Data Management Plan

Towill collected approximately 75 square miles of coast in Oregon, 486 square miles of coast in Washington and California, and an additional 44 square miles for USACE defined harbors. The data was collected in a corridor approximately 500 meters wide. The nominal pulse spacing for this project was 1 point every 0.35 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground, 7-Low Noise, 9-Water, 10-Ignored Ground due to breakline proximity, 17- Bridges, 18-High Noise, 64- Flown Outside of Low Tide Window and 65- Temporal Ground. Dewberry produced 3D breaklines and combined these with the final LiDAR data to produce seamless DEMs for the project area. The data was formatted according to the USNG tile naming convention with each tile covering an area of 1,500 meters by 1,500 meters. A total of 2377 UTM 10 DEM and LAS tiles and 487 UTM 11 DEM and LAS tiles were produced for the project.

Original contact information:

Contact Org: USGS

Title: Program Manager

Phone: (303)202-4419

Email: kyoder@usgs.gov

Process Steps:

• 2016-05-01 00:00:00 - Data for the West Coast El Nino LiDAR project was acquired by Towill. Towill collected approximately 75 square miles of coast in Oregon, 486 square miles of coast in Washington and California, and an additional 44 square miles for USACE defined harbors about 500 meters wide. LiDAR sensor data were collected with the Optech Orion M300 LiDAR system. The data was delivered in the UTM coordinate system, meters, zone 10 and Zone 11, horizontal datum NAD83(2011), vertical datum NAVD88, Geoid 12B. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud, survey control, and a final acquisition/calibration report. 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. Process steps to achieve this are as follows: 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. 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. Vertical accuracy is achieved through the adjustment to ground control survey points within the finished product. Although the base station has absolute vertical accuracy, adjustments to sensor parameters introduces vertical error that must be normalized in the final (mean) adjustment. Final calibrated swaths were delivered in LAS format 1.4. The withheld and overlap bits are set and all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.
• 2016-05-01 00:00:00 - Dewberry utilizes a variety of software suites for inventory management, classification, and data processing. All LiDAR related processes begin by importing the data into the GeoCue task management software. The swath data is tiled according to project specifications (1,500 m x 1,500 m). The tiled data is then opened in Terrascan where Dewberry classifies edge of flight line points that may be geometrically unusable to a separate class. These points are separated from the main point cloud so that they are not used in the ground algorithms. Dewberry then uses proprietary ground classification routines to remove any non-ground points and generate an accurate ground surface. The ground routine consists of three main parameters (building size, iteration angle, and iteration distance); by adjusting these parameters and running several iterations of this routine an initial ground surface is developed. The building size parameter sets a roaming window size. Each tile is loaded with neighboring points from adjacent tiles and the routine classifies the data section by section based on this roaming window size. The second most important parameter is the maximum terrain angle, which sets the highest allowed terrain angle within the model. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. Dewberry classified areas flown outside of low tide to class 64 and temporal ground areas to class 65. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a supervisor manual inspection is completed on a percentage of the classified tiles based on the project size and variability of the terrain. After the ground classification and bridge deck corrections are completed, the dataset is processed through a water classification routine that utilizes breaklines compiled by Dewberry to automatically classify hydrographic features. The water classification routine selects ground points within the breakline polygons and automatically classifies them as class 9, water. During this water classification routine, points that are within 1x NPS or less of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. Overage points are then identified in Terrascan and GeoCue is used to set the overlap bit for the overage points and the withheld bit is set on the withheld points previously identified in Terrascan before the ground classification routine was performed. A final QC is performed on the data. The LAS files are then converted from v1.2 to v1.4 using GeoCue software. At this time, all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools. The data was classified as follows: Class 1 = Unclassified. This class includes vegetation, buildings, noise etc. Class 2 = Ground Class 7= Low Noise Class 9 = Water Class 10 = Ignored Ground due to breakline proximity Class 17 = Bridge Decks Class 18 = High Noise Class 64 = Flown Outside of Low Tide Window Class 65 = Temporal Ground The LAS header information was verified to contain the following: Class (Integer) Adjusted GPS Time (0.0001 seconds) Easting (0.003 m) Northing (0.003 m) Elevation (0.003 m) Echo Number (Integer) Echo (Integer) Intensity (scaled to 16 bit integer) Flight Line (Integer) Scan Angle (degree)
• 2017-03-24 00:00:00 - The NOAA Office for Coastal Management (OCM) received 2377 files (UTM10) and 487 files (UTM11) files in las 1.4 format from Dewberry. The files contained lidar intensity and elevation measurements for the project area. Horizontal positions were provided in UTM Zones 10 and 11 coordinates, in meters. Vertical elevations were provided in NAVD88 (Geoid 12b), meters. OCM noted that while not indicated in the original Dewberry metadata, the data set inlcluded points classified as 15. OCM contacted Dewberry, who advised that any points classiifed as 15, should be moved to class 64. OCM performed the following processing on the data for Digital Coast storage and provisioning purposes: 1. Converted data from NAVD88 elevations to ellipsoid elevations using Geoid12b 2. Converted data from UTM Zones 10 and 11 coordinates to geographic coordinates. 3. Class 15 points were reclassified to Class 64. 4. Sorted by gpstime 5. Compressed the data using laszip.

This data can be obtained on-line at the following URL: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=6259;