Hydro-Flattened Digital Elevation Models (DEMs)

U.S. Geological Survey 201601 Unknown Hydro-Flattened Digital Elevation Models (DEMs) Elevation Data Leading Edge Geomatics (LEG) collected 994 square miles in the Virginia counties of Accomack and Northampton. The nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground (bare earth points identified as Model Key Points are flagged with the Model Key Point bit), 7-Low Noise, 9-Water, 10-Ignored Ground due to breakline proximity, 17- Bridge Decks, 18-High Noise. Dewberry produced 3D breaklines and combined these with the final LiDAR data to produce seamless hydro flattened DEMs for the project area. The data was formatted according to the VBMP tile naming convention with each tile covering an area of 5,000 feet by 5,000 ft. A total of 1375 LAS tiles and 1310 DEM tiles were produced for the entire project. The purpose of this LiDAR data was to produce high accuracy 3D elevation products, including tiled LiDAR in LAS 1.4 format, 3D breaklines, and 2.5 foot cell size hydro flattened Digital Elevation Models (DEMs). All products follow and comply with USGS Lidar Base Specification Version 1.2. A complete description of this dataset is available in the Final Project Report submitted to the USGS. 20150411 20150424 ground condition Complete As needed -76.093949 -75.227100 38.064707 37.062181 None DTM Elevation Lidar LAS DEM Hydro Flattened Breaklines None Virginia Eastern Shores Accomack County Northampton County USA None This data was produced for the USGS according to specific project requirements. This information is provided "as is". Further documentation of this data can be obtained by contacting: USGS/NGTOC, 1400 Independence Road, Rolla, MO 65401. Telephone (573) 308-3810. USGS USGS NGTOC mailing and physical address

