2011 Delaware Department of Natural Resources and Environmental Control (DNREC) Lidar: Bombay Hook National Wildlife Refuge

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This graphic shows the lidar coverage for the Bombay Hook National Wildlife Refuge data set.

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.

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Terrapoint used their Optech ALTM 3100EA LiDAR sensor to collect the data for the Bombay Hook project.

Three flight missions were required to obtain data over the entire project area. Flightlines were flown in a North-South orientation to

optimize flying time in regards to the project layout. 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. Terrapoint used three existing

published survey monuments and one newly established control station to control all flight missions and kinematic and static ground

surveys.

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 (13 degrees above the horizon) and with a PDOP of better than 4. Distances from base station to aircraft were

kept to a maximum of 40 km. For all flights, the GPS data can be classified as excellent, with GPS residuals of 3 cm average or better

but no larger than 10 cm being recorded.

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. The swath data is tiled into the client provided tiling schema

(1700 m x 1700 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 first part of the ground routine is to classify all points with a scan

angle greater than 20 degrees to class 12, overlap. These points have the highest potential to cause issues in the ground surface.

Therefore, they are classified to class 12 immediately where they will not be available for the remaining ground routine. 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. The ground routine also identifies extremely low or high points that should be excluded from the ground surface and classifies

them as class 7, noise. 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. 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 (These points were removed from the data set by NOAA OCM during processing for data storage and Digital Coast provisioning).

Class 9 = Water

Class 12= Overlap Points with a scan angle greater than 20 degrees

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) received the files in las format. The files contained LiDAR elevation as well as intensity

values. The data were in Delaware State Plane, NAD83 coordinates and NAVD88 heights. OCM performed the following processing for data

storage and Digital Coast provisioning purposes:

1. The data were converted from Delaware State Plane, NAD83 coordinates to geographic coordinates.

2. The data were converted from NAVD88 heights to ellipsoid heights using Geoid09.

3. Data points that were classified as 7 (noise) were removed from the data set.

4. The data were sorted and converted to LAZ format.