2007 US Army Corps of Engineers (USACE), Jacksonville District US Virgin Islands LiDAR

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Date that the source FGDC record was last modified.

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This Light Detection and Ranging (LiDAR) LAS dataset is a topographic survey conducted for the USACE USVI LiDAR Project. These data

were produced for The Corps of Engineers Jacksonville District. The USVI LiDAR Survey consists of the islands of St. Croix, St. Thomas,

and St. John. The LiDAR point cloud was flown at a density sufficient to support the Federal Emergency Management Agency (FEMA)

guidelines and specifications. 3001 Inc. acquired the USVI LiDAR Survey between November 16, 2007 and November 29, 2007. The USVI

LiDAR Survey was collected under the guidance of a Professional Mapper/Surveyor.

| Source Geospatial Form: remote-sensing image | Type of Source Media: digital tape media

The Airborne Global Position System (ABGPS), inertial measurement unit (IMU), and raw scans are collected during the LiDAR aerial survey.

The ABGPS monitors the xyz position of the sensor and the IMU monitors the orientation of the aircraft. During the aerial survey laser

pulses reflected from features on the surface and are detected by the receiver optics and collected by the data logger. GPS locations

are based on data collected by receivers on the aircraft and base stations on the ground. The ground base stations are placed no more

than 35 km radius from the flight survey area.

The ABGPS, IMU, and raw scans are integrated using proprietary software developed by the Leica Geosystems and delivered with the Leica

ALS50 System. The resultant file is in a LAS binary file format. The LAS file version 1.1 format can be easily transferred from one file

format to another. It is a binary file format that maintains information specific to the LiDAR data (return #, intensity value, xyz, etc.).

The resultant points are produced in the Florida State Plane West Zone coordinate system, with units in feet and referenced to the NAD83

horizontal datum and GRS80/Geoid03 vertical datum.

The unedited data are classified to facilitate the application of the appropriate feature extraction filters. A combination of proprietary

filters is applied as appropriate for the production of bare-earth digital terrain models (DTMs). Interactive editing methods are applied to

those areas where it is inappropriate or impossible to use the feature extraction filters, based upon the design criteria and/or limitations

of the relevant filters. These same feature extraction filters are used to produce elevation height surfaces.

Filtered and edited data are subjected to rigorous QA/QC according to the 3001 Inc. Quality Control Plan and procedures. Very briefly, a

series of quantitative and visual procedures are employed to validate the accuracy and consistency of the filtered and edited data. Ground

control is established by 3001, Inc. and GPS-derived ground control points (GCPs) points in various areas of dominant and prescribed land

cover. These points are coded according to landcover, surface material, and ground control suitability. A suitable number of points are

selected for calculation of a statistically significant accuracy assessment as per the requirements of the National Standard for Spatial

Data Accuracy. A spatial proximity analysis is used to select edited LiDAR data points within a specified distance of the relevant GCPs.

A search radius decision rule is applied with consideration of terrain complexity, cumulative error, and adequate sample size. Accuracy

validation and evaluation is accomplished using proprietary software to apply relevant statistical routines for calculation of Root Mean

Square Error (RMSE) and the National Standard for Spatial Data Accuracy (NSSDA) according to Federal Geographic Data Committee (FGDC)

specifications.

The LiDAR mass points were delivered in American Society for Photogrammetry and Remote Sensing LAS 1.1 format. The header file for each

dataset is complete as define by the LAS 1.1 specification. In addition the following fields are included: Flight Date Julian, Year, and Class.

The data were classified as follows:

Class 1 = Unclassified- this class includes vegetation, buildings, noise etc.;

Class 2 = Ground

Class 7 = Noise

Class 9 = Water

Class 12 = Overlap

A triangulated irregular network (TIN) is a set of irregularly spaced points that contain an explicit topographic value. Each point is a

vertice and is connected to any three points to represent an area of uniform topography. TINs retain precise topological location and are

excellent sources for statistical calculations. The TINs were created in Terrasolid's "Terrascan" software. The first step of the process

included the separation of the bare-earth points from the artifacts. The breaklines (vectors) are then concerted to x, y, z (ASCII) files

and imported into the bare-earth mass points. The final step is to create the TIN using an ESRI Arc macro language (aml) script. To ensure

the quality of the TIN each TIN is viewed independently to ensure that the hydro-breaklines are enforced and that the vegetation has been

removed.

The breaklines were constructed under the model representation that water courses vary linearly in elevation or are constant in elevation

between critical points established in the breakline model. These breaklines were determined from LiDAR and orthophoto data of specific

dates and that model the land/water contributions and extents on those dates. The 3-D vector line work was created using stereo-compilation,

digitizing, and manual editing. A thorough QC procedure was implemented to verify the elevation of the breaklines and to ensure no zero

elevations were found except in coastal areas where it is possible to find z values equal to mean sea level. The breaklines are hydrologically

correct 3-D product that represents a continuous dendritic network. This dataset is topologically correct. Stream and river features

that are 0.5 miles or greater in length will be captured. Features that are 8 feet are less in width shall be captured as a single breaklines.

Features greater than 8 feet in width shall be captured as double breakline features. All features will be captured as three-dimensional

breaklines. Coastal shorelines shall be captured as three-dimensional linear features. Coastal breaklines will merge seamlessly with

linear hydrographic features at the approximate maximum extent of tidal influence. The shoreline of islands within water bodies shall be

captured as three-dimensional breaklines. Terramodel software was used to display the edited LiDAR points and associated GEOTIFF orthophotos

to construct breaklines for the water's edge of wide channel rivers and canals, the shoreline of lakes and the shoreline of islands within

water bodies. Breakline elevations were linearly ramped between identified critical elevation points along flowing water courses or were set

at a fixed level for lakes based on the lowest observed shoreline elevation or water return elevation. The breakline files are edge-matched

and a shapefile for the project was created. Three-dimensional breaklines were derived through on screen digitizing based on the LiDAR data

and orthophotography. The line work was captured as two dimensional lines with x/y coordinates only. Principal breaklines that support

hydrologic and hydraulic models were captured which includes stream shorelines and hydraulic features such as dams, bridges, and culverts

that constrict or impede the flow of water.

The NOAA Office for Coastal Management (OCM) received files in LAS format. The files contained LiDAR intensity and elevation measurements.

OCM performed the following processing on the data to make it available within Digital Coast:

1. The data were converted from State Plane Puerto Rico coordinates to UTM Zone 20 coordinates.

2. The data were converted from NAVD88 feet to NAVD88 meters.

3. The data were re-tiled in MARS and all point classifications were set to 0 (class 0).

4. The data were filtered to Bare Earth (class 2) and Unclassified (class 1) using an automated process in LASEdit.

5. The data were converted from UTM coordinates to geographic coordinates.

6. The data were converted from NAVD88 heights to ellipsoid heights using Geoid03.

7. The LAS header fields were sorted by latitude and updated.