Data Management Plan

Watershed Sciences, Inc. collected Light Detection and Ranging (LiDAR) data for the U.S. Geological Survey (USGS) Umpqua River

study site in collaboration with the Puget Sound LiDAR Consortium (PSLC). The areas for LiDAR collection have been designed

as part of a collaborative effort of state, federal, and local agencies in order to meet a wide range of project goals.

This LiDAR data set was collected between April 21 - July 13, 2009 and falls in Douglas County, Oregon.

This data set consists of bare earth and unclassified points. The average pulse density is 8.80 pulses per square meter over

terrestrial surfaces. The area of interest (AOI) encompasses approximately 67,408 acres and the total area flown (TAF) covers

69,925 acres. The TAF acreage is greater than the original AOI acreage due to buffering and flight planning optimization.

In some areas of heavy vegetation or forest cover, there may be relatively few ground points in the LiDAR data. Elevation values

for open water surfaces are not valid elevation values because few lidar points are returned from water surfaces. Lidar intensity

values were also collected.

Original contact information:

Contact Name: Ian Madin

Contact Org: DOGAMI

Phone: 971-673-1542

Email: ian.madin@dogami.state.or.us

Process Steps:

• 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.
• 2012-12-01 00:00:00 - 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.

This data can be obtained on-line at the following URL:

https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=1429

This data set is dynamically generated based on user-specified parameters.

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