Ayres Associates 20230314 Lidar point cloud The McHenry County, IL lidar project area covers approximately 667 square miles. The lidar data was acquired at an aggregate nominal point density (ANPD) of 8ppsm. Project specifications are based on McHenry County requirements. The data was developed based on a horizontal projection/datum of NAD83(2011) / Illinois State Plane East Zone (ftUS) (EPSG Code: 3435), and vertical datum of NAVD88 - Geoid 18 (US Survey Feet). LiDAR data was acquired using a REIGL 1560 Lidar sensor from April 12th, 2022 to April 22, 2022 in 5 total lifts. Acquisition occurred with leaves absent from deciduous trees, when no snow was present on the ground, and with rivers at or below normal levels. 2.1 REIGL 1560 8 0.32 8 0.32 8 900 160 58 100 1400 8 .24 1064 1 .16 1273 30 National Geodetic Survey (NGS) Geoid18 This data set was produced to meet ASPRS Positional Accuracy Standard for Digital Geospatial Data (2014) for a 23-cm RMSEz Hirizontal Accuracy Class. This dataset was not independently tested for horizontal accuracy 0.345 69 0.345 69 1.4 6 Withheld (ignore) points were identified in these files using the standard LAS Withheld bit. 1 Processed, but Unclassified 2 Bare Earth Ground 5 High Vegetation 6 Buildings 7 Low Noise 9 Water 17 Bridge Deck 18 High Noise 20 Ignored Ground 20220412 20220422 ground condition Complete None Planned -88.720289 -87.521435 42.500824 42.142892 879898.2413 1022599.9978 2125099.99885 1994896.01125 None Model LAS Point Cloud Remote Sensing Elevation Data Lidar None Illinois City of Woodstock McHenry County No restrictions apply to this 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be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions.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. Terrasolid 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 complete and data passes Vertical Accuracy specifications. The project specifications require that only Non-Vegetated Vertical Accuracy (NVA) be computed for raw lidar point cloud swath files. The required accuracy (ACCz) 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. These 63 points were not used in the calibration or post processing of the lidar point cloud data. The checkpoints were distributed throughout the project area and were surveyed using GPS techniques. Elevations from the unclassified lidar surface were measured for the x,y location of each check point. Elevations interpolated from the lidar surface were then compared to the elevation values of the surveyed control points. AccuracyZ has been prepared 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. 0.345 Tested 0.345 feet NVA at a 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA). These 69 survey points were not used in the calibration or post processing of the lidar point cloud data The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used Leica software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technician then analyzed the results and made any necessary additional adjustment until it is acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps match for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 7 cm RMSEz within individual swaths and less than or equal to 10 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed check points after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met. Point classification was performed according to USGS Lidar Base Specification 2.1, and breaklines were collected for water features. Bare earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening. 20230314 LAS Point Cloud Classification: LiDAR data processing for the point cloud deliverable consists of classifying the LiDAR using a combination of automated classification and manual edit/reclassification processes. On most projects the automated classification routines McHenrycorrectly classify 90-95 percent of the LiDAR points. The remaining 5-10 percent of the bare earth ground class must undergo manual edit and reclassification. Because the classified points serve as the foundation for the Terrain, DEM and breakline products, it is necessary for the QA/QC supervisor to review the completed point cloud deliverables prior to the production of any additional products. The following workflow steps are followed for automated LiDAR classification: 1. Lead technicians review the group of LiDAR tiles to determine which automated classification routines McHenryachieve the best results. Factors such as vegetation density, cultural features, and terrain can affect the accuracy of the automated classification. The lead technicians have the ability to edit or tailor specific routines in order to accommodate the factors mentioned above, and achieve the best results and address errors. 2. Distributive processing is used to maximize the available hardware resources and speed up the automated processing as this is a resource-intensive process. 3. Once the results of the automated classification have been reviewed and passed consistent checks, the supervisor then approves the data tiles for manual classification. The following workflow steps are followed for manual edits of the LiDAR bare earth ground classification: 1. LiDAR technicians review each tile for errors made by the automated routines and correctly address errors any points that are in the wrong classification. By methodically panning through each tile, the technicians view the LiDAR points in profile, with a TIN surface, and as a point cloud. 