Dewberry
201803
Unknown
South Carolina Georgetown County Lidar Bridge Deck Polygons
Elevation Data
Precision Aerial Reconnaissance (PAR) collected 902 square miles in the South Carolina county of Georgetown. 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: 1-Unclassified, 2-Ground, 7-Low Noise, 8-Model Keypoints, 9-Water, 10-Ignored Ground due to breakline proximity, 13-Culverts, 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 SC DNR tile naming convention with each tile covering an area of 5,000 feet by 5,000 ft. A total of 1100 LAS tiles and 1100 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 that was submitted to the SC DNR
20161216
20170309
ground condition
As needed
-79.687357
-78.994310
33.787475
33.101837
None
Elevation
lidar
LAS
DEM
Breaklines
2D Bridge Deck Polygons
None
South Carolina
Coastal Region
Georgetown County
USA
None
This data was produced for the SC DNR according to specific project requirements. This information is provided "as is". Further documentation of this data can be obtained by contacting: SC DNR, 1000 N Assembly St Suite 123, Columbia, SC 29201. Telephone (803)734-3162.
Dewberry
Project Manager
mailing and physical address
990 S. Broadway, Suite 400
Denver
CO
80209
USA
(813)421-8632
jnovac@Dewberry.com
Microsoft Windows 7 Enterprise Service Pack 1; ESRI ArcCatalog 10.3
Data covers the project boundary.
A visual qualitative assessment was performed to ensure data completeness and bare earth data cleanliness. No void or missing data and data passes vertical accuracy specifications.
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.
3.28 ft (100 cm)
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.
This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 1.35 ft (41 cm) RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 3.28 ft (1 meter) at a 95% confidence level. No (0) checkpoints were photo-identifiable so no horizontal accuracy testing was conducted.
The vertical accuracy of the source lidar and final bare earth DEMs was tested by Dewberry with 101 independent survey checkpoints. The survey checkpoints are evenly distributed throughout the project area and are located in areas of non-vegetated terrain, including bare earth, open terrain (26), and urban terrain (24), and vegetated terrain, including forest (16), brush (17), tall weeds, crops, and high grass (18). The vertical accuracy of the lidar is tested by comparing survey checkpoints to a triangulated irregular network (TIN) that is created from the lidar ground points. Checkpoints are always compared to interpolated surfaces created from the lidar point cloud because it is unlikely that a survey checkpoint will be located at the location of a discrete lidar point. The vertical accuracy of the final bare earth DEMs 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. Accuracy results may vary between the source lidar and final DEM deliverable. DEMs are created by averaging several lidar points within each pixel which may result in slightly different elevation values at each survey checkpoint when compared to the source LAS, which is tested by comparing survey checkpoints to TINs. TINs do not average several lidar points together but interpolate (linearly) between two or three points to derive an elevation value. The accuracy results reported for the overall project are the accuracy results of the source lidar. Bare earth DEM accuracy results are reported in the DEM metadata file. 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 lidar 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 (6.1 cm), equating to +/- 0.40 ft (12.2 cm) at 95% confidence level.
0.59 ft (18.0 cm)
This lidar 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 +/- 0.59 ft (18.0 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 0.61 ft (18.6 cm) to 0.81 ft (24.7 cm).
Data for the South Carolina Georgetown County Lidar Project was acquired by Precision Aerial Reconnaissance (PAR).
The project area included approximately 873 contiguous square miles or 2261 square kilometers for the county of Georgetown in South Carolina.
Lidar sensor data were collected with the Riegl Q1560 lidar system. The data was delivered in the State Plane coordinate system, international feet, South Carolina, horizontal datum NAD83, vertical datum NAVD88, U.S. Survey Feet, Geoid 12B. 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.
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.
201712
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). The tiled data is then opened in Terrascan where Dewberry identifies edge of flight line points that may be geometrically unusable with the withheld bit. These points are separated from the main point cloud so that they are not used in the ground algorithms. Overage points are then identified with the overlap bit. 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. A final QC is performed on the data. 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
Class 7= Low Noise
Class 8 = Model Keypoints
Class 9 = Water
Class 10 = Ignored Ground due to breakline proximity
Class 13 = Culverts
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)
201712
Dewberry used GeoCue software to produce intensity imagery from the source lidar. The intensity imagery is georeferenced ortho-imagery that spatially aligns with the source lidar. The intensity imagery is created from the full point cloud to show the full representation of the lidar dataset and was created with a 2.5 foot pixel resolution. The final format of the imagery is 8-bit, unsigned integer, grayscale GeoTIFF.
201712
Dewberry digitzed 2D bridge deck polygons from the intensity imagery and used these polygons to classify bridge deck points in the LAS to class 17. As some bridges are hard to identify in intensity imagery, Dewberry then used ESRI software to generate bare earth elevation rasters. Bare earth elevation rasters do not contain bridges. As bridges are removed from bare earth DEMs but DEMs are continuous surfaces, the area between bridge abutments must be interpolated. The rasters are reviewed to ensure all locations where the interpolation in a DEM indicates a bridge have been collected in the 2D bridge deck polygons.
201801
Vector
Lambert Conformal Conic (State Plane South Carolina FIPS 3900)
32.500
34.833
-81.000
31.833
2000000.000
0.000
coordinate pair
0.001
0.001
International Feet
North American Datum of 1983 (2011)
Geodetic Reference System 80
6378137.000000
298.257222
North American Vertical Datum of 1988 (Geoid 12B)
0.001
U.S. Survey Feet
Explicit elevation coordinate included with horizontal coordinates
Bridge_Decks
2D polygon depicting elevated bridge decks
Georgetown County Lidar Project
OBJECTID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
SHAPE
Feature geometry.
ESRI
Coordinates defining the features.
SHAPE_Length
Length of feature in internal units.
ESRI
Positive real numbers that are automatically generated.
SHAPE_Area
Area of feature in internal units squared.
ESRI
Positive real numbers that are automatically generated.
201804
Dewberry
Josh Novac
Project Manager
mailing and physical address
990 S. Broadway, Suite 400
Denver
CO
80209
USA
(813)421-8632
jnovac@Dewberry.com
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
local time
http://www.esri.com/metadata/esriprof80.html
ESRI Metadata Profile