2016 - 2017 USGS Lidar DEM: Puerto Rico

The initial lidar aerial acquisition was conducted from January 26, 2016 through May 15, 2016. However, only eighty percent (80%) of the project areas were surveyed during this acquisition period due to persistent, low lying cloud cover in the southeast region of Puerto Rico. After discussions with local officials, meteorologists, and USGS the project team determined that this cloud cover would persist through the summer and likely into the fall. Therefore, the decision was made to resume the lidar survey in December 2016 when cloud cover was expected to be less impactful. The subsequent lidar survey commenced on December 8, 2016 and was completed on March 16, 2017.

The initial lidar aerial acquisition was conducted from January 26, 2016 through May 15, 2016. However, only eighty percent (80%) of the project areas was surveyed during this acquisition period due to persistent, low lying cloud cover in the southeast region of Puerto Rico. After discussions with local officials, meteorologists, and USGS the project team determined that this cloud cover would persist through the summer and likely into the fall. Therefore, the decision was made to resume the lidar survey in December 2016 when cloud cover was expected to be less impactful. The subsequent lidar survey commenced on December 8, 2016 and was completed on March 16, 2017.

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This graphic displays the footprint for this lidar data set.

Link to data set report.

Link to the survey report.

Link to the breaklines.

Link to custom download the lidar point data from which these raster Digital Elevation Model (DEM) data were created.

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.

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)

Dewberry used GeoCue software to produce intensity imagery and raster stereo models from the source lidar for use in lidargrammetry techniques. Dewberry then produced full point cloud intensity imagery, bare earth ground models, density models, and slope models. These files were ingested into eCognition software, segmented into polygons, and training samples were created to identify water. eCognition used the training samples and defined parameters to identify water segments throughout the project area. Water segments were then reviewed for completeness, separated into project defined feature classes, merged, and smoothed. Elevations derived from a bare earth lidar terrain were applied to each feature for 3D attribution.

The delineation of lakes and ponds and tidal waters, or other water bodies at a constant elevation, was achieved using eCognition software. Lidargrammetry was used to monotonically collect streams and rivers, or features that have gradient 3D elevations. All breaklines were collected according to specifications for the project.

Dewberry digitzed 2D bridge deck polygons from the intensity imagery and used these polygons to classify bridge deck points in the LAS to class 17. As some bridges are hard to identify in intensity imagery, Dewberry then used ESRI software to generate bare earth elevation rasters. Bare earth elevation rasters do not contain bridges. As bridges are removed from bare earth DEMs but DEMs are continuous surfaces, the area between bridge abutments must be interpolated. The rasters are reviewed to ensure all locations where the interpolation in a DEM indicates a bridge have been collected in the 2D bridge deck polygons.

The bridge deck polygons are loaded into Terrascan software. Lidar points and surface models created from ground lidar points are reviewed and 3D bridge breaklines are compiled in Terrascan. Typically, two breaklines are compiled for each bridge deck-one breakline along the ground of each abutment. The bridge breaklines are placed perpendicular to the bridge deck and extend just beyond the extents of the bridge deck. Extending the bridge breaklines beyond the extent of the bridge deck allows the compiler to use ground elevations from the ground lidar data for each endpoint of the breakline. The 3D endpoints of each breakline are used to enforce a continous slope on the ground under the bridge deck along the collected breakline. These breaklines are used in the final DEM production and help to reduce the appearance of bridge saddles.

Breaklines are reviewed against lidar intensity imagery to verify completeness of capture. All breaklines are then compared to ESRI terrains created from ground only points prior to water classification. The horizontal placement of breaklines is compared to terrain features and the breakline elevations are compared to lidar elevations to ensure all breaklines match the lidar within acceptable tolerances. Some deviation is expected between hydrographic breakline and lidar elevations due to monotonicity, connectivity, and flattening rules that are enforced on the hydrographic breaklines. Once completeness, horizontal placement, and vertical variance is reviewed, all breaklines are reviewed for topological consistency and data integrity using a combination of ESRI Data Reviewer tools and proprietary tools. Corrections are performed within the QC workflow and re-validated.

Class 2, ground, lidar points are exported from the LAS files into an Arc Geodatabase (GDB) in multipoint format. The 3D breaklines, Inland Lakes and Ponds, Inland Streams and Rivers, Tidal Water, and bridge breaklines are imported into the same GDB. An ESRI Terrain is generated from these inputs. The surface type of each input is as follows:

Ground Multipoint: Masspoints

Inland Lakes and Ponds: Hard Replace

Inland Rivers and Streams : Hard Line

Tidal: Hard Line

Bridge Breaklines: Hard Line

The ESRI Terrain is converted to a raster. The raster is created using linear interpolation with a 1 meter cell size. The DEM is reviewed with hillshades in both ArcGIS and Global Mapper. Hillshades allow the analyst to view the DEMs in 3D and to more efficiently locate and identify potential issues. Analysts review the DEM for missed lidar classification issues, incorrect breakline elevations, incorrect hydro-flattening, and artifacts that are introduced during the raster creation process.

The corrected and final DEM is clipped to individual tiles. Dewberry uses a proprietary tool that clips the DEM to each tile located within the final Tile Grid, names the clipped DEM to the Tile Grid Cell name, and verifies that final extents are correct. All individual tiles are loaded into Global Mapper for the last review. During this last review, an analsyt checks to ensure full, complete coverage, no issues along tile boundaries, tiles seamlessly edge-match, and that there are no remaining processing artifacts in the dataset.

The NOAA Office for Coastal Management (OCM) downloaded 4333 PR_PuertoRico_2015 Digital Elevation Model (DEM) files from this USGS site: ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/OPR/. OCM checked with USGS about an area where tiles appeared to be missing. USGS confirmed that these tiles were not published to the rockyftp site for downloads because these were areas of no collection, only tinning. The data were in geographic coordinates and Puerto Rico Vertical Datum 2002 (PRVD02) elevations in meters. The bare earth raster files were at a 1 meter grid spacing.

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

1. Copied the files to https