2018 NOAA National Geodetic Survey Topobathy Lidar DEM (interpolated): Potomac River, Chesapeake Bay
Office for Coastal Management
Data Set
(DS)
| ID: 56273
| Published / External
Created: 2019-05-13
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Last Modified: 2024-01-10
Project (PRJ) | ID: 47844
ID: 56273
Data Set (DS)
* Discovery• First Pass
» Metadata Rubric
Item Identification
* » Title | 2018 NOAA National Geodetic Survey Topobathy Lidar DEM (interpolated): Potomac River, Chesapeake Bay |
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Short Name | VA1803 M8732 |
* Status | Completed |
Creation Date | |
Revision Date | |
• Publication Date | 2019-05 |
* » Abstract |
These data were collected by Quantum Spatial, Inc. (QSI) for the National Oceanic and Atmospheric Administration (NOAA), National Geodetic Survey (NGS), Remote Sensing Division (RSD), Coastal Mapping Program (CMP) using a Riegl VQ880G system. The Initial Award lidar data were acquired from 20180215 - 20180417 in eighteen missions. 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), water column (25), bathymetric bottom or submerged topography (26), submerged feature (29), submerged aquatic vegetation (30), temporal bathy bottom (31), and unclassified withheld/edge clip (129) in accordance with project specifications. The contracted project consists of approximately 309.125 square miles (197,840 acres) of the states of Virginia and Maryland covering the confluence of the Potomac River and the Chesapeake Bay. To ensure complete coverage and adequate point densities around survey area boundaries, the Area of Interest (AOI) was buffered by 100m. The full Initial Award project area including buffered area is approximately 478.151 square miles (306,017 acres). LAS files were compiled by 500 m x 500 m tiles. The final classified LiDAR data were then transformed to orthometric heights and used to create topobathymetric DEMs in IMG format with 1m pixel resolution. |
* Purpose |
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. |
Notes | |
Other Citation Details | |
• Supplemental Information |
The NOAA Chesapeake Bay Initial Award 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. Classes 2 (ground), 26 (submerged topography), and 29 (submerged features) were used to create the final DEMs. |
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Keywords
Theme Keywords
Thesaurus | Keyword |
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Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > BATHYMETRY |
Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > SEAFLOOR TOPOGRAPHY |
Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION |
ISO 19115 Topic Category | elevation |
Temporal Keywords
Thesaurus | Keyword |
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None | 20180215 |
None | 20180417 |
* Spatial Keywords
Thesaurus | Keyword |
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Global Change Master Directory (GCMD) Location Keywords | CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MARYLAND |
Global Change Master Directory (GCMD) Location Keywords | CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > VIRGINIA |
Global Change Master Directory (GCMD) Location Keywords | Continent > North America > United States of America > Chesapeake Bay |
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Physical Location
• » Organization | Office for Coastal Management |
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• » City | Charleston |
• » State/Province | SC |
• Country | USA |
• » Location Description |
Data Set Information
* Data Set Scope Code | Data Set |
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• Data Set Type | Elevation |
• Maintenance Frequency | None Planned |
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» Data Presentation Form | |
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Distribution Liability |
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. |
Data Set Credit | NOAA National Geodetic Survey (NGS). |
Support Roles
* » Support Role | Data Steward |
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* » Date Effective From | 2019-05 |
Date Effective To | |
Organization | NOAA Office for Coastal Management (NOAA/OCM) |
Address |
2234 South Hobson Ave Charleston, SC 29405-2413 |
Email Address | coastal.info@noaa.gov |
Phone | (843) 740-1202 |
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URL | https://coast.noaa.gov |
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Contact Instructions |
* » Support Role | Distributor |
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* » Date Effective From | 2019-05 |
Date Effective To | |
Organization | NOAA Office for Coastal Management (NOAA/OCM) |
Address |
2234 South Hobson Ave Charleston, SC 29405-2413 |
Email Address | coastal.info@noaa.gov |
Phone | (843) 740-1202 |
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URL | https://coast.noaa.gov |
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Contact Instructions |
* » Support Role | Metadata Contact |
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* » Date Effective From | 2019-05 |
Date Effective To | |
Organization | NOAA Office for Coastal Management (NOAA/OCM) |
Address |
2234 South Hobson Ave Charleston, SC 29405-2413 |
Email Address | coastal.info@noaa.gov |
Phone | (843) 740-1202 |
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URL | https://coast.noaa.gov |
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* » Support Role | Point of Contact |
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* » Date Effective From | 2019-05 |
Date Effective To | |
Organization | NOAA Office for Coastal Management (NOAA/OCM) |
Address |
2234 South Hobson Ave Charleston, SC 29405-2413 |
Email Address | coastal.info@noaa.gov |
Phone | (843) 740-1202 |
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URL | https://coast.noaa.gov |
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Extents
Currentness Reference | Ground Condition |
