Data Management Plan

Leading Edge Geomatics (LEG) collected 3451 square miles in Puerto Rico. The nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground, 7-Low Noise, 9-Water, 10-Ignored Ground due to breakline proximity, 17-Bridges, 18-High Noise. Dewberry produced 3D breaklines and combined these with the final lidar data to produce seamless hydro flattened DEMs for the project area. The data was formatted according to the USNG tile naming convention with each tile covering an area of 1,500 meters by 1,500 meters. A total of 4,714 tiles were produced for the entire Puerto Rico project. An interim delivery was submitted to USGS in December 2016. There are 2,048 tiles in the tile grid, 1,995 LAS files, and 1,941 DEMs included in this second delivery. The discrepancy between the tile count and the LAS/DEM files is because several tiles cover only open water.

Process Steps:

• 2017-10-01 00:00:00 - Data for the Puerto Rico Lidar project was acquired by Leading Edge Geomatics, Inc (LEG). The project area included approximately 3,451 contiguous square miles or 8,938 square kilometers for Puerto Rico and smaller municipal islands. Lidar sensor data were collected with the Riegl 680i and Riegl 780 lidar systems. The data was delivered in the Puerto Rico State Plane coordinate system, meters, horizontal datum NAD83 (2011), vertical datum PRVD02, Geoid 12B. The lidar data were acquired over two different acquisition campaigns. The first campaign occurred from January 26, 2016 through May 15, 2016 and acquired two thousand three hundred sixteen (2,316) square miles of topographic lidar data. The second campaign occurred from December 8, 2016 through March 16, 2017 and acquired one thousand seven hundred seventy nine (1,779) square miles of topographic lidar data. Deliverables for the project included a raw (unclassified) calibrated lidar point cloud, survey control, and a final acquisition/calibration report. 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. Process steps to achieve this are as follows: Rigorous lidar calibration: 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 25 miles. 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. The withheld and overlap bits are set and 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.
• 2017-11-01 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 is tiled according to project specifications (1,500 m x 1,500 m). The tiled data is then opened in Terrascan where Dewberry classifies 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. Overage points are then identified with the overlap bit. 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. 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 and bridge deck 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, points that are within 1x NPS or less of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. 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. The data was classified as follows: Class 1 = Unclassified. This class includes vegetation, buildings, noise etc. Class 2 = Ground Class 7= Low Noise Class 9 = Water Class 10 = Ignored Ground due to breakline proximity Class 17 = Bridge Decks Class 18 = High Noise The LAS header information was verified to contain the following: Class (Integer) Adjusted GPS Time (0.0001 seconds) Easting (0.003 m) Northing (0.003 m) Elevation (0.003 m) Echo Number (Integer) Echo (Integer) Intensity (16 bit integer) Flight Line (Integer) Scan Angle (degree)
• 2018-11-14 00:00:00 - Data were obtained via ftp from USGS. Data were converted from Puerto Rico state plane to geographic coordinates and the geoid12b model was removed to obtain ellipsoid heights. The data was then ingested into the Digital Coast Data Access Viewer.

Data is available online for custom downloads