Data Management Plan

The dataset encompasses 1221.6 square miles in portions of the greater Portland Metro area in the state of 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 was originally Oregon Statewide Lambert Conformal Conic, units are in International Feet. The horizontal datum is NAD 83 (2011) and the vertical datum was 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). Watershed Sciences, Inc. collected the lidar and created this data set for the Oregon Department of Geology and Mineral Industries (DOGAMI).

Original contact information:

Contact Name: Jacob Edwards

Contact Org: DOGAMI

Phone: 971-673-1557

Email: jacob.edwards@oregon.gov

Process Steps:

• 2014-07-09 00:00:00 - Acquisition. LiDAR data acquisition was conducted from July 9th, 2014 to September 7th, 2014. The survey utilized a Leica ALS70 and an Optech Orion H laser system mounted in a Cessna 208B. 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.
• 2014-09-07 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).
• 2017-09-01 00:00:00 - The NOAA Office for Coastal Management (OCM) received the files in the proprietary ESRI grid format. The files were converted to floating point GeoTIFF format and ingested into the Digital Coast Data Access Viewer system. (Citation: DEM files)

This data can be obtained on-line at the following URL: https://coast.noaa.gov/dataviewer

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