2020 SC DNR Lidar DEM: 5 County (Cherokee, Chester, Fairfield, Lancaster, Union), SC

Dates of collection for the 5 County project in South Carolina.

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, South Carolina State Plane NAD83(2011), international feet coordinates and NAVD88 (Geoid12B) elevations in feet.

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 acquisition report.

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

Link to the lidar data processing report.

Link to the lidar survey report.

Link to the breaklines and building polygons footprint.

Data was acquired by Quantum Spatial for the 5-county lidar project. The project area encompassed approximately 3,016 square miles. Data were collected using linear mode Leica ALS-80 sensors, serial numbers 3061 and 3546. The data were delivered in the State Plane coordinate system, international feet, South Carolina, horizontal datum NAD83, vertical datum NAVD88, U.S. Survey Feet, Geoid 12B. Deliverables for the project included a raw (unclassified) calibrated lidar point cloud and an acquisition report The lidar calibration process was conducive to postprocessing an accurate data set. Significant attention was given to GPS baseline distances and GPS satellite constellation geometry and outages during the trajectory processing. Verification that proper ABGPS surveying techniques were followed including: pre and post mission static initializations and review of In-air Inertial Measurement Unit (IMU) alignments, if performed, both before and after on-site collection activities to ensure proper self-calibration of the IMU accelerometers and gyros were achieved. Cross flights were planned throughout the project area across all flightlines and over roadways where possible. The cross-flights provided a common control surface used to remove any vertical discrepancies in the lidar data between flightlines and aided in the bundle adjustment process with review of the roll, pitch, heading (omega, phi, kappa). The cross-flight design was critical to ensure flight line ties across the sub-blocks and the entire project area. The areas of overlap between flightlines were used to calibrate (aka boresight) the lidar point cloud to achieve proper flight line to flight line alignment in all 6 degrees of freedom. This included adjustment of IMU and scanner-related variables such as roll, x, y, z, pitch, heading, and timing interval (calibration range bias by return) Each lidar mission flown was independently reviewed, bundle adjusted (boresighted), and/if necessary, improved by a hands-on boresight refinement in the office. Once the relative accuracy adjustment was complete, the data was adjusted to the high order GPS calibration control to achieve a zero-mean bias for fundamental accuracy computation, verification, and reporting. Internal accuracy testing procedures and methods were compliant with SCDNR and USGS specifications.

Field survey was conducted for Cherokee, Lancaster, Fairfield, Chester, and Union Counties to establish ground survey control in support of lidar data calibration processes and to establish independent lidar checkpoints used to internally verify calibration results. A total of 70 calibration survey points were established for the purpose of data calibration and a total of 161 checkpoints comprised of bare earth, forested, urban, low and medium height vegetation types were used to verify calibration results independent of the calibration process. Each location was double-occupied to validate accuracy. The control was used to facilitate calibration of lidar flight lines/blocks, perform mean adjustment, and test final fundamental accuracy of the data. Control was established under the following conditions: 1. Located only in open terrain where there is a high probability that the sensor will have detected the ground surface without influence from surrounding vegetation. 2. On flat or uniformly sloping terrain at least five (5) meters away from any breakline where there is a change in slope. 3. Checkpoint accuracy satisfied a Local Network accuracy of 5 cm at the 95% confidence level. 4. Field photos will be taken of each point, in multiple directions (generally cardinal directions). ESP prepared and delivered a Report of Survey which included a "as collected" control locations map, survey methodology, QA/QC methodology, control coordinates, field pictures, and any field comments. As part of this deliverable, Excel .CSV files were delivered with the control coordinates and elevation values for calibration and checkpoint locations. The report was signed and sealed by the surveyor in charge. National Geodetic Survey data sheets were included for any Network Control Points used to control the topographic data acquisition and ground surveys.

The ESP team utilized multiple software and data management methods throughout the lidar processing workflow. The workflow post-acquisition began at team member Quantum's production facility with the lidar calibration process. The calibration process ensured that all lidar acquisition missions were carried out in a manner conducive to postprocessing an accurate data set. Significant attention was given to GPS baseline distances and GPS satellite constellation geometry and outages during the trajectory processing. Verification that proper Airborne GPS (AGPS) surveying techniques were followed including: pre and post mission static initializations and review of In-air IMU alignments, if performed, both before and after on-site collection to ensure proper self-calibration of IMU accelerometers and gyros were achieved. Relative accuracy was achieved by establishing cross flights throughout each project block area across all flight lines and over roadways where possible. The cross-flight provides a common control surface used to remove any vertical discrepancies in the lidar data between flight lines and aids in bundle adjustment process with review of roll, pitch, heading (omega, phi, kappa). The cross-flight is critical to ensure flight line ties across the sub-blocks and the entire project area. Areas of overlap between flight lines are used to calibrate the lidar point cloud to achieve proper flight line to flight line alignment in all 6 degrees of freedom. This includes adjustment of IMU and scanner-related variables such as roll, x, y, z, pitch, heading, and timing interval (calibration range bias by return). Each LiDAR mission flown was independently reviewed, bundle adjusted, and/if necessary, improved by a hands-on boresight refinement in the office. Once the relative accuracy adjustment was complete, data was adjusted to the high order GPS calibration control to achieve a zero-mean bias for fundamental accuracy computation, verification, and reporting. Internal accuracy testing procedures, methods were compliant with ASPRS and USGS specifications. ESP utilized a combination of Terrasolid products and proprietary software such as ESP Analyst and ESP Utilities to conduct post-calibration, lidar point cloud processing tasks.

