Data Management Plan

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 September 2018, Quantum Spatial (QSI) was contracted by the Washington Department of Natural Resources (WADNR) to collect Light Detection and Ranging (LiDAR) data in the fall of 2018 for the Chelan site in central Washington. Data were collected to aid WADNR in assessing the topographic and geophysical properties of the study area to support natural resources planning and management for Chelan County, Washington. A total of 335,876 acres of 8 pulses per square meter LIDAR were acquired between 10/18/2018 and 10/27/2018. .

In addition to these the lidar point data, bare earth Digital Elevation Model (DEM) data, created from the points, are also available. These data are available for download at the link provided in the URL section of this metadata record.

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

Process Steps:

• Airborne Survey The LiDAR survey was accomplished using a Riegl VQ-1560i sensor system mounted in a Cessna Caravan. Settings were used to yield an average pulse density of 8 pulses/m2 over the Chelan project area. The Riegl laser system can record unlimited range measurements (returns) per pulse, though 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 ≥50% (≥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.
• Once the LiDAR data arrived in the laboratory, QSI employed a suite of automated and manual techniques for processing tasks. Processing tasks included: GPS, kinematic corrections, calculation of laser point position, relative accuracy testing, classification of ground and non-ground points, and assessments of statistical absolute accuracy. The general workflow for calibration of the LiDAR data was as follows: Resolve GNSS kinematic corrections for aircraft position data using kinematic aircraft GNSS (collected at 2Hz) and IMU (collected at 200Hz) data with Trimble CenterPoint PP-RTX methodologies. Used POSGNSS, PosPac MMS Develop a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with attitude data. Sensor heading, position, and attitude are calculated throughout the survey. Used POSGNSS, PosPac MMS Calculate laser point position by associating SBET position to each laser point return time, with offsets relative to scan angle, intensity, etc. This process creates the raw laser point cloud data for the entire survey in *.las (ASPRS v 1.2) format, in which each point maintains the corresponding scan angle, return number (echo), intensity, and x, y, z information. These data are converted to orthometric elevation (NAVD88 & NGVD29) by applying a Geoid correction. Used RiProcess and RiWorld Test relative accuracy using ground classified points per each flight line. Perform automated line-to-line calibrations for system attitude parameters (pitch, roll, heading), mirror flex (scale), and GNSS/IMU drift. Calibrations are performed on ground classified points from paired flight lines. Every flight line is used for relative accuracy calibration. Used TerraMatch, TerraScan, QSI Proprietary Software Assess NVA via direct comparisons of ground classified points to ground RTK survey data. Point classifications are assigned for features of interest via a combination of QSI custom algorithms and manual inspection. Used TerraScan, TerraMatch, Trimble Business Center
• 2022-06-06 00:00:00 - The NOAA Office for Coastal Management (OCM) downloaded 1387 lidar point files in LAZ 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 data had the following point classifications: 1 - Unclassified, 2 - Ground, 7 - Noise, 9 - Water, 17 - Bridge Deck, 21 - Snow. 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. That report is linked within this metadata record, please see it for additional information. 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 2011), US survey feet coordinates to geographic coordinates, to convert from elevations in feet to meters, to assign the geokeys, to sort the data by gps time and zip the data to database and to the Amazon s3 bucket.

QSI has high standards and adheres to best practices in all efforts. In the laboratory, quality checks are built in throughout processing steps, and automated methodology allows for rapid data processing. QSI's innovation and adaptive culture rises to technical challenges and the needs of clients like Washington DNR. Reporting and communication to our clients are prioritized through regular updates and meetings.

Data is available online for bulk and custom downloads.

Data is backed up to tape and to cloud storage.