1400 Independence Road

Rolla MO 65401 USA (573) 308-3810 pemmett@usgs.gov Microsoft Windows 7 Enterprise Service Pack 1; ESRI ArcCatalog 10.3 A total of 1375 LAS tiles and 1310 DEM tiles were produced for the entire project. A visual qualitative assessment was performed to ensure data completeness and full tiles. No void or missing data exists. The DEMs are derived from the source LiDAR and 3D breaklines created from the LiDAR. Horizontal accuracy is not performed on the DEMs or breaklines. Only checkpoints photo-identifiable in the intensity imagery can be used to test the horizontal accuracy of the LiDAR. Photo-identifiable checkpoints in intensity imagery typically include checkpoints located at the ends of paint stripes on concrete or asphalt surfaces or checkpoints located at 90 degree corners of different reflectivity, e.g. a sidewalk corner adjoining a grass surface. The xy coordinates of checkpoints, as defined in the intensity imagery, are compared to surveyed xy coordinates for each photo-identifiable checkpoint. These differences are used to compute the tested horizontal accuracy of the LiDAR. As not all projects contain photo-identifiable checkpoints, the horizontal accuracy of the LiDAR cannot always be tested. 2.13 ft (65 cm) The DEMs are derived from the source LiDAR and 3D breaklines created from the LiDAR. Horizontal accuracy is not performed on the DEMs or breaklines. Lidar vendors calibrate their lidar systems during installation of the system and then again for every project acquired. Typical calibrations include cross flights that capture features from multiple directions that allow adjustments to be performed so that the captured features are consistent between all swaths and cross flights from all directions. Dewberry tested the horizontal accuracy of the LiDAR by comparing photo-identifiable survey checkpoints to the LiDAR Intensity Imagery. As only seventeen (17) checkpoints were photo-identifiable, the results are not statistically significant enough to report as a final tested value but the results of this testing are shown below. Using NSSDA methodology (endorsed by the ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014)), horizontal accuracy at the 95% confidence level (called ACCURACYr) is computed by the formula RMSEr * 1.7308 or RMSExy * 2.448. Actual positional accuracy of this dataset was found to be RMSEx = 0.83 ft (25 cm) and RMSEy = 0.91 ft (28 cm) which equates to +/- 2.13 ft (65 cm) at 95% confidence level. The DEMs are derived from the source LiDAR and 3D breaklines created from the LiDAR. The DEMs are created using controlled and tested methods to limit the amount of error introduced during DEM production so that any differences identified between the source LiDAR and final DEMs can be attributed to interpolation differences. DEMs are created by averaging several LiDAR points within each pixel which may result in slightly different elevation values at a given location when compared to the source LAS, which is tested by comparing survey checkpoints to a triangulated irregular network (TIN) that is created from the LiDAR ground points. TINs do not average several LiDAR points together but interpolate (linearly) between two or three points to derive an elevation value. The vertical accuracy of the final bare earth DEMs was tested by Dewberry with 113 independent checkpoints. The same checkpoints that were used to test the source LiDAR data were used to validate the vertical accuracy of the final DEM products. The survey checkpoints are evenly distributed throughout the project area and are located in areas of non-vegetated terrain (61 checkpoints), including bare earth, open terrain, and urban terrain, and vegetated terrain (52 checkpoints), including forest, brush, tall weeds, crops, and high grass. The vertical accuracy is tested by extracting the elevation of the pixel that contains the x/y coordinates of the checkpoint and comparing these DEM elevations to the surveyed elevations. All checkpoints located in non-vegetated terrain were used to compute the Non-vegetated Vertical Accuracy (NVA). Project specifications required a NVA of 0.64 ft (19.6 cm) at the 95% confidence level based on RMSEz (0.33 ft/10 cm) x 1.9600. All checkpoints located in vegetated terrain were used to compute the Vegetated Vertical Accuracy (VVA). Project specifications required a VVA of 0.96 ft (29.4 cm) based on the 95th percentile. 0.40 ft (12.2 cm) This DEM dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 0.33 ft (10 cm) RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be RMSEz =0.20 ft (6.10 cm), equating to +/- 0.40 ft (12.2 cm) at 95% confidence level. 0.60 ft (18.3 cm) This DEM dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 0.33 ft (10 cm) RMSEz Vertical Accuracy Class. Actual VVA accuracy was found to be +/- 0.60 ft (18.3 cm) at the 95th percentile. The 5% outliers consisted of 3 checkpoints that are larger than the 95th percentile. These checkpoints have DZ values ranging between 0.62 ft (18.9 cm) and 0.87 ft (26.5 cm). Data for the Eastern Shores Virginia QL2 LiDAR project was acquired by Leading Edge Geomatics (LEG). The project area included approximately 994 contiguous square miles or 2574.45 square kilometers for the counties of Accomack and Northampton in Virginia. LiDAR sensor data were collected with the Riegl 680i LiDAR system. The data was delivered in the State Plane coordinate system, feet, Virginia South, horizontal datum NAD83, vertical datum NAVD88, Geoid 12a. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud, survey control, and a final acquisition/calibration report. The calibration process considered all errors inherent with the equipment including errors in GPS, IMU, and sensor specific parameters. Adjustments were made to achieve a flight line to flight line data match (relative calibration) and subsequently adjusted to control for absolute accuracy. Process steps to achieve this are as follows: Rigorous LiDAR calibration: all sources of error such as the sensor's ranging and torsion parameters, atmospheric variables, GPS conditions, and IMU offsets were analyzed and removed to the highest level possible. This method addresses all errors, both vertical and horizontal in nature. Ranging, atmospheric variables, and GPS conditions affect the vertical position of the surface, whereas IMU offsets and torsion parameters affect the data horizontally. The horizontal accuracy is proven through repeatability: when the position of features remains constant no matter what direction the plane was flying and no matter where the feature is positioned within the swath, relative horizontal accuracy is achieved. Absolute horizontal accuracy is achieved through the use of differential GPS with base lines shorter than 25 miles. The base station is set at a temporary monument that is 'tied-in' to the CORS network. The same position is used for every lift, ensuring that any errors in its position will affect all data equally and can therefore be removed equally. Vertical accuracy is achieved through the adjustment to ground control survey points within the finished product. Although the base station has absolute vertical accuracy, adjustments to sensor parameters introduces vertical error that must be normalized in the final (mean) adjustment. A copy of the final calibrated swaths are maintained in LAS format 1.2 for production utilizing Terrascan software. A second, identical version of final calibrated swaths are converted from v1.2 to v1.4 using GeoCue software. The withheld and overlap bits are set and all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools. Airborne Global Positioning System Data Inertial Measurement Unit 201504 Calibrated LiDAR Point Cloud LAS 1.4 format Leading Edge Geomatics mailing and physical address

