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2019-2020 NOAA NGS Topobathy Lidar: Coastal VA, NC, SC
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            title:  2019-2020 NOAA NGS Topobathy Lidar: Coastal VA, NC, SC
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                date:  2022
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                        linkage: https://www.fisheries.noaa.gov/inport/item/66707
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                        description:  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.
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        abstract:  NOAA Florence Topobathymetric Lidar data were collected by NV5 Geospatial (NV5) in 9 blocks from 20191126 - 20200825 using the follow sensors: Block01 -Riegl VQ880GII system Block02 - Riegl VQ-880-G and Riegl VQ-880-GII systems Block03 - Riegl VQ880G, Riegl VQ880GII, and Riegl VQ880GH systems Block04 - Riegl VQ880GII and Leica Chiroptera 4x systems Block05 - Riegl VQ880GII, Leica Chiroptera 4x and Hawkeye systems Block06 - Riegl VQ880GII, Leica Chiroptera 4x and Hawkeye systems Block07 - Riegl VQ880G, Riegl VQ880GII, and Leica Chiroptera 4x systems Block08 - Riegl VQ880G and Riegl VQ880GII systems Block09 - Riegl VQ880G and Riegl VQ880GII systems This dataset includes topobathymetric data in a LAS format 1.4, point data record format 6, with classifications in accordance with project specifications and the American Society for Photogrammetry and Remote Sensing (ASPRS) classification standards. This data set also includes LiDAR intensity values, number of returns, return number, time, and scan angle. The 100 meter buffered project area consists of approximately 3,075,010 acres along the Eastern coast of Virginia, North Carolina, and South Carolina. This data set is an LAZ (compressed LAS) format file containing LIDAR point cloud data.
        purpose:  This lidar data was required by National Oceanic and Atmospheric Administration (NOAA) and 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.
        credit:  National Oceanic and Atmospheric Administration (NOAA), National Geodetic Survey (NGS), Remote Sensing Division (RSD), Coastal Mapping Program (CMP) The custom download may be cited as National Oceanic and Atmospheric Administration (NOAA) Digital Coast Data Access Viewer. Charleston, SC: NOAA Office for Coastal Management. Accessed Aug 01, 2023 at https://coast.noaa.gov/dataviewer.
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                    city:  Charleston
                    administrativeArea:  SC
                    postalCode:  29405-2413
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            keyword:  EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION
            keyword:  EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > BATHYMETRY > COASTAL BATHYMETRY
            keyword:  EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION
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            keyword:  CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > SOUTH CAROLINA
            keyword:  CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > VIRGINIA
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            keyword:  DOC/NOAA/NOS/NGS > National Geodetic Survey, National Ocean Service, NOAA, U.S. Department of Commerce
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                    date:  2017-04-24
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            otherConstraints:  Cite As: National Geodetic Survey, [Date of Access]: 2019-2020 NOAA NGS Topobathy Lidar: Coastal VA, NC, SC [Data Date Range], https://www.fisheries.noaa.gov/inport/item/66707.
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            useLimitation:  NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose.
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            otherConstraints:  Access Constraints: None | 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. | Distribution Liability: Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office for Coastal Management or its partners
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        supplementalInformation:  This dataset 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. Depth values were adjusted per sensor to NOAA provided ground truth data. Bathymetric intensity values were normalized for water depth. Classes 2 (ground), 40 (submerged topography), and 43 (submerged object) were used to create the final DEMs. The full workflow used for this project is documented in the NOAA Florence Topobathymetric Lidar final report.
