2010 Oregon Department of Geology and Mineral Industries (DOGAMI) Lidar: Yambo Study Area

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This graphic shows the lidar coverage for the Yambo Study Area.

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Date that the source FGDC record was last modified.

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The LiDAR data were collected between January 3 and July 14, 2010. The survey used Leica ALS60 and Leica ALS50 Phase II laser

sensors mounted in multiple Cessna Caravans 208B. The systems were set to acquire greater than or equal to 105,000 laser pulses

per second (i.e. 105 kHz pulse rate) and flown at 900 and 1300 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 density of greater than

or equal to 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 study area 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. During the LiDAR

survey of the study area, static (1 Hz recording frequency) ground surveys were conducted over either known or set monuments.

After the airborne survey, the static GPS data are processed using triangulation with CORS (continuously operating reference station) and

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.

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 for data storage and provisioning purposes:

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.