2010 ARRA Lidar: Golden Gate (CA)

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This graphic shows the lidar coverage for the 2010 ARRA Golden Gate project area.

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Sensor data collected for this project include simultaneous capture of LiDAR, multi-spectral (color), and hyperspectral imagery for the San Francisco State University LiDAR project. The project area includes 835 sq miles in Marin and San Francisco counties, Point Reyes National Seashore, the Golden Gate National Recreation Area as well as portions of San Mateo and Sonoma Counties. The data were delivered in the UTM coordinate system, meters, zone 10 north, horizontal datum NAD83, vertical datum NGVD88, via 1500 x 1500 meter tiles.

All sensors were installed together in a Cessna 207 aircraft. The LiDAR sensor used was a Leica ALS60 MPiA(multi-pulse in air) sensor collecting multiple return x, y, and z data, full

wave form data as well as intensity data. The LiDAR data acquisition plan was developed to support a nominal post spacing of 2 pts/sq meter at 15 percent side-lap with 28 degree FOV to support a 1 foot contour accuracy with waveform data included. The aircraft speed was 120 kts, and flight altitude 8500 ft aMSL. The base station equipment used was a Trimble R7 with a Zephyr geodetic model 2 antenna. The control points were collected with a Trimble R8 integrated receiver and antenna unit. The calibration process considered all errors inherent with the equipment including errors in GPS, IMU, and sensor specific parameters. Adjustments were made to achieve a flight line to flight line data match (relative calibration) and subsequently adjusted to control for absolute accuracy.

Compliance with the accuracy standard was ensured through the collection of GPS ground control during the acquisition of aerial LiDAR and the establishment of a GPS base station operating at the airport (Figure 2). In addition to the base station, CORS bases may have been used to supplement the solutions. The following criteria were adhered to during control point collection.

1. Each point was collected during periods of very low less than 2 PDOP. 2. No point was collected with a base line greater than 25 miles. 3. Each point was collected at a place of constant slope so as to minimize any errors introduced through LiDAR triangulation. 4. Each point was collected at moderate intensity surfaces so any intensity based anomalies could be avoided.

The following methods were used to ensure LiDAR accuracy:

1. Rigorous LiDAR calibration: all sources of error such as the sensor's ranging and torsion parameters, atmospheric variables, GPS conditions, and IMU offsets were analyzed and removed to the highest level possible. This method addresses all errors, both vertical and horizontal in nature. Ranging, atmospheric variables, and GPS conditions affect the vertical position of the surface, whereas IMU offsets and torsion parameters affect the data horizontally. The horizontal accuracy is proven through repeatability: when the position of features remains constant no matter what direction the plane was flying and no matter where the feature is positioned within the swath, relative horizontal accuracy is achieved.

2. Absolute horizontal accuracy is achieved through the use of differential GPS with base lines shorter than 25 miles. The base station is set at a temporary monument that is 'tied-in' to the CORS network. The same position is used for every lift, ensuring that any errors in its position will affect all data equally and can therefore be removed equally. The auto-classification or 'filtering' step was performed with TerraScan to create a bare earth ground model and building classification by comparing each point's relationship with its neighbors. Algorithms consider lope, angular relationships and distance in its computations which were successful in accurately defining he ground in at least 95% of the project area.

An additional automated ground classification pass was made on the original TerraScan generated ground class (2) points to improve the quality of the points representing the ground. This was done since a significant amount of vegetation and building footprints existed in the original ground class. More than 10% of the original ground classified points were reclassified. However, this did soften the edges in terrain with very rapidly changing slope and reduced the ground point density in areas with above ground vegetation.

Manual editing of the ground class (2) was done after filtering to reclassify the remaining below ground noise blunders, the remaining buildings and the most obvious remaining near ground vegetation. Ground points were manually added back in for selected coastal cliffs, large offshore rocks and large land rock outcrops in Marin County. While this manual editing was systematic and purposeful, it may have inadvertently missed some details. Manual editing to classify noise (7) in the point cloud was done to minimize the significant amount of noise points that remained in the other classes after the original vendor provided TerraScan classification. The noise points were often mostly associated with reflective surfaces in city, urban and around water. A tile by tile effort eliminated essentially all significant high and below ground noise points. Another pass eliminated the majority of the close-in noise near structures, vegetation, roads and water.

The NOAA Office for Coastal Management (OCM) received the files in laz format from USGS via an FTP online repository. The files contained lidar elevation and intensity measurements. The data were in State Plane California Zone 3 and Zone 4, NAVD88 (orthometric) heights in meters. The California Coastal Project was divided into two projects: State Plane Zone 3 and State Plane Zone 4 respectively. OCM performed the following processing for data storage and Digital Coast provisioning purposes:

1. The data were converted from state plane coordinates to geographic coordinates.

2. The data were converted from NAVD88 (orthometric) heights in meters to GRS80 (ellipsoid) heights in meters using Geoid 09.

3. The LAS Noise class was dropped and Class 4 (medium vegetation scrutinized). All Class 4 points are considered to include all vegetation and man-made objects.