2019 USGS Topobathy Lidar: West Everglades National Park, FL

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 Albers Equal Area, NAD83(2011), meters and a vertical datum of NAVD88 (GEOID12B) and units in meters. This url links to the USGS copy of the files. 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 reports, breaklines, metadata, spatial metadata, and shapefiles.

Link to the USGS project report which provides information about the project, the vertical accuracy results, the point classifications and the sensors used.

Link to the lidar report.

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 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 RMSDz within individual swaths and less than or equal to 8 cm RMSDz or within swath overlap (between adjacent swaths). The sensors utilized on this project had both a NIR laser and a green laser so intraswath analyses, including intraswath polygon generation and review, were performed for each laser separately. The eastern add-on portion of the FL West Everglades NP project area was particularly difficult to align due to relatively few hard surfaces present which could reliably be used during the alignment process. Only one road is present in this add-on area. Due to the nature of relative swath alignment being based off the ground surface within each swath (which in reality is just the lowest plane of points), the process of using the lowest points can introduce some discrepancies due to these assumptions. In some areas one sensor may penetrate the vegetation to a greater extent, resulting in a lower last return surface. The alignment process bases the corrections from the statistical trends found in these offsets. This means that if there is a much greater coverage of vegetated areas than hard surfaces, those vegetated areas can have a much larger impact on the relative alignment of the data. In the case of this area the discrepancy resulted in a misalignment along a portion of the roadway while the vegetated areas nearby show no misalignment. Examining the park road in the east of the project shows that several of the swaths have some bias between the NIR and green swaths (approximately 7-11 cm), but there are 3-4 flightlines with larger offsets approaching 15 cm. Areas along the road exhibiting the most measureable offsets are identified in the provided shapefile, named W_Everglades_NP_Lidar_Interswath_Issues. Please see the project report for more details on interswath and intraswath testing and review. 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed check points to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) is met. Point classification was performed according to USGS Lidar Base Specification 1.3 including the addition of bathy domain classes. Refraction extents (2D) were generated from the refracted lidar points. Topobathy DEMs were generated from the classified point cloud.

Automated grounding was performed using Terrascan software. This routine classifies any obvious low outliers in the dataset to class 7 and high outliers in the dataset to class 18. Points along flight line edges that are geometrically unusable are identified by their scan angle and classified to a separate class so that they will not be used in the initial ground algorithm. These point with higher scan angles will be set to withheld later in the lidar processing. After points that could negatively affect the ground are removed from class 1, the ground layer is extracted from this remaining point cloud. The ground extraction process encompassed in this routine takes place by building an iterative surface model. The final refraction extents are then used to classify ground points within the refraction extents as bathymetric bottom. The refraction extents are also used as part of the classification routines to ensure water surface and water column points are classified correctly. Each tile was then imported into Terrascan and a surface model was created to examine the ground (class 2) and bathy bottom (class 40) classification. Dewberry analysts employ 3D visualization techniques to view the point cloud at multiple angles and in profile to ensure that non-ground points are removed from the ground classification and that class 40 accurately represents submerged topography. Dewberry analysts visually reviewed the surface models and corrected errors in the ground classification such as vegetation, buildings, and bridges that were present following the initial processing conducted by Dewberry. Bridge decks are manually classified to class 17. The withheld bit is set on the points with higher scan angles previously identified in Terrascan before the ground classification routine was performed. After manual classification, the LAS tiles were peer reviewed and then underwent a final QA/QC. After the final QA/QC and corrections, all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in proprietary software and then verified using proprietary Dewberry tools.

Due to two lasers being utilized per each sensor and the high amount of overlap, the average calculated (A)NPD is much hihger than the project requirements of 0.35 meter NPS and 8 points per square meter (ppsm). A portion of this project was funded after the original Task Order, as an add-on. The average calculated (A)NPD of the original AOI is 23 ppsm (0.21 m NPS) and the average (A)NPD of the add-on portion of this AOI is 14 ppsm (0.27 m NPS). 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 calculate the average number of points within each cell.

The NOAA Office for Coastal Management (OCM) downloaded 2601 laz point data files from this USGS site:

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/FL_WestEvergladesNP_2018_B18/FL_WestEvergladesNP_topobathymetric_2018/LAZ/

The data were in Albers Equal Area (NAD83 2011), meters coordinates and NAVD88 (Geoid12B) elevations in meters. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 17 - Bridge Decks, 18 - High Noise, 40 - Bathymetric Bottom, 41 - Water Surface, 45 - No bathymetric bottom found (water column). OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 7, 17, 18, 40, 41, 45.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. Internal OCM scripts were run to check the number of points by classification and by flight ID and the gps, elevation, and intensity ranges.

2. Internal OCM scripts were run on the laz files to:

a. Convert from orthometric (NAVD88) elevations to NAD83 (2011) ellipsoid elevations using the Geoid12B model

b. Convert the laz files from Albers Equal Area (NAD83 2011), meters coordinates to geographic coordinates

c. Assign the geokeys, sort the data by gps time and zip the data to database.