U.S. Geological Survey
201409
Unknown
LAS
map
Aerial Cartographics of America (ACA) collected 2233 square miles in the New York counties of Chautauqua, Orleans, Wayne, Cayuga, Jefferson, Oswego and St. Lawerence. The nominal pulse spacing for this project was no greater than 0.7 meters. Dewberry used proprietary procedures to classify the LAS into an initial ground surface. Dewberry then used proprietary procedures to classify the LAS and performed manual classifications according to project specifications: 1-Unclassified, 2-Ground, 7-Noise, 9-Water, 10-Ignored Ground due to breakline proximity, 11- Witheld. The LiDAR data were processed to a bare-earth digital terrain model (DTM). Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Deliverables were produced in UTM Zone 18, meters. The data was formatted according to the USNG Tile naming convention with each tile covering an area of 1,500 meters by 1,500 meters. A total of 3070 tiles were produced for the entire project encompassing an area of approximately 2233 sq. mile. The pilot delivery consists of Chautauqua County that includes 218 tiles and encompasses approximately 130 sq. miles.
The purpose of this LiDAR data was to produce high accuracy 3D elevation products, including tiled LiDAR in LAS 1.2 format, 3D breaklines, 1 meter cell size hydro flattened Digital Elevation Models (DEMs) and 1 foot contours. All products follow and comply with USGS Program Lidar Base Specification Version 1.0.
A complete description of this dataset is available in the Final Project Report submitted to the USGS.
20140506
20140507
ground condition
As needed
-79.964273
-75.249830
44.836563
42.224863
None
DTM
Elevation
Lidar
LAS
DEM
Hydro Flattened
Breaklines
Contours
None
New York
Great Lakes Region
Chautauqua County
Cayuga County
Jefferson County
Orleans County
Oswego County
St. Lawrence County
Wayne County
USA
None
This data was produced for the U.S. Geological Survey according to specific project requirements. This information is provided "as is". Further documentation of this data can be obtained by contacting: USGS, 1400 Independence Road, Rolla, MO 65401. Telephone (573) 308-3756.
U.S. Geological Survey
Program Manager
mailing and physical address
1400 Independence Road
Rolla
MO
65401
USA
(573) 308-3756
gdunn@usgs.gov
Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 10.0
Data covers the tile scheme provided for the project area.
A visual qualitative assessment was performed to ensure data completeness and bare earth data cleanliness. No void or missing data, the bare earth surface is of good quality and data passes vertical accuracy specifications.
Lidar source produced to meet 1 meter horizontal accuracy.
Project specifications required a horizontal accuracy of 1 m based on a RMSEr (0.578m) x 1.7308. 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.
1 meter
LiDAR vendors perform calibrations on the LiDAR sensor and compare data to adjoining flight lines to ensure LiDAR meets the 1 meter horizontal accuracy standard at the 95% confidence level.
However, Dewberry tested the horizontal accuracy of the LiDAR by comparing photo-identifiable survey checkpoints to the LiDAR Intensity Imagery. As only two (2) checkpoints were photoidentifiable, the results are not statistically significant enough to report as a final tested value. However, the results are reported below.
Using NSSDA methodology, horizontal accuracy at the 95% confidence level (called Accuracyr) is computed by the formula RMSEr x 1.7308. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.196 m horizontal accuracy at 95% confidence level, based on RMSEr (0.113 m) x 1.7308.
Please see the final project report delivered to the U.S. Geological Survey for more details.
The interim vertical accuracy of the source LiDAR and final bare earth DEMs for Chautauqua County was tested by Dewberry with 8 independent survey checkpoints. The survey checkpoints were evenly distributed throughout the project area and were located in areas of bare earth and open terrain (2), grass weeds and crops (1), brush and low trees (1), forested and fully grown (2), and urban (2).
The vertical accuracy 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. Checkpoints in open terrain will be used to compute the Fundamental Vertical Accuracy (FVA). Project specifications required a FVA of 0.18 m based on a RMSEz (0.0925m) x 1.9600. All checkpoints will be used to compute the Consolidated Vertical Accuracy (CVA). Project specifications required a CVA of 0.269 m based on the 95th percentile.
