2010 Northern San Francisco Bay Area Lidar: Portions of Alameda, Contra Costa, Marin, Napa, San Francisco, Solano, and Sonoma Counties

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Fugro EarthData, Inc. collected ALS60-derived LiDAR over northern San Francisco Bay, CA with a 1 m, nominal post spacing using a Piper

Navajo twin engine aircraft. The collection for the entire project area was accomplished on February 25, 26, and 28; March 1, 24,

and 26; and April 3, 15, and 16, 2010. The collection was performed by Fugro EarthData, Inc., using a Leica ALS60 MPiA LiDAR system,

serial number 113, including an inertial measuring unit (IMU) and a dual frequency GPS receiver. This project required 9 lifts of

flight lines to be collected. The lines were flown at an average of 6,250 feet above mean terrain using a pulse rate of 121,300

pulses per second.

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TerraSurv under contract to Fugro EarthData, Inc. successfully established ground control for Northern San Francisco Bay, CA. A total

of 41 ground control points were acquired. GPS was used to establish the control network. The horizontal datum was the North American

Datum of 1983 (NAD83, NSRS2007). The vertical datum was the North American Vertical Datum of 1988 (NAVD88).

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All acquired LiDAR data went through a preliminary review to assure that complete coverage was obtained and that there were no gaps

between flight lines before the flight crew left the project site. Once back in the office, the data is run through a complete

iteration of processing to ensure that it is complete, uncorrupted, and that the entire project area has been covered without gaps

between flight lines. There are essentially three steps to this processing;

1. GPS/IMU Processing.

Airborne GPS and IMU data was immediately processed using the airport GPS base station data, which was available to the flight

crew upon landing the plane. This ensured the integrity of all the mission data. These results were also used to perform the

initial LiDAR system calibration test.

2. Raw LiDAR Data Processing.

Technicians processed the raw data to LAS format flight lines with full resolution output before performing QC. A starting

configuration file was used in this process, which contain the latest calibration parameters for the sensor. The technician also

generated flight line trajectories for each of the flight lines during this process.

3. Verification of Coverage and Data Quality.

Technicians checked flight line trajectory files to ensure completeness of acquisition for project flight lines, calibration lines,

and cross flight lines. The intensity images were generated for the entire lift at the required post spacing for the project.

The technician visually checked the intensity images against the project boundary to ensure full coverage. The intensity histogram

was analyzed to ensure the quality of the intensity values. The technician also thoroughly reviewed the data for any gaps in project

area. The technician generated a sample TIN surface to ensure no anomalies were present in the data. Turbulence was inspected for

and if it affected the quality of the data, the flight line was rejected and reflown. The technician also evaluated the achieved

post spacing against project specified post spacing.

The following steps describe the Raw Data Processing and Boresight process;

1. The calibration flight lines were first processed with the starting configuration file which contains the latest calibration

parameters for the sensor. The boresight for each lift was done individually as the solution may change slightly from lift to lift.

2. Lift boresighting was accomplished using the tri-directional calibration flight lines over the project area.

3. Once the boresighting was done for the calibration flight lines, the adjusted settings were applied on all of the flight lines

of the lift and checked for consistency. The technician selected a series of areas in the dataset to be inspected where adjacent

flight lines overlay. A routine was run to calculate the misalignment of the adjacent flight lines and a statistical report was

generated. The technician analyzed the result and applied more adjustment if necessary to optimize the result for the entire lift.

Color coded elevation difference images were generated for all flight line overlaps including cross ties in the lift once the

boresight adjustment was complete. The technician reviewed these images to ensure that systematic errors were eliminated for the

lift and the results met the project specifications.

4. Once the boresight adjustment was completed for each lift individually, the technician checked and corrected the vertical

misalignment of all flight lines and also the matching between data and ground truth. This process included calculating the z bias

value for each flight line so that all flight lines are aligned vertically. The entire dataset was then matched to ground control

points within the project specified accuracy range.

5. The technician ran a final vertical accuracy check after the z correction. The result was analyzed against the project specified

accuracy to make sure it met the project requirements.

Fugro EarthData, Inc. has developed a unique method for processing LiDAR data to identify and re-classify elevation points falling on

vegetation, building, and other above ground structures into separate data layers. The steps are as follows;

1. Fugro EarthData, Inc. utilized commercial software as well as proprietary software for automatic filtering. The parameters used in

the process were customized for each terrain type to obtain optimum results.

2. The Automated Process typically re-classifies 90-98% of points falling on vegetation depending on terrain type. Once the automated

filtering was completed, the files were run through a visual inspection to ensure that the filtering was not too aggressive or not

aggressive enough. In cases where the filtering was too aggressive and important terrain features were filtered out, the data was

either run through a different filter or was corrected during the manual filtering process.

3. Interactive editing was completed in 3D visualization software which also provides manual and automatic point classification tools.

Fugro EarthData, Inc. used commercial and proprietary software for this process. Vegetation and artifacts remaining after automatic

data post-processing were reclassified manually through interactive editing. The hard edges of ground features that were automatically

filtered out during the automatic filtering process were brought back into ground class during manual editing. Auto-filtering routines

were utilized as much as possible within fenced areas during interactive editing for efficiency. The technician reviewed the LiDAR

points with color shaded TINs for anomalies in ground class during interactive filtering.

4. Upon the completion of peer review and finalization of bare earth filtering, the classified LiDAR point cloud work tiles went

through a water classification routine based on the collected hydro-flattened water polygons.

5. Upon the completion of peer review and finalization of the classified LiDAR point cloud work tiles, the tiles were reprojected to

NAD83 (NSRS2007), UTM zone 10 north, meters; NAVD88, meters, using GEOID09. The data was also cut to the approved tile layout.

The classified LiDAR point cloud data is in LAS format after this process. The technician checked the output LAS files for coverage

and format.

6. The classified LiDAR point cloud data were delivered in LAS 1.2 format;

2 - ground,

1 - unclassified,

9 - water,

7 - low points/noise, and

12 - overlap points.

The NOAA Office for Coastal Management (OCM) received the lidar files in las format. The files contained lidar intensity

and elevation measurements. OCM performed the following processing for data storage and Digital Coast provisioning purposes:

1. Data converted from UTM Zone 10 coordinates to geographic coordinates.

2. Data converted from NAVD88 heights to ellipsoid heights using GEOID09.

3. The LAS data were sorted by latitude and the headers were updated.