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        transferOptions:  (MD_DigitalTransferOptions)
            onLine:  (CI_OnlineResource)
                linkage: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/9467/index.html
                protocol:  WWW:LINK-1.0-http--link
                name:  Bulk Download
                description:  Bulk download of data files in LAZ format, geographic coordinates, orthometric heights. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum.
                function:  (CI_OnLineFunctionCode) download
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    dataQualityInfo:  (DQ_DataQuality)
        scope:  (DQ_Scope)
            level:  (MD_ScopeCode) dataset
        report:  (DQ_QuantitativeAttributeAccuracy)
            nameOfMeasure:  Accuracy
            evaluationMethodDescription:  Lidar horizontal accuracy is a function of Global Navigation Satellite System (GNSS) derived positional error, flying altitude, and INS derived attitude error. The obtained RMSEr value is multiplied by a conversion factor of 1.7308 to yield the horizontal component of the National Standards for Spatial Data Accuracy (NSSDA) reporting standard where a theoretical point will fall within the obtained radius 95 percent of the time (ACCr). Absolute vertical accuracy was assessed using Non-Vegetated Vertical Accuracy (NVA) survey methods. Survey check points were evenly distributed as feasible 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 unclassified lidar point cloud. NVA is a measure of the accuracy of lidar point data in open areas with level slope (less than 20°) 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. Submerged topography points were tested separately to calculate accuracy and usually occurred in depths up to 1m. Project specifications require submerged topography shall meet a vertical RMSE of QL2b specified in the Draft National Coastal Mapping strategy 1.0 which is equivalent to 0.30 m RMSE at a depth of 1 m. Please refer to the NOAA Hurricane Florence Topobathymetric lidar final data report for final accuracies, to be provided upon project completion.
            result: (missing)
        report:  (DQ_AbsoluteExternalPositionalAccuracy)
            nameOfMeasure:  Horizontal Positional Accuracy
            evaluationMethodDescription:  Block01: Based on a flying altitude of 400 meters, an IMU error of 0.002 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 1 area is 0.029 meters, with a ACCr of 0.05 meters at the 95% confidence level. Block02:Based on a flying altitude of 400 meters, an IMU error of 0.005 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 2 area is 0.064 meters, with a ACCr of 0.11 meters at the 95% confidence level. Block03:Based on a flying altitude of 400 meters, an IMU error of 0.006 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 3 area is 0.076 meters, with a ACCr of 0.13 meters at the 95% confidence level. The project specification requires horizontal positions to be accurate to 1.0m(RMSE). Block04:Based on a flying altitude of 400 meters, an IMU error of 0.003 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 4 area is 0.040 meters, with a ACCr of 0.07 meters at the 95% confidence level. Block05: Based on a flying altitude of 400 meters, an IMU error of 0.003 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 5 area is 0.040 meters, with a ACCr of 0.07 meters at the 95% confidence level. Block06: Based on a flying altitude of 400 meters, an IMU error of 0.005 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 6 area is 0.064 meters, with a ACCr of 0.11 meters at the 95% confidence level. Block07:Based on a flying altitude of 400 meters, an IMU error of 0.005 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 7 area is 0.064 meters, with a ACCr of 0.11 meters at the 95% confidence level. Block08: Based on a flying altitude of 400 meters, an IMU error of 0.005 decimal degrees, and a GNSS positional error of 0.015 meters, the RMSEr value for the Delivery 8 area is 0.064 meters, with a ACCr of 0.11 meters at the 95% confidence level. Block09: Based on a flying altitude of 400 meters, an IMU error of 0.006 decimal degrees, and a GNSS positional error of 0.023 meters, the RMSEr value for the Delivery 9 area is 0.078 meters, with a ACCr of 0.14 meters at the 95% confidence level.
