ds765_metadata: Coastal Topography--Northeast Atlantic Coast, Post-Hurricane Sandy, 2012

Using an Optech Gemini LiDAR Sensor, 11 flight lines of high density data, at a nominal pulse spacing (NPS) of 1 meter, were collected by Woolpert along the southern shore of Long Island, New York (approximately 15 square miles). Data Acquisition Height = 3,500 feet AGL - Aircraft Speed = 125 Knots. Multiple returns were recorded for each laser pulse along with an intensity value for each return. A total of on (1) mission was flown on November 05. Two airborne global positioning system (GPS) base stations were used in support of the lidar data acquisition. Eight ground control points were surveyed through static methods. The GEOID used to reduce satellite derived elevations to orthometric heights was GEOID96. Data for the task order is referenced to the UTM Zone 18N, North American Datum of 1983 (NAD83), and North American Vertical Datum of 1988 (NAVD88), in meters. Airborne GPS data were differentially processed and integrated with the post-processed IMU data to derive a smoothed best estimate of trajectory (SBET). The SBET was used to reduce the lidar slant range measurements to a raw reflective surface for each flight line. The coverage was classified to extract a bare earth digital elevation model (DEM) and separate last returns. In addition to the LAS deliverables, one layer of coverage was delivered in the Imagine (IMG) Format: bare-earth.

The lidar calibration and system performance is verified on a periodic basis using Woolpert's calibration range. The calibration range consists of a large building and runway. The edges of the building and control points along the runway have been located using conventional survey methods. Inertial measurement unit (IMU) misalignment angles and horizontal accuracy are calculated by comparing the position of the building edges between opposing flight lines. The scanner scale factor and vertical accuracy is calculated through comparison of lidar data against control points along the runway. Field calibration is performed on all flight lines to refine the IMU misalignment angles. IMU misalignment angles are calculated from the relative displacement of features within the overlap region of adjacent (and opposing) flight lines. The raw lidar data is reduced using the refined misalignment angles.

Once the data acquisition and GPS processing phases are complete, the lidar data were processed immediately by Woolpert to verify the coverage had no voids. The GPS and IMU data was post processed using differential and Kalman filter algorithms to derive a best estimate of trajectory. The quality of the solution was verified to be consistent with the accuracy requirements of the project.

The individual flight lines were inspected by Woolpert to ensure the systematic and residual errors have been identified and removed. Then, the flight lines were compared to adjacent flight lines for any mismatches to obtain a homogenous coverage throughout the project area. The point cloud underwent a classification process to determine bare-earth points and non-ground points utilizing "first and only" as well as "last of many" lidar returns. This process determined Default (Class 1), Ground (Class 2), Noise (Class 7), Water (Class 9), Ignored Ground (Class 10), Overlap Default (Class 17), and Overlap Ground (class 18) classifications. The bare-earth (Class 2 - Ground) lidar points underwent a manual QA/QC step to verify that artifacts have been removed from the bare-earth surface. The surveyed ground control points are used to perform the accuracy checks and statistical analysis of the lidar dataset.

Photo Science, Inc. located a total of 29 calibration control points used in the post processing of the lidar data. The points were located on relatively flat terrain on surfaces that generally consisted of grass, gravel, or bare earth. Applanix software was used in the post processing of the airborne GPS and inertial data that is critical to the positioning and orientation of the sensor during all flights. POSPac MMS provides the smoothed best estimate of trajectory (SBET) that is necessary for Optech or Leica's post processor to develop the point cloud from the lidar missions. The point cloud is the mathematical three-dimensional collection of all returns from all laser pulses as determined from the aerial mission. The GEOID used to reduce satellite derived elevations to orthometric heights was GEOID96. Data for the task order are referenced to the UTM Zone 18N, NAD83, and NAVD88, in meters. At this point the data are ready for analysis, classification, and filtering to generate a bare-earth surface model in which the above ground features are removed from the dataset. The point cloud was manipulated within the Optech or Leica software; GeoCue, TerraScan, and TerraModeler software were used for the automated data classification, manual cleanup, and bare earth generation from the data. Project specific macros were used to classify the ground and to remove the side overlap between parallel flight lines. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. All ground (ASPRS Class 2) lidar data inside of the Lake Pond and Double Line Drain hydro flattening breaklines were then classified to water (ASPRS Class 9) using TerraScan macro functionality. All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) were reclassified to the correct classification after the automated classification was completed. All overlap data were processed through automated functionality provided by TerraScan to classify the overlapping flight line data to approved classes by USGS. The overlap data were classified to Class 17 (USGS Overlap Default) and Class 18 (USGS Overlap Ground). These classes were created through automated processes only and were not verified for classification accuracy. Data were then run through additional macros to ensure deliverable classification levels matching the ASPRS LAS Version 1.2 Classification structure. GeoCue functionality was then used to ensure correct LAS Versioning. In-house software was used as a final QA/QC check to provide LAS Analysis of the delivered tiles. QA/QC checks were performed on a per tile level to verify final classification metrics and full LAS header information.

