2009 Oregon Department of Geology and Mineral Industries (DOGAMI) Oregon Lidar: North Coast

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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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The LiDAR data was collected between October 23, 2008 and August 9, 2009. The survey used a Leica ALS50 Phase II laser system

mounted in a Cessna Caravan 208B. The system was set to acquire > or = 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 were

developed to yield points with an average native density of > or = 8 points per square meter over terrestrial surfaces. The

native pulse density is the number of pulses emitted by the LiDAR system. 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 lightly variable according to distributions of terrain, land cover, and water bodies. The completed areas

were surveyed with opposing flight line side-lap of > or = 50% (> or = 100% 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. During the LiDAR survey of the study area, a static (1 Hz recording frequency) ground

survey was conducted over monuments with known coordinates. After the airborne survey, the static GPS data are processed

using triangulation with CORS stations checked against the Online Positioning User Service (OPUS) to quantify daily variance.

Multiple sessions are processed over the same monument to confirm the antenna height measurements and reported position accuracy.

Multiple DGPS units are used for the ground real-time kinematic (RTK) portion of the survey. To collect accurate ground

surveyed points, a GPS base unit is set up over monuments to broadcast a kinematic correction to a roving GPS unit. The

ground crew uses a roving unit to receive radio-relayed kinematic corrected positions from the base unit. This method is

referred to as real-time kinematic (RTK) surveying and allows precise location measurement (sigma < or = 1.5 cm (0.6 in)).

1. Laser point coordinates are 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) are assigned

an associated (x, y, z) coordinate along with unique intensity values (0-255). The data are output into large LAS v. 1.1 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 are too large to process. To facilitate laser point processing, bins (polygons) are created to divide

the dataset into manageable sizes (< 500 MB). Flightlines and LiDAR data are then reviewed to ensure complete coverage of the study area

and positional accuracy of the laser points.

3. Once the laser point data are imported into bins in TerraScan, a manual calibration is 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 is resolved and

corrected if necessary.

4. The LiDAR points are then filtered for noise, pits, and birds by screening for absolute elevation limits, isolated points, and height

above ground. Each bin is then inspected for pits and birds manually; spurious points are removed. For a bin containing approximately

7.5-9.0 million points, an average of 50-100 points are 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.

5. The internal calibration is refined using TerraMatch. Points from overlapping lines are tested for internal consistency and final

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

scale corrections yield 3-5 cm improvements in the relative accuracy. Once the system misalignments are corrected, vertical GPS drift

is then resolved and removed per flight line, yielding a slight improvement (<1 cm) in relative accuracy. At this point in the workflow,

data have passed a robust calibration designed to reduce inconsistencies from multiple sources (i.e. sensor attitude offsets,

mirror scale, GPS drift) using a procedure that is comprehensive (i.e. uses all of the overlapping survey data). Relative accuracy

screening is complete.

6. 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.

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 Oregon Lambert (NAD83), International Feet coordinates

and NAVD88 (Geoid 03) vertical datum. OCM performed the following processing to the data to make it available within

the Digital Coast:

1. The data were converted from Oregon Lambert (NAD83), International Feet coordinates to geographic coordinates.

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

3. The vertical units of the data were converted from International feet to meters.

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