            result: (missing)
        report:  (DQ_AbsoluteExternalPositionalAccuracy)
            nameOfMeasure:  Vertical Positional Accuracy
            evaluationMethodDescription:  Block01dataset Non-Vegetated Vertical Accuracy tested 0.045 m at the 95% confidence level against the unclassified point cloud in open terrain using 10 ground check points, based on RMSEz (0.018 m) x 1.9600. Submerged topography accuracy tested 0.069 m at the 95% confidence level against the classified points cloud using 34 submerged check points, based on RMSEz (0.035 m) x 1.9600. Block02 dataset Non-Vegetated Vertical Accuracy tested 0.081 m at the 95% confidence level against the ground classified point cloud in open terrain using 14 ground check points, based on RMSEz (0.041 m) x 1.9600. Submerged topography accuracy tested 0.102 m at the 95% confidence level against the classified points cloud using 89 submerged check points, based on RMSEz (0.052 m) x 1.9600. Submerged topography checkpoints usually occur in depths up to 1m. Block03 dataset Non-Vegetated Vertical Accuracy tested 0.055 m at the 95% confidence level against the unclassified point cloud in open terrain using 19 ground check points, based on RMSEz (0.028 m) x 1.9600. Submerged topography accuracy tested 0.140 m at the 95% confidence level against the classified points cloud using 60 submerged check points, based on RMSEz (0.072 m) x 1.9600. Block04 dataset Non-Vegetated Vertical Accuracy tested 0.053 m at the 95% confidence level against the unclassified point cloud in open terrain using 8 ground check points, based on RMSEz (0.027 m) x 1.9600. Submerged topography accuracy tested 0.058 m at the 95% confidence level against the classified points cloud using 34 submerged check points, based on RMSEz (0.030 m) x 1.9600. Block05 dataset Non-Vegetated Vertical Accuracy tested 0.045 m at the 95% confidence level against the unclassified point cloud in open terrain using 27 ground check points, based on RMSEz (0.023 m) x 1.9600. Submerged topography accuracy tested 0.129 m at the 95% confidence level against the classified points cloud using 308 submerged check points, based on RMSEz (0.066 m) x 1.9600. Block06 dataset Non-Vegetated Vertical Accuracy tested 0.101 m at the 95% confidence level against the unclassified point cloud in open terrain using 27 ground check points, based on RMSEz (0.051 m) x 1.9600. Submerged topography accuracy tested 0.171 m at the 95% confidence level against the classified points cloud using 165 submerged check points, based on RMSEz (0.087 m) x 1.9600. Block07 dataset Non-Vegetated Vertical Accuracy tested 0.047 m at the 95% confidence level against the unclassified point cloud in open terrain using 24 ground check points, based on RMSEz (0.024 m) x 1.9600. Submerged topography accuracy tested 0.086 m at the 95% confidence level against the classified points cloud using 124 submerged check points, based on RMSEz (0.044 m) x 1.9600. Block08 dataset Non-Vegetated Vertical Accuracy tested 0.071 m at the 95% confidence level against the unclassified point cloud in open terrain using 23 ground check points, based on RMSEz (0.036 m) x 1.9600. Submerged topography accuracy tested 0.092 m at the 95% confidence level against the classified points cloud using 123 submerged check points, based on RMSEz (0.047 m) x 1.9600. Block09 dataset Non-Vegetated Vertical Accuracy tested 0.076 m at the 95% confidence level against the unclassified point cloud in open terrain using 34 ground check points, based on RMSEz (0.039 m) x 1.9600. Submerged topography accuracy tested 0.132 m at the 95% confidence level against the classified points cloud using 11 submerged check points, based on RMSEz (0.067 m) x 1.9600.
            result: (missing)
        report:  (DQ_CompletenessCommission)
            nameOfMeasure:  Completeness Measure
            evaluationMethodDescription:  Block01 is comprised of 1048 - 500 m x 500 m LAS tiles covering 117,958 acres. Block02 is comprised of 5,381- 500 m x 500 m LAS tiles covering 266,749 acres. Block03 is comprised of 6,549 - 500 m x 500 m LAS tiles covering 316,143 acres. Block04 is comprised of 5,529 - 500 m x 500 m LAS tiles covering 313,361 acres. Block05 is comprised of 5,182 - 500 m x 500 m LAS tiles covering 351,277 acres. Block06 is comprised of 6,180 - 500 m x 500 m LAS tiles covering 350,238 acres. Block07 is comprised of 8,896 - 500 m x 500 m LAS tiles covering 396,241 acres. Block08 is comprised of 9,593 - 500 m x 500 m LAS tiles covering 514,295 acres. Block09 is comprised of 13,337 - 500 m x 500 m LAS tiles covering 678,311 acres.
