53150
2016-2017 NOAA NGS Topobathy: Coastal South Carolina
Data Set
Published / External
59025
NGS Lidar
Project
Completed
2018-08-24
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.
Theme
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > TOPOGRAPHICAL RELIEF MAPS
Theme
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > SEAFLOOR TOPOGRAPHY
Theme
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION
Theme
ISO 19115 Topic Category
elevation
Spatial
Global Change Master Directory (GCMD) Location Keywords
CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > SOUTH CAROLINA
Theme
CoRIS Discovery Thesaurus
Geographic Information > LiDAR
Theme
CoRIS Theme Thesaurus
EARTH SCIENCE > Land Surface > Topography> Terrain Elevation
Theme
CoRIS Theme Thesaurus
EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Bathymetry
Theme
NOAA NOS Harvest Catalog
CoRIS
Temporal
20161002
Temporal
20170301
Spatial
CoRIS Place Thesaurus
COUNTRY/TERRITORY > United States of America > South Carolina
Spatial
CoRIS Place Thesaurus
OCEAN BASIN > Atlantic Ocean >South Carolina
Office for Coastal Management
Charleston
SC
Data Set
None Planned
Model (digital)
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.
We request that you credit the National Oceanic and Atmospheric Administration (NOAA) when you use these data in a report, publication, or presentation.
72033
NOAA NGS Topobathy Lidar Class Scheme: Coastal South Carolina
Published / External
Completed
Other
Yes
Class table describing the class numbers used in the lidar data file. Each point in the lidar file has a class associated.
1
1 - Unclassified
Point class number
No
No
Active
Unclassified point
2
2- Ground
Point class number
No
No
Active
Ground Point
3
7 - Low Point (Noise)
Point class number
No
No
Active
Low Point (Noise)
4
25 - Water Column
Point class number
No
No
Active
Point classified as water column
5
26 - Bathymetric Point
Point class number
No
No
Active
Point classified as bathymetric ground.
6
27 - Water Surface
Point class number
No
No
Active
Point classified as water surface
7
29 - Submerged Object
Point class number
No
No
Active
Submerged object, not otherwise specified (e.g., wreck, rock, submerged piling).
8
31 - Temporal Bathy Bottom
Point class number
No
No
Active
Bathymetric point (e.g., seafloor or riverbed; also known as submerged topography).
Data Steward
2018-07-24
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Distributor
2017-07-24
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Metadata Contact
2018-07-24
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Point of Contact
2018-07-24
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
D1/D2 area
-80.041115
-79.98658
32.691547
32.578384
Range
2016-12-03
2017-03-01
Acquisition Dates: 20161203, 20161209, 20161210, 20161228, 20161229, 20161230, 20170105, 20170228 and 20170301
D3 area
-80.575337
-80.307752
32.542629
32.254916
Range
2016-10-02
2017-02-19
Data acquired on 20161002, 20161211, 20161212, 20161213, 20161215, 20161216, 20161221, 20161222, 20161228, 20161229, 20161230, 20161231, 20170102, 20170104 and 20170219
D4 area
-80.893994
-80.574798
32.372078
32.117508
Range
2016-12-11
2017-02-04
Data acquired on 20161213, 20170102, 20170104, 20170111, 20170112, 20170113, 20170115 , 20170118, 20170119 and 20170204
D5 area
-81.092181
-80.787823
32.189411
31.878403
Range
2017-01-12
2017-02-04
Data acquired on 20170112, 20170113, 20170115, 20170118,
20170119, 20170120, 20170129, 20170130, 20170201, 20170202, and 20170204
D6 area
-79.933722
-79.609965
32.905082
32.647866
Range
2016-12-03
2017-03-01
Data acquired on 20161203, 20161210, 20170109, 20170110, 20170210, 20170211, 20170212 , 20170213, 20170214, 20170228 and 20170301
D7 area
-79.717039
-79.327277
33.117057
32.886564
Range
2017-02-12
2017-02-21
Data acquired from 20170212-20170214, 20170217-20170221
D8 area
-79.428248
-79.130611
33.228379
33.064631
Range
2017-02-18
2017-02-27
Data acquired from 20170218-20170221 and 20170226-20170226
Yes
Unclassified
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.
https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8575
2017-07-24
Organization
NOAA Office for Coastal Management
Customized Download
Create custom data files by choosing data area, product type, map projection, file format, datum, etc.
Zip
Zip
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/8575/index.html
2017-07-24
Organization
NOAA Office for Coastal Management
Bulk Download
Simple download of data files.
