2022 DOGAMI/USGS Lidar DEM: Baker County, OR

Create custom data files by choosing data area, map projection, file format, etc. A new metadata will be produced to reflect your request using this record as a base.

Bulk download of data files in the original coordinate system.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used.

Link to the reports, breaklines, metadata, spatial metadata, and shapefiles.

Link to the NV5 Geospatial lidar report.

Lidar data acquisition occurred etween May 20 and July 1, 2022. The survey utilized the Riegl 1560ii laser systems mounted in a Cessna Caravan. 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 survey points collected to verify the vertical and horizontal accuracies. 9. Points were output as laser points, TINed and GRIDed surfaces.

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. Points that were determined to be geometrically invalid, or invalid surface returns, were removed from the data set.

Raster DEM Processing: Class 2 (Ground) LiDAR points were used to create a 1-meter raster DEM. Using automated scripting routines within LasTools a GeoTIFF file was created for each tile. Each surface is reviewed using ArcMap to check for any surface anomalies or incorrect elevations found within the surface.

The NOAA Office for Coastal Management (OCM) created references to the USGS GeoTIFF files that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the raster data as it resides on Amazon Web Services (AWS).

These are the GeoTIFF files that are being accessed:

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/OR_Malheur_A22/OR_Malheur_2_A22/TIFF/