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            <gco:CharacterString>Only checkpoints photo-identifiable in the intensity imagery can be used to test the horizontal accuracy of the lidar. Photo-identifiable checkpoints in intensity imagery typically include checkpoints located at the ends of paint stripes on concrete or asphalt surfaces or checkpoints located at 90 degree corners of different reflectivity, e.g. a sidewalk corner adjoining a grass surface. The xy coordinates of checkpoints, as defined in the intensity imagery, are compared to surveyed xy coordinates for each photo-identifiable checkpoint. These differences are used to compute the tested horizontal accuracy of the lidar. As not all projects contain photo-identifiable checkpoints, the horizontal accuracy of the lidar cannot always be tested.

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. Lidar vendors calibrate their lidar systems during installation of the system and then again for every project acquired. Typical calibrations include cross flights that capture features from multiple directions that allow adjustments to be performed so that the captured features are consistent between all swaths and cross flights from all directions. Horizontal positional accuracy is achieved through rigorous processing of airborne GPS and IMU, use of control, and calibration procedures. This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 41 cm RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 1 meter at a 95% confidence level. 11 photo-identifiable checkpoints were used to test horizontal accuracy for this lidar dataset. Horizontal accuracy from this small dataset cannot be considered statistically significant; however, it was still calculated and found to be 26.2 cm.</gco:CharacterString>
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This DEM dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10 cm RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be RMSEz = 5.7 cm, equating to +/- 11.2 cm at 95% confidence level.

This DEM dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10 cm RMSEz Vertical Accuracy Class. Actual VVA accuracy was found to be +/- 15.1 cm at the 95th percentile. The 5% outliers consisted of 1 checkpoint that is larger than the 95th percentile. This checkpoint has a DZ value of 30.2 cm.

This DEM dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 30.0 cm RMSEz Vertical Accuracy Class. Actual Bathymetric accuracy was found to be RMSEz = 15.5 cm, equating to +/- 30.4 cm at 95% confidence level.</gco:CharacterString>
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                <gco:CharacterString>Data were processed to an initial LAS format using Teledyne CARIS software. CARIS was also utilized for GPS and inertial processing, and Terrasolid and LAStools were used for data visualization, 3D editing, and export to final LAS/LAZ. Data were processed using NAD83(2011) horizontal and vertical datums. The data are in UTM Zone 17 coordinates and ellipsoid elevations in meters. The data classifications are: unclassified (1); ground (2); noise (7); water surface (topographic sensor) (18); bathymetric bottom (40); water surface (41); derived water surface (42); submerged object, not otherwise specified (e.g., wreck, rock, submerged piling) (43); and no bottom found (bathymetric lidar point for which no detectable bottom return was received) (45) in accordance with project specifications; temporal surface not used in bathymetric classification (65).

DEMs created from lidar ground classes (2 and 40) were transformed from ellipsoid heights to orthometric heights, referenced to NAVD88 Geoid 18, meters. All tiled DEMs incorporate the use of the void polygons to enforce areas of NoData (no valid bathymetric returns) greater than 9 sq m in the bathymetry.</gco:CharacterString>
              </gmd:description>
              <gmd:dateTime>
                <gco:DateTime>2021-10-28T00:00:00</gco:DateTime>
              </gmd:dateTime>
              <gmd:processor>
                <gmd:CI_ResponsibleParty>
                  <gmd:organisationName>
                    <gco:CharacterString>National Geodetic Survey</gco:CharacterString>
                  </gmd:organisationName>
                  <gmd:role>
                    <gmd:CI_RoleCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#gmd:CI_RoleCode" codeListValue="processor">processor</gmd:CI_RoleCode>
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                </gmd:CI_ResponsibleParty>
              </gmd:processor>
            </gmd:LI_ProcessStep>
          </gmd:processStep>
          <gmd:source>
            <gmd:LI_Source>
              <gmd:sourceCitation>
                <gmd:CI_Citation>
                  <gmd:title>
                    <gco:CharacterString>Acquisition and Processing</gco:CharacterString>
                  </gmd:title>
                  <gmd:date gco:nilReason="missing" />
                  <gmd:citedResponsibleParty>
                    <gmd:CI_ResponsibleParty>
                      <gmd:organisationName>
                        <gco:CharacterString>Dewberry</gco:CharacterString>
                      </gmd:organisationName>
                      <gmd:role>
                        <gmd:CI_RoleCode codeList="http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#gmd:CI_RoleCode" codeListValue="originator">originator</gmd:CI_RoleCode>
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                    </gmd:CI_ResponsibleParty>
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          </gmd:source>
        </gmd:LI_Lineage>
      </gmd:lineage>
    </gmd:DQ_DataQuality>
  </gmd:dataQualityInfo>
</gmi:MI_Metadata>