LAZ
LAS/LAZ - LASer
Zip
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/8575/supplemental/sc2016_ngs_topobathy_m8575.kmz
Browse Graphic
Browse Graphic
This graphic shows the lidar footprint for the 2017 NOAA NGS topobathy lidar project of the South Carolina coast.
https://coast.noaa.gov
Office for Coastal Management
Online Resource
https://coast.noaa.gov/dataviewer
Digital Coast Data Access Viewer
Online Resource
https://coast.noaa.gov/digitalcoast/data/home.html
Digital Coast
Online Resource
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.
Lidar
1
Green and NIR LiDAR data were acquired with the Riegl sensor VQ-880G.
QSI reviewed flight lines to ensure complete coverage and positional accuracy of the laser points. The collected LiDAR data is processed in the field by QSI to allow QA\QC measures. An initial SBET was created in POSPAC MMS 7.1 and used in RiProcess which applies pre-calibrated angular misalignment corrections of scanner position to extract the raw point cloud into geo-referenced LAS files. These files were inspected for sensor malfunctions and then passed through automated classification routines (TerraScan) to develop a rough topo-bathymetric ground model for an initial assessment of bathymetric coverage.
QSI concurrently conducted multiple static Global Navigation Satellite System (GNSS) ground surveys (1 Hz recording frequency). After the airborne survey, the static GPS data were triangulated with nearby Continuously Operating Reference Stations (CORS) using the Online Positioning User Service (OPUS) for precise positioning. Multiple independent sessions over the same monument were processed to confirm antenna height measurements and to refine position accuracy. QSI then resolved kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. A final smoothed best estimate trajectory (SBET) was developed. Sensor head position and attitude are calculated throughout the survey. The software Trimble Business Center v.3.90, Blue Marble Geographic Calculator 2016, and PosPac MMS 7.1 SP3 are used for these processes.
Next, QSI used RiProcess 1.8.1 to calculate laser point positioning of the Riegl VQ-880G data by associating SBET positions to each laser point return time, scan angle, intensity, etc. A raw laser point cloud is created in Riegl data format. Within RiProcess the RiHydro tool was used to classify water surface and create a water surface model. QSI used the green water surface points and as needed NIR water surface points to create water surface models. These models are used in the RiHydro refraction tool to determine the depth of bathymetric points and are created for single swaths to ensure temporal differences and wave or water surface height variations between flight lines do not impact the refraction of the bathymetric data. All LiDAR data below water surface models were classified as water column to be refracted. The refraction tool corrects for this difference by adjusting depth (distance traveled) and horizontal position (change of angle/direction) of the LiDAR data. Using raster-based QC methods, the output data is verified to ensure the refraction tool functioned properly. QSI used their proprietary LASMonkey refraction tool to correct some refraction in back bay/lake pond areas where RiHydro refraction was found to be inadequate.
Once all green data had been refracted it was exported to LAS 1.2 format and combined into 500 m x 500 m tiles. Data was then further calibrated using TerraScan, TerraModeler, and TerraMatch. QSI used custom algorithms in TerraScan to create the initial ground/submerged topography surface. Relative accuracy of the green swaths was compared to overlapping and adjacent swaths and verified through the use Delta-Z (DZ) orthos created using QSI's DZ Ortho creator. Absolute vertical accuracy of the calibrated data was assessed using ground RTK survey data and complete coverage was again verified.
QSI then performed manual editing to review all classification and improve the final topobathy surface. QSI's LasMonkey was used to update LAS header information, including all projection and coordinate reference system information. The final LiDAR data are in LAS format 1.2 and point data record format 3.
2
The NOAA Office for Coastal Management (OCM) received files in las format. The files contained lidar elevation and intensity measurements. The data were in UTM Zone 17 coordinates and ellipsoid elevations in meters. OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:
1. Converted from UTM Zone 17 to geographic coordinates
2. Sorted by gps time
3. Compressed the data using laszip
4. Converted laz files to version 1.4
5. Reclassified overlap classes 19 to 1, 20 to 2, 21 to 25 and 22 to 27 with overlap flags
The data have the following classification: unclassified (1), ground (2), noise (7), water column (25), bathymetric bottom or submerged topography (26), water surface (27), and temporal bathy bottom (31)
Organization
Office for Coastal Management
OCM
2234 South Hobson Avenue
Charleston
SC
29405-2413
https://www.coast.noaa.gov/
72033
Entity
NOAA NGS Topobathy Lidar Class Scheme: Coastal South Carolina
gov.noaa.nmfs.inport:53150
Maryellen Sault
2018-07-27T10:00:28
Maryellen Sault
2024-02-02T11:37:19
2022-03-16
National Geodetic Survey
NGS
1002
Public
No
2022-03-16
1 Year
2023-03-16