Data Management Plan (Deprecated)

Original Data Collection:

Dewberry collected 822 square miles of lidar data in Pasco County, Florida. The nominal pulse spacing for this project was 1 point every 0.25 meters or a nominal pulse density of 8 points per square meter. Dewberry used proprietary procedures to classify the LAS according to project specifications: 1-Unclassified, 2-Ground, 6-Building Rooftops, 7-Low Noise, 9-Water, 17- Bridge Decks, 18-High Noise. Geometrically unreliable points, ground points within 2 feet of breaklines, and ground points within 3 feet of building rooftops have been identified with the Withheld Flag. Overage points have been identified with the Overlap Flag. Final lidar deliverables are in LAS v1.4. The data were tiled according to the Florida Statewide Lidar Index tiling scheme with each tile covering an area of 5,000 feet by 5,000 ft.

In addition to the lidar point data, bare earth Digital Elevation Models (DEMs) at a 2.5 ft grid spacing, created from the lidar point data, are also available from the NOAA Digital Coast Data Access Viewer (DAV). A link to the bare earth DEM data is provided in the URL section of this metadata record.

Lineage Statement:
The NOAA Office for Coastal Management (OCM) received the Pasco County, FL lidar data from the Southwest Florida Water Management District (SWFWMD). NOAA OCM processed the data to make it available for custom downloads from the NOAA Digital Coast Data Access Viewer and for bulk downloads from AWS S3.

Process Steps:

• 2018-03-01 00:00:00 - 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.
• 2018-08-01 00:00:00 - 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
• 2024-09-12 00:00:00 - 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

Data is available online for bulk and custom downloads.

Data is backed up to cloud storage.