2020 USGS Lidar: Hurricane Michael, FL

Dates of collection for Work Unit 229804 (Block 7: Bay (portion), Calhoun (portion) , Franklin (portion) , Gulf, Liberty (portion), and Washington (portion)) counties,

Dates of collection for Work Unit 188515 (Block 1, Wakulla and Jefferson Counties)

Dates of collection for Work Unit 212018 (Block 2, Wakulla and Jefferson Counties)

Dates of collection for Work Unit 217056 (Block 4, Leon, Gadsden, Liberty and Franklin Counties)

Dates of collection for Work Unit 227988 (Block 5, Washington, Holmes, Walton Counties)

Dates of collection for Work Unit 229801 (Block 6, Bay, Calhoun, Franklin, Gadsden, Gulf, Holmes, Jackson, Liberty, and Walton Counties)

Dates of collection for Work Unit 216141 (Block 3, Leon, Gadsden, Liberty, and Franklin Counties)

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, based on a horizontal datum/projection of Florida State Plane North, NAD83(2011), US feet and a vertical datum of NAVD88 (GEOID12B) and units in feet. This url links to the USGS copy of the files, from which the Entwine Point Tile files originated. These have not been reviewed by OCM and the link is provided here for convenience.

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.

Link to the USGS project report.

Link to the reports, breaklines, and spatial metadata.

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

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

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

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

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

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

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

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 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 technician then analyzed the results and made any necessary additional adjustment until it is 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 match 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 6 cm RMSEz within individual swaths and less than or equal to 8 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 check points after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met. Point classification was performed according to USGS Lidar Base Specification 2.1, and breaklines were collected for water features. Bare earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening. Synthetic points generated by Riegl processing software are present in this dataset and were used to fill MTA zones. Please see the project report for more details on the synthetic points.

LAS Point Classification: The point classification is performed as described below. The bare earth surface is then manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface is finalized, it is then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) lidar data inside of the WATERBODY, COASTAL and Double Line Drain hydro flattening breaklines were then classified to water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 2.29 feet was also used around each hydro-flattened feature as well as Single Line Drains to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All WATERBODY Island, COASTAL 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. Buildings (ASPRS Class 6) were classified through an automated process in the initial ground routine. All data was 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. The withheld bit was set on the withheld points previously identified in TerraScan before the ground classification routine was performed. The withheld bit was set on class 7 and class 18 in TerraScan after all classification was complete. These LAS files contain synthetic points. These points were generated by the Riegl processsing software to fill Multiple Time Around (MTA) zones, which are a physical phenomenon that exists in any time of flight lidar system which has multiple pulses in air and wishes to record all of them seamlessly without range gate limitations. The MTA zones only exist in narrow bands of ranges and typically are dependent on flight planning parameters and project area topography. Dewberry proprietary software was then used to create the deliverable industry-standard LAS files for both the All Point Cloud Data and the Bare Earth. Dewberry 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.

Data was tested at 0.27 meter nominal pulse spacing and 14.22 points per square meter (ppsm). The average density was tested on the LAS data using geometrically reliable (withheld and noise points excluded) first-return points. (A)NPD was tested using rasters which produce the average number of points within each cell.

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 the vertical units were converted to meters (NAVD88 GEOID12B).

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/FL_HurricaneMichael_1_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_HurricaneMichael_2_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_HurricaneMichael_3_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_HurricaneMichael_4_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_HurricaneMichael_5_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_HurricaneMichael_6_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_HurricaneMichael_7_2020/ept.json (Processed to DAV Feb. 7, 2024)