Quantum Spatial, Inc.
20181011
New York FEMA 2017 LiDAR
Lidar point cloud
Product: This lidar data set includes unclassified swath LAS 1.4 files, classified LAS 1.4 files, hydro and bridge breaklines, hydro-flattened digital elevation models (DEMs), intensity imagery, and topographic contours.
Geographic Extent: Five counties in New York, covering approximately 1,935 total square miles.
Dataset Description: The New York FEMA 2017 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD83 (2011), State Plane New York East FIPS 3101, meters and vertical datum of NAVD88 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 2,477 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 44 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 129 independent accuracy checkpoints, 71 in Bare Earth and Urban landcovers (71 NVA points), 58 in Tall Weeds categories (58 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
To acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation modeling, etc. Classified LAS files are used to show the manually reviewed bare earth surface. This allows the user to create intensity images, breaklines and raster DEMs. The purpose of these LiDAR data was to produce high accuracy 3D hydro-flattened digital elevation models (DEMs) with a 1-meter cell size. These raw LiDAR point cloud data were used to create classified LiDAR LAS files, intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
USGS Contract No. G16PC00016, Task Order No. 140G0218F0009; CONTRACTOR: Quantum Spatial, Inc.
USGS-NGP Base Specification v1.2
Leica ALS80
Unlimited
0.71
2
0.62
2.6
2013
155
18
51.6
270.4
4
0.44
1064
1
0.22
1305.17
42.87
GEOID 12B
NAD 1983 (2011) State Plane New York East FIPS 3101, meters
0
0.129
71
1.4
6
Withheld (ignore) points were identified in these files using the standard LAS Withheld bit
Swath "overage" points were identified in these files using the standard LAS overlap bit
16
1
Processed, but Unclassified
2
Bare-Earth Ground
7
Low Noise
9
In-land Water
10
Ignored Ground
17
Bridge Decks
18
High Noise
20180506
20180509
ground condition
None planned
-75.17140886
-73.92525473
44.67069081
42.94240939
None
Model
LAS Point Cloud
Remote Sensing
Elevation Data
Lidar
Hydrology
Breaklines
Raster
DEM
Intensity Image
Tile Index
QC Checkpoints
Processing Boundary
Calibration Points
Contours
None
New York
Franklin County
Fulton Couny
Herkimer County
Montgomery County
Saratoga County
No restrictions apply to these 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 their limitations.
Data cover the entire area specified for this project.
All files are inspected to ensure that they conform to the specified file naming conventions, all files load in their correct geographic position, all files conform to the project specifications for file standard and content.
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. The NVA was tested with 71 checkpoints located in bare earth and urban (non-vegetated) areas. These checkpoints 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. See survey report for additional survey methodologies. Elevations from the unclassified LiDAR surface were measured for the x,y location of each checkpoint. Elevations interpolated from the LiDAR surface were then compared to the elevation values of the surveyed control points. AccuracyZ has been tested 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.
The project specifications require the accuracy (ACCz) of the derived DEM be calculated and reported in two ways: 1. The required NVA 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. This is a required accuracy. The NVA was tested with 71 checkpoints located in bare earth and urban (non-vegetated) areas. 2. Vegetated Vertical Accuracy (VVA): VVA shall be reported for "tall weeds" land cover classes. The target VVA is: 29.4 cm at the 95th percentile, derived according to ASPRS Guidelines, Vertical Accuracy Reporting for LiDAR Data, i.e., based on the 95th percentile error in all vegetated land cover classes combined. This is a target accuracy. The VVA was tested with 58 checkpoints located in forested and tall weeds (vegetated) areas. The checkpoints were distributed throughout the project area and were surveyed using GPS techniques. See survey report for additional survey methodologies. AccuracyZ has been tested 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.
0.129
Tested 0.129 meters NVA at a 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA). The NVA of the raw LiDAR point cloud swath files was calculated against TINs derived from the final calibrated and controlled swath data using 71 independent checkpoints located in Bare Earth and Urban land cover classes.
0.129
Tested 0.129 meters NVA at a 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA). The NVA of the DEM was calculated using 71 independent checkpoints located in the Bare Earth and Urban land cover categories.
0.217
Tested 0.217 meters VVA was calculated using 58 checkpoints located in the Forested and Tall Weeds land cover categories at the 95th percentile, derived according to ASPRS Guidelines, Vertical Accuracy Reporting for LiDAR Data. Tested against the DEM.
Raw Data and Boresight Processing: 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 Quantum Spatial, Inc. proprietary and commercial 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 was 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 matched 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 checkpoints after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met.