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Extent Group 1
Extent Description |
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Extent Group 1 / Geographic Area 1
* » W° Bound | -76.791568 |
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* » E° Bound | -76.176395 |
* » N° Bound | 38.332869 |
* » S° Bound | 37.715495 |
* » Description |
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* » Time Frame Type | Range |
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* » Start | 2018-02-15 |
End | 2018-04-17 |
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* » Security Class | Unclassified |
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• Data Access Policy | |
» Data Access Procedure |
Users may access the data from either a custom download or bulk download link. |
• » Data Access Constraints |
None |
• Data Use Constraints |
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. |
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Distribution Information
Start Date | 2019-05 |
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End Date | Present |
» Download URL | https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8732 |
Distributor | NOAA Office for Coastal Management (NOAA/OCM) (2019-05 - Present) |
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Description |
Link to custom download |
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File Type (Deprecated) | ZIP |
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Compression | Zip |
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Start Date | 2019-05 |
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End Date | Present |
» Download URL | https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/8727/supplemental/2018_ngs_topobathy_potomac_river_m8727.kmz |
Distributor | NOAA Office for Coastal Management (NOAA/OCM) (2019-05 - Present) |
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KMZ displays the footprint for this lidar data set. |
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File Type (Deprecated) | KMZ |
Distribution Format | KML/KMZ - Keyhole Markup Language |
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Compression | Zip |
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Start Date | 2019-05 |
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End Date | Present |
» Download URL | https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/dem/NGS_PotomacRiver_DEM_Interpolated_2018_8732/ |
Distributor | NOAA Office for Coastal Management (NOAA/OCM) (2019-05 - Present) |
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File Type (Deprecated) | LAZ |
Distribution Format | LAS/LAZ - LASer |
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URL | https://coast.noaa.gov/dataregistry |
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URL Type | Online Resource |
File Resource Format | HTML |
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Web page to the Digital Coast for finding coastal data |
URL | https://coast.noaa.gov |
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URL Type | Online Resource |
File Resource Format | HTML |
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Web page for the NOAA Office for Coastal Management |
URL | https://coast.noaa.gov/dataviewer |
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Data Access Viewer for discovering coastal imagery, land cover, and elevation data |
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Data Quality
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Accuracy | |
Analytical Accuracy | |
Horizontal Positional Accuracy | |
Vertical Positional Accuracy |
Using NSSDA and FEMA methodology, the derived DEM non-vegetated vertical accuracy (NVA) at the 95% confidence level (called Accuracyz) was computed by the formula RMSEz x 1.9600. The NOAA Chesapeake Bay Initial Award West 02 and West 03 dataset tested 0.046 m vertical accuracy at 95% confidence level against the derived bare earth DEM in open terrain using 27 ground check points, based on RMSEz (0.024 m) x 1.9600. Using NDEP and ASPRS methodology, vegetated vertical accuracy (VVA) was computed using the 95th percentile method against the derived bare earth DEM. The NOAA Chesapeake Bay Initial Award West 02 and West 03 dataset tested 0.155 m vegetated vertical accuracy at 95th percentile against the derived bare earth DEM using 17 mixed landclass points. 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 NOAA Chesapeake Bay Initial Award West 02 and West 03 dataset tested 0.070 m vertical accuracy at 95% confidence level against the classified points cloud using 727 submerged check points, based on RMSEz (0.036 m) x 1.9600. Submerged topography checkpoints usually occur in depths up to 1m. |
Quantitation Limits |
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 throughout the project area as feasible. 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 unclassified LiDAR point cloud as well as the derived gridded bare earth DEM. 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). In the Initial Award West 02 and West 03 areas, 27 survey checkpoints were used to assess the non-vegetated vertical accuracy. 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 four land cover categories: deciduous forest, mixed forest, and tall grass, and shrubland. In the Initial Award West 02 and West 03 areas, 17 survey checkpoints were used to assess the vegetated vertical accuracy. Project specifications require VVA meet 0.36 m based on the 95th percentile tested against the derived bare earth DEM. Submerged topography points were tested separately. In the Initial Award West 02 and West 03 areas, 727 survey checkpoints were used to assess the submerged topography accuracy. Submerged topography checkpoints usually occur in depths up to 1m. Project specifications require submerged topography to meet 0.490 m at the 95% confidence level based on RMSEz x 1.9600. |
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» Do these Data Comply with the Data Access Directive? | |
