Data Management Plan (Deprecated)

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 link provided in the URL section of this metadata record.

In October 2017, Quantum Spatial (QSI) was contracted by the Lummi Nation to collect topobathymetric Light Detection and Ranging (LiDAR) data in the spring of 2018 for the South Fork Nooksack River site in Washington. The South Fork Nooksack River area of interest stretches approximately 19 miles upriver from Saxon, in northwestern Washington State. Traditional near-infrared (NIR) LiDAR was fully integrated with green wavelength (bathymetric) LiDAR in order to provide a comprehensive topobathymetric LiDAR dataset. Data were collected to aid the Lummi Nation in assessing the channel morphology and topobathymetric surface of the study area to support river restoration activities on the South Fork Nooksack River.

Lineage Statement:
The NOAA Office for Coastal Management (OCM) downloaded the GeoTiff files from the Washington Lidar Portal.

Process Steps:

• Planning: In preparation for data collection, QSI reviewed the project area and developed a specialized flight plan to ensure complete coverage of the South Fork Nooksack River LiDAR study area at the target combined point density of greater than or equal to 12 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, and channel flow rates were reviewed. Due to a number of environmental and logistical issues the acquisition of the project area data was split into two collection windows.
• Ground Survey Points Ground survey points were collected using real time kinematic (RTK) survey techniques. The WSRN RTN broadcasted kinematic corrections to a roving 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, the rover records data while stationary for five seconds, then calculates the pseudorange position using at least three one-second epochs. 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. QSI typically collects bathymetric survey points along the water's edge and in submerged locations within the project area in order to assess topobathymetric surface model accuracy; however, QSI ground crew were unable to collect bathymetric survey points for this project due to safety and access concerns along the South Fork Nooksack River. Base Stations Two permanent base stations from the Washington State Reference Network (WSRN) were utilized as static base stations for the SF Nooksack airborne acquisition, providing control within 13 nautical miles of the mission areas for LiDAR flight. To correct the continuously recorded onboard measurements of the aircraft position, QSI utilized static Global Navigation Satellite System (GNSS) data collected at 1 Hz recording frequency by the base station. During post-processing, the static GPS data were triangulated with nearby Continuously Operating Reference Stations (CORS) using the Online Positioning User Service (OPUS) to verify and update record positions as needed to align with the National Spatial Reference System (NSRS).
• Airborne Survey The LiDAR survey was accomplished using a Leica ALS80 system dually mounted with a Riegl VQ-820-G topobathymetric sensor 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 South Fork Nooksack River project area. The Leica ALS80 laser system can record unlimited range measurements (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. The settings used were to yield an average pulse density of greater than or equal to 12 points/m2 over the South Fork Nooksack River project area. All areas were surveyed with an opposing flightline side-lap of greater than or equal to 50% (greater than or equal to100% 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.8.0 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, TerraMatch v.18 Import raw laser points into manageable blocks (less than 500 MB) to perform manual relative accuracy calibration and filter erroneous points. Classify ground points for individual flightlines. Software used - TerraScan v.18 Using ground classified points per each flightline, 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 flightlines and apply results to all points in a flightline. Use every flightline for relative accuracy calibration. Software used - TerraMatch v.18, RiProcess v1.8.5 Apply refraction correction to all subsurface returns. Software used - LAS Monkey 2.3.3 (QSI proprietary software) Classify resulting data to ground and other client designated ASPRS classifications (Table 6). 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 1.0 meter pixel resolution. Software used - TerraScan v.18, TerraModeler v.18, ArcMap v. 10.3.1
• 2022-08-26 00:00:00 - The NOAA Office for Coastal Management (OCM) downloaded 4 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.

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Data is backed up to tape and to cloud storage.