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2016-2017 NOAA NGS Topobathy: Coastal South Carolina
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                city:  Charleston
                administrativeArea:  SC
                postalCode:  29405-2413
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    identificationInfo:  (MD_DataIdentification)
        citation:  (CI_Citation)
            title:  2016-2017 NOAA NGS Topobathy: Coastal South Carolina
            alternateTitle:  SC1601
            date:  (CI_Date)
                date:  2018-08-24
                dateType:  (CI_DateTypeCode) publication
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                  Anchor:  InPort Catalog ID 53150
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                        linkage: https://www.fisheries.noaa.gov/inport/item/53150
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                        linkage: https://coast.noaa.gov
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                        linkage: https://coast.noaa.gov/dataviewer
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                        linkage: https://coast.noaa.gov/digitalcoast/data/home.html
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                        name:  Digital Coast
                        description:  Online Resource
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        abstract:  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 data set is an LAZ (compressed LAS) format file containing LIDAR point cloud data.
        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.
        credit:  We request that you credit the National Oceanic and Atmospheric Administration (NOAA) when you use these data in a report, publication, or presentation. 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 Jul 01, 2023 at https://coast.noaa.gov/dataviewer.
        status:  (MD_ProgressCode) completed
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            organisationName:  NOAA Office for Coastal Management
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                    voice:  (843) 740-1202
                address:  (CI_Address)
                    deliveryPoint:  2234 South Hobson Ave
                    city:  Charleston
                    administrativeArea:  SC
                    postalCode:  29405-2413
                    country: (missing)
                    electronicMailAddress:  coastal.info@noaa.gov
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                    linkage: https://coast.noaa.gov
                    protocol:  WWW:LINK-1.0-http--link
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            organisationName:  NOAA Office for Coastal Management
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                    voice:  (843) 740-1202
                address:  (CI_Address)
                    deliveryPoint:  2234 South Hobson Ave
                    city:  Charleston
                    administrativeArea:  SC
                    postalCode:  29405-2413
                    country: (missing)
                    electronicMailAddress:  coastal.info@noaa.gov
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                    linkage: https://coast.noaa.gov
                    protocol:  WWW:LINK-1.0-http--link
                    name:  NOAA Office for Coastal Management Website
                    description:  NOAA Office for Coastal Management Home Page
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            role:  (CI_RoleCode) custodian
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        graphicOverview:  (MD_BrowseGraphic)
            fileName: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/8575/supplemental/sc2016_ngs_topobathy_m8575.kmz
            fileDescription:  This graphic shows the lidar footprint for the 2017 NOAA NGS topobathy lidar project of the South Carolina coast.
        descriptiveKeywords:  (MD_Keywords)
            keyword:  EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > TOPOGRAPHICAL RELIEF MAPS
            keyword:  EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > SEAFLOOR TOPOGRAPHY
            keyword:  EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION
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                title:  Global Change Master Directory (GCMD) Science Keywords
                date: (missing)
                edition:  12.3
        descriptiveKeywords:  (MD_Keywords)
            keyword:  CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > SOUTH CAROLINA
            type:  (MD_KeywordTypeCode) place
            thesaurusName:  (CI_Citation)
                title:  Global Change Master Directory (GCMD) Location Keywords
                date: (missing)
                edition:  12.3
        descriptiveKeywords:  (MD_Keywords)
            keyword:  Geographic Information > LiDAR
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            keyword:  EARTH SCIENCE > Land Surface > Topography> Terrain Elevation
            keyword:  EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Bathymetry
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                title:  NOAA NOS Harvest Catalog
                date: (missing)
        descriptiveKeywords:  (MD_Keywords)
            keyword:  COUNTRY/TERRITORY > United States of America > South Carolina
            keyword:  OCEAN BASIN > Atlantic Ocean >South Carolina
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        descriptiveKeywords:  (MD_Keywords)
            keyword:  20161002
            keyword:  20170301
            type:  (MD_KeywordTypeCode) temporal
        descriptiveKeywords:  (MD_Keywords)
            keyword:  DOC/NOAA/NOS/NGS > National Geodetic Survey, National Ocean Service, NOAA, U.S. Department of Commerce
            type:  (MD_KeywordTypeCode) dataCentre
            thesaurusName:  (CI_Citation)
                title:  Global Change Master Directory (GCMD) Data Center Keywords
                date:  (CI_Date)
                    date:  2017-04-24
                    dateType:  (CI_DateTypeCode) publication
                edition:  8.5
                citedResponsibleParty:  GCMD Landing Page
        descriptiveKeywords:  (MD_Keywords)
            keyword:  NGS Lidar
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            otherConstraints:  Cite As: National Geodetic Survey, [Date of Access]: 2016-2017 NOAA NGS Topobathy: Coastal South Carolina [Data Date Range], https://www.fisheries.noaa.gov/inport/item/53150.
        resourceConstraints:  (MD_Constraints)
            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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            useConstraints:  (MD_RestrictionCode) otherRestrictions
            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 National Geodetic Survey, the Office for Coastal Management, or its partners.
