- Watershed Sciences, Inc.
- DOC/NOAA/NOS/OCM > Office for Coastal Management, National Ocean Service, National
Oceanic and Atmospheric Administration, U.S. Department of Commerce
- DOC/NOAA/NESDIS/NCEI > National Centers of Environmental Information, NESDIS, NOAA,
U.S. Department of Commerce
| Processing Steps
- No metadata was provided to NOAA CSC with this data set. The following process step
is derived from the Watershed Sciences, Inc. lidar report. This report may be accessed
at: ftp://ftp.csc.noaa.gov/pub/crs/beachmap/qa_docs/or/sandy/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 Coastal Services Center (CSC) 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.
CSC 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 4. The data were converted
to LAZ format.
- The NOAA National Geophysical Data Center (NGDC) received lidar data files via ftp
transfer from the NOAA Coastal Services Center. The data are currently
being served via NOAA CSC Digital Coast at http://www.csc.noaa.gov/digitalcoast/.
The data can be used to re-populate the system. The data are archived in LAS or LAZ
The LAS format is an industry standard for LiDAR data developed by the American Society
of Photogrammetry and Remote Sensing (ASPRS); LAZ is a loseless compressed version
LAS developed by Martin Isenburg (http://www.laszip.org/). The data are exclusively
in geographic coordinates (either NAD83 or ITRF94). The data are referenced vertically
the ellipsoid (either GRS80 or ITRF94), allowing for the ability to apply the most
up to date geoid model when transforming to orthometric heights.