The lidar classification process encompassed a series of automated and manual steps to classify the calibrated point cloud dataset. Each project represents unique characteristics in terms of cultural features (urbanized vs. rural areas), terrain type, and vegetation coverage. These characteristics were thoroughly evaluated at the onset of the project to ensure that the appropriate automated filters were applied and that subsequent manual filtering yielded correctly classified data. Automated filtering macros, which may contain one or more filtering algorithms, were developed and executed to derive LAS files with points separated into the different classification groups as defined in the ASPRS classification table. The macros were tested in several portions of project area to verify the appropriateness of the filters. At times, a combination of several filter macros optimized the filtering based on the unique characteristics of the project. Automatic filtering generally yields a ground surface that is 85-90% valid, so additional editing (hand filtering) was required to produce a more robust ground surface. The data were classified as follows: Class 1 = Unclassified (non-ground) Class 2 = Ground (bare earth) Class 3 = Low Vegetation Class 4 = Medium Vegetation Class 5 = High Vegetation Class 6 = Buildings Class 7= Low Noise Class 8 = Model Keypoints Class 9 = Water Class 11 = Withheld Points Class 13 = Roads Class 17 = Bridge Decks Class 18 = High Noise Class 20 = Ignored Ground (breakline proximity buffer) Class 21 = Culverts. Header records for the LAS files were reviewed to ensure that the expected classifications were present along with project information, x/y/z limits and scale, and that time, intensity and angle were also populated.

ESP technicians reviewed the auto-classified lidar point clouds to manually re-classify (or hand-filter) "noise" and other features that may have remained in the ground classification as well as to correct any gross mis-classifications by the software. Cross-sections and TIN surfacing tools were used to assist technicians in the reclassification of non-ground data artifacts. Certain features such as berms, hilltops, cliffs and other features that may have been aggressively auto-filtered had points re-classified into the ground classification. Conversely, above-ground artifacts such as decks, bushes, and other subtle features that may have remained in the ground classification after automated filtering were corrected via a manual editing process.

Hydro-flattening breaklines were collected and compiled using proprietary techniques within ESP Analyst software for drainage features that drain approximately 0.5 sq. mi. or more. A minimum of four feature attributes we included in the linework 1. Single Line Stream (Polyline Z), 2. Stream Centerline/Connector (Polyline Z), 3. Stream Banks (Polygon Z), 4. Waterbodies (Polygon Z) Centerlines were captured for all streams that were > 20 feet in width as well as for lakes and ponds. Banks and centerlines/connectors were captured for streams >20 feet in width and closed water bodies and islands that equaled or exceeded 1 acre in surface area were delineated. Line intersections were noded and linework for adjoining counties in the project were edge-matched in the z,y and z. In addition, lines were collected under bridge locations to ensure that the TIN would be enforced around bridge abutments. To ensure that only closed water bodies that met the SCDNR criteria were drawn, ESP utilized a minimum map unit (MMU) tool within ESP Analyst to assist the technicians in determining whether or not island, ponds and other closed water bodies needed to be collected based on the project minimum map units of >1 acre for permanent islands and >1 acre for closed water bodies. ESRI geodatabase files were created by county, containing all linework.

ESP generated hydro-flattened DEMs using the classified ground and road lidar points in conjunction with the collected hydro-flattening and under-bridge breaklines. The DEMs were generated using ESP's proprietary ESP Utilities software and then inspected for completeness and hydro-enforcement. DEMs were generated at a 5-ft resolution in ESRI Grid format.

The NOAA Office for Coastal Management (OCM) received the files in GeoTiff format from the South Carolina Department of Natural Resources (SC DNR). The bare earth raster file was at a 5 foot grid spacing. The data were in South Carolina State Plane NAD83(2011), international feet coordinates and in NAVD88 (Geoid12B) elevations in feet. OCM copied the raster files to https for Digital Coast storage and provisioning purposes.