Supplemental Vertical Accuracy (SVA) will be computed on each individual land cover category other than open terrain. Target specifications for SVA are 0.269 m based on the 95th percentile. NDEP and ASPRS testing methodologies allow individual SVA's to fail as long as the mandatory CVA passes project specifications.
0.033 m
Based on the vertical accuracy testing conducted by Dewberry, using NSSDA and FEMA methodology, vertical accuracy at the 95% confidence level (called Accuracyz) is computed by the formula RMSEz x 1.9600. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.033 m vertical accuracy at 95% confidence level in open terrain, based on RMSEz (0.017 m) x 1.9600.
0.070 m
Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, consolidated vertical accuracy (CVA) is computed using the 95th percentile method. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.070 m consolidated vertical accuracy at 95th percentile in all land cover categories combined.
The 5% outliers consist of 1 checkpoint that is larger than the 95th percentile. This checkpoint has a DZ value of -0.077.
0.074 m
Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.074 m supplemental vertical accuracy at 95th percentile in the forested and fully grown land cover category.
0.056 m
Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.056 m supplemental vertical accuracy at 95th percentile in the urban land cover category.
0.030 m
Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.030 m supplemental vertical accuracy at 95th percentile in the grass, weeds, and crops land cover category.
0.039 m
Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The Pilot dataset for the New York Great Lakes LiDAR project satisfies the criteria:
Lidar dataset tested 0.039 m supplemental vertical accuracy at 95th percentile in the brush land and trees land cover category.
Data for the New York Great Lakes LiDAR project was acquired by Aerial Cartographics of America, Inc (ACA).
The project area included approximately 2233 square miles in the New York counties of Chautauqua, Orleans, Wayne, Cayuga, Jefferson, Oswego and St. Lawerence. The pilot area of Chautauqua encompasses approximately 130 sq. miles. LiDAR sensor data was collected with the RIEGL LMS-Q680i LiDAR system. No imagery was requested or delivered. The data was delivered in the UTM coordinate system, meters, zone 18, horizontal datum NAD83 (2011), vertical datum NAVD88, Geoid 12A. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud, survey control, and a final control report.
A preliminary RMSEz error check is performed at this stage of the project life cycle in the raw LiDAR dataset against GPS static and kinematic data and compared to RMSEz project specifications. The LiDAR data is examined in open, flat areas away from breaks. Lidar ground points for each flightline generated by an automatic classification routine are used.
Overall the LiDAR data products collected by ACA meet or exceed the requirements set out in the Statement of Work. The quality control requirements of ACAs quality management program were adhered to throughout the acquisition stage of this project to ensure product quality.
LIDAR acquisition began on May 06, 2014 (julian day 126) and was completed on May 07, 2014 (julian day 127). A total of 2 survey missions were flown to complete the project. ACA utilized an RIEGL LMS-Q680i for the acquisition. The flight plan was flown as planned with no modifications. There were no unusual occurrences during the acquisition and the sensor performed within specifications. There were 29 flight lines required to complete the project.
The initial step of calibration is to verify availability and status of all needed GPS and Laser data against field notes and compile any data if not complete.
Subsequently the mission points are output using RIEGL's RiProcess, initially with default values from RIEGL or the last mission calibrated for system. The initial point generation for each mission calibration is verified within RIEGL's RiProcess for calibration errors. If a calibration error greater than specification is observed within the mission, the roll pitch and scanner scale corrections that need to be applied are calculated. The missions with the new calibration values are regenerated and validated internally once again to ensure quality.
Data collected by the LiDAR unit is reviewed for completeness, acceptable density, and to make sure all data is captured without errors or corrupted values. In addition, all GPS, aircraft trajectory, mission information, and ground control files are reviewed and logged into a database.
