2013 USGS Lidar: NY post-Sandy, Ulster, Dutchess, Orange Counties

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This graphic displays the footprint for this lidar data set.

Link to data set report.

Folder containing breaklines for the project.

Data for the Ulster, Dutchess, and Orange Counties NY LiDAR project was acquired by The Atlantic Group (TAG) using a Partenavia S.P.A. P 68 C/TC (N775MW) and a Cessna T210L (N732JE).

The project area included approximately 2846 square miles in the New York counties of Ulster, Dutchess, and Orange. LiDAR sensor data was collected with the Leica ALS70-HP 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 Atlantic meet or exceed the requirements set out in the Statement of Work. The quality control requirements of Atlantic's quality management program were adhered to throughout the acquisition stage fo this project to ensure product quality.

LIDAR acquisition began on November 20, 2013 (julian day 324) and was completed on June 01, 2014 (julian day 152). A total of 22 survey missions were flown to complete the project. TAG utilized a Leica ALS70-HP LiDAR system 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 499 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 Leica's ALS Post Processor, initially with the most recent boresight values. The initial point generation for each mission calibration is verified within Microstation/Terrascan 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.

All missions are validated against the adjoining missions for relative vertical biases and collected GPS validation points for absolute vertical accuracy purposes.

On a project level, a supplementary coverage check is carried out to ensure no data voids unreported by Field 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

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 . Class 7 was only used if needed when points could manually be identified as low/high points.

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

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 (16 bit integer)

Flight Line (Integer)

Scan Angle (Integer degree)

All hydrographic breaklines were collected by Tuck Mapping Solutions using LP360. LP360 allows the user to view both the intensity and elevation of the dataset when placing the breaklines. The breaklines are then conflated in LP360 to match the lidar elevations. Monotonicity is enforced on linear hydrographic features during the conflate process and water bodies are assigned the lowest elevation during the conflate process so that each water body is set to one constant elevation that is below the surrounding terrain. The breaklines were collected in accordance with the Data Dictionary.

Inland Lakes and Ponds and Inland Streams and Rivers were collected according to specifications for the Ulster, Dutchess, and Orange Counties NY LiDAR Project.

NOAA OCM obtained the data from the USGS rocky ftp site. Data were converted to geographic coordinates and ellipsoid heights for ingest into the Digital Coast Data Access Viewer. NGS Geoid12a was used for the conversion to ellipsoid height.

Pollpel Island in the Hudson River was found unclassified. Global Mapper was used to classify ground on the island. The continually updated shoreline product was used to then classify the water that had been classified as ground.