2009 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Snohomish River Estuary

Create custom data files by choosing data area, product type, map projection, file format, datum, etc.

Simple download of data files.

This graphic shows the lidar coverage for the 2009 Snohomish River Estuary collection area in Washington.

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.

Partial upload of Positional Accuracy fields only.

Partial upload to move data access links to Distribution Info.

Point Generation. The points are generated as Terrascan binary Format using Terrapoint's

proprietary Laser Postprocessor Software.

This software combines the Raw Laser file and GPS/IMU information to generate a point cloud for each

individual flight. All the point cloud files encompassing the project area were then divided into

quarter quad tiles. The referencing system of these tiles is based upon the project boundary minimum

and maximums. This process is carried out in Terrascan.

The bald earth is subsequently extracted from the raw LiDAR points using Terrascan in a

Microstation environment. The automated vegetation removal process takes place by building an

iterative surface model. This surface model is generated using three main parameters:

Building size, Iteration angle and Iteration distance.

The initial model is based upon low points selected by a roaming window and are

assumed to be ground points. The size of this roaming window is determined by the

building size parameter. These low points are triangulated and the remaining points

are evaluated and subsequently added to the model if they meet the Iteration angle and

distance constraints (fig. 1). This process is repeated until no additional points are

added within an iteration.

There is also a maximum terrain angle constraint that determines the maximum

terrain angle allowed within the model.

Multiple process dates, report compiled 20050331.

Applications and Work Flow Overview

1. Resolved kinematic corrections for aircraft position data using kinematic aircraft GPS and static

ground GPS data.

Software: Waypoint GPS v.8.10, Trimble Geomatics Office v.1.62

2. Developed a smoothed best estimate of trajectory (SBET) file that blends post-processed

aircraft position with attitude data Sensor head position and attitude were calculated

throughout the survey. The SBET data were used extensively for laser point processing.

Software: IPAS v.1.4

3. Calculated laser point position by associating SBET position to each

laser point return time, scan angle, intensity, etc. Created raw laser

point cloud data for the entire survey in *.las (ASPRS v1.1) format.

Software: ALS Post Processing Software v.2.69

4. Imported raw laser points into manageable blocks (less than 500 MB) to perform manual

relative accuracy calibration and filter for pits/birds. Ground points were then classified for

individual flight lines (to be used for relative accuracy testing and calibration).

Software: TerraScan v.9.001

5. Using ground classified points per each flight line, the relative accuracy was tested.

Automated line-to-line calibrations were then performed for system attitude parameters

(pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calibrations were performed on

ground classified points from paired flight lines. Every flight line was used for relative

accuracy calibration.

Software: TerraMatch v.9.001

6. Position and attitude data were imported. Resulting data were classified as ground and nonground

points. Statistical absolute accuracy was assessed via direct comparisons of ground

classified points to ground RTK survey data. Data were then converted to orthometric

elevations (NAVD88) by applying a Geoid03 correction. Ground models were created as a

triangulated surface and exported as ArcInfo ASCII grids at a 1-meter pixel resolution.

Software: TerraScan v.9.001, ArcMap v9.3, TerraModeler v.9.001

Laser point coordinates were 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) were assigned an associated

(x, y, z) coordinate along with unique intensity values (0-255). The data were 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.

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

(< 500 MB). Flightlines and LiDAR data were then reviewed to ensure complete coverage of

the survey area and positional accuracy of the laser points.

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.

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.

LiDAR Data Acquisition and Processing: Snohomish River Estuary, WA

Prepared by Watershed Sciences, Inc.

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 (<1 cm) in relative accuracy.

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) downloaded topographic files in text format from PSLC's website.

The files contained lidar easting, northing, elevation, intensity, return number, class, scan angle

and GPS time measurements. The data were received in Washington State Plane North Zone 4601, NAD83

coordinates and were vertically referenced to NAVD88 using the Geoid03 model. The vertical units of

the data were feet. OCM performed the following processing for data storage and Digital Coast

provisioning purposes:

1. The All-Return ASCII txt files were parsed to convert GPS Week Time to Adjusted Standard GPS Time.

2. The All-Return ASCII files were converted from txt format to las format using LASTools' txt2las tool and

reclassified to fit the OCM class list, N=1 (unclassified), G=2 (ground).

3. The las files were converted from orthometric (NAVD88) heights to ellipsoidal heights using Geoid03.

4. The las files' vertical units were converted from feet to meters, removing bad elevations.

5. The las files were converted from a Projected Coordinate System (WA SP North) to a Geographic Coordinate system (NAD83)

6. The las files' horizontal units were converted from feet to decimal degrees and converted to laz format.

7. The laz tiles containing only water areas were removed and remaining tiles were clipped to remove excess noise.