2018 SWFWMD Lidar: Pasco County, 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, 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.

Link to custom download, from the Data Access Viewer (DAV), the raster Digital Elevation Model (DEM) data that were created from this lidar data set.

Link to the dataset breaklines.

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.

This kmz shows the lidar coverage for the 2018 lidar acquisition for Pasco County, FL.

Dewberry collected 822 square miles of lidar data in Pasco County, Florida. Lidar data were collected with the Riegl VQ-1560i lidar system in conjunction with a high accuracy airborne GPS and IMU unit. Sixteen missions were flown between January 14, 2018 and January 25, 2018 at an average flying height of 1300 meters above mean ground level. A maximum of five base stations were used and the maximum baseline or distance from base stations during acquisition never exceeded 40 kilometers. Maximum GPS PDOP allowed was 2.5. The flight lines were planned with 55% overlap with a nominal swath width on the ground of approximately 4800 feet and distance between flight lines approximately 1900 feet. The missions were planned with a nominal pulse spacing of 0.35 m. The Riegl VG-1560i is capable of capturing an unlimited number of returns per pulse.

Boresight calibration flights were performed in Wauchula, Florida and cross flights were flown as part of every mission. The calibration process considered all errors inherent with the equipment including errors in GPS, IMU, and sensor specific parameters. Adjustments were made to achieve a flight line to flight line data match (relative calibration) and subsequently adjusted to control for absolute accuracy. All sources of error such as the sensor's ranging and torsion parameters, atmospheric variables, GPS conditions, and IMU offsets were analyzed and removed to the highest level possible. This method addresses all errors, both vertical and horizontal in nature. Ranging, atmospheric variables, and GPS conditions affect the vertical position of the surface, whereas IMU offsets and torsion parameters affect the data horizontally. The horizontal accuracy is proven through repeatability: when the position of features remains constant no matter what direction the plane was flying and no matter where the feature is positioned within the swath, relative horizontal accuracy is achieved.

Absolute horizontal accuracy is achieved through the use of differential GPS with base lines shorter than 40 kilometers. The base station is set at a temporary monument that is 'tied-in' to the CORS network. The same position is used for every lift, ensuring that any errors in its position will affect all data equally and can therefore be removed equally.

Vertical accuracy is achieved through the adjustment to ground control survey points within the finished product. Although the base station has absolute vertical accuracy, adjustments to sensor parameters introduces vertical error that must be normalized in the final (mean) adjustment.

Riegl Riprocess, Applanix POSPac MMS v8, Microstation Connect, and the Terra suite (Terrascan, Terramodel, and Terramatch) were used to process and calibrate the swath data.

After calibration is performed, final point density, spatial distribution of the lidar point cloud, within swath relative vertical accuracy (hard surface repeatability), between swath relative vertical accuracy (overlap relative accuracy), horizontal alignment, and absolute vertical accuracy (Fundamental Vertical Accuracy) is verified on the final swath data. See the final Report of Survey for results from these tests and verifications.

Dewberry utilizes a variety of software suites for inventory management, classification, and data processing. All lidar related processes begin by importing the data into the GeoCue task management software. The swath data are tiled according to project specifications (5,000 ft x 5,000 ft). The tiled data are then opened in Terrascan where Dewberry identifies edge of flight line points that may be geometrically unusable with the withheld bit. These points are separated from the main point cloud so that they are not used in the ground algorithms. Dewberry then uses proprietary ground classification routines to remove any non-ground points and generate an accurate ground surface. The ground routine consists of three main parameters (building size, iteration angle, and iteration distance); by adjusting these parameters and running several iterations of this routine an initial ground surface is developed. The building size parameter sets a roaming window size. Each tile is loaded with neighboring points from adjacent tiles and the routine classifies the data section by section based on this roaming window size. The second most important parameter is the maximum terrain angle, which sets the highest allowed terrain angle within the model. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. Points within the building rooftop polygons compiled by Dewberry are then classified to class 6. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a supervisor manual inspection is completed on a percentage of the classified tiles based on the project size and variability of the terrain. After the ground classification, bridge deck, and rooftop corrections are completed, the dataset is processed through a water classification routine that utilizes breaklines compiled by Dewberry to automatically classify hydrographic features. The water classification routine selects ground points within the breakline polygons and automatically classifies them as class 9, water. During this water classification routine, ground points that are within 2 feet of the hydrographic features are flagged with the withheld bit. Ground points within 3 feet of the building rooftops are flagged with the withheld bit. Overage points are then identified with the overlap bit. A final QC is performed on the data. All headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

These data were classified as follows:

Class 1 = Unclassified. This class includes vegetation, buildings, noise etc.

Class 2 = Ground

Class 6 = Building Rooftops

Class 7 = Low Noise

Class 9 = Water

Class 17 = Bridge Decks

Class 18 = High Noise

The NOAA Office for Coastal Management (OCM) received the Pasco County, FL lidar data from the Southwest Florida Water Management District (SWFWMD). The data were in Florida State Plane West NAD83(2011), US survey feet coordinates and in NAVD88 (Geoid12B) elevations in feet. The data were classified as: 1 - Unclassified, 2 - Ground, 6 - Building Rooftops, 7 - Low Noise, 9 - Water, 17- Bridge Decks, 18 - High Noise. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 6, 7, 9, 17, 18.

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

1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.

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

a. Convert the files from FL State Plane West NAD83(2011), US survey feet coordinates to geographic coordinates

b. Convert the files from NAVD88 (Geoid12B) elevations to ellipsoid (NAD83 2011) elevations

c. Convert the files from elevations in feet to meters

d. Assign the geokeys, to sort the data by gps time and zip the data to database and to AWS S3