2017 WRCD USFS Lidar DEM: Wenatchee, WA

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Information on the NOAA Office for Coastal Management (OCM)

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This graphic displays the footprint for this lidar data set.

Link to data set report.

This data source was used (along with the airborne GPS/IMU Data) to georeferencing of the Lidar point cloud data used to create all contracted Lidar derivative products. Version of the LAS Standard 1.4 / point data record format 6.

Aerial Lidar Acquisition: Aerial data collection was acquired in five (5) flight missions, using the ALS70 SN#7123/7225 at an altitude of 2900 meters AGL. Airborne GPS and IMU data was collected during the acquisition. Data acquisition was completed on October 15, 2017.

Lidar Pre-processing: Airborne GPS and IMU data were merged to develop a Smoothed Best Estimate Trajectory (SBET) of the lidar system for each lift. Lidar ranging data were initially calibrated using previous best parameters for this instrument and aircraft. Relative calibration was evaluated using advanced plane-matching analysis and parameter corrections derived. This process was repeated interactively until residual errors between overlapping swaths, across all project lifts, was reduced to 2 cm or less. Data were then block adjusted to match surveyed calibration control. Raw data NVA were checked using independently surveyed check points. Swath overage points were identified and tagged within each swath file

Initial processing of the GPS data was processed using Inertial Explorer. The solution file was generated and CloudPro software was used to generate georeferenced laser returns which were then processed in strip form allowing for the QC of the overlap between strips (lines). The data from each line were combined and automated classification routines run to determine the initial surface model. This initial surface model was then verified to the surveyed test points.

Lidar Post-Processing: The calibrated and controlled lidar swaths were processed using automatic point classification routines in TerraSolid 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 are stored in the original swath files. After completion of classification and final QC approval, the NVA and VVA for the project are 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.

Lidar Classification: The calibrated Lidar data was run through automated classification routines and then manually checked and edited. The data was classified into the following classes: 1-unclassified*, 2-ground, 7-low noise and 18-high noise

Bare-Earth DEM Creation: Bare-Earth digital elevation models (DEMs) were derived using the bare-earth (ground) lidar points, all DEMs were created with a grid spacing of 1 meter. The DEMs were cut to tiles of 1500 meters X 1500 meters index.

Data were received from US Forest Service by NOAA Office for Coastal Management in Imagine (*.img) format with the Albers USFS region 6 projection (EPSG:9674) in meters. Vertical values were NAVD88 with Geoid12b. The format was converted to cloud optimized geotiff for ingest into the Digital Coast Data Access Viewer.