2009 U.S. Geological Survey (USGS) Lidar: Umpqua River Study Area

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This graphic shows the lidar coverage for the Umpqua 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/geoid18/1429/supplemental/Umpqua_River_LiDAR_Data_Report.pdf

Acquisition

1. The lidar data were collected between April 21 - July 13, 2009.

2. The survey used a Leica ALS50 Phase II and an ALS60 Phase II sensor mounted in a Cessna Caravan 208B.

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 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. 1.2 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 are too large to process. To facilitate laser point processing, bins (polygons) are created

to divide the dataset into manageable sizes (less than 500 MB). Flightlines and LiDAR data are then reviewed to ensure complete

coverage of the study areas 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 (less than 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 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 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.