Quantum Spatial, Inc. 20181011 LiDAR Point Cloud Product: These lidar data are processed Classified LAS 1.4 files, formatted to 9,312 3000 ft x 3000 ft tiles. (Please note that 5 tiles contain no data due to their size and location over water and therefore do not exist. These tiles include WA3_07786, WA3_08558, WA3_08745, WA3_08821, and WA3_08825.) Geographic Extent: This dataset and derived products encompass an area covering approximately 2,917 square miles of south eastern Washington. The full area is a mix of QL1 and QL2 lidar. Dataset Description: RAW flight line swaths were processed to create 9,312 classified LAS 1.4 files delineated in 3000 ft x 3000 ft tiles. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. From the classified point cloud additional derived products include intensity images, hydro-flattened breaklines, hydro-flattened DEMs, and highest hit surface models of the study area. Ground Control Points were acquired and calibrated by Quantum Spatial, Inc. Data acquisition was coordinated by Quantum Spatial and all lidar data calibration, and follow-on processing were completed by Quantum Spatial Ground Conditions: Acquisition below aircraft free of smoke, fog and cloud cover. A small area with remaining snow on the ground was noticed in the data. The difference was only noticed due to the area being acquired at two different times. The high snow level was classified to class 21 and the overlapping mission acquired without snow on the ground was used for ground model creation. A shape of this area has been provided. The purpose of the lidar data was to produce a high accuracy 3D dataset that meets all necessary standards laid out by the 3DEP initiative. The raw lidar point cloud data were used to create classified lidar LAS files, intensity images, hydro-flattened DEMs, highest hit surface models, and 3D breaklines of rivers, lakes, and bridges within the study area. USGS Contract No. G16PC00016 CONTRACTOR: Quantum Spatial, Inc. Ground Control Points were acquired and calibrated by Quantum Spatial, Inc. Data acquisition was coordinated by Quantum Spatial and all lidar data calibration, and follow-on processing were completed by Quantum Spatial, the prime contractor. U.S. Geological Survey National Geospatial Program LIDAR Base Specification, Version 1.2 Leica ALS80 15 0.5 m 4 0.35 8 1650 145 30 48 335000 2.5 0.75 1064 1 0.22 884 67 National Geodetic Survey (NGS) Geoid12B U.S. Geological Survey National Geospatial Program LIDAR Base Specification, Version 1.2 Leica ALS80 15 1 1 0.70 2 2100 145 40 38 250000 2.5 0.75 1064 1 0.22 1529 63 National Geodetic Survey (NGS) Geoid12B 0.5 0.083 85 0.077 85 .206 79 1.4 6 Witheld points are identified in these files using the standard LAS Witheld bits. Swath overage points are identified in these files using the standard LAS Overlap bits 16 1 Processed, but Unclassified 2 Bare earth ground 7 Low Noise 9 Water 10 Ignored Ground Near Breakline 17 Bridge Decks 21 Snow 20171003 20180704 ground condition Complete None Planned -119.048030 -117.183731 46.732836 45.961812 2009394.211531 2472308.459371 513792.507063 246441.682612 none model LAS Point Cloud remote sensing Elevation data lidar none Washington 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. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data. Data covers the entire area specified for this project. These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications. The specifications require that raw Non-vegetated Vertical Accuracy (NVA) be computed from the both the raw lidar point cloud swath files and the derived DEMs. Additionally, the Vegetated Vertical Accuracy (VVA) is computed from the derived DEMs. The NVA was tested with 85 independent check points located in open terrain, and distributed throughout the project as feasible, while VVA was tested with 79 independent check points located in a variety of vegetation classes distributed throughout the project area. These check points were not used in the calibration or post processing of the lidar point cloud data. Specifications for this project require that the NVA be 19.6 cm meters or better AccuracyZ at 95% confidence level and that the VVA be 29.4 cm or better AccuracyZ at the 95th percentile. 0.083 Tested 0.083 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 raw lidar point cloud files was calculated against TINs derived from the final calibrated and controlled data using 85 independent checkpoints located in Bare Earth and Urban land cover classes. 0.077 Tested 0.077 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 classified lidar point cloud files was calculated against rasters derived from the final calibrated, controlled, and classified data using 85 independent checkpoints located in Bare Earth and Urban land cover classes. 0.206 Tested 0.206 meters VVA at the 95th percentile as defined by the National Standards for Spatial Data Accuracy (NSSDA). The VVA of the classified lidar point cloud files was calculated against rasters derived from the final calibrated, controlled, and classified data using 85 independent checkpoints located in forest, shrub, and tall grass land cover classes. Quantum Spatial 20181011 vector digital and tabular data Corvallis, OR Quantum Spatial hard drive 20181011 publication date Base_Station_Control This data source was used (along with the airborne GPS/IMU data) to georeference the LiDAR point cloud data. Quantum Spatial 20181011 vector digital and tabular data Corvallis, OR Quantum Spatial hard drive 20181011 publication date SBETs This data source was used (along with base station control data) to georeference the LiDAR point cloud data. Quantum Spatial 20181011 vector digital and tabular data Corvallis, OR Quantum Spatial hard drive 20181011 publication date SGCPs This data source was used to refine airborne GPS positional accuracy during the calibration process. Quantum Spatial 20181011 vector digital and tabular data Corvallis, OR Quantum Spatial hard drive 20181011 publication date QCPs This data source was used to assess the accuracy of LiDAR point cloud data. Quantum Spatial 20181011 LiDAR data Corvallis 20181011 hard drive 20171003 20180704 ground condition RAW_LiDAR This data source was used to populate the LiDAR point cloud data. 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 (less than 500 MB) 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 20181011 Quantum Spatial mailing and physical

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Corvallis OR 97330 USA 541-752-1204 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 final NVA and VVA for the project using ground control quality check points. Base_Station_Control, SBETs, QCPs, RAW_LiDAR 20181011 Classified_LiDAR Quantum Spatial mailing and physical

1100 NE Circle Blvd, Suite 126

Corvallis OR 97330 USA 541-752-1204 Point State Plane Coordinate System 1983 4602 45.83333333333334 -120.5 45.33333333333334 1640416.666666667 0.0 coordinate pair 0.01 0.01 US survey feet North American Datum of 1983 2011 Geodetic Reference System 80 6378137.0 298.257222101 North American Vertical Datum of 1988 Geoid12B 0.01 US survey feet Explicit elevation coordinate included with horizontal coordinates 20181011 Quantum Spatial, Inc. mailing and physical

1100 NE Circle Blvd, Suite 126

Corvallis OR 97330 USA 541-752-1204 FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998