793)

Create custom data files by choosing data area, product type, map projection, file format, datum, etc. A new metadata will be produced to reflect your request using this record as a base. Change to an orthometric vertical datum is one of the many options.

Bulk download of data files in LAZ format, geographic coordinates, orthometric heights. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer.

Link to the reports, breaklines, metadata, and spatial metadata.

Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used.

Link to the NV5 lidar report for NC_HurricaneFlorence_1

Link to the NV5 lidar report for NC_HurricaneFlorence_2

Link to the NV5 lidar report for NC_HurricaneFlorence_3

Link to the NV5 lidar report for NC_HurricaneFlorence_4

Link to the NV5 lidar report for NC_HurricaneFlorence_5

Link to the NV5 lidar report for NC_HurricaneFlorence_6

Link to the NV5 lidar report for NC_HurricaneFlorence_7

Link to the NV5 lidar report for NC_HurricaneFlorence_8

Link to the NV5 lidar report for NC_HurricaneFlorence_9

Link to the NV5 lidar report for NC_HurricaneFlorence_10

Link to the NV5 lidar report for NC_HurricaneFlorence_11

Raw Data and Boresight Processing: The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used NV5 Geospatial proprietary and commercial software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie, and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technicians then analyzed the results and made any necessary additional adjustment until it was acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps matched for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 7 cm RMSEz within individual swaths and less than or equal to 10 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed checkpoints after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met.

LAS Point Classification: The point classification was performed as described below. The bare earth surface was manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface was finalized, it was then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) LiDAR data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Water (ASPRS Class 9) using proprietary tools. A buffer of 0.5 meter was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. Any noise that was identified either through manual review or automated routines was classified to the appropriate class ( ASPRS Class 7 and/or ASPRS Class 18) followed by flagging with the withheld bit. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS files for point cloud data. NV5 Geospatial proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.

Original point clouds in LAS/LAZ format were restructured as Entwine Point Tiles and stored on Amazon Web Services. The data were re-projected horizontally to WGS84 web mercator (EPSG 3857) and no changes were made to the vertical (NAVD88 GEOID18 meters).

The NOAA Office for Coastal Management (OCM) created references to the Entwine Point Tiles (EPT) that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the point cloud as it resides on Amazon Web Services (AWS) under the usgs-lidar-public container.

These are the AWS URLs being accessed:

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_1_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_2_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_3_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_4_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_5_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_6_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_7_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_8_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_9_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_10_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NC_HurricaneFlorence_11_2020/ept.json