Oregon Cascades Bare Earth DEM LiDAR Survey

www.nv5.com/geospatial October 31, 2025 OSU Cascade Snow Survey, Oregon 2025 Lidar Technical Data Report Prepared For: Prepared By: Oregon State University David Hill, PhD Civil and Construction Engineering Oregon State University 220 Owen Hall Corvallis, OR 97331 NV5 Corvallis 1100 NE Circle Blvd, Ste. 126 Corvallis, OR 97330 PH: 541-752-1204 Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project TABLE OF CONTENTS INTRODUCTION ................................................................................................................................................. 1 Deliverable Products ................................................................................................................................. 2 ACQUISITION .................................................................................................................................................... 4 Planning ..................................................................................................................................................... 4 Airborne Survey ......................................................................................................................................... 4 Ground Survey ........................................................................................................................................... 7 Base Stations.......................................................................................................................................... 7 Ground Survey Points (GSPs) ................................................................................................................. 7 PROCESSING ................................................................................................................................................... 10 NIR Lidar Data .......................................................................................................................................... 10 Feature Extraction ................................................................................................................................... 12 Highest Hit Digital Surface Model Rasters ........................................................................................... 12 Intensity Image Processing .................................................................................................................. 12 RESULTS & DISCUSSION .................................................................................................................................... 13 Lidar Density ............................................................................................................................................ 13 Lidar Accuracy Assessments .................................................................................................................... 16 Lidar Non-Vegetated Vertical Accuracy ............................................................................................... 16 Lidar Relative Vertical Accuracy .......................................................................................................... 19 Lidar Horizontal Accuracy .................................................................................................................... 20 CERTIFICATIONS .............................................................................................................................................. 21 SELECTED IMAGES ............................................................................................................................................ 22 GLOSSARY ...................................................................................................................................................... 23 APPENDIX A – ACCURACY CONTROLS .................................................................................................................. 24 Cover Photo: A northern view from Sparks Lake showcasing South Sister and Broken Top, photographed by the NV5 Ground Survey team. Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project LIST OF FIGURES Figure 1: Location map of the OSU Cascade Snow Survey site in Oregon .................................................... 3 Figure 2: Flightlines map ............................................................................................................................... 6 Figure 3: Ground survey location map .......................................................................................................... 9 Figure 4: Frequency distribution of first return point density values per 100 x 100 m cell ....................... 14 Figure 5: Frequency distribution of ground-classified return point density values per 100 x 100 m cell .. 14 Figure 6: First return and ground-classified point density map for the OSU Cascade Snow Survey site (100 m x 100 m cells) .................................................................................................................................. 15 Figure 7: Frequency histogram for lidar classified LAS deviation from ground checkpoint values (NVA) . 17 Figure 8: Frequency histogram for the lidar bare earth DEM surface deviation from ground checkpoint values (NVA) ................................................................................................................................................ 18 Figure 9: Frequency histogram for the lidar surface deviation from ground control point values ............ 