2016 - 2017 FEMA Lidar DEM: Southwest Virginia & Northeast West Virginia

Southwest Virginia (VA_FEMA_R3_Southwest_A and VA_FEMA_R3_Southwest_B)

Dates of collection for Southwest Virginia (VA_FEMA_R3_Southwest_A and VA_FEMA_R3_Southwest_B)

Northeast West Virginia (VA_FEMA_R3_Northeast)

Dates of collection for Northeast West Virginia (VA_FEMA_R3_Northeast)

Create custom data files by choosing data area, map projection, file format, etc. A new metadata will be produced to reflect your request using this record as a base.

Bulk download of data files in GeoTIFF format, UTM 17N NAD83(2011) meters coordinates and elevations in NAVD88 (Geoid12B) meters.

Bulk download of data files in GeoTIFF format, UTM 17N NAD83(2011) meters coordinates and elevations in NAVD88 (Geoid12B) meters.

Bulk download of data files in GeoTIFF format, UTM 17N NAD83(2011) meters coordinates and elevations in NAVD88 (Geoid12B) meters.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

Link to the lidar report

Link to the vertical accuracy report.

Link to the reports, breaklines, metadata, and spatial metadata.

Data for the Virginia portion of the VA FEMA Lidar project was acquired by Leading Edge Geomatics (LEG).

The project area included approximately 6069.91 contiguous square miles or 15720.99 square kilometers for a portion of Virginia. Leading Edge Geomatics, LLC operated a Piper Navajo C-GKCN outfitted with a Riegl 780 dual channel laser scanner lidar system . The data was delivered in the UTM coordinate system, meters, zone 17, horizontal datum NAD83 (2011), vertical datum NAVD88, Geoid 12B. 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.

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.

Data for the West Virginia portion of the VA FEMA Lidar project was acquired by Axis Geospatial, LLC (Axis).

The project area included approximately 2,640 contiguous square miles or 6,837 square kilometers for a portion of West Virginia. Axis Geospatial, LLC operated a Cessna 206H single engine aircraft (N223TC) outfitted with a Riegl LMS-Q1560 dual channel laser scanner lidar system . The data was delivered in the UTM coordinate system, meters, zone 17, horizontal datum NAD83 (2011), vertical datum NAVD88, Geoid 12B. 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.

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 (1,500 m x 1,500 m). The tiled data is then opened in Terrascan where Dewberry classifies edge of flight line points that may be geometrically unusable with the withheld bit. These points are separated from the main point cloud so that they are not used in the ground algorithms. Overage points are then identified with the overlap bit. 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. A final QC is performed on the data. 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

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)

Dewberry used GeoCue software to produce intensity imagery and raster stereo models from the source lidar for use in lidargrammetry techniques. Dewberry then produced full point cloud intensity imagery, bare earth ground models, density models, and slope models. These files were ingested into eCognition software, segmented into polygons, and training samples were created to identify water. eCognition used the training samples and defined parameters to identify water segments throughout the project area. Water segments were then reviewed for completeness, separated into project defined feature classes, merged, and smoothed. Elevations derived from a bare earth lidar terrain were applied to each feature for 3D attribution. The delineation of lakes and ponds and tidal waters, or other water bodies at a constant elevation, was achieved using eCognition software. Lidargrammetry was used to monotonically collect streams and rivers, or features that have gradient 3D elevations. All breaklines were collected according to specifications for the project.

All hydrographic breaklines were collected by Kinetics. Breaklines were compiled for all Lakes and Ponds that were 2 acres or greater using the LiDAR intensity data and a surface terrain model of the entire project area in ESRI's ArcMap 10.5 and GeoCue's LP360 2017.1.54.7. This collection environment allows for the analyst to determine shore lines with a high level of accuracy. After the collection of hydro lines all features were validated for vertical difference, to ensure that Lakes and ponds are at or slightly below the immediately surrounding terrain. Breaklines were compiled for all Rivers and Streams that were greater than 100 ft nominal width using the LiDAR intensity data and a surface terrain model of the entire project area in ESRI's ArcMap 10.5 and GeoCue's LP360 2017.1.54.7. This collection environment allows for the analyst to determine the location of the bank with a high level of accuracy. After the collection of hydro lines all features were validated for monotonicity to ensure that they represent a gradient downhill water surface and vertical difference to ensure that the water surface edge is at or slightly below the immediately surrounding terrain.

Dewberry digitized 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. The 3D endpoints of each breakline are used to enforce a continous slope on the ground under the bridge deck along the collected breakline. These breaklines are used in the final DEM production and help to reduce the appearance of bridge saddles.

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 hydrographic breakline and lidar elevations due to monotonicity, connectivity, and flattening rules that are enforced on the hydrographic 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, lidar points are exported from the LAS files into an Arc Geodatabase (GDB) in multipoint format. The 3D breaklines, Inland Lakes and Ponds, Inland Streams and Rivers, and bridge breaklines 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 Inland Rivers and Streams : Hard Line Bridge Breaklines: Hard Line

The ESRI Terrain is converted to a raster. The raster is created using linear interpolation with a 1 meter 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 analyst 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.

The NOAA Office for Coastal Management (OCM) created references to the USGS GeoTIFF files that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the raster data as it resides on Amazon Web Services (AWS).

These are the GeoTIFF files that are being accessed:

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/VA_FEMA_R3_SW_Lidar_2016_D16/VA_FEMA_R3_Northeast_2016/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/VA_FEMA_R3_SW_Lidar_2016_D16/VA_FEMA_R3_Southwest_A_2016/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/VA_FEMA_R3_SW_Lidar_2016_D16/VA_FEMA_R3_Southwest_B_2016/TIFF/