2011 Oregon Parks and Recreation Department Lidar: Northeast (Clyde Holliday, Cove Palisades, Lake Owyhee, and White River Falls State Parks)

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This kmz file shows the extent of coverage of the 2011 Oregon Parks and Recreation Department

Northeast Lidar 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.

Partial upload of Positional Accuracy fields only.

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Acquisition

The LiDAR survey used a Leica ALS50 Phase II sensor in a Cessna Caravan 208B. The sensor operates with Automatic Gain Control (AGC) for

intensity correction. Depending on acquisition day, weather, and terrain, the Leica system was set to acquire 105,000 (120,000 on

6/1/11) laser pulses per second (i.e. 105-120 kHz pulse rate) and flown at 900 (700 on 6/1/11) meters above ground level (AGL),

capturing a scan angle of plus or minus 14 degrees from nadir. These settings were developed to yield points with an average native

pulse density of greater than or equal to 8 pulses per square meter over terrestrial surfaces. It is not uncommon for some types of

surfaces (e.g. dense vegetation or water) to return fewer pulses than the laser originally omitted. These discrepancies between

the native and delivered density will vary depending on terrain, land cover, and the prevalence of water bodies.

All areas were surveyed with an opposing flight line side-lap of greater than or equal to 50 percent (equal to 100 percent

overlap) to reduce laser shadowing and increase surface laser painting. The Leica laser systems allow up to four

range measurements (returns) per pulse, and all discernible laser returns were processed for the output data set.

To accurately solve for laser point position (geographic coordinates x,y,z) the positional coordinates of the airborne

sensor and the attitude of the aircraft were recorded continuously throughout the LiDAR data collection mission. Aircraft

position was 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). To allow for

post-processing correction and calibration, aircraft/sensor position and attitude data were indexed to GPS time.

Processing

1. 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,z) coordinate along with unique intensity values (0 to 255).

The data were output into large LAS v1.2 files; each point maintains the corresponding scan angle, return number

(echo), intensity, and x,y,z (easting, northing, and elevation) information.

2. These initial laser point files were too large for subsequent processing. To facilitate laser point processing, bins

(polygons) were created to divide the data set into manageable sizes (less than 500 MB). Flightlines and LiDAR data were then

reviewed to ensure complete coverage of the survey area and positional accuracy of the laser points.

3. Laser point data were imported into processing bins in TerraScan and manual calibration was performed to assess the

system offsets for pitch, roll, heading, and scale (mirror flex). Using a geometric relationship developed by

Watershed Sciences, each of these offsets were resolved and corrected if necessary.

4. LiDAR points were then filtered for noise, pits (artificial low points) and birds (true birds, as well as erroneously

high points) by screening for absolute elevation limits, isolated points, and height above ground. Each bin was then

manually inspected for remaining pits and birds and spurious points were removed. In a bin containing approximately

7.5 to 9.0 million points, an average of 50 to 100 points are typically found to be artificially low or high. Common sources

of non-terrestrial returns are clouds, birds, vapor, haze, decks, brush piles, etc.

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

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

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

corrected, vertical GPS drift was then resolved and removed per flight line, yielding a slight improvement (less than 1 cm)

in relative accuracy.

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

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

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

modeling was performed in site-specific areas to improve ground detail. This manual editing of ground often occurs in

areas with known ground modeling deficiencies such as: bedrock outcrops, cliffs, deeply incised stream banks, and

dense vegetation. In some cases, automated ground point classification erroneously included known vegetation (i.e.,

understory, low/dense shrubs, etc.). These points were manually reclassified as non-grounds. Ground surface rasters

were 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 Lambert Conformal Conic projection and NAVD88 Geoid 03 vertical datum. OCM performed the

following processing for data storage and Digital Coast provisioning purposes:

1. The data were converted from Lambert Conformal Conic coordinates to geographic coordinates.

2. The data were converted from NAVD88 (orthometric) heights in feet to GRS80 (ellipsoid) heights in meters using Geoid 03.

3. The data were filtered to remove outliers.

4. The LAS data were sorted by latitude and the headers were updated.