2017 WA Dept. of Ecology Lidar: Edgewater Beach, WA

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Bulk download of data files in LAZ format, in geographic coordinates and NAVD88 (Geoid18) elevations in meters.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

This graphic displays the footprint for this lidar data set.

Link to custom download, from the Data Access Viewer (DAV), the raster Digital Elevation Model (DEM) data that were created from this lidar data set.

Link to view the point cloud (using the Entwine Point Tile (EPT) format) in the 3D Potree viewer.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Link to the WA Dept. of Ecology lidar report.

Data Collection

Geodetic Control

A local GNSS base station was set up during each survey over the same known position to transmit RTK corrections to the POS MV and GNSS rovers. The base station also logged static GNSS raw data every second at its location for post-processing. During the boat-based lidar survey the base station receiver logged ~7 hours of raw GNSS data at the same location.

Boat Based Lidar

Boat-based lidar data were collected along 5 km of shoreline from Sanderson Harbor south of Edgewater Beach, heading north around Hunter Point, and ending at the small embayment between Hunter Point and Carlyon Beach on the south shore of Squaxin Passage. Data were collected on June 22, 2017, almost eight months after removal of shoreline armoring. Data were collected at low tide during maximum exposure of the beach; however, multiple passes of select areas, such as the Edgewater Beach restoration site, were made at a higher tide to achieve greater data density and higher resolution on the upper beach.

During lidar data acquisition, the vessel slowly moved alongshore at a speed of ~1 kt while the laser continuously scanned in a vertical line pattern. The angular interval between laser pulses was set at 0.09 degrees, which equates to a vertical point spacing of 1.6 cm at distance of 100 m. An object's range was determined using the last returned laser pulse. Data from the laser scanner and IMU were integrated in Quality Positioning Services (QPS) QINSy hydrographic software (v8.16.1), which was also used for navigation. Position and orientation data from the IMU were logged at 10 Hz for post-processing. High-resolution digital photographs of the shoreline were taken from the vessel simultaneously to document the landscape.

Ground Based GNSS

During the laser scanning, ground elevation data were collected by land-based surveyors walking on the beach with RTK-GNSS receivers mounted to backpacks. Data were collected along the shore, one point per meter, distributed throughout the survey area in locations that were clearly surveyed by the lidar system. These data are primarily used as a means of quality assurance to ensure accuracy in the vertical component of the laser data. In some cases, the ground-based GNSS data may help to supplement the lidar data by filling in gaps or shadows that can be present due to large objects on the beach or where the beach is wet.

Several ground-control targets were set up on the beach throughout the survey area for checking the positional alignment of the lidar point cloud with independently surveyed GNSS points. Targets made of 1 m by 1 m sheet metal, spray-painted flat white, were mounted to wooden stakes and placed on the upper beach. A smaller, rectangular sheet metal target (0.61 m high by 0.76 m wide), also spray-painted flat white, was set up near the water's edge and moved several times during the course of the survey. During the 2017 survey, a spherical target (0.73 m diameter) made from an inflatable ball covered in aluminum foil was also used as ground control. The advantage of the spherical target is that regardless of the direction the target is scanned, the spherical shape can be modeled from the lidar returns, and a more accurate target center can be obtained from the point cloud. After each target was set up level and plumb, surveyors on land measured the position of the target center by obtaining a 10-second average using RTK-GNSS.

Data Processing

Geodetic Control

An accurate position for the location of the base station was determined in the office by processing the static GNSS data logged during the first boat-based lidar survey and the following day through the National Geodetic Survey's Online Positioning User Service (OPUS; accessible at: https://www.ngs.noaa.gov/OPUS/) and computing the average of the two solutions. These coordinates were used during post-processing of the boat-based lidar and ground-based GNSS data for both the first and second surveys to ensure all data are identically georeferenced.

The GNSS data logged by the base station receiver during the second survey were processed through OPUS to compare the solution with the established coordinates. The values varied by 1.9 cm in Easting, 2.4 cm in Northing, and only 0.7 cm in elevation. Small variations in the coordinates are expected as more data is collected over the same point and will ultimately converge onto a well-established set of coordinates. This does, however, show that the reference point has not significantly moved between the two surveys.

Boat Based Lidar

Data logged by the base station during the survey, along with the final coordinates from OPUS, were used to post-process the vessel's position in Applanix POSPac Mobile Mapping Suite software (POSPac MMS v8.0) using Applanix IN-Fusion Single Base Station Processing to correct for RTK dropouts experienced in the field and establish accurate vessel positioning. The resultant Smoothed Best Estimate of Trajectory (SBET) file was applied to the lidar data in Qimera v1.5 to adjust the point-cloud position.

An initial cleaning of the post-processed point cloud was performed in Qloud v2.3 to remove high-fliers, reflections, and other noise due to sun glare or debris on the water surface. Final cleaning and point-cloud classification was completed in the QPS 3D Editor (available in both Qimera v1.5 and Fledermaus v7.7) by examining cross-sections of the point cloud in three dimensions to remove all vegetation, buildings, large woody debris, and to define a clear waterline, resulting in a bare-earth surface. Backshore protection structures (i.e., armoring) were left in the point cloud as a contiguous part of the ground surface. Data upland of the bluff crest were rejected. Digital photos taken during the survey, along with aerial imagery from Google Earth and oblique shoreline photos from the Washington Coastal Atlas, were used when needed to interpret and classify the lidar point cloud. Photomosaics for select areas were made by stitching overlapping photos taken from the boat together using Autopano Giga Pro v3.0.

Point-cloud data from individual passes along the shoreline were compared to one another in MATLAB and adjusted for agreement. Areas of the point cloud on the beach with low standard deviation, a relatively uniform slope, and gravel-sized or finer texture were extracted for comparison with the ground-based GNSS data.

Ground Based GNSS

GNSS data collected on the beach and at each laser target were processed in Trimble Business Center v3.70 using the final coordinates computed for the base station location. Data points between surveyors within a 30-cm radius were compared in MATLAB, and each surveyor's data were adjusted for vertical agreement based on the average of individual comparisons to produce the final XYZ coordinates for the GNSS data.

The final GNSS data were compared to surrounding lidar points within a 30-cm radius to determine an average vertical offset between the two datasets. For the 2017 survey, an offset was calculated for each pass made by the vessel since certain sections of the beach were scanned multiple times at different tide levels. The lidar point cloud was adjusted vertically (+Z) to match the GNSS data.

Point Cloud Classification

Detailed classification of the 2017 lidar point cloud was performed for the Edgewater Beach restoration site. Features in the point cloud were identified and classified into four main groups: ground, vegetation, large woody debris, and armoring. With the point cloud classified, different groups of points can be turned on or off to examine and quantify various morphological and ecological aspects of the shoreline.

The NOAA Office for Coastal Management (OCM) received 2 laz files from the Washington Dept. of Ecology.

No metadata record was provided with the data. This record is populated with information from the WA Dept. of Ecology report that was provided along with the laz data. A link to the report is provided in the URL section of this metadata record.

The data were in Washington State Plane South (NAD83 2011), meters coordinates and NAVD88 (Geoid12B) elevations in meters. The two laz files had all points classified as 0. One file contained all the ground classified points and the other file had all the unclassified points. The unclassified points included vegetation, structures, boats.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.

2. Internal OCM scripts were run on the laz files to:

a. reclassify the points in the ground classified file from 0 to 2 and the points in the unclassified file from 0 to 1 using Global Mapper 25.1.

b. convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid12B model, to convert from Washington State Plane South (NAD83 2011), meters coordinates to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database and to the Amazon s3 bucket.