On a project level, a supplementary coverage check is carried out to ensure no data voids unreported by Fields Operations are present,
The initial points for each mission calibration are inspected for flight line errors, flight line overlap, slivers or gaps in the data, point data minimums, or issues with the LiDAR unit or GPS. Roll, pitch and scanner scale are optimized during the calibration process until the relative accuracy is met.
Relative accuracy and internal quality are checked using at least 3 regularly spaced QC blocks in which points from all lines are loaded and inspected. Vertical differences between ground surfaces of each line are displayed. Color scale is adjusted so that errors greater than the specifications are flagged. Cross sections are visually inspected across each block to validate point to point, flightline to flightline and mission to mission agreement.
For this project the specifications used are as follow:
Relative accuracy <= 7cm RMSEZ within individual swaths and <=10 cm RMSEZ or within swath overlap (between adjacent swaths).
UTM coordinate system, meters, zone 18, horizontal datum NAD83 (2011), vertical datum NAVD88, Geoid 12A
Airborne Global Positioning System Data
Inertial Measurement Unit
20140506
20140507
Calibrated LiDAR Point Cloud LAS 1.2 format
Aerial Cartographics of America, Inc.
mailing and physical address
423 S KELLER RD STE 300
Orlando
FL
32810
USA
407-937-1519
407-855-8250
8:00 - 5:00 EST
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 (1,500 m x 1,500 m). The tiled data is then opened in Terrascan where Dewberry 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. Once the ground routine has been completed a manual quality control routine is done using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review and 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 corrections were completed, the dataset was 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 0.3 meter of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. In addition to classes 1, 2, 9, and 10, there is a Class 7, noise points and Class 11, withheld . Class 7 was only used if needed when points could manually be identified as low/high points and class 11 points are points that contain a scan angle greater than 20 degrees.
The fully classified dataset is then processed through Dewberry's comprehensive quality control program.
The data was classified as follows:
Class 1 = Unclassified. This class includes vegetation, buildings, noise etc.
Class 2 = Ground
Class 7= Noise
Class 9 = Water
Class 10=Ignored
Class 11= Withheld
The LAS header information was verified to contain the following:
Class (Integer)
GPS Week Time (0.0001 seconds)
Easting (0.003 m)
Northing (0.003 m)
Elevation (0.003 m)
Echo Number (Integer 1 to 4)
Echo (Integer 1 to 4)
Intensity (8 bit integer)
Flight Line (Integer)
Scan Angle (Integer degree)
Calibrated LiDAR Point Cloud LAS 1.2 format
201408
Final Tiled LiDAR datasets
Dewberry - Geospatial Services Group
Amar Nayegandhi
Project Manager
mailing and physical address
1000 N. Ashley Drive, Suite 801
Tampa
FL
33602
USA
813.421.8642
813.225.1385
anayegandhi@dewberry.com
8:00 - 5:00 EST
Vector
Point
4075691754
Universal Transverse Mercator
18
0.999600
-75.000000
0.000000
500000.000000
0.000000
coordinate pair
0.000100
0.000100
meters
North American Datum of 1983(2011)
Geodetic Reference System 80
6378137.000000
298.257222
North American Vertical Datum of 1988 (Geoid 12A)
0.000100
meters
Explicit elevation coordinate included with horizontal coordinates
LiDAR points in LAS 1.2 format
none
USGS
Program Manager
mailing and physical address
1400 Independence Road
Rolla
MO
65401
USA
(573) 308-3756
gdunn@usgs.gov
Downloadable Data
This data was produced for the U.S. Geological Survey according to specific project requirements. This information is provided "as is". Further documentation of this data can be obtained by contacting: USGS, 1400 Independence Road, Rolla, MO 65401. Telephone (573) 308-3587.
20140927
U.S. Geological Survey
Gail Dunn
Program Manager
mailing and physical address
1400 Independence Road
Rolla
MO
65401
USA
(573) 308-3756
gdunn@usgs.gov
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
local time
http://www.esri.com/metadata/esriprof80.html
ESRI Metadata Profile