NV5 Geospatial 20231117 Lidar Point Cloud Product: Classified LAS 1.4 files, formatted to 5,757 individual 1,000m x 1,000m tiles covering the CA North Sierra 8 2022 project area. Geographic Extent: This dataset and derived products encompass an area covering approximately 1,386,980 acres of Sierra Nevada Northern region. Dataset Description: The CA North Sierra 8 2022 Lidar project called for the planning, acquisition, and processing of lidar data collected to Quality Level 1 (QL1) Standards. Project specifications are based on the U.S. Geological Survey National Geospatial Program Lidar Base Specification, Version 2022 rev. A. The data was developed based on the NAD83 2011 horizontal datum and the NAVD88 Geoid 18 vertical datum. Data was projected in UTM Zone 11 N, Meters. Lidar data was delivered as calibrated and classified LAS 1.4 files. Non-Vegetated Vertical Accuracy (NVA) was assessed using 0 check points located on bare earth in clear, unobstructed areas. Vegetated Vertical Accuracy (VVA) was assessed using 0 check point in the Tall Grass, Forest, and Shrub landcover types. Single swath nominal pulse spacing (NPS) was designed to be 0.35m at nadir. Aggregate Nominal Pulse Spacing (ANPS) was calculated to be 0.22m using all valid first return points. Derived products include: Intensity Imagery, Hydro-flattened Bare Earth Digital Elevation Models (DEMs), Interpolated DEMs, Highest Hit Digital Surface Models (DSMs), and 3D waters edge breaklines. Ground Condition: Acquisition occurred free of smoke, fog and cloud cover The purpose of the lidar data was to produce a high accuracy 3D dataset that meets all necessary standards laid out by the 3D Elevation Program and the CA North Sierra 8 2022 contract. The lidar point cloud data were used to create classified lidar LAS files, intensity images, hydro-flattened DEMs, highest hit DSMs, and 3D breaklines of rivers and lakes within the study area. USGS Contract No. 140G0221D0012 CONTRACTOR: NV5 Geospatial. Ground Control Points were acquired and calibrated by NV5 Geospatial. All lidar data acquisition, calibration, and follow-on processing were completed by NV5 Geospatial. U.S. Geological Survey National Geospatial Program LIDAR Base Specification, Version 2022 rev. A. Riegl 1560ii-S 15 0.35 8 0.22 20.35 2,500 145 58.5 Uniform Point Spacing 757 3 0.9 1,064 1 0.23 2,800 55 Geoid 18 0 0 0 1.4 6 Withheld (ignore) points are identified in these files using the standard LAS Withheld bit. Swath "overage" points was not used for the CA_SierraNevada 2022 work unit. 16 1 Processed, but Unclassified 2 Bare earth ground 7 Low Noise 9 Water 17 Bridge Decks 18 High Noise 20 Ignored Ground Near Breaklines 21 Snow 22 Temporal Exclusion 20211118 20220825 ground condition In work As needed -120.061858 -118.902833 39.333061 38.008265 236000.000000 333000.000000 4355528.016000 4211000.000000 none model LAS Point Cloud remote sensing Elevation data Lidar none California These data are considered provisional as they have not yet been reviewed by client or designated agency. 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. Bayes Strip Align 2.20, POSPac MMS v.8.5, PofImport v.1.7.4, Georun V.6.1.1 Microstation CONNECT version, TerraScan Version 20, TerraModeler Version 20, TerraMatch Version 20, ESRI ArcGIS 11.7, Windows 11 Operating System Classified LAS files were tested by NV5 for vertical accuracy. All data is seamless from one tile to the next, no gaps or no data areas. 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. This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 0 (cm) RMSEx / RMSEy Horizontal Accuracy Class which equates to the Positional Horizontal Accuracy = +/- 0 cm at a 95% confidence level. 0 Lidar horizontal accuracy is a function of Global navigation Satellite System (GNSS) derived positional error, flying altitude, and INS derived attitude error. The obtained RMSEr value is multiplied by a conversion factor of 1.7308 to yield the horizontal component of the National Standards for Spatial Data Accuracy (NSSDA) reporting standard where a theoretical point will fall within the obtained radius 95 percent of the time. Using a flying altitude of 2,500 meters, an IMU error of 0 decimal degrees, and a GNSS positional error of 0 meters, this project was compiled to meet 0 m meters horizontal accuracy at the 95% confidence level. This data set was produced to meet ASPRS Positional Accuracy Standard for Digital Geospatial Data (2014) for a 11-cm RMSEz Vertical Accuracy Class. The NVA was tested with 0 independent check points located in open terrain and distributed throughout the project as feasible. 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 0.196 meters or better AccuracyZ at 95% confidence level. Vegetated Vertical Accuracy (VVA) is also to be computed from the classified lidar point cloud. The VVA was tested with 0 check points in the Tall Grass, Forest, Pine, and Shrub land cover types. These check points was also withheld from the calibration and post processing of the lidar point cloud data and distributed throughout the project area as feasible. Specifications for this project require that the VVA be 0.300 meters or better AccuracyZ at the 95th percentile. 0 The 0 independent NVA check points were surveyed using the closed level loop technique. Elevations interpolated from the ground classified lidar surface were compared to the elevation values of the surveyed NVA check points. The RMSE was computed to be 0 meters resulting in an AccuracyZ at the 95% confidence level of 0 meters. NVA AccuracyZ has been tested and meets the required 0.196 meter NVA at 95% confidence level using (RMSEz * 1.9600) for the ground classified lidar point cloud as defined by the National Standards for Spatial Data Accuracy (NSSDA) and herein reported using National Digital Elevation Program (NDEP)/ASPRS Guidelines. 0 The 0 VVA check points were surveyed using the closed level loop technique. Elevations for this point was compared to the elevations of the ground classified lidar point cloud resulting in an AccuracyZ of 0 meters at the 95th percentile. AccuracyZ has been tested on the classified point cloud and meets the required 0.300 meter VVA using the 95th percentile of the absolute value of all vertical errors in all combined vegetation classes as defined by the National Standards for Spatial Data Accuracy (NSSDA); and herein reported using National Digital Elevation Program (NDEP)/ASPRS Guidelines. 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 18 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. CA North Sierra 8 2022 Ground Control 20231117 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 values for the project using ground control quality check points. Final density information will also be calculated. 20231117 Vector Point 155,627,037,187 Universal Transverse Mercator 11 0.9996 -117.0 0.0 500000.0 0.0 coordinate pair 0.01 0.01 Meters North American Datum of 1983 2011 GRS_1980 6378137.0 298.257222101 North American Vertical Datum of 1988 Geoid 18 0.01 Meters Explicit elevation coordinate included with horizontal coordinates 20231117 20231117 NV5 Geospatial mailing and physical

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