18 Figure 10: Frequency plot for relative vertical accuracy between flightlines ............................................. 19 Figure 11: A view looking northeast over Lewis Glacier Cirque Lake in the OSU Cascade Snow Survey AOI. The image was created from the lidar bare earth model overlaid with Virtual Earth imagery. ................ 22 Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project LIST OF TABLES Table 1: Acquisition dates, acreage, and data types collected on the OSU Cascade Snow Survey site ....... 1 Table 2: Deliverable product projection information ................................................................................... 2 Table 3: Products delivered to OSU for the OSU Cascade Snow Survey site ................................................ 2 Table 4: Lidar specifications and aerial survey settings ................................................................................ 5 Table 5: Base station positions for the OSU Cascade Snow Survey acquisition. Coordinates are on the NAD83 (2011) datum, epoch 2010.00 .......................................................................................................... 7 Table 6: NV5 ground survey equipment identification ................................................................................. 8 Table 7: ASPRS LAS classification standards applied to the OSU Cascade Snow Survey dataset ............... 10 Table 8: Lidar processing workflow ............................................................................................................ 11 Table 9: Average lidar point densities ......................................................................................................... 14 Table 10: Absolute accuracy results............................................................................................................ 17 Table 11: Relative accuracy results ............................................................................................................. 19 Table 12: Horizontal Accuracy .................................................................................................................... 20 Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 1 INTRODUCTION In June 2025, NV5 was contracted by Oregon State University (OSU) to collect Light Detection and Ranging (lidar) data in the summer of 2025 for the OSU Cascade Snow Survey site in Oregon. Data was collected to aid OSU in assessing the topographic properties of the study area to support snow depth and water equivalency modeling in the Cascade Mountains of western Oregon. This report accompanies the delivered lidar data and documents contract specifications, data acquisition procedures, processing methods, and analysis of the final dataset including lidar accuracy and density. Acquisition dates and acreage are shown in Table 1, deliverable projection information is shown in Table 2, a complete list of contracted deliverables provided to OSU is shown in Table 3, and the project extent is shown in Figure 1. Table 1: Acquisition dates, acreage, and data types collected on the OSU Cascade Snow Survey site Project Site Contracted Acres Buffered Acres Aerial Acquisition Dates Data Type OSU Cascade Snow Survey, Oregon 164,497 168,102 8/23/2025 NIR - Lidar This photo, taken by NV5 acquisition staff, shows a view of Broken Top Mountain in the OSU Cascade Snow Survey site in Oregon. Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 2 Deliverable Products Table 2: Deliverable product projection information Projections Horizontal Datum Vertical Datum Units UTM Zone 10 North NAD83 (2011) NAVD88 (GEOID18) Meters Table 3: Products delivered to OSU for the OSU Cascade Snow Survey site Product Type File Type Product Details Points LAS v.1.4 (*.las) • All Classified Returns Rasters 1.0 meter GeoTIFF (*.tiff) • Bare Earth Digital Elevation Model (DEM) • Highest Hit Digital Surface Model (DSM) • Intensity Images Vectors Shapefiles (*.shp) • Survey Boundary • Lidar Tile Index • Ground Survey Data • Snow polygons Metadata Extensible Markup Language (*.xml) • Metadata Reports Adobe Acrobat (*.pdf) • Lidar Technical Data Report Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 3 Figure 1: Location map of the OSU Cascade Snow Survey site in Oregon Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 4 ACQUISITION Planning In preparation for data collection, NV5 reviewed the project area and developed a specialized flight plan to ensure complete coverage of the OSU Cascade Snow Survey lidar study area at the target pulse density of at least 8 pulse/m2. Acquisition parameters including orientation relative to terrain, flight altitude, pulse rate, scan angle, and ground speed were adapted to optimize flight paths and flight times while meeting all contract specifications. Figure 2 shows these optimized flight paths and dates. Factors such as satellite constellation availability and weather windows must be considered during the planning stage. Any weather hazards or conditions affecting the flight were continuously monitored due to their potential impact on the daily success of airborne and ground operations. In addition, logistical considerations including private property access and potential air space restrictions were reviewed. Airborne Survey The lidar survey was accomplished using a Riegl VQ-1560ii-S system mounted in a Cessna Grand Caravan. Table 4 summarizes the settings used to yield an average pulse density of at least 8 pulse/m2 over the OSU Cascade Snow Survey project area. The Riegl VQ-1560ii-S laser system can record unlimited range measurements (returns) per pulse; however, a maximum of 15 returns can be stored due to LAS v.1.4 file limitations. The typical number of returns digitized from a single pulse range from 1 to 8 in the OSU Cascade Snow Survey project dataset. It is not uncommon for some types of surfaces (e.g., dense vegetation or water) to return fewer pulses to the lidar sensor than the laser originally emitted. The discrepancy between first return and overall delivered density will vary depending on terrain, land cover, A view from inside NV5’s Cessna Grand during acquisition for OSU Snow Survey project. Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 11 Table 8: Lidar processing workflow Lidar Processing Step Software Used Resolve kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. Develop a SBET file that blends post-processed aircraft position with sensor head position and attitude recorded throughout the survey. POSPac MMS v.9.4 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 (ASPRS v.1.4) format. Convert data to orthometric elevations by applying a geoid correction. RiUnite v.1.0.5 Import raw laser points into manageable blocks to perform manual relative accuracy calibration and filter erroneous points. Classify ground points for individual flightlines. TerraScan v.25.007 Using ground classified points per each flightline, 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 flightlines and apply results to all points in a flightline. Use every flightline for relative accuracy calibration. StripAlign v.2.25 Classify resulting data to ground and other client designated ASPRS classifications (Table 7). Assess statistical absolute accuracy via direct comparisons of ground classified points to ground control survey data. TerraScan v.25.007 TerraModeler v.25.003 Generate bare earth models as triangulated surfaces. Generate highest hit models as a surface expression of all classified points. Export all surface models as Cloud Optimized GeoTIFFs at a 1 meter pixel resolution. LAS Product Creator v.4.0 (NV5 proprietary) Export intensity images as Cloud Optimized GeoTIFFs at a 1 meter pixel resolution. Las Monkey v.2.6.11 (NV5 proprietary) LAS Product Creator v.4.0 (NV5 proprietary) Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 12 Feature Extraction Highest Hit Digital Surface Model Rasters Highest Hit Digital Surface Model (DSM) rasters represent a lidar-derived product illustrating the earth's surface elevation with all natural and anthropogenic features included. NV5’s proprietary software utilizes all valid classified lidar returns, excluding those flagged with a withheld bit, and creates tiled rasters using the highest hit algorithm. This model has been mosaicked with bare earth DEM data using the maximum operator in order to interpolate across areas in the ground model where there is no elevation data. Data extending past the tile edge is incorporated in this process so that proper gridding can occur. The raster product is then clipped back to the tile edge so that no overlapping cells remain across the project area. A 32-bit floating point GeoTIFF was generated for each tile with a pixel size of 1 meter. NV5’s proprietary software was used to write appropriate horizontal and vertical projection information as well as applicable header values into the file during product generation. Each Highest Hit DSM raster is reviewed in a GIS to check for any anomalies and to ensure a seamless dataset. NV5 uses a proprietary tool called FOCUS on Delivery to check all formatting requirements of the DSMs against what is required before final delivery. Intensity Image Processing Intensity images represent reflectivity values collected by the lidar sensor during acquisition. NV5 proprietary software generates intensity images using all valid first returns and excluding those flagged with a withheld bit. Intensity images are linearly scaled to a value range specific to the project area and sensor to standardize the images and reduce differences between individual flightlines. Appropriate horizontal projection information as well as applicable header values are written during product generation. NV5 uses a proprietary tool called FOCUS on Delivery to check all formatting requirements of the images against what is required before final delivery. Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 13 RESULTS & DISCUSSION Lidar Density The acquisition parameters were designed to acquire an average first-return density of 8 pulse/m2. First return density describes the density of pulses emitted from the laser that return at least one echo to the system. Multiple returns from a single pulse were not considered in first return density analysis. Some types of surfaces (e.g., breaks in terrain, water, and steep slopes) may have returned fewer pulses than originally emitted by the laser. First returns typically reflect off the highest feature on the landscape within the footprint of the pulse. In forested or urban areas, the highest feature could be a tree, building, or power line, while in areas of unobstructed ground, the first return will be the only echo and represents the bare earth surface. The density of ground-classified lidar returns was also analyzed for this project. Terrain character, land cover, and ground surface reflectivity all influenced the density of ground surface returns. In vegetated areas, fewer pulses may penetrate the canopy, resulting in lower ground density. The average first-return density of lidar data for the OSU Cascade Snow Survey project was 14.29 points/m2 while the average ground classified density was 4.32 points/m2 (Table 9). The statistical and spatial distributions of first return densities and classified ground return densities per 100 m x 100 m cell are portrayed in Figure 4 through Figure 6. This 2 meter lidar cross section shows a view of vegetation, a structure and bare ground in the OSU Cascade Snow Survey AOI, colored by point laser echo. Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 14 Table 9: Average lidar point densities Classification Point Density First-Return 14.29 points/m2 Ground Classified 4.32 points/m2 Figure 4: Frequency distribution of first return point density values per 100 x 100 m cell Figure 5: Frequency distribution of ground-classified return point density values per 100 x 100 m cell Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 15 Figure 6: First return and ground-classified point density map for the OSU Cascade Snow Survey site (100 m x 100 m cells) Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 16 Lidar Accuracy Assessments The accuracy of the lidar data collection can be described in terms of absolute accuracy (the consistency of the data with external data sources) and relative accuracy (the consistency of the dataset with itself). See Appendix A for further information on sources of error and operational measures used to improve relative accuracy. Lidar Non-Vegetated Vertical Accuracy Absolute accuracy was assessed using Non-Vegetated Vertical Accuracy (NVA) reporting designed to meet guidelines presented in the FGDC National Standard for Spatial Data Accuracy.2 NVA compares known ground checkpoint data that were withheld from the calibration and post-processing of the lidar point cloud to the triangulated surface generated by the classified lidar point cloud as well as the derived gridded bare earth DEM. NVA is a measure of the accuracy of lidar point data in open areas where the lidar system has a high probability of measuring the ground surface. This dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data, Edition 2, Version 2 (2024) for a 10 cm RMSE Vertical Accuracy Class (Table 10). The mean and standard deviation (sigma, σ) of divergence of the ground surface model from quality assurance point coordinates are also considered during accuracy assessment. The number of points evaluated for NVA is large enough that the error for x, y, and z is approximately normally distributed. The skew and kurtosis of distributions are also considered when evaluating error statistics in order to better evaluate the magnitude and distribution of the estimated error. For the OSU Cascade Snow Survey, 37 ground checkpoints were withheld from the calibration and post processing of the lidar point cloud. The Non-Vegetated Vertical Accuracy (NVA) was found to be RMSE = 0.031 meters as compared to classified LAS, and RMSE = 0.037 meters as compared to the bare earth DEM (Figure 7, Figure 8). NV5 also assessed absolute accuracy using 180 ground control points. Although these points were used in the calibration and post-processing of the lidar point cloud, they still provide a good indication of the overall accuracy of the lidar dataset, and therefore have been provided in Table 10 and Figure 9. 2 Federal Geographic Data Committee, ASPRS POSITIONAL ACCURACY STANDARDS FOR DIGITAL GEOSPATIAL DATA EDITION 2, Version 2.0, 2024. https://publicdocuments.asprs.org/PositionalAccuracyStd-Ed2-V2 Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 17 Table 10: Absolute accuracy results Parameter NVA, as compared to classified LAS NVA, as compared to bare earth DEM Ground Control Points Sample 37 points 37 points 180 points 95% Confidence (1.96*RMSE) 0.060 m 0.072 m 0.085 m Average 0.002 m 0.006 m -0.001 m Median 0.004 m 0.010 m 0.002 m RMSE 0.031 m 0.037 m 0.043 m Standard Deviation (1σ) 0.031 m 0.037 m 0.043 m Figure 7: Frequency histogram for lidar classified LAS deviation from ground checkpoint values (NVA) Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 18 Figure 8: Frequency histogram for the lidar bare earth DEM surface deviation from ground checkpoint values (NVA) Figure 9: Frequency histogram for the lidar surface deviation from ground control point values Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 19 Lidar Relative Vertical Accuracy Relative vertical accuracy refers to the internal consistency of the dataset as a whole: the ability to place an object in the same location given multiple flightlines, GPS conditions, and aircraft attitudes. When the lidar system is well calibrated, the swath-to-swath vertical divergence is low (<0.10 meters). The relative vertical accuracy was computed by comparing the ground surface model of each individual flightline with its neighbors in overlapping regions. The average (mean) line to line relative vertical accuracy for the OSU Cascade Snow Survey Lidar project was 0.032 meters (Table 11, Figure 10). Table 11: Relative accuracy results Parameter Relative Accuracy Sample 45 flight line surfaces Average 0.032 m Median 0.032 m RMSE 0.032 m Standard Deviation (1σ) 0.004 m 1.96σ 0.008 m Figure 10: Frequency plot for relative vertical accuracy between flightlines Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E Lidar Technical Data Report – OSU Cascade Snow Survey Lidar Project Page 20 Lidar Horizontal Accuracy 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. Based on a flying altitude of 2532 meters, an IMU error of 0.003 decimal degrees, and a GNSS positional error of 0.023 meters, this project was produced to meet a 0.238 meter RMSEH Horizontal Positional Accuracy Class (Table 12). Table 12: Horizontal Accuracy Parameter Horizontal Accuracy RMSEr 0.238 m ACCr 0.412 m Docusign Envelope ID: A4E0090E-DDC9-4EB7-9F39-FE66F951004E