2016-2017 NOAA NGS Topobathy Lidar DEM: Coastal South Carolina

These data were collected by Quantum Spatial, Inc. (QSI) for the National Oceanic and Atmospheric Administration (NOAA), National Geodetic Survey (NGS), Remote Sensing Devision (RSD), Coastal Mapping Program (CMP) using a Riegl VQ880G system. The Delivery 1 and Delivery 2 (D1/D2) data were acquired from 20161203 - 20170301 in nine missions. The missions were flown on 20161203, 20161209, 20161210, 20161228, 20161229, 20161230, 20170105, 20170228, and 20170301. The Delivery 3 (D3) data were acquired from 20161002 - 20170219 in fifteen missions. Data acquired on 10/02, pre-Hurricane Matthew, was only used to fill a small gap in data entirely over water where no bathymetric coverage was achieved. The Delivery 4 (D4) data were acquired from 20161211 - 20170204 in thirteen missions. The Delivery 5 (D5) data were acquired from 20170112 - 20170204 in eleven missions. The Delivery 6 (D6) data were acquired from 20161203 - 20170301 in eleven missions. The Delivery 7 (D7) data were acquired from 20170212 - 20170221 in eight missions. The Delivery 8 (D8) data were acquired from 20170218 - 20170227 in six missions.

This lidar data (and digital camera imagery collected under the same task order) was required by National Oceanic and Atmospheric Administration (NOAA), the National Geodetic Survey (NGS), Remote Sensing Division Coastal Mapping Program (CMP) to enable accurate and consistent measurement of the national shoreline. The CMP works to provide a regularly updated and consistent national shoreline to define America's marine territorial limits and manage coastal resources.

An automated grounding classification algorithm was used to determine bare earth and submerged topography point classification. The automated grounding was followed with manual editing. Classes 2 (ground) and 26 (submerged topography) were used to create the final DEMs.

Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the National Geodetic Survey, the Office for Coastal Management, or its partners.

This data can be obtained on-line at the following URL: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8585

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Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations.

OS Independent

The DEMs are derived from the source LiDAR and inherit the accuracy of the source data. The DEMs are created using controlled and tested methods to limit the amount of error introduced during DEM production so that any differences identified between the source LiDAR and final DEMs can be attributed to interpolation differences. DEMs are created by averaging several LiDAR points within each pixel which may result in slightly different elevation values at a given location when compared to the source LAS, which does not average several LiDAR points together but may interpolate (linearly) between two or three points to derive an elevation value.

Absolute accuracy was assessed using both Non-Vegetated Vertical Accuracy (NVA) and Vegetated Vertical Accuracy (VVA) survey methods. Survey checkpoints were evenly distributed, as much as possible, throughout the project area. NVA compares known ground check point data that were withheld from the calibration and post-processing of the LiDAR point cloud to the triangulated surface generated by the LiDAR points that were calibrated and post-processed. NVA is a measure of the accuracy of LiDAR point data in open areas with level slope (less than 20 degrees) where the LiDAR system has a high probability of measuring the ground surface and is evaluated at the 95% confidence interval (1.96*RMSE.) Project specifications require NVA meet 0.196 m accuracy at the 95% confidence interval. Ground check points located in land cover categories other than bare earth, urban, or submerged topography were used to compute the Vegetated Vertical Accuracy (VVA). QSI assessed three land cover categories: tall grass, brush and small trees, and forested. Overall the NOAA South Carolina D1/D2 area, 9 survey checkpoints were used to assess the vegetated vertical accuracy. Project specifications require VVA meet 0.36 m based on the 95th percentile. Submerged topography points were also tested separately. Project specifications require submerged topography to meet 0.49 m at the 95% confidence level based on RMSEz x 1.9600.

Using NSSDA and FEMA methodology, the non-vegetated vertical accuracy (NVA) at the 95% confidence level (called Accuracyz) was computed by the formula RMSEz x 1.9600.

Using NSSDA and FEMA methodology, bathymetric vertical accuracy at the 95% confidence level for submerged topography was computed by the formula RMSEz x 1.9600.

The D1/D2 dataset tested 0.06 m non-vegetated vertical accuracy at 95% confidence level in open terrain using 23 ground check points, based on RMSEz (0.031 m) x 1.9600. The D3 dataset tested 0.068 m non-vegetated vertical accuracy at 95% confidence level in open terrain using 30 ground check points, based on RMSEz (0.035 m) x 1.9600. The D4 dataset tested 0.108 m non-vegetated vertical accuracy at 95% confidence level in open terrain using 21 ground check points, based on RMSEz (0.055 m) x 1.9600. The D5 dataset tested 0.035 m non-vegetated vertical accuracy at 95% confidence level in open terrain using 30 ground check points, based on RMSEz (0.018 m) x 1.9600. The D6 dataset tested 0.043 m non-vegetated vertical accuracy at 95% confidence level in open terrain using 64 ground check points, based on RMSEz (0.022 m) x 1.9600. The D7 dataset tested 0.047 m non-vegetated vertical accuracy at 95% confidence level in open terrain using 46 ground check points, based on RMSEz (0.024 m) x 1.9600

The bathymetric vertical accuracy of the D1/D2 Lidar dataset tested 0.231 m vertical accuracy at 95% confidence level in submerged topography using 38 submerged control points, based on RMSEz (0.118 m) x 1.9600. The D3 Lidar dataset tested 0.082 m vertical accuracy at 95% confidence level in submerged topography using 31 submerged control points, based on RMSEz (0.042 m) x 1.9600. The D5 Lidar dataset tested 0.073 m vertical accuracy at 95% confidence level in submerged topography using 8 submerged control points, based on RMSEz (0.035 m) x 1.9600. D6 Lidar dataset tested 0.062 m vertical accuracy at 95% confidence level in submerged topography using 15 submerged control points, based on RMSEz (0.031 m) x 1.9600. The D7 Lidar dataset tested 0.132 m vertical accuracy at 95% confidence level in submerged topography using 52 submerged control points, based on RMSEz (0.067 m) x 1.9600.

Using NDEP and ASPRS methodology, vegetated vertical accuracy (VVA) was computed using the 95th percentile method. The D1/D2 dataset tested 0.301 m vegetated vertical accuracy at 95th percentile using 9 mixed landclass points. The D3 dataset tested 0.049 m vegetated vertical accuracy at 95th percentile using 2 mixed landclass points. The D4 dataset tested 0.108 m vertical accuracy at 95% confidence level in open terrain using 21 ground check points, based on RMSEz (0.055 m) x 1.9600. The D5 dataset tested 0.052 m vegetated vertical accuracy at 95th percentile using 3 mixed landclass points. The D6 dataset tested 0.294 m vegetated vertical accuracy at 95th percentile using 11 mixed landclass points. The D7 dataset tested 0.107 m vegetated vertical accuracy at 95th percentile using 8 mixed landclass points.

Due to the predominately marshy terrain, no survey checkpoints, ground check points, or submerged topobathy checkpoints were able to be collected within the D8 delivery area.