2. Any ancillary data available, such as Google Earth, is used to identify any features that may not be identifiable as points so that the technician can make the determination to which classification the feature belongs. The QA/QC processes for the LiDAR processing phase consist of: 1. The lead technician reviews all automated classification results and adjust the macros as necessary to achieve the optimal efficiency. This is an iterative process, and the technician may need to make several adjustments to the macros, depending upon the complexity of the features in the area being processed.  During the manual editing process, the LiDAR technicians use a system of QA, whereby they check each other’s edits. This results in several benefits to the process:  There is a greater chance of catching minor blunders  It increases communication between technicians on technique and appearance  Solutions to problems are communicated efficiently  To ensure consistency across the project area, the supervisor reviews the data once the manual editing is complete. For this phase of a project, the following specifications are checked against: • Point cloud – all points must be classified according to the USGS classification standard for LAS. The all-return point cloud must be delivered in fully-compliant LAS version 1.4. • LAS files McHenryuse the Spatial Reference Framework according to project specification and all files shall be projected and defined. • General Point classifications:  Class 1. Processed, but unclassified  Class 2. Bare Earth  Class 7. Noise  Class 9. Water  Class 17. Bridge Decks  Class 18. High Noise  Class 20. Ignored ground (Breakline proximity) • Outliers, noise, blunders, duplicates, geometrically unreliable points near the extreme edge of the swath, and other points deemed unusable are to be identified using the "Withheld" flag. This applies primarily to points which are identified during pre-processing or through automated post-processing routines. Subsequently identified noise points may be assigned to the standard Noise Classes (Class 7). • Point classification shall be consistent across the entire project. Noticeable variations in the character, texture, or quality of the classification between tiles, swaths, lifts, or other non-natural divisions McHenrybe cause for rejection. • Once the data is imported into GeoCue and has undergone and passed the QC process, the strip data McHenrybe tiled to the 4500’ x 4500’ tiling scheme. 20230314 Illinois State Geological Survey received a copy of the initial delivery and performed a QAQC analysis on the data. The classification scheme skips classes 3 and 4 (low and medium vegetation) even though there are points in those classes. On tile #22700 there is a portion of a house that has been misclassified as ground. (has been fixed in the final derivatives. ISGS creates the LiDAR data derivatives (DTM and DSM with Hillshades in county wide mosaics, raster format) The preceding Terrains were produced using a LP360 thinned version of the LAS files, and the provided breaklines and project boundary specified as a hardline and soft clip respectively. Raster derivatives were created from the terrain datasets using a cell size of 1.0 feet. Hillshades were created from each raster with a Z factor of 2.9. Observation: Near tile #21968 and #21969 there is an island breakline that is adjacent to a river breakline. In a situation like this the island breakline should have an elevation equal to the stream breakline (perpendicular to the stream flow). In this instance, the island breakline has an elevation of 0.1’ lower than the stream. So the water seems to be slightly flowing toward the island in this area. (If the contract was open, we would send it back to the vendor to be fixed, if the elevation difference was larger than 0.1’ we would try and fix it at this stage.) Observation: There appears to have been an approximately 30’ buffer applied around a good portion of the buildings. Any point that varies slightly from the ground elevation in this buffer area, in some cases elevation differences of around a tenth of a foot, are placed in the low vegetation category. Regardless if there is vegetation there or not, i.e. parking lots. (Generally speaking, this data has a lot of points classified as low vegetation throughout the dataset that really should not be classified as low vegetation, since the points do not represent low vegetation.) Observation: There is a sporadic issue of points in one of the advanced classifications, i.e. points that represent structures, trees, etc., being misclassified into one of the other classes. 20240308 Point Point State Plane Coordinate System 1983 0.999975 -88.33333333333333 36.66666666666666 984250.0 0.0 coordinate pair 0.01 0.01 US survey feet NAD83 (National Spatial Reference System 2011) GRS 1980 6378137.0 298.257222101 North American Vertical Datum of 1988, GEOID 18 0.01 US Survey feet Explicit elevation coordinate included with horizontal coordinates 20230314 20230314 Ayres Associates mailing and physical

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Madison Wisconsin 53718 USA (608) 443-1200 FGDC-STD-001-1998 FGDC-STD-001-1998 None Unclassified None None None IL_McHenry_DTM_2022