2018 WA DNR Lidar: Green River Watershed, WA

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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 Quantum Spatial, Inc. Technical Lidar Report from the Washington Lidar Portal.

Planning:

In preparation for data collection, QSI reviewed the project area and developed a specialized flight plan to ensure complete coverage of the Green River Watershed LiDAR study area at the target point density greater than or equal to 8.0 points/m2 (0.74 points/ft2). Acquisition parameters including orientation relative to terrain, flight altitude, pulse rate, scan angle, and ground speed were adapted to optimize flight paths and flight times while meeting all contract specifications.

Factors such as satellite constellation availability and weather windows must be considered during the planning stage. Any weather hazards or conditions affecting the flights were continuously monitored due to their potential impact on the daily success of airborne and ground operations. In addition, logistical considerations including private property access and potential air space restrictions were reviewed.

Ground Survey Points

Ground control surveys, including base stations and ground survey points (GSPs) were conducted to support the airborne acquisition. Ground control data were used to geospatially correct the aircraft positional coordinate data and to perform quality assurance checks on final LiDAR data.

Base Stations

A combination of Washington State Reference Network (WSRN) Real-Time Network (RTN) base stations and a QSI-established monument were utilized for the Green River Watershed LiDAR project. Base stations were used to correct the flightline positional coordinate data, while QSI's monument was used to support collection of ground survey points using real time kinematic (RTK) and fast static (FS) survey techniques. QSI utilized seven existing base stations and established one new monument for the Green River Watershed LiDAR project. New monumentation was set using a 6-inch PK nail with a reference washer. QSI's professional land surveyor, Evon Silvia (WAPLS#53957) oversaw and certified the establishment of all monuments.

QSI utilized static Global Navigation Satellite System (GNSS) data collected at 1 Hz recording frequency for each base station. During post-processing, the static GNSS data were triangulated with nearby Continuously Operating Reference Stations (CORS) using the Online Positioning User Service (OPUS1) for precise positioning. Multiple independent sessions over the same monument were processed to confirm antenna height measurements and to refine position accuracy.

Ground survey points were collected using real time kinematic (RTK) and fast-static (FS) survey techniques. For RTK surveys, a roving receiver receives corrections from a nearby base station or Real-Time Network (RTN) via radio or cellular network, enabling rapid collection of points with relative errors less than 1.5 cm horizontal and 2.0 cm vertical. FS surveys compute these corrections during post-processing to achieve comparable accuracy. RTK surveys record data while stationary for at least five seconds, calculating the position using at least three one-second epochs. FS surveys record observations for up to fifteen minutes on each GSP in order to support longer baselines. All GSP measurements were made during periods with a Position Dilution of Precision (PDOP) of less than or equal to 3.0 with at least six satellites in view of the stationary and roving receivers.

GSPs were collected in areas where good satellite visibility was achieved on paved roads and other hard surfaces such as gravel or packed dirt roads. GSP measurements were not taken on highly reflective surfaces such as center line stripes or lane markings on roads due to the increased noise seen in the laser returns over these surfaces. GSPs were collected within as many flightlines as possible; however, the distribution of GSPs depended on ground access constraints and monument locations and may not be equitably distributed throughout the study area.

Airborne Survey

The 2018 LiDAR survey was accomplished using a Riegl VQ-1560i sensor system mounted in a Cessna Caravan. Additionally, a portion of the Green River Watershed LiDAR dataset was collected with a Leica ALS80 sensor system on December 10th, 2017. Settings were used to yield an average pulse density of greater than or equal to 8 pulses/m2 over the Green River Watershed project area. The Riegl laser system records unlimited range measurements (returns) per pulse; however, it is not uncommon for some types of surfaces (e.g., dense vegetation or water) to return fewer pulses to the LiDAR sensor than the laser originally emitted. The discrepancy between first return and overall delivered density will vary depending on terrain, land cover, and the prevalence of water bodies. All discernible laser returns were processed for the output dataset.

All areas were surveyed with an opposing flight line side-lap of greater than or equal to 50% (greater than or equal to 100% overlap) in order to reduce laser shadowing and increase surface laser painting. To accurately solve for laser point position (geographic coordinates x, y and z), the positional coordinates of the airborne sensor and the attitude of the aircraft were recorded continuously throughout the LiDAR data collection mission. Position of the aircraft was measured twice per second (2 Hz) by an onboard differential GPS unit, and aircraft attitude was measured 200 times per second (200 Hz) as pitch, roll and yaw (heading) from an onboard inertial measurement unit (IMU). To allow for post-processing correction and calibration, aircraft and sensor position and attitude data are indexed by GPS time.

Upon completion of data acquisition, QSI processing staff initiated a suite of automated and manual techniques to process the data into the requested deliverables. Processing tasks included GPS control computations, smoothed best estimate trajectory (SBET) calculations, kinematic corrections, calculation of laser point position, sensor and data calibration for optimal relative and absolute accuracy, and LiDAR point classification. Processing methodologies were tailored for the landscape.

Lidar Processing Steps

Resolve kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. Develop a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with sensor head position and attitude recorded throughout the survey. Software used - Waypoint Inertial Explorer v.8.7

Calculate laser point position by associating SBET position to each laser point return time, scan angle, intensity, etc. Create raw laser point cloud data for the entire survey in *.las (ASPRS v. 1.4) format. Convert data to orthometric elevations by applying a geoid correction. Software used - Waypoint Inertial Explorer v.8.7 Leica CloudPro v. 1.2.4

Import raw laser points into manageable blocks to perform manual relative accuracy calibration and filter erroneous points. Classify ground points for individual flight lines. Software used - TerraScan v.18

Using ground classified points per each flight line, test the relative accuracy. Perform automated line-to-line calibrations for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calculate calibrations on ground classified points from paired flight lines and apply results to all points in a flight line. Use every flight line for relative accuracy calibration. Software used - TerraMatch v.18

Classify resulting data to ground and other client designated ASPRS classifications. Assess statistical absolute accuracy via direct comparisons of ground classified points to ground control survey data. Software used - TerraScan v.18, TerraModeler v.18

The NOAA Office for Coastal Management (OCM) downloaded this data set from the Washington Lidar Portal. The total number of files downloaded and processed was 547.

No metadata record was provided with the data. This record is populated with information from the Quantum Spatial, Inc. technical report downloaded from the Washington Dept. of Natural Resources Washington Lidar Portal. The technical report is available for download from the Washington Lidar Portal. The link is provided in the URL section of this metadata record.

The data were in Washington State Plane South (NAD83 HARN), US survey feet coordinates and NAVD88 (Geoid12B) elevations in feet. From the provided report, the data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 17 - Bridge Deck. The presence of Class 10 points were noted during OCM processing. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available in the DAV are: 1, 2, 7, 9, 10, 17.

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 convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid12B model, to convert from Washington State Plane South (NAD83 HARN), US survey feet coordinates to geographic coordinates, to convert from elevations in feet to meters, to filter out negative elevations less than -200 feet, to assign the geokeys, to sort the data by gps time and zip the data to database and to the Amazon s3 bucket.