2015 USGS Lidar DEM: Eastern Shore VA

This survey report provides accompanying information about the collection and processing of the check point data.

This graphic displays the footprint for this lidar data set.

This lidar report provides accompanying information about the collection and processing of this lidar data set.

Link to download the breakline data

Link to the Data Access Viewer (DAV) where elevation, imagery, and land cover data are available for custom download.

Link to the Digital Coast Data where many types of coastal data are available for download.

Link to the NOAA Office for Coastal Management (OCM) homepage.

Link to custom download the lidar point data from which these raster Digital Elevation Model (DEM) data were created.

Data for the Eastern Shores Virginia QL2 LiDAR project was acquired by Leading Edge Geomatics (LEG).

The project area included approximately 994 contiguous square miles or 2574.45 square kilometers for the counties of Accomack and Northampton in Virginia. LiDAR sensor data were collected with the Riegl 680i LiDAR system. The data was delivered in the State Plane coordinate system, feet, Virginia South, horizontal datum NAD83, vertical datum NAVD88, Geoid 12a. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud, survey control, and a final acquisition/calibration 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.

A copy of the final calibrated swaths are maintained in LAS format 1.2 for production utilizing Terrascan software. A second, identical version of final calibrated swaths are converted from v1.2 to v1.4 using GeoCue software. The withheld and overlap bits are set and all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

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 (5,000 ft x 5,000 ft). Dewberry extended the client provided boundary where tiles had ground to include thirty four extra tiles. The tiled data is then opened in Terrascan where Dewberry classifies edge of flight line points that may be geometrically unusable to a separate class. These points are separated from the main point cloud so that they are not used in the ground algorithms. Dewberry then 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. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a 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 and bridge deck corrections are completed, the dataset is 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 1x NPS or less of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. Next, an intelligently thinned ground classification identified model key points and are flagged with the Model Key Point bit. Overage points are then identified in Terrascan and GeoCue is used to set the overlap bit for the overage points and the withheld bit is set on the withheld points previously identified in Terrascan before the ground classification routine was performed. A final QC is performed on the data. The LAS files are then converted from v1.2 to v1.4 using GeoCue software. At this time, all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

The data was classified as follows:

Class 1 = Unclassified. This class includes vegetation, buildings, noise etc.

Class 2 = Ground (bare earth points identified as Model Key Points are flagged with the Model Key Point bit)

Class 7 = Low Noise

Class 9 = Water

Class 10 = Ignored Ground due to breakline proximity

Class 17 = Bridge Decks

Class 18 = High Noise

The LAS header information was verified to contain the following:

Class (Integer)

Adjusted GPS Time (0.0001 seconds)

Easting (0.003 m)

Northing (0.003 m)

Elevation (0.003 m)

Echo Number (Integer)

Echo (Integer)

Intensity (16 bit integer)

Flight Line (Integer)

Scan Angle (degree)

Existing lidar data acquired and processed as part of the NOAA Sandy Supplemental project were re-processed and combined with the LEG data to supplement/complete data coverage along the eastern portion of this project

Dewberry used GeoCue software to produce intensity imagery and raster stereo models from the source LiDAR. The raster resolution was 2.5 feet.

LiDAR intensity stereopairs were viewed in 3-D stereo using Socet Set for ArcGIS softcopy photogrammetric software. The breaklines are collected directly into an ArcGIS file geodatabase to ensure correct topology. The LiDARgrammetry was performed under the direct supervision of an ASPRS Certified Photogrammetrist. The breaklines were stereo-compiled in accordance with the Data Dictionary. Inland Lakes and Ponds and Tidal were collected according to specifications for the Eastern Shores Virginia QL2 LiDAR Project.

Dewberry digitzed 2D bridge deck polygons from the intensity imagery and used these polygons to classify bridge deck points in the LAS to class 17. As some bridges are hard to identify in intensity imagery, Dewberry then used ESRI software to generate bare earth elevation rasters. Bare earth elevation rasters do not contain bridges. As bridges are removed from bare earth DEMs but DEMs are continuous surfaces, the area between bridge abutments must be interpolated. The rasters are reviewed to ensure all locations where the interpolation in a DEM indicates a bridge have been collected in the 2D bridge deck polygons

The bridge deck polygons are loaded into Terrascan software. LiDAR points and surface models created from ground LiDAR points are reviewed and 3D bridge breaklines are compiled in Terrascan. Typically, two breaklines are compiled for each bridge deck-one breakline along the ground of each abutment. The bridge breaklines are placed perpendicular to the bridge deck and extend just beyond the extents of the bridge deck. Extending the bridge breaklines beyond the extent of the bridge deck allows the compiler to use ground elevations from the ground LiDAR data for each endpoint of the breakline.

Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture. All breaklines are then compared to ESRI terrains created from ground only points prior to water classification. The horizontal placement of breaklines is compared to terrain features and the breakline elevations are compared to LiDAR elevations to ensure all breaklines match the LiDAR within acceptable tolerances. Some deviation is expected between breakline and LiDAR elevations due to monotonicity, connectivity, and flattening rules that are enforced on the breaklines. Once completeness, horizontal placement, and vertical variance is reviewed, all breaklines are reviewed for topological consistency and data integrity using a combination of ESRI Data Reviewer tools and proprietary tools. Corrections are performed within the QC workflow and re-validated.

Class 2, ground, and Class 8, model key points, LiDAR points are exported from the LAS files into an Arc Geodatabase (GDB) in multipoint format. The 3D breaklines, Inland Lakes and Ponds and Tidal are imported into the same GDB. An ESRI Terrain is generated from these inputs. The surface type of each input is as follows: Ground Multipoint: Masspoints Inland Lakes and Ponds: Hard Replace Tidal : Hard Replace

The ESRI Terrain is converted to a raster. The raster is created using linear interpolation with a 2.5 foot cell size. The DEM is reviewed with hillshades in both ArcGIS and Global Mapper. Hillshades allow the analyst to view the DEMs in 3D and to more efficiently locate and identify potential issues. Analysts review the DEM for missed LiDAR classification issues, incorrect breakline elevations, incorrect hydro-flattening, and artifacts that are introduced during the raster creation process.

The corrected and final DEM is clipped to individual tiles. Dewberry uses a proprietary tool that clips the DEM to each tile located within the final Tile Grid, names the clipped DEM to the Tile Grid Cell name, and verifies that final extents are correct. All individual tiles are loaded into Global Mapper for the last review. During this last review, an analsyt checks to ensure full, complete coverage, no issues along tile boundaries, tiles seamlessly edge-match, and that there are no remaining processing artifacts in the dataset.

Original project data were provided to the National Geospatial Technical Operations Center (NGTOC) in Rolla, MO and/or Denver, CO. The data were reviewed for quality and accuracy before publication. Data are provided in the original source resolution and spatial reference.

This is the Original Product Resolution (OPR) Digital Elevation Model (DEM) as provided to the USGS. This DEM is delivered in the original resolution, with the original spatial reference. These data may be used as the source of updates to the seamless layers of the 3D Elevation Program, which serves as the elevation layer of the National Map. These data can be used by scientists and resource managers for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications.

The NOAA Office for Coastal Management (OCM) downloaded 1310 raster files from the USGS rockyftp site. The data were in VA State Plane South coordinates and NAVD88 (Geoid12A) elevations in feet. The bare earth raster files were at a 2.5 ft grid spacing.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. Converted the raster files from elevations in feet to meters using gdal_translate

2. Copied the files to https