2011 U.S. Department of Agriculture- Natural Resources Conservation Service (USDA-NRCS) Topographic Lidar: North West Connecticut

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This graphic shows the lidar coverage for Litchfield and Fairfield Counties, Connecticut.

Date that the source FGDC record was last modified.

Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details.

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Data for the U.S. Corp of Engineers High Resolution LiDAR Data Acquisition & Processing for Portions of Connecticut project was acquired by Earth Eye, LLC.

The project area included approximately 657 contiguous square miles for portions of Connecticut including a buffer of 200 meters. LiDAR sensor data were collected with an Optech ALTM3100EA LIDAR System. No imagery was requested

or delivered. The data was delivered in the UTM coordinate system, meters, zone 18, horizontal datum NAD83, vertical datum NGVD88, Geoid 09. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud,

survey control, and a final control 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 minimum

expected horizontal accuracy was tested during the boresight process to meet or exceed the National Standard for Spatial Data Accuracy (NSSDA) for a Horizontal accuracy of 1 meter RMSE or better and a Vertical Accuracy of

RMSE(z) 9.25 cm.

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 (1,000 m x 1,000 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. In addition to classes 1, 2, and 9, there is a Class 7, noise

points. This class 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

The LAS header information was verified to contain the following:

Class (Integer)

GPS Week Time (0.0001 seconds)

Easting (0.01 foot)

Northing (0.01 foot)

Elevation (0.01 foot)

Echo Number (Integer 1 to 4)

Echo (Integer 1 to 4)

Intensity (8 bit integer)

Flight Line (Integer)

Scan Angle (Integer degree)

The NOAA Office for Coastal Management (OCM) received topographic files in LAS V1.2 format. The files contained lidar elevation measurements, intensity values, scan angle values, return information, flightline information,

and adjusted standard GPS time. The data were received in UTM Zone 18N, NAD83 coordinates and were vertically referenced to NAVD88 using the Geoid09 model. The vertical units of the data were meters. OCM performed the

following processing for data storage and Digital Coast provisioning purposes:

1. The LAS header's total point count did not match the actual number of points contained in several files. The header counts were repaired using LASTools' Lasinfo.

2. The Global Encoding Bit of each LAS file was set to '1' to reflect the use of Adjusted Standard GPS Time. (NOTE: Tile 18TXM6117 does not contain GPS Time information)

3. The topographic las files were converted from orthometric (NAVD88) heights to ellipsoidal heights using Geoid09.

4. The topographic las files were converted from a Projected Coordinate System (UTM Zone 18N) to a Geographic Coordinate System (NAD83).

5. The topographic las files' horizontal units were converted from meters to decimal degrees.