2018
LAS Point Classification: The point classification was performed as described below. The bare earth surface was manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface was finalized, it was then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) LiDAR data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 1 meter was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. All overlap data was processed through automated functionality provided by TerraScan to classify the overlapping flight line data to approved classes by USGS. The overlap data was classified using standard LAS overlap bit. These classes were created through automated processes only and were not verified for classification accuracy. Due to software limitations within TerraScan, these classes were used to trip the withheld bit within various software packages. These processes were reviewed and accepted by USGS through numerous conference calls and pilot study areas. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS files for both the All Point Cloud Data and the Bare Earth. Quantum Spatial, Inc. proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.
2018
Hydro-Flattened Breakline Processing: Class 2 (ground) LiDAR points were used to create a bare earth surface model. The surface model was then used to heads-up digitize 2D breaklines of inland streams and rivers with a 100-foot nominal width and inland ponds and lakes of 2 acres or greater surface area. Elevation values were assigned to all Inland Ponds and Lakes, Inland Pond and Lake Islands, Inland Stream and River Islands, using TerraModeler functionality. Elevation values were assigned to all inland streams and rivers using Quantum Spatial, Inc. proprietary software. All Ground (ASPRS Class 2) LiDAR data inside of the collected inland breaklines were then classified to Water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 1 meter was also used around each hydro-flattened feature. These points were moved from ground (ASPRS Class 2) to Ignored Ground (ASPRS Class 20). The breakline files were then translated to Esri file geodatabase format using Esri conversion tools. Breaklines were reviewed against LiDAR intensity imagery to verify completeness of capture. All breaklines were then compared to TINs (triangular irregular networks) created from ground only points prior to water classification. The horizontal placement of breaklines was compared to terrain features and the breakline elevations were compared to LiDAR elevations to ensure all breaklines matched the LiDAR within acceptable tolerances. Some deviation was expected between breakline and LiDAR elevations due to monotonicity, connectivity, and flattening rules that were enforced on the breaklines. Once completeness, horizontal placement, and vertical variance were reviewed, all breaklines were reviewed for topological consistency and data integrity using a combination of Esri Data Reviewer tools and proprietary tools.
2018
Hydro-Flattened Raster DEM Processing: Class 2 (Ground) LiDAR points in conjunction with the hydro-breaklines were used to create a 1-meter hydro-flattened raster DEM. Using automated scripting routines within ArcMap, an ERDAS Imagine .IMG file was created for each tile. Each surface was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface.
2018
Intensity Image Generation Processing: GeoCue software was used to create the deliverable intensity images. All overlap classes were ignored during this process. This helps to ensure a more aesthetically pleasing image. The GeoCue software was then used to verify full project coverage as well. TIF/TWF files were then provided as the deliverable for this dataset requirement.
2018
Tile Index Processing: Tiles were created using a 0,0 origin point to ensure proper divisibility of raster and image cells. A 1,500-meter x 1,500-meter tile size was used as called for in the Task Order. Tile index was output in Esri shapefile format. Tile names are derived from the US National Grid.
2018
QC Checkpoint Processing: Please see the survey report for more information on control point location methodologies. The QC checkpoint shapefiles were generated from XYZ text files using a combination of Global Mapper and ArcMap software.
2018
Processing Boundary Processing: The processing boundary was created using the original client-provided AOI shapefile. The original file was buffered by 100 meters in order to meet task order requirements for data coverage.
2018
Calibration Point Processing: Please see the survey report for more information on control point location methodologies. The calibration control point shapefiles were generated from XYZ text files using a combination of Global Mapper and ArcMap software.
2018
Contour Processing: Using an automated process and no manual editing, 1 foot contours were generated from the hydro-flattened surface and delivered in file geodatabase format. No depressions, hidden contours, or spot elevations were included yielding only intermediate and index contours.
2018
Point
State Plane Coordinate System 1983
3101
0.9999
-74.5
38.83333333
150000
0
coordinate pair
0.01
0.01
meters
NAD 1983 (2011)
Geodetic Reference System 80
6378137.0
298.257222101
NAVD 1988 (GEOID 12B)
0.01
meters
Explicit elevation coordinate included with horizontal coordinates
20181212
Quantum Spatial
mailing and physical
523 Wellington Way
Lexington
KY
40503
USA
859-277-8700
859-277-8901
jbowen@quantumspatial.com
Monday through Friday 8:00 AM to 5:00 PM (Eastern Time)
If unable to reach the contact by telephone, please send an email. You should get a response within 24 hours.
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998
None.
None.
None.
Unclassified
NONE