            result: (missing)
        report:  (DQ_ConceptualConsistency)
            nameOfMeasure:  Conceptual Consistency
            evaluationMethodDescription:  Not applicable
            result: (missing)
        lineage:  (LI_Lineage)
            statement: (missing)
            processStep:  (LI_ProcessStep)
                description:  Data for the NOAA Florence Topobathymetric Lidar project area was acquired by NV5 Geospatial (NV5). All derived LAS data is referenced to: Horizontal Datum-NAD83(2011) epoch: 2010.00 Projection-UTM Zone 18N Horizontal Units-meters Vertical Datum-GRS80 Ellipsoid Vertical Units-meters The collected lidar data were immediately processed in the field by NV5 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 8.3 SP3 and loaded into 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 raster generation using LAStools to develop an initial assessment of bathymetric coverage. NV5 reviewed all acquired flight lines to ensure complete coverage and positional accuracy of the laser points. These rasters were also used to create an initial product in Quick Look Coverage Maps. These Quick Look files are not fully processed data or final products but provide rapid assessment of approximate coverage and depth penetration. NV5 resolved kinematic corrections for aircraft position data using aircraft GNSS and Applanix's proprietary PP-RTX solution. When PP-RTX was not used NV5 conducted static Global Navigation Satellite System (GNSS) ground surveys (1 Hz recording frequency) using base stations over known monument locations during flights. After the airborne survey, 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 base station were performed to confirm antenna height measurements and to refine position accuracy. This data was used to correct the continuous on board measurements of the aircraft position recorded throughout the flight. A final smoothed best estimate trajectory (SBET) was developed that blends post-processed aircraft position with attitude data. Using the SBETs, sensor head position and attitude were then calculated throughout the survey. Trimble Business Center v.3.90, Blue Marble Geographic Calculator 2019, and PosPac MMS 8.3 SP3 were used for these processes.
                dateTime:
                  DateTime:  2020-10-02T00:00:00
                processor:  (CI_ResponsibleParty)
                    organisationName:  NGS Communications and Outreach Branch
                    contactInfo:  (CI_Contact)
                        phone:  (CI_Telephone)
                            voice:  (301) 713-3242
                        address:  (CI_Address)
                            electronicMailAddress:  ngs.infocenter@noaa.gov
                    role:  (CI_RoleCode) processor
            processStep:  (LI_ProcessStep)
                description:  Following final SBET creation, NV5 used RiProcess 1.8.5 to calculate laser point positioning by associating SBET positions to each laser point return time, scan angle, and intensity. Terra 19 and LasTools were used to classify water surface and create a water surface model. They 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. These models are used in NV5's LasMonkey refraction tool to determine the accurate positioning of bathymetric points. All lidar data below water surface models were classified as water column to be refracted. Light travels at different speeds in air versus water and its direction of travel or angle is changed or refracted when entering the water column. The refraction tool corrects for this difference by adjusting the depth (distance traveled) and horizontal positioning (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. In addition, for Blocks 4 - 7, following final SBET creation for the Leica Chiroptera 4X and Hawkeye systems, NV5 used Leica Lidar Survey Studio (LSS) to calculate laser point positioning by associating SBET positions to each laser point return time, scan angle, and intensity. Leica LSS was used to derive a synthetic water surface to create a water surface model. Light travels at different speeds in air versus water and its direction of travel or angle is changed or refracted when entering the water column. The refraction tool corrects for this difference by adjusting the depth (distance traveled) and horizontal positioning (change of angle/direction) of the lidar data. All lidar data below water surface models were classified as water column to correct for refraction. Using raster-based QC methods, the output data is verified to ensure the refraction tool functioned properly.
