2010 ARRA Lidar: Eleven County Virginia

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 of the eleven counties of Virginia collected in 2010 to support the ARRA of 2009.

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 Eleven County Virginia LiDAR project was acquired by both Terrapoint and LMSI.

Three Optech ALTM 3100EA LiDAR systems were utilized to collect the data. The three aircraft used for the survey were Piper Navaho, registrations C-FVTL (Terrapoint) and C-FVYW (Terrapoint) with an endurance of approximately 6 hours and a Piper Turbo Aztec registration N40204 (LMSI) with an endurance of approximately 7 hours.

Fifty-four (54) flight missions resulting in 477 production lines were required to obtain data over the entire project area. A combination of Sokkia GSR 2600 and NovAtel DL-4+ dual-frequency GPS receivers were used to support the airborne operations of this survey and to establish the GPS control network. Five existing published survey monuments and two newly surveyed points were used as base stations for all missions. Forty-eight (48) additional points were established by LMSI and used for quality control and calibration of the data.

Airborne GPS kinematic data was processed on-site using GrafNav kinematic On-The-Fly (OTF) software. Flights were flown with a minimum of 6 satellites in view (13o above the horizon) and with a PDOP of better than 3. Distances from base station to aircraft were kept to a maximum of 24km.

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 Optech's Dashmap, initially with default values from Optech 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 kinematic 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.

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. GeoCue allows the data to retain its delivered tiling scheme (5000 ft by 5000 ft). 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 which are in close proximity (3 ft) to the hydrographic features are moved to class 10, an ignored ground. In addition to classes 1, 2, 9, and 10, there is a Class 7, noise points. This class was only used if needed when points could manually be identified as low/high 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 Ground

The LAS header information was verified to contain the following:

Class (Integer); GPS Week Time (0.0001 seconds); Easting (0.01 foot); Northing (0.01 foot); Elevation (0.01 foot); Echo Number (Integer 1 to 4); Echo (Integer 1 to 4); Intensity (8 bit integer); Flight Line (Integer); Scan Angle (Integer degree)

The NOAA Office for Coastal Management (OCM) downloaded topographic files in LAZ format from the website for William and

Mary's Center for Geospatial Analysis. The files contained lidar easting, northing, elevation, intensity,

return number, class, scan angle and GPS time measurements; the data was received in state plane

Virginia South (in feet) and vertical coordinates were referenced to NAVD88 in feet using the Geoid03 model.

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

1. The All-Return LAZ files were checked for erroneous outliers removed, as well as variable length records.

2. The laz files were converted from a Projected Coordinate System (SP 4502) to a Geographic Coordinate system (NAD83)

3. The laz files were then converted to ellipsoidal vertical units in meters using the geoid03 conversion.