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                  <gco:CharacterString>Vertical Positional Accuracy Report</gco:CharacterString>
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                  <gco:CharacterString>We measured a mean offset of -5.4 cm between 24 control points and the point cloud. This offset was reduced to +4.8 cm by applying a constant vertical correction to the lidar point data. We used six checkpoints to determine the vertical accuracy of the point cloud ground class using a Triangulated Irregular Network (TIN) approach. The project vertical accuracy has a mean absolute error (MAE) of 9.1 cm. We evaluated the relative accuracy for this dataset as the interswath overlap consistency and measured a root mean square error of 10.0 cm.</gco:CharacterString>
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                  <gco:CharacterString>DGGS visually inspected the images for data errors such as shifts, seamline mismatches, and water noise overlapping land. Data quality is consistent throughout the survey.</gco:CharacterString>
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                        <gco:CharacterString>Ground survey - Ground control points were collected September 5, 2019. We deployed a Trimble R7 GNSS receiver with Zephyr-2 antenna at benchmark AI4980 in downtown Juneau to provide a base station occupation and real-time kinematic (RTK) corrections to points we surveyed with a rover Trimble R8-4 GNSS receiver (internal antenna). We collected 30 ground control points and checkpoints to use for calibration and to assess the vertical accuracy of the point cloud. Checkpoints were collected on bare earth (gravel or pavement).</gco:CharacterString>
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                        <gco:DateTime>2019-09-05T00:00:00</gco:DateTime>
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                        <gco:CharacterString>Aerial survey - DGGS used a Riegl VUX1-LR laser scanner integrated with a global navigation satellite system (GNSS) and Northrop Grumman LN-200C inertial measurement unit (IMU) designed by Phoenix LiDAR Systems. The sensor can collect a maximum of 820,000 points per second at a range of 150 m or a minimum of 50,000 points per second at a range of 820 m (ranges assume greater than or equal to 20 percent natural reflectance). The scanner operated with a pulse refresh rate of 50,000-400,000 pulses per second and a scan rate of 80-220 revolutions per second. We used a Bell 206 Jet Ranger helicopter platform to survey from an elevation of approximately 130 m above ground level, at a ground speed of approximately 30 m/s, and with a scan angle set from 80 to 280 degrees. The total survey area covers approximately 33.7 km2. The survey area was accessed by air from the Juneau International Airport. Data collection occurred between 5:10 pm and 7:00 pm (ADT), and the weather throughout the survey was clear with no wind.</gco:CharacterString>
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                     <gmd:dateTime>
                        <gco:DateTime>2019-09-06T00:00:00</gco:DateTime>
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                     <gmd:description>
                        <gco:CharacterString>Lidar dataset processing - We processed point data in SDCimport software for initial filtering and multiple-time-around (MTA) disambiguation. MTA errors, corrected in this process, result from ambiguous interpretations of received pulse time intervals and occur more frequently with higher pulse refresh rates. IMU and GNSS data were processed in Inertial Explorer, and flightline information was integrated with the point cloud in Spatial Explorer. We calibrated the point data at an incrementally precise scale of sensor movement and behavior, incorporating sensor velocity, roll, pitch, and yaw fluctuations throughout the survey. For the lidar data collection, the average pulse density is 17.6 pulses/m2, and the average pulse spacing is 23.8 cm. We created macros in Terrasolid software and classified points in accordance with the American Society for Photogrammetry &amp;amp; Remote Sensing (ASPRS) 2019 guidelines (ASPRS, 2019). Once classified, we applied a geometric transformation and converted the points from ellipsoidal heights to GEOID12B (Alaska) orthometric heights. Raster products were derived from the point cloud in ArcGIS Pro. A 1-m DSM was interpolated from ground, vegetation, bridge deck, and building classes using a triangulation method. A 1-m DTM was interpolated from all ground class returns, also using a triangulation method. We also produced a 1-m intensity image for the entire area using average binning, with no normalization or corrections applied. We hydro-conditioned the DTM and DSM in ArcGIS Pro by masking the shoreline and assigning any pixels located underwater an elevation of 0 m asl. This assigned elevation value does not account for tide activity, so the shoreline may appear abrupt in some areas.</gco:CharacterString>
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                     <gmd:dateTime>
                        <gco:DateTime>2023-01-01T00:00:00</gco:DateTime>
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            <gco:CharacterString>Metadata Use Constraints: If the user has modified the data in any way they are obligated to describe the types of modifications they have performed in the supporting metadata file. User specifically agrees not to imply that changes they made were approved by the Alaska Department of Natural Resources or Division of Geological &amp; Geophysical Surveys.</gco:CharacterString>
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                              <gco:CharacterString>USA</gco:CharacterString>
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               </gmd:contactInfo>
               <gmd:role>
                  <gmd:CI_RoleCode codeList="http://schemas.opengis.net/iso/19139/20070417/resources/codelist/gmxCodelists.xml#CI_RoleCode"
                                   codeListValue="pointOfContact">pointOfContact</gmd:CI_RoleCode>
               </gmd:role>
            </gmd:CI_ResponsibleParty>
         </gmd:contact>
      </gmd:MD_MaintenanceInformation>
   </gmd:metadataMaintenance>
</gmd:MD_Metadata>
