Quantum Spatial, Inc. 20191113 Raster Digital Data Product: The highest hit digital surface model (DSM) represents the earth's surface elevation with all natural and anthropogenic features included. It was derived from NIR LiDAR data using the highest hit method. Geographic Extent: This dataset and derived products encompass Olympic Peninsula Area 2, an approximately 1,547 square mile portion of the Olympic Peninsula 3DEP project area, which covers approximately 5,352 square miles of Western Washington in the Olympic Peninsula region. Dataset Description: RAW flight line swaths were processed to create 3,371 classified LAS 1.4 files delineated in 1/100th USGS Quadrangle tiles. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. Additional derived products include intensity images, hydro-flattened DEMs, highest hit surface models, and 3D breaklines of rivers, lakes, coastlines and bridges within the study area. Tiled deliverables that are split between delivery boundaries have been given an extension of "_[delivery#]" at the end of the file name. For this delivery, tiled deliverables have been given an "_2" extension. Ground Conditions: Acquisition below aircraft free of smoke, fog and cloud cover. Ground Control Points were acquired and calibrated by Quantum Spatial, Inc. The purpose of the lidar data was to produce a high accuracy 3D dataset that meets all necessary standards laid out by the Olympic Peninsula 3DEP contract. The raw lidar point cloud data were used to create classified lidar LAS files, intensity images, hydro-flattened DEMs, and 3D breaklines of rivers, lakes, coastlines, and bridges within the study area. CONTRACTOR:Quantum Spatial, Inc. Ground Control Points were acquired and calibrated by Quantum Spatial, Inc. Data acquisition was coordinated by Quantum Spatial. Quantum Spatial, Eagle Aerial, and Airborne Imaging all acquired portions of this project area. All lidar data calibration, and follow-on processing were completed by Quantum Spatial. Raster File Type = ESRI GRID Bit Depth/Pixel Type = 32-bit float Raster Cell Size = 3 foot Interpolation or Resampling Technique = Highest Hit Method Required Vertical Accuracy = 19.6 cm 20180219 20190425 ground condition Complete None Planned -124.165360 -123.019618 46.991462 46.118543 725542.297654 1001924.230949 615029.021279 307270.921794 none model LAS Point Cloud remote sensing Elevation data lidar High Hit none Washington Olympic Peninsula Pacific County Lewis County Wahkiakum County Grays Harbor County No restrictions apply to these data. None. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Acknowledgment of the U.S. Geological Survey would be appreciated for products derived from these data. Classified LAS files were tested by QSI for both vertical and horizontal accuracy. All data is seamless from one tile to the next, no gaps or no data areas. LAS files include all data points collected. No points have been removed or excluded. Shaded relief images have been visually inspected for data errors such as pits, border artifacts, and shifting. Lidar flight lines have been examined to ensure consistent elevation values across overlapping flight lines. The raw point cloud is of good quality and data passes Non Vegetated Vertical Accuracy specifications. The project specifications require the accuracy (ACCz) of the derived DEM be calculated and reported in two ways: 1. The required NVA is: 19.6 cm at a 95% confidence level, derived according to NSSDA, i.e., based on RMSE of 10 cm in the “bare earth” and "urban" land cover classes. This is a required accuracy. The NVA was tested with 82 checkpoints located in bare earth and urban (non-vegetated) areas. 2. Vegetated Vertical Accuracy (VVA): VVA shall be reported for "brushlands/low trees" and "tall weeds/crops" land cover classes. The target VVA is: 29.4 cm at the 95th percentile, derived according to ASPRS Guidelines, Vertical Accuracy Reporting for Lidar Data, i.e., based on the 95th percentile error in all vegetated land cover classes combined. This is a target accuracy. The VVA was tested with 63 checkpoints located in tall weeds/crops and brushlands/low trees (vegetated) areas. The checkpoints were distributed throughout the project area and were surveyed using GPS techniques. See survey report for additional survey methodologies. AccuracyZ has been tested to meet 19.6 cm or better Non-Vegetated Vertical Accuracy at 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA); assessed and reported using National Digital Elevation Program (NDEP)/ASRPS Guidelines. 0.086 Tested 0.086 meters NVA at a 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA). The NVA of the derived raster DEM was calculated using 68 independent checkpoints located in the Bare Earth and Urban land cover categories with a resulting RMSE of 0.044 meters. 0.219 Tested 0.219 meters VVA at the 95th percentile. The VVA of the derived raster DEM was calculated using 55 independent checkpoints located in forest, shrub, and tall grass land cover classes. LiDAR Pre-Processing: 1. Review flight lines and data to ensure complete coverage of the study area and positional accuracy of the laser points. 2. Resolve kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. 3. Develop a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with sensor head position and attitude recorded throughout the survey. 4. Calculate laser point position by associating SBET position to each laser point return time, scan angle, intensity, etc. Create raw laser point cloud data for the entire survey in *.las format. Convert data to orthometric elevations by applying a geoid correction. 5. Import raw laser points into manageable blocks to perform manual relative accuracy calibration and filter erroneous points. Classify ground points for individual flight lines. 6. Using ground classified points per each flight line, test the relative accuracy. Perform automated line-to-line calibrations for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calculate calibrations on ground classified points from paired flight lines and apply results to all points in a flight line. Use every flight line for relative accuracy calibration. 7. Adjust the point cloud by comparing ground classified points to supplemental ground control points. Base_Station_Control, SBETs, SGCPs, RAW_LiDAR 20190425 LiDAR Post-Processing: 1. Classify data to ground and other client designated classifications using proprietary classification algorithms. 2. Manually QC data classification 3. After completion of classification and final QC approval, calculate NVA and VVA, and density information for the project. 20190425 High Hit DEM creation: High Hit DSMs were created using the highest hit method from all valid return. 20190425 Raster Pixel State Plane Coordinate System 1983 4602 45.83333333 47.33333333 -120.5 45.33333333 1640416.667 0 coordinate pair 3 3 U.S. Survey Feet North American Datum of 1983 (CORS96) defined (HARN) GRS_1980 6378137.0 298.257223563 North American Vertical Datum of 1988, Geoid 03 0.01 U.S. Survey Feet Explicit elevation coordinate included with horizontal coordinates 20190810 20190810 Quantum Spatial, Inc. mailing and physical

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