All Photo Science elevation points classified as ground (class 2), water (class 9), and unclassified (class 1) were extracted from the .las files and converted to ASCII xyz point files using LASTools las2las.exe. The ASCII point files were then written to netcdf format using MATLAB 8.0.0.783.

All Woolpert .laz files were extracted to .las and converted to ASCII xyz point files using LASTools las2las.exe. The ASCII point files were then written to netcdf format using MATLAB 8.0.0.783.

Elevation data from lidar surveys were interpolated in MATLAB 8.0.0.783 to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 meters in the longshore direction and 2.5 meters in the cross-shore direction. The interpolation method applies spatial filtering with a Hanning window that is twice as wide as the grid resolution. The shoreline position was determined as the intersection of the 20-year mean high water contour line with the cross-shore profiles of the gridded elevations at 10-meter intervals along the coast using MATLAB 8.0.0.783. Reference for 20-year mean high water line: Weber, K.M., List, J.H., and Morgan, K.L.M., 2005, An operational mean high water datum for determination of shoreline position from topographic lidar data: U.S. Geological Survey Open-File Report 2005-1027, available at http://pubs.usgs.gov/of/2005/1027/.

Elevation data from lidar surveys were interpolated in MATLAB 8.0.0.783 to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 meters in the longshore direction and 2.5 meters in the cross-shore direction. The interpolation method applies spatial filtering with a Hanning window that is twice as wide as the grid resolution. The position and height of the crest of the primary dune were then extracted from cross-shore profiles of the gridded elevations at 10-meter intervals along the coast using an automated algorithm in MATLAB 8.0.0.783. Dune crest height was then interpolated to the 20-year mean high water line over 1-kilometer sections of coast and smoothed using a Hanning window with a width of 2 kilometers. Reference for automated algorithm: Stockdon, H.F., Doran, K.J., Thompson, D.M., Sopkin, K.L., Plant, N.G., and Sallenger, A.H., 2012, National assessment of hurricane-induced coastal erosion hazards: Gulf of Mexico: U.S. Geological Survey Open-File Report 2012-1084, 51 p.

Elevation data from lidar surveys were interpolated in MATLAB 8.0.0.783 to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 meters in the longshore direction and 2.5 meters in the cross-shore direction. The interpolation method applies spatial filtering with a Hanning window that is twice as wide as the grid resolution. The position and height of the primary dune toe, defined as the location of maximum change in slope between the shoreline and the primary dune crest for each cross-shore profile, were then extracted from the gridded elevations at 10-meter intervals along the coast using an automated algorithm in MATLAB 8.0.0.783. Dune toe elevation was then interpolated to the 20-year mean high water line over 1-kilometer sections of coast and smoothed using a Hanning window with a width of 2 kilometers. Reference for automated algorithm: Stockdon, H.F., Doran, K.J., Thompson, D.M., Sopkin, K.L., Plant, N.G., and Sallenger, A.H., 2012, National assessment of hurricane-induced coastal erosion hazards: Gulf of Mexico: U.S. Geological Survey Open-File Report 2012-1084, 51 p.

Dune crest and toe were interpolated in Matlab (version 2012a) and smoothed to a 1 km alongshore spacing using a hanning window filter. Sections with greater than 75 percent of data missing are flagged with the invalid number of 999. The 1 km smoothed dune crest, toe and rms errors for each were written to line shapefiles using Matlab's shapewrite.m script. Shoreline position was exported from Matlab into point shapefiles and converted to lines using XTools Pro for ArcGIS desktop (version 9.1). The resulting line connected points across inlets and channels, so these areas were hand-edited in ArcMap (version 10.1).

Keywords section of metadata optimized for discovery in USGS Coastal and Marine Geology Data Catalog.

Added keywords section with USGS persistent identifier as theme keyword.