                dateTime:
                  DateTime:  2020-12-17T00:00:00
                processor:  (CI_ResponsibleParty)
                    organisationName:  National Geodetic Survey
                    role:  (CI_RoleCode) processor
            processStep:  (LI_ProcessStep)
                description:  Once all data was refracted by flight line data was exported to LAS 1.4 format and combined into 500 m x 500 m tiles. Data were then further calibrated using TerraMatch. NV5 used custom algorithms in TerraScan to classify the initial ground/submerged topography surface points. Relative accuracy of overlapping swaths was compared and verified through the use Delta-Z (DZ) orthos created using NV5's Las Product Creator. Absolute vertical accuracy of the calibrated data was assessed using ground survey data and complete coverage was again verified. Post automated classification NV5 then performed manual editing to review all classification and improve the final topobathymetric surface. NV5'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 1.4 and point data record format 6. The delivered dataset used the following classification scheme for Block01, Block02, Block03, Block08 and Block09: 1 - Unclassified 2 - Ground 7 - Noise 40 - Bathymetric Bottom or Submerged Topography 41 - Water Surface 43 - Submerged feature 45 - Water Column 46 - Temporal Bathymetric Bottom 71 - Overlap Default 72 - Overlap Ground 81 - Overlap Water Surface 85 - Overlap Water Column 1-Overlap - Edge Clip The delivered dataset used the following classification scheme for Block04, Block05, Block06 and Block07: 1 - unclassified 2 - ground 7 - noise 40 - bathymetric bottom or submerged topography 41 - water surface 42 Synthetic- Chiroptera synthetic water surface 43 - submerged feature 45 - water column 46 - overlap bathy bottom - temporally different from a separate lift 71 - unclassified associated with areas of overlap bathy bottom/temporal bathymetric differences 72 - ground associated with areas of overlap bathy bottom/temporal bathymetric differences 81 - water surface associated with areas of overlap bathy bottom/temporal bathymetric differences 81 Synthetic - Chiroptera synthetic water surface associated with areas of overlap bathy bottom/temporal bathymetric differences 85 - water column associated with areas of overlap bathy bottom/temporal bathymetric differences 1 Overlap - edge clip 1 Withheld - unrefracted green data from Chiroptera sensor
                dateTime:
                  DateTime:  2022-01-29T00:00:00
                processor:  (CI_ResponsibleParty)
                    organisationName:  National Geodetic Survey
                    role:  (CI_RoleCode) processor
            processStep:  (LI_ProcessStep)
                description:  The NOAA Office for Coastal Management (OCM) received files in laz format. The files contained lidar elevation and intensity measurements. The data were in UTM Zone 18 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 18 to geographic coordinates 2. Sorted by gps time. 3. Moved data from class 46 to class 22, class 71 to class 1, class 72 to class 22 and class 85 to class 1
                dateTime:
                  DateTime:  2022-04-07T00:00:00
                processor:  (CI_ResponsibleParty)
                    organisationName:  Office for Coastal Management
                    role:  (CI_RoleCode) processor
            source:  (LI_Source)
                sourceCitation:  (CI_Citation)
                    title:  Acquisition and Processing
                    date: (missing)
                    citedResponsibleParty:  (CI_ResponsibleParty)
                        organisationName:  NV5 Geospatial, Inc
                        role:  (CI_RoleCode) originator
            processStep:  (LI_ProcessStep)
                description:   The vertical values in this data set have been converted to reference North American Vertical Datum of 1988 (NAVD88) (GEOID18) meters, using the GEOID18 grids provided by the National Geodetic Survey. Any datum and projection transformations were then done with the Office for Coastal Management 'datum_shift' program. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org) Processing notes:
                dateTime:
                  DateTime:  2023-08-01T07:35:38
                processor:  (CI_ResponsibleParty)
                    individualName:  NOAA Office for Coastal Management
                    contactInfo:  (CI_Contact)
                        address:  (CI_Address)
                            electronicMailAddress:  coastal.info@noaa.gov
                    role:  (CI_RoleCode) processor