Data Management Plan

The dataset encompasses 522 square miles in portions of Wallowa and Union Counties, Oregon. The highest hit digital surface models (DSM) represent the earth's surface with all vegetation and human-made structures included. The highest hit DSMs were derived from lidar data using TIN processing of the all-return point cloud. The bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from lidar data using TIN processing of the ground point returns. The DSM and DEM grid cell size is 3 feet. The projection is is Oregon Statewide Lambert Conformal Conic, units are in International Feet.The horizontal datum is NAD 83 (2011)and vertical datum is NAVD88 (Geoid 12A). Some elevation values have been interpolated across areas in the models where there is no elevation data (e.g. over water, over dense vegetation). Watershed Sciences, Inc. collected the lidar and created this data set for the Oregon Department of Geology and Mineral Industries (DOGAMI).

Lineage Statement:
Watershed Sciences Inc. has collected Light Detection and Ranging (LIDAR) data in the Milton-Freewater Study Area for the Oregon Department of Geology and Mineral Industries (DOGAMI). NOAA OCM has taken the data and ingested it into the Digital Coast Data Access Viewer for distribution.

Process Steps:

• 2016-05-28 00:00:00 - Acquisition. LiDAR data acquisition was conducted from 23-May to 5-Jun. The survey utilized a Leica ALS 80 laser system mounted in a Cessna 208. Scan angles near nadir were used to increase penetration of vegetation to ground surfaces. Ground level GPS and aircraft IMU were collected during the flight.
• 2016-07-05 00:00:00 - Processing. 1. TRAJECTORY: Aircraft trajectory (position and attitude) were calculated based on on-board GPS and IMU data with post-processing refinement through coincident static GPS collection. 2. POST-PROCESSING: Laser point return coordinates (x,y,z) were computed using sensor-specific post processing software, combining LiDAR return range and intensity information with aircraft trajectory information. 3. INITIAL QAQC: The post-processed LiDAR files were assembled into flight lines and reviewed for gaps and consistency, as well as systematic noise. 4. CALIBRATION: Custom algorithms evaluated individual swaths for misalignments based on IMU configuration as well as aircraft attitude variability. Offsets were resolved through surface and linear matching algorithms that minimize variability in elevation and slope. Descriptive statistics, thresholds, and specifications providing transparency for data calibration are discussed in the accompanying Data Report. 5. GROUND MODELING: Ground classified point cloud was generated through proprietary data processing tools, with settings and thresholds appropriate to landscape and vegetation condition. 6. ARTIFACT FILTRATION: Noise and processing artifacts were filtered using post-processing software and proprietary quality control methods. 7. ACCURACY ASSESSMENT: Vertical accuracy for the LiDAR dataset was assessed against Ground Check Points (GCP) distributed throughout the study area. See the accompanying Data Report for methodology, descriptive statistics, and relevant standards and reporting language. 8. DATA PRODUCT: LiDAR points classified as 'ground' were output as a digital elevation model (DEM). (Citation: raw lidar data)
• 2019-07-10 00:00:00 - NOAA/OCM received the data from DOGAMI in oregon lambert coordinates and NAVD88 (geoid12a) heights with international feet both horizontally and vertically. Data were converted to geographic coordinates and ellipsoid heights in meters for ingest into the Digital Coast Data Access Viewer. (Citation: processed lidar data)

The approach adopted for DOGAMI lidar surveys was comprised of three components:

1) Verify the horizontal and vertical coordinates established by Watershed Sciences for a select number of survey monuments used to calibrate the lidar survey. These surveys typically involved a minimum of two hours of GPS occupation over a known point. The collected data were then submitted to the National Geodetic Survey (NGS) Online Positioning User Service (OPUS) for post-processing against several Continuously Operating Reference Stations (CORS) operated by the NGS.

2) Collect GCPs along relatively flat surfaces (roads, paths, parking lots etc.). This step involved the collection of both continuous measurements (from a vehicle as well as from a backpack) as well as static measurements (typically 5 epochs).

3) Having collected the GCP data, the GPS data was post-processed using Trimble Business Center software. Data post-processing typically involved calibrations against at least three CORS stations as well as from local site calibrations performed in the field using those benchmarks that had been independently verified. Data is post processed to refine measurements so that horizontal and vertical errors are less than 0.02 meters (0.065 feet). Horizontal accuracy of data is tested by reoccupying a sample subset of survey monuments used for processing of lidar data. Each occupation's x and y coordinates are compared with the vendor coordinates for offsets.

Data is available online for custom downloads

Data is backed up to tape and to cloud storage.