Data Management Plan

This dataset represents the Oregon LiDAR Consortium Big Windy project area. The dataset encompasses 131,357 acres in Josephine, Douglas, and Curry Counties, Oregon. The native projection is Oregon Statewide Lambert, Lambert Conformal Conic, units are in international feet. The native horizontal datum is NAD83(2011) and the native vertical datum is NAVD88 (Geoid 12B). Quantum Geospatial Inc. collected the lidar and created this dataset for the Oregon Department of Geology and Mineral Industries (DOGAMI).

OCM received 13 ESRI GRID files from the Oregon Lidar Consortium and processed them to the Data Access Viewer (DAV) as well as html. Additionally, the lidar point cloud data are available and may be found in the Related Items section below.

Process Steps:

• 2017-09-11 00:00:00 - Lidar data acquisition began on June 18th, 2017 and was completed June 21, 2017. The survey utilized the Leica ALS80 laser systems mounted in a Cessna Caravan 208B. Near nadir scan angles were used to increase penetration of vegetation to ground surfaces. Ground level GPS and aircraft IMU were collected during the flight. Processing.1. Airborne GPS and IMU data were merged to develop a Single Best Estimate (SBET) of the lidar system trajectory for each flight line. Flight lines and data were reviewed to ensure complete coverage of the study area and positional accuracy of the laser points. 2. Laser point return coordinates were computed using ALS Post Processor software and IPAS Pro GPS/INS software, based on independent data from the LiDAR system, IMU, and aircraft. 3. The raw LiDAR file was assembled into flight lines per return with each point having an associated x, y, and z coordinate. 4. Visual inspection of swath to swath laser point consistencies within the study area were used to perform manual refinements of system alignment. 5. Custom algorithms were designed to evaluate points between adjacent flight lines. Automated system alignment was computed based upon randomly selected swath to swath accuracy measurements that consider elevation, slope, and intensities. Specifically, refinement in the combination of system pitch, roll and yaw offset parameters optimize internal consistency. 6. Noise (e.g., pits and birds) was filtered using ALS postprocessing software, based on known elevation ranges and included the removal of any cycle slips. 7. Using TerraScan and Microstation, ground classifications utilized custom settings appropriate to the study area. 8. The corrected and filtered return points were compared to the RTK ground control points collected to verify the vertical and horizontal accuracies. 9. Points were output as laser points, TINed and GRIDed surfaces. (Citation: See the survey report PDF for this project area.)
• 2017-09-13 00:00:00 - Lidar Post-Processing: The calibrated and controlled lidar swaths were processed using automatic point classification routines in proprietary software. These routines operate against the entire collection (all swaths, all lifts), eliminating character differences between files. Data were then distributed as virtual tiles to experienced lidar analysts for localized automatic classification, manual editing, and peer-based QC checks. Supervisory QC monitoring of work in progress and completed editing ensured consistency of classification character and adherence to project requirements across the entire project. All classification tags were stored in the original swath files. After completion of classification and final QC approval, the NVA and VVA for the project were calculated. Sample areas for each land cover type present in the project were extracted and forwarded to the client, along with the results of the accuracy tests. Upon acceptance, the complete classified lidar swath files were delivered to the client.
• 2019-10-21 00:00:00 - The NOAA Office for Coastal Management (OCM) received 13 DEM files in ESRI GRID format from DOGAMI. The data were in Oregon Lambert (NAD83 2011), international feet coordinates and NAVD88 (Geoid12B) elevations in international feet. OCM performed the following processing on the data for Digital Coast storage and provisioning purposes: 1. An internal OCM script was run to supply geo-referencing tags and convert the data to cloud-optimized GeoTIFF format (COGS).

Data is available online for custom downloads

Data is backed up to tape and to cloud storage.