<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20221210</CreaDate>
<ArcGISFormat>1.0</ArcGISFormat>
<ArcGISstyle>FGDC CSDGM Metadata</ArcGISstyle>
<SyncOnce>FALSE</SyncOnce>
<DataProperties>
<itemProps>
<itemName Sync="TRUE">SCWA_Sonoma_Creek_Lidar_Intensity_NIR_2021.tif</itemName>
<itemLocation>
<protocol Sync="TRUE">Local Area Network</protocol>
</itemLocation>
<imsContentType Sync="TRUE" export="False">002</imsContentType>
<nativeExtBox>
<westBL Sync="TRUE">6392154.000000</westBL>
<eastBL Sync="TRUE">6444024.000000</eastBL>
<southBL Sync="TRUE">1845000.000000</southBL>
<northBL Sync="TRUE">1925532.000000</northBL>
<exTypeCode Sync="TRUE">1</exTypeCode>
</nativeExtBox>
</itemProps>
<coordRef>
<type Sync="TRUE">Projected</type>
<geogcsn Sync="TRUE">GCS_NAD_1983_2011</geogcsn>
<csUnits Sync="TRUE">Linear Unit: Foot_US (0.304801)</csUnits>
<projcsn Sync="TRUE">NAD_1983_2011_StatePlane_California_II_FIPS_0402_Ft_US</projcsn>
<peXml Sync="TRUE">&lt;ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.7'&gt;&lt;WKT&gt;PROJCS[&amp;quot;NAD_1983_2011_StatePlane_California_II_FIPS_0402_Ft_US&amp;quot;,GEOGCS[&amp;quot;GCS_NAD_1983_2011&amp;quot;,DATUM[&amp;quot;D_NAD_1983_2011&amp;quot;,SPHEROID[&amp;quot;GRS_1980&amp;quot;,6378137.0,298.257222101]],PRIMEM[&amp;quot;Greenwich&amp;quot;,0.0],UNIT[&amp;quot;Degree&amp;quot;,0.0174532925199433]],PROJECTION[&amp;quot;Lambert_Conformal_Conic&amp;quot;],PARAMETER[&amp;quot;False_Easting&amp;quot;,6561666.666666666],PARAMETER[&amp;quot;False_Northing&amp;quot;,1640416.666666667],PARAMETER[&amp;quot;Central_Meridian&amp;quot;,-122.0],PARAMETER[&amp;quot;Standard_Parallel_1&amp;quot;,38.33333333333334],PARAMETER[&amp;quot;Standard_Parallel_2&amp;quot;,39.83333333333334],PARAMETER[&amp;quot;Latitude_Of_Origin&amp;quot;,37.66666666666666],UNIT[&amp;quot;Foot_US&amp;quot;,0.3048006096012192],AUTHORITY[&amp;quot;EPSG&amp;quot;,6418]]&lt;/WKT&gt;&lt;XOrigin&gt;-115211800&lt;/XOrigin&gt;&lt;YOrigin&gt;-93821500&lt;/YOrigin&gt;&lt;XYScale&gt;36983428.057351544&lt;/XYScale&gt;&lt;ZOrigin&gt;-100000&lt;/ZOrigin&gt;&lt;ZScale&gt;10000&lt;/ZScale&gt;&lt;MOrigin&gt;-100000&lt;/MOrigin&gt;&lt;MScale&gt;10000&lt;/MScale&gt;&lt;XYTolerance&gt;0.0032808333333333331&lt;/XYTolerance&gt;&lt;ZTolerance&gt;0.001&lt;/ZTolerance&gt;&lt;MTolerance&gt;0.001&lt;/MTolerance&gt;&lt;HighPrecision&gt;true&lt;/HighPrecision&gt;&lt;WKID&gt;103004&lt;/WKID&gt;&lt;LatestWKID&gt;6418&lt;/LatestWKID&gt;&lt;/ProjectedCoordinateSystem&gt;</peXml>
</coordRef>
<RasterProperties>
<General>
<PixelDepth Sync="TRUE">8</PixelDepth>
<HasColormap Sync="TRUE">FALSE</HasColormap>
<CompressionType Sync="TRUE">LZW</CompressionType>
<NumBands Sync="TRUE">1</NumBands>
<Format Sync="TRUE">TIFF</Format>
<HasPyramids Sync="TRUE">TRUE</HasPyramids>
<SourceType Sync="TRUE">continuous</SourceType>
<PixelType Sync="TRUE">unsigned integer</PixelType>
<NoDataValue Sync="TRUE">0</NoDataValue>
</General>
</RasterProperties>
</DataProperties>
<SyncDate>20221210</SyncDate>
<SyncTime>07533900</SyncTime>
<ModDate>20221210</ModDate>
<ModTime>07533900</ModTime>
<scaleRange>
<minScale>150000000</minScale>
<maxScale>5000</maxScale>
</scaleRange>
<ArcGISProfile>FGDC</ArcGISProfile>
<CreaTime>07284600</CreaTime>
</Esri>
<dataIdInfo>
<envirDesc Sync="TRUE"> Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595</envirDesc>
<dataLang>
<languageCode Sync="TRUE" value="eng"/>
<countryCode Sync="TRUE" value="USA"/>
</dataLang>
<idCitation>
<resTitle Sync="FALSE">SCWA_Sonoma_Creek_Lidar_Intensity_NIR_2021</resTitle>
<presForm>
<PresFormCd Sync="TRUE" value="005"/>
<fgdcGeoform>raster digital data</fgdcGeoform>
</presForm>
<date>
<pubDate>2021-04-21</pubDate>
</date>
<citRespParty>
<rpOrgName>NV5 Geospatial</rpOrgName>
<rpCntInfo>
<cntAddress addressType="both">
<delPoint>1100 Circle Blvd. Suite 126</delPoint>
<city>Corvallis</city>
<adminArea>OR</adminArea>
<postCode>97330</postCode>
<country>US</country>
</cntAddress>
<cntPhone>
<voiceNum tddtty="">541-752-1204</voiceNum>
</cntPhone>
<cntOnlineRes>
<linkage>http://www.nv5.com/geospatial</linkage>
</cntOnlineRes>
</rpCntInfo>
<displayName>NV5 Geospatial</displayName>
<role>
<RoleCd value="006"/>
</role>
</citRespParty>
</idCitation>
<spatRpType>
<SpatRepTypCd Sync="TRUE" value="002"/>
</spatRpType>
<dataExt>
<geoEle>
<GeoBndBox esriExtentType="search">
<exTypeCode Sync="TRUE">1</exTypeCode>
<westBL Sync="TRUE">-122.591889