2016-2017 NOAA NGS Topobathy: 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 dataset includes topobathy data in a LAS 1.2 format file with the following classification: unclassified (1), ground (2), noise (7), overlap default (19), overlap ground (20), overlap water column (21), overlap water surface (22), water column (25), bathymetric bottom or submerged topography (26), water surface (27), and temporal bathy bottom (31), in accordance with project specifications. The contracted project consisted of approximately 519,590 acres along the Atlantic Coast of South Carolina. To ensure complete coverage and adequate point densities around survey area boundaries, the Area of Interest (AOI) was buffered by 100m. LAS files were compiled by 500 m x 500 m tiles.

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.

The data includes all lidar returns. An automated grounding classification algorithm was used to determine bare earth and submerged topography point classification. The automated grounding was followed with manual editing.

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.

D1/D2 area

D3 area

D4 area

D5 area

D6 area

D7 area

D8 area

This data can be obtained on-line at the following URL: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8575

None

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be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions.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

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.). 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.

D1/D2 dataset tested 0.301 m vegetated vertical accuracy at 95th percentile using 9 mixed landclass points. The non-vegetated accuracy (NVA) tested 0.060 m vertical accuracy at 95% confidence level in open terrain using 23 ground check points based on RMSEz (0.031 m) x 1.9600. The bathymetric vertical accuracy 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.

D3 dataset tested 0.049 m vegetated vertical accuracy at 95th percentile using 2 mixed landclass points. The non-vegetated accuracy (NVA) tested 0.068 m vertical accuracy at 95% confidence level in open terrain using 30 ground check points based on RMSEz (0.035 m) x 1.9600. The bathymetric vertical accuracy 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.

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.

D5 dataset tested 0.052 m vegetated vertical accuracy at 95th percentile using 3 mixed landclass points. The non-vegetated accuracy (NVA) tested 0.035 m vertical accuracy at 95% confidence level in open terrain using 30 ground check points based on RMSEz (0.018 m) x 1.9600. The bathymetric vertical accuracy 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 dataset tested 0.294 m vegetated vertical accuracy at 95th percentile using 11 mixed landclass points. The non-vegetated accuracy (NVA) tested 0.043 m vertical accuracy at 95% confidence level in open terrain using 64 ground check points based on RMSEz (0.22 m) x 1.9600. The bathymetric vertical accuracy tested 0.062 m vertical accuracy at 95% confidence level in submerged topography using 8 submerged control points, based on RMSEz (0.031 m) x 1.9600.

D7 dataset tested 0.107 m vegetated vertical accuracy at 95th percentile using 8 mixed landclass points. The non-vegetated accuracy (NVA) tested 0.047 m vertical accuracy at 95% confidence level in open terrain using 46 ground check points based on RMSEz (0.24 m) x 1.9600. The bathymetric vertical accuracy 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.

Due to marshy terrain, no survey checkpoints, ground check points, or submerged topobathy checkpoints were able to be collected within the D8 delivery area. Therefore, the NVA, VVA, and submerged topography accuracies were unable to be assessed for the D8 Delivery area.