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                            linkage: https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/ngs/dmp/pdf/53150.pdf
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        language:  eng; US
        topicCategory:  (MD_TopicCategoryCode) elevation
        environmentDescription:  OS Independent
        extent:  (EX_Extent)
            description:  D1/D2 area
            geographicElement:  (EX_GeographicBoundingBox)
                westBoundLongitude:  -80.041115
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                southBoundLatitude:  32.578384
                northBoundLatitude:  32.691547
            temporalElement:  (EX_TemporalExtent)
                extent:
                  TimePeriod:
                    description:  Acquisition Dates: 20161203, 20161209, 20161210, 20161228, 20161229, 20161230, 20170105, 20170228 and 20170301
                    beginPosition:  2016-12-03
                    endPosition:  2017-03-01
        extent:  (EX_Extent)
            description:  D3 area
            geographicElement:  (EX_GeographicBoundingBox)
                westBoundLongitude:  -80.575337
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                southBoundLatitude:  32.254916
                northBoundLatitude:  32.542629
            temporalElement:  (EX_TemporalExtent)
                extent:
                  TimePeriod:
                    description:  Data acquired on 20161002, 20161211, 20161212, 20161213, 20161215, 20161216, 20161221, 20161222, 20161228, 20161229, 20161230, 20161231, 20170102, 20170104 and 20170219
                    beginPosition:  2016-10-02
                    endPosition:  2017-02-19
        extent:  (EX_Extent)
            description:  D4 area
            geographicElement:  (EX_GeographicBoundingBox)
                westBoundLongitude:  -80.893994
                eastBoundLongitude:  -80.574798
                southBoundLatitude:  32.117508
                northBoundLatitude:  32.372078
            temporalElement:  (EX_TemporalExtent)
                extent:
                  TimePeriod:
                    description:  Data acquired on 20161213, 20170102, 20170104, 20170111, 20170112, 20170113, 20170115 , 20170118, 20170119 and 20170204
                    beginPosition:  2016-12-11
                    endPosition:  2017-02-04
        extent:  (EX_Extent)
            description:  D5 area
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                westBoundLongitude:  -81.092181
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                southBoundLatitude:  31.878403
                northBoundLatitude:  32.189411
            temporalElement:  (EX_TemporalExtent)
                extent:
                  TimePeriod:
                    description:  Data acquired on 20170112, 20170113, 20170115, 20170118, 20170119, 20170120, 20170129, 20170130, 20170201, 20170202, and 20170204
                    beginPosition:  2017-01-12
                    endPosition:  2017-02-04
        extent:  (EX_Extent)
            description:  D6 area
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                westBoundLongitude:  -79.933722
                eastBoundLongitude:  -79.609965
                southBoundLatitude:  32.647866
                northBoundLatitude:  32.905082
            temporalElement:  (EX_TemporalExtent)
                extent:
                  TimePeriod:
                    description:  Data acquired on 20161203, 20161210, 20170109, 20170110, 20170210, 20170211, 20170212 , 20170213, 20170214, 20170228 and 20170301
                    beginPosition:  2016-12-03
                    endPosition:  2017-03-01
        extent:  (EX_Extent)
            description:  D7 area
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                westBoundLongitude:  -79.717039
                eastBoundLongitude:  -79.327277
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                northBoundLatitude:  33.117057
            temporalElement:  (EX_TemporalExtent)
                extent:
                  TimePeriod:
                    description:  Data acquired from 20170212-20170214, 20170217-20170221
                    beginPosition:  2017-02-12
                    endPosition:  2017-02-21
        extent:  (EX_Extent)
            description:  D8 area
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                eastBoundLongitude:  -79.130611
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                northBoundLatitude:  33.228379
            temporalElement:  (EX_TemporalExtent)
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                    description:  Data acquired from 20170218-20170221 and 20170226-20170226
                    beginPosition:  2017-02-18
                    endPosition:  2017-02-27
        supplementalInformation:  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.
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                        deliveryPoint:  2234 South Hobson Ave
                        city:  Charleston
                        administrativeArea:  SC
                        postalCode:  29405-2413
                        country: (missing)
                        electronicMailAddress:  coastal.info@noaa.gov
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                        linkage: https://coast.noaa.gov
                        protocol:  WWW:LINK-1.0-http--link
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                linkage: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/8575/index.html
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                name:  Bulk Download
                description:  Simple download of data files.
                function:  (CI_OnLineFunctionCode) download
        transferOptions:  (MD_DigitalTransferOptions)
            onLine:  (CI_OnlineResource)
                linkage: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8575
                protocol:  WWW:LINK-1.0-http--link
                name:  Customized Download
                description:  Create custom data files by choosing data area, product type, map projection, file format, datum, etc.
                function:  (CI_OnLineFunctionCode) download
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    dataQualityInfo:  (DQ_DataQuality)
        scope:  (DQ_Scope)
            level:  (MD_ScopeCode) dataset
        report:  (DQ_NonQuantitativeAttributeAccuracy)
            nameOfMeasure:  Representativeness
            evaluationMethodDescription:  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.
            result: (missing)
        report:  (DQ_AbsoluteExternalPositionalAccuracy)
            nameOfMeasure:  Vertical Positional Accuracy
            evaluationMethodDescription:  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.
            result: (missing)
        lineage:  (LI_Lineage)
            statement: (missing)
            processStep:  (LI_ProcessStep)
                description:  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.
            processStep:  (LI_ProcessStep)
                description:  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)
                processor:  (CI_ResponsibleParty)
                    organisationName:  Office for Coastal Management
                    role:  (CI_RoleCode) processor
            source:  (LI_Source)
                sourceCitation:  (CI_Citation)
                    title:  Lidar
                    date: (missing)
            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:  2023-07-01T08:50:52
                processor:  (CI_ResponsibleParty)
                    individualName:  NOAA Office for Coastal Management
                    contactInfo:  (CI_Contact)
                        address:  (CI_Address)
                            electronicMailAddress:  coastal.info@noaa.gov
                    role:  (CI_RoleCode) processor