2384 Route 102 Highway

Lincoln NB E3B 7G1 Canada 506-446-4403 506-446-4402 8:00 - 5:00 Dewberry utilizes a variety of software suites for inventory management, classification, and data processing. All LiDAR related processes begin by importing the data into the GeoCue task management software. The swath data is tiled according to project specifications (5,000 ft x 5,000 ft). Dewberry extended the client provided boundary where tiles had ground to include thirty four extra tiles. The tiled data is then opened in Terrascan where Dewberry classifies edge of flight line points that may be geometrically unusable to a separate class. These points are separated from the main point cloud so that they are not used in the ground algorithms. Dewberry then uses proprietary ground classification routines to remove any non-ground points and generate an accurate ground surface. The ground routine consists of three main parameters (building size, iteration angle, and iteration distance); by adjusting these parameters and running several iterations of this routine an initial ground surface is developed. The building size parameter sets a roaming window size. Each tile is loaded with neighboring points from adjacent tiles and the routine classifies the data section by section based on this roaming window size. The second most important parameter is the maximum terrain angle, which sets the highest allowed terrain angle within the model. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a supervisor manual inspection is completed on a percentage of the classified tiles based on the project size and variability of the terrain. After the ground classification and bridge deck corrections are completed, the dataset is processed through a water classification routine that utilizes breaklines compiled by Dewberry to automatically classify hydrographic features. The water classification routine selects ground points within the breakline polygons and automatically classifies them as class 9, water. During this water classification routine, points that are within 1x NPS or less of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. Next, an intelligently thinned ground classification identified model key points and are flagged with the Model Key Point bit. Overage points are then identified in Terrascan and GeoCue is used to set the overlap bit for the overage points and the withheld bit is set on the withheld points previously identified in Terrascan before the ground classification routine was performed. A final QC is performed on the data. The LAS files are then converted from v1.2 to v1.4 using GeoCue software. At this time, all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools. The data was classified as follows: Class 1 = Unclassified. This class includes vegetation, buildings, noise etc. Class 2 = Ground (bare earth points identified as Model Key Points are flagged with the Model Key Point bit) Class 7 = Low Noise Class 9 = Water Class 10 = Ignored Ground due to breakline proximity Class 17 = Bridge Decks Class 18 = High Noise The LAS header information was verified to contain the following: Class (Integer) Adjusted GPS Time (0.0001 seconds) Easting (0.003 m) Northing (0.003 m) Elevation (0.003 m) Echo Number (Integer) Echo (Integer) Intensity (16 bit integer) Flight Line (Integer) Scan Angle (degree) Calibrated LiDAR Point Cloud LAS 1.2 format 201509 Final Tiled LiDAR datasets in LAS 1.4 format Dewberry - Geospatial Services Group Elise MacPherson Project Manager mailing and physical address

1000 N Ashley Drive, Suite 801

Tampa FL 33602 USA 813.421.8647 813.225.1385 emacpherson@dewberry.com 8:00 - 5:00 EST Existing lidar data acquired and processed as part of the NOAA Sandy Supplemental project were re-processed and combined with the LEG data to supplement/complete data coverage along the eastern portion of this project Calibrated LiDAR Point Cloud LAS 1.2 format 201509 Final Tiled LiDAR datasets in LAS 1.4 format Dewberry - Geospatial Services Group Elise MacPherson Project Manager mailing and physical address

1000 N Ashley Drive, Suite 801

Tampa FL 33602 USA 813.421.8647 813.225.1385 emacpherson@dewberry.com 8:00 - 5:00 EST Dewberry used GeoCue software to produce intensity imagery and raster stereo models from the source LiDAR. The raster resolution was 2.5 feet. Final Tiled LiDAR datasets 201509 Lidar Intensity Stereopairs and Lidar Intensity Ortho-Imagery Dewberry - Geospatial Services Group Elise MacPherson Project Manager mailing and physical address