» Is Access to the Data Limited Based on an Approved Waiver? | |
» If Distributor (Data Hosting Service) is Needed, Please Indicate | |
» Approximate Delay Between Data Collection and Dissemination | |
» If Delay is Longer than Latency of Automated Processing, Indicate Under What Authority Data Access is Delayed |
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Citation Title | Lidar |
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Process Steps
Process Step Number | 1 |
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» Description |
Data was acquired by Quantum Spatial (QSI) using a Riegl VQ-880G Topobathy LiDAR system. All delivered LiDAR data is referenced to: Horizontal Datum-NAD83 (2011) epoch: 2010 Projection-UTM Zone 18N Horizontal Units-meters Vertical Datum-GRS 80 Ellipsoid Vertical Units-meters The dataset encompasses 5,974 500m x 500m tiles covering the states of Virginia and Maryland at the confluence of the Potomac River and the Chesapeake Bay. Green and NIR (for water surface model creation that is used during refraction of the green bathymetric data) LiDAR data were acquired with the Riegl sensor VQ-880G. QSI reviewed all acquired flight lines to ensure complete coverage and positional accuracy of the laser points. QSI creates an initial product call Quick Look Coverage Maps. These Quick Look files are not fully processed data or final products. The collected LiDAR data is immediately processed in the field by QSI to a level that will allow QA\QC measures to determine if the sensor is functioning properly and assess the coverage of submerged topography. An initial SBET was created in POSPAC MMS 8.1 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 topobathymetric ground model for an initial assessment of bathymetric coverage. To correct the continuous onboard measurements of the aircraft position recorded throughout the missions, QSI concurrently conducted multiple static Global Navigation Satellite System (GNSS) ground surveys (1 Hz recording frequency) over established monuments located in or around the project area. 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 that blends post-processed aircraft position with attitude data. Sensor head position and attitude are calculated throughout the survey. The SBET data are used extensively for laser point processing. The software Trimble Business Center v.3.90, Blue Marble Geographic Calculator 2017, and PosPac MMS 8.1 SP3 are used for these processes. |
Process Date/Time | 2018-08-31 00:00:00 |
Process Contact | National Geodetic Survey (NGS) |
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Process Step Number | 2 |
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» Description |
Next, QSI used RiProcess 1.8.5 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 and erroneous points are filtered. 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 angle of incidence and ranging time under water. They are created for a single swaths to ensure temporal differences and wave or water surface height variations between flight lines do not impact the refraction correction of the bathymetric data. All lidar data below water surface models were classified as water column and corrected for refraction. The green laser light travels at slower speed in water than air and its direction of travel is changed when entering water. The refraction tool corrects positioning of under water points by adjusting the ranging distance in water and horizontal position change due to the angle of refraction. 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/inland pond areas where RiHydro refraction was found to be inadequate. Once all green data had been refracted by flight line all data was exported to LAS 1.2 format and are 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. Once all green data had been refracted by flight line all data was exported to LAS 1.2 format and are 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. The classification scheme is as follows: 1-Unclassified 2-Ground 7-Noise 19-Overlap Default 20-Overlap Ground 21-Overlap Water Column 25-Water Column 26-Bathymetric Bottom or Submerged Topography 29-Submerged feature 30-Submerged Aquatic Vegetation* 31-Temporal Bathymetric Bottom *Class 30 was submerged aquatic vegetation QSI identified within the bathymetric void shapes that precluded bathymetric bottom returns. |
Process Date/Time | 2018-08-31 00:00:00 |
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Process Step Number | 3 |
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» Description |
QSI then transformed the ellipsoid heights of the final LiDAR data to into orthometric heights referenced to NAVD88 using Geoid 12B to create the DEMs. The topobathymetric DEM was output at 1 meter resolution in IMG format into 5000 m x 5000 m tiles. Interpolated DEM dataset-These DEMs represent a continuous surface with all void areas interpolated. No void layer was incorporated into this DEM and there are no areas of No Data, regardless of whether the LiDAR data fully penetrated to the submerged topography. |
Process Date/Time | 2018-08-31 00:00:00 |
Process Contact | National Geodetic Survey (NGS) |
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Acquisition Information
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Catalog Details
Catalog Item ID | 56273 |
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Metadata Record Created By | Maryellen Sault |
Metadata Record Created | 2019-05-13 13:00+0000 |
Metadata Record Last Modified By | Kirk Waters |
» Metadata Record Last Modified | 2024-01-10 19:05+0000 |
Metadata Record Published | 2024-01-10 |
Owner Org | OCM |
Metadata Publication Status | Published Externally |
Do Not Publish? | N |
Metadata Workflow State | Published / External |
Metadata Last Review Date | 2022-03-16 |
Metadata Review Frequency | 1 Year |
Metadata Next Review Date | 2023-03-16 |
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