2008 USDA Forest Service Lidar: Sandy River Study Area

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This graphic shows the lidar coverage for the Sandy River 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 was provided to NOAA OCM with this data set. The following process step is derived from the Watershed Sciences, Inc.

lidar report. This report may be accessed at:

https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/supplemental/sandy_report.doc

Acquisition

1. The lidar data were collected between Sept 29 - Oct 1, 2008.

2. The survey used a Leica ALS50 Phase II sensor.

3. Near nadir scan angles were used to increase penetration of vegetation to ground surfaces.

4. Ground level GPS and aircraft IMU were collected during the flight.

Processing

1. Laser point coordinates were computed using the REALM software 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 (0-255). The data were 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 were too large for subsequent processing. To facilitate laser point processing, bins

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

reviewed to ensure complete coverage of the study 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 was 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 - 9.0 million points, an average of 50 - 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 3-5 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 grounds 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 UTM Zone 10 NAD83(CORS96) projection, NAVD88 (Geoid03) vertical datum

and units were in meters. OCM performed the following processing for data storage and Digital Coast provisioning purposes:

1. The data were converted from UTM Zone 10 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.