2010 OR DOGAMI: Glass Buttes Study Area

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This graphic shows the lidar coverage for the Glass Buttes Study Area.

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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No metadata for the lidar point data was provided to NOAA OCM with this data set. The following process step contains information

derived from the Ormat Technologies lidar report. This lidar report may be accessed at:

https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1475/supplemental/GlassButtes.pdf

Acquisition

Ground Survey - Instrumentation and Methods

During the LiDAR survey, static (1 Hz recording frequency) ground surveys were conducted over monuments with known coordinates.

After the airborne survey, the static GPS data were processed using triangulation with CORS stations and checked against the Online

Positioning User Service (OPUS2) to quantify daily variance. Multiple sessions were processed over each monument to confirm antenna

height measurements and reported position accuracy.

Multiple differential GPS units were used in the ground based real-time kinematic (RTK) portion of the survey. To collect

accurate ground surveyed points, a GPS base unit was set up over monuments to broadcast a kinematic correction to a roving

GPS unit. The ground crew used a roving unit to receive radio-relayed kinematic corrected positions from the base unit.

This RTK survey allowed precise location measurement (sigma less than or equal to 1.5 cm).

Lidar Point Data

The lidar data were collected between May 8 and May 14, 2010. The LiDAR survey utilized a Leica ALS60 sensor mounted in a

Cessna Caravan 208B. The Leica ALS60 system was set to acquire greater than or equal to 105,000 laser pulses per second

(i.e., 105 kHz pulse rate) and flown at 900 meters above ground level (AGL), capturing a scan angle of 14 degrees from nadir.

These settings are developed to yield points with an average native pulse density of greater than or equal to 8 points per

square meter over terrestrial surfaces. Some types of surfaces (i.e., dense vegetation or water) may return fewer pulses

than the laser originally emitted. Therefore, the delivered density can be less than the native density and vary according to

distributions of terrain, land cover, and water bodies.

The area of interest was surveyed with opposing flight line side-lap of greater than or equal to 50 percent (greater than or equal to

100 percent overlap) to reduce laser shadowing and increase surface laser painting. The system allows up to four

range measurements per pulse, and all discernible laser returns were processed for the output dataset. To solve for laser point

position, an accurate description of aircraft position and attitude is vital. Aircraft position is described as x, y, and z

and measured twice per second (2 Hz) by an onboard differential GPS unit. Aircraft attitude was measured 200 times

per second (200 Hz) as pitch, roll, and yaw (heading) from an onboard inertial measurement unit (IMU).

Processing

Aircraft Kinematic GPS and IMU Data

LiDAR survey datasets were referenced to 1 Hz static ground GPS data collected over a pre-surveyed monument with known

coordinates. While surveying, the aircraft collected 2 Hz kinematic GPS data and the inertial measurement unit (IMU)

collected 200 Hz attitude data. Waypoint GraphNav v.8.10 was used to process the kinematic corrections for the aircraft.

The static and kinematic GPS data were then post-processed after the survey to obtain an accurate GPS solution and aircraft

positions. IPAS Pro v.1.3 was used to develop a trajectory file including corrected aircraft position and attitude information.

The trajectory data for the entire flight survey session were incorporated into a final smoothed best estimated trajectory (SBET)

file containing accurate and continuous aircraft positions and attitudes.

Lidar Point Data

Laser point coordinates were computed using the IPAS and ALS Post Processor software suites based on independent data

from the LiDAR system (pulse time, scan angle), and aircraft trajectory data (SBET). Laser point returns (first through fourth)

were assigned an associated (x, y, and z) coordinate along with unique intensity values (0-255). The data were output into

large LAS v. 1.2 files; each point maintaining the corresponding scan angle, return number (echo), intensity, and x, y, and

z (easting, northing, and elevation) information. Flightlines and LiDAR data were then reviewed to ensure complete coverage

of the study area and positional accuracy of the laser points.

Once the laser point data were imported into TerraScan, a manual calibration was performed to assess the system offsets

for pitch, roll, heading and mirror scale. Using a geometric relationship developed by Watershed Sciences, each of these

offsets was resolved and corrected if necessary.

The LiDAR points were then filtered for noise, pits and birds by screening for absolute elevation limits, isolated points

and height above ground. The data were then inspected for pits and birds manually, and spurious points were removed.

For a .las file containing approximately 7.5-9.0 million points, an average of 50-100 points were typically found to be

artificially low or high. These spurious non-terrestrial laser points must be removed from the dataset. Common sources of

non-terrestrial returns are clouds, birds, vapor, and haze.

Internal calibration was refined using TerraMatch. Points from overlapping lines were tested for internal consistency

and final adjustments made for system misalignments (i.e., pitch, roll, heading offsets and mirror scale). Automated sensor

attitude and scale corrections yielded 3-5 cm improvements in the relative accuracy. Once the system misalignments were

corrected, vertical GPS drift was resolved and removed per flight line, yielding a slight improvement (<1 cm) in relative

accuracy. In summary, the data must complete a robust calibration designed to reduce inconsistencies from multiple sources

(i.e., sensor attitude offsets, mirror scale, GPS drift).

The TerraScan software suite is designed specifically for classifying near-ground points (Soininen, 2004). The processing

sequence begins by removing all points that are not near the earth based on geometric constraints used to evaluate

multi-return points. The resulting bare earth (ground) model is visually inspected and additional ground point modeling is

performed in site-specific areas (over a 50-meter radius) to improve ground detail. This is only done in areas with known

ground modeling deficiencies, such as: bedrock outcrops, cliffs, deeply incised stream banks, and dense vegetation.

In some cases, ground point classification includes known vegetation (i.e., understory, low/dense shrubs, etc.) and these

points are manually reclassified as non-grounds. Ground surface rasters are developed from triangulated irregular

networks (TINs) of ground points.

The NOAA Office for Coastal Management (OCM) received the files in las format. The files contained LiDAR

elevation and intensity measurements. The data were in UTM Zone 11 (NAD83) coordinates and NAVD88 (Geoid03) vertical datum.

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

1. The data were converted from UTM Zone 11 (NAD83) coordinates to geographic coordinates.

2. The data were converted from NAVD88 (orthometric) heights to GRS80 (ellipsoid) heights using Geoid03.

3. The data were sorted by time and zipped to laz format.

4. The metadata was updated in 2014 to reflect DOGAMI as an originator and the title of the project was also altered to reflect the association with existing DOGAMI projects. All acknowledgements for Ormat Technologies remain the same.