2014 U.S. Geological Survey CMGP LiDAR: Post Sandy (Connecticut)

Create custom data files by choosing data area, product type, map projection, file format, datum, etc.

Simple download of data files.

This graphic shows the lidar coverage for the 2014 lidar acquisition for Connecticut.

Date that the source FGDC record was last modified.

Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details.

Partial upload of Positional Accuracy fields only.

Partial upload to move data access links to Distribution Info.

Data for the USGS Connecticut Sandy LiDAR project was acquired by Leading Edge Geomatics (LEG)

The project area included approximately 1,526 contiguous square miles for portions of Connecticut. LiDAR sensor data were collected with the Riegl 680i LiDAR system. The data was delivered in UTM Zone 18, horizontal datum NAD83(2011), vertical datum NAVD88, Geoid 12A. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud, survey control, and a final acquisition/calibration report.

A preliminary RMSEz error check is performed at this stage of the project life cycle in the raw LiDAR dataset against GPS static and kinematic data and compared to RMSEz project specifications. The LiDAR data is examined in open, flat areas away from breaks. Lidar ground points for each flightline generated by an automatic classification routine are used.

Overall the LiDAR data products collected by LEG meet or exceed the requirements set out in the Statement of Work. The quality control requirements of LEGs quality management program were adhered to throughout the acquisition stage of this project to ensure product quality.

LIDAR acquisition began on April 27, 2014 and was completed on May 29, 2014. A total of 40 survey missions were flown to complete the project. LEG utilized an Riegl 680i LiDAR system for the acquisition. The flight plan was flown as planned with no modifications. There were no unusual occurrences during the acquisition and the sensor performed within specifications. There were 428 flight lines required to complete the project.

The initial step of calibration is to verify availability and status of all needed GPS and Laser data against field notes and compile any data if not complete.

Subsequently the mission points are output using Trimble Business Center (TBC), initially with default

values from Trimble or the last mission calibrated for system. The initial point generation

for each mission calibration is verified within Microstation/Terrascan for calibration errors.

If a calibration error greater than specification is observed within the mission, the roll pitch

and scanner scale corrections that need to be applied are calculated. The missions with the

new calibration values are regenerated and validated internally once again to ensure

quality.

All missions are validated against the adjoining missions for relative vertical biases and

collected GPS validation points for absolute vertical accuracy purposes.

On a project level, a supplementary coverage check is carried out to ensure no data voids

unreported by Field Operations are present.

The initial points for each mission calibration are inspected for flight line errors, flight line overlap, slivers or gaps in the data, point data minimums, or issues with the LiDAR unit or GPS. Roll, pitch and scanner scale are optimized during the calibration process until the relative accuracy is met.

Relative accuracy and internal quality are checked using at least 3 regularly spaced QC blocks in which points from all lines are loaded and inspected. Vertical differences between ground surfaces of each line are displayed. Color scale is adjusted so that errors greater than the specifications are flagged. Cross sections are visually inspected across each block to validate point to point, flightline to flightline and mission to mission agreement.

For this project the specifications used are as follow:

Relative accuracy <= 7cm RMSEZ within individual swaths and <=10 cm RMSEZ or within swath overlap (between adjacent swaths).

UTM coordinate system, meters, zone 18, horizontal datum NAD83(2011), vertical datum NAVD88, Geoid 12A

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 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. Once the ground routine has been completed a manual quality control routine is done using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review and 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 corrections were completed, the dataset was 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 1 meter of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. In addition to classes 1, 2, 9, and 10, there is a Class 7, noise points . This class was used for both low and high noise points.

The fully classified dataset is then processed through Dewberry's comprehensive quality control program.

The data was classified as follows:

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

Class 2 = Ground

Class 7= Noise

Class 9 = Water

Class 10 = Ignored

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 1 to 4)

Echo (Integer 1 to 4)

Intensity (8 bit integer)

Flight Line (Integer)

Scan Angle (Integer degree)

Dewberry used GeoCue software to develop raster stereo models from the LiDAR intensity. The raster resolution was 0.3 m.

LiDAR intensity stereopairs were viewed in 3-D stereo using Socet Set for ArcGIS softcopy photogrammetric software. The breaklines are collected directly into an ArcGIS file geodatabase to ensure correct topology. The LiDARgrammetry was performed under the direct supervision of an ASPRS Certified Photogrammetrist. The breaklines were stereo-compiled in accordance with the Data Dictionary.

Inland Lakes and Ponds, Streams and Rivers, Tidal Waters, and Bridge Breaklines were collected according to specifications for the Connecticut LiDAR Project.

Dewberry 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 identify locations where the interpolation between bridge abutments created bridge saddles.

The locations of identified bridge saddles 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-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 continuous 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 breakline and LiDAR elevations due to monotonicity, connectivity, and flattening rules that are enforced on the 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, Streams and Rivers, Tidal Waters, 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

Streams and Rivers: Hard Line

Tidal Waters: Hard Replace

Bridge Breaklines: Hard Line

The ESRI Terrain is converted to rasters. The rasters are created to pre-defined extents so that multiple rasters are created over the project area. Creating multiple rasters rather than one large raster over a large project area makes the data more manageable to work with. The rasters are created with 2 tiles of overlap. This allows us to ensure seamless coverage and edge-matching in the final tiled product. These rasters were created with a 1 meter cell size.

The DEMs that are created over large areas are reviewed in ArcGIS with hillshades. Hillshades allow the analyst to view the DEMs in 3D and to more efficiently locate and identify potential issues. The first review is done on the area DEMs as this increases the efficiency of any corrections that may be performed. Performing corrections on area DEMs allows the analyst to perform corrections on multiple tiles at once and helps prevent errors from occurring along individual tile seamlines. Analysts review the area DEMs for incorrect water elevations and artifacts that are introduced during the raster creation process.

The corrected and final area DEMs are clipped to individual tiles. Dewberry uses a proprietary tool that clips the area DEMs 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) received the topographic files in classified LAZ format from USGS' ftp site. The data were received in UTM Zone 18N NAD83 coordinates (meters) and vertically referenced to NAVD88 using the Geoid12a model in meters. OCM performed the following processing for data storage and Digital Coast provisioning purposes:

1. LAS files were compressed to LAZ format with LASTools.

2. LAS points which were duplicate or extraneous points (below -25 m) were removed.

3. The LAS files were transformed to geographic (decimal degrees), ellipsoidal coordinates (meters) referenced to the Geoid12a model.