2018 WA DNR Topobathy Lidar DEM: Tacoma Water Service Area - Green River, WA

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Bulk download of data files in GeoTiff format, WA State Plane South NAD83(HARN) US survey feet coordinates and orthometric heights in feet.

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 lidar point data from which these raster Digital Elevation Model (DEM) data were created.

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 Tacoma Water Service Area: Green River LiDAR study area at the target combined point density of greater than or equal to 12.0 points/m2. 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, potential air space restrictions, channel flow rates, and water clarity were reviewed.

Ground Survey Points

Ground control surveys, including monumentation 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.

Ground survey points were collected using real time kinematic (RTK), post-processed kinematic (PPK), and fast-static (FS) survey techniques. A QSI Trimble R7 base unit or the WSRN RTN broadcasted a kinematic correction to a roving Trimble R8 GNSS or Trimble R6 GNSS receiver. 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. When collecting RTK and PPK data, the rover records data while stationary for five seconds, then calculates the pseudorange 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 for post-processing. Relative errors for any GSP position must be less than 1.5 cm horizontal and 2.0 cm vertical in order to be accepted.

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 base station locations and may not be equitably distributed throughout the study area.

Base Stations

Base stations were used for collection of ground survey points using real time kinematic (RTK), post processed kinematic (PPK), and fast static (FS) survey techniques. Base station locations were selected with consideration for satellite visibility, field crew safety, and optimal location for GSP coverage. QSI established one monument and utilized four permanent base stations from the Washington State Reference Network (WSRN) for the Tacoma Water Service Area: Green River project. QSI's professional land surveyor, Mark Meade (WAPLS#54019) oversaw and certified the establishment of all monuments and the ground survey.

QSI collected static Global Navigation Satellite System (GNSS) occupations (1 Hz recording frequency) for the base station locations. 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 to ensure alignment with the National Spatial Reference System (NSRS). Multiple independent sessions for each position were processed to confirm height measurements and to refine positional accuracy.

Monuments were established according to the national standard for geodetic control networks, as specified in the Federal Geographic Data Committee (FGDC) Geospatial Positioning Accuracy Standards for geodetic networks. This standard provides guidelines for classification of monument quality at the 95% confidence interval as a basis for comparing the quality of one control network to another.

For the Tacoma Water Service Area: Green River topobathymetric LiDAR project, the monument coordinates contributed no more than 2.8 cm of positional error to the geolocation of the final ground survey points and LiDAR, with 95% confidence.

Airborne Survey

The LiDAR survey was accomplished using a Riegl VQ-820G green laser system combined with an Optech Orion-H NIR laser system dually mounted in a Cessna Caravan. The Riegl VQ-820-G uses a green wavelength (532 nm) laser that is capable of collecting high resolution vegetation and topography data, as well as penetrating the water surface with minimal spectral absorption by water. The recorded waveform enables range measurements for all discernible targets for a given pulse. The typical number of returns digitized from a single pulse range from 1 to 7 for the Tacoma Water Service Area: Green River project area. The Optech laser system can record unlimited range measurements (returns) per pulse, but typically does not record more than 5 returns per pulse. 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 percent (greater than or equal to 100 percent 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.

Riegl's RiProcess software was used to facilitate bathymetric return processing. Once bathymetric points were differentiated, they were spatially corrected for refraction through the water column based on the angle of incidence of the laser. QSI refracted water column points using QSI's proprietary LAS processing software, LAS Monkey. The resulting point cloud data were classified using both manual and automated techniques. Processing methodologies were tailored for the landscape. Brief descriptions of these tasks are shown below.

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 - POSPac MMS v.8.2

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 - RiProcess v1.8.5

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 and RiProcess v1.8.5

Apply refraction correction to all subsurface returns. Software used - LAS Monkey 2.3.4 (QSI proprietary software)

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

Generate bare earth models as triangulated surfaces. Generate highest hit models as a surface expression of all classified points. Export all surface models as ESRI GRIDs at a 3.0 foot pixel resolution. Software used - TerraScan v.18.1 TerraModeler v.18.1 ArcMap v. 10.3.1

The NOAA Office for Coastal Management (OCM) downloaded 7 raster DEM files in GeoTiff format from the Washington Lidar Portal. The data were in Washington State Plane South NAD83(HARN), US survey feet coordinates and NAVD88 (Geoid12B) elevations in feet. The bare earth raster files were at a 3 feet grid spacing. No metadata record was provided with the data. This record is populated with information from the Quantum Geospatial, Inc. technical report downloaded from the Washington Dept. of Natural Resources Washington Lidar Portal.

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

1. Used internal an script to assign the EPSG codes (Horizontal EPSG: 2927 and Vertical EPSG: 6360) to the GeoTiff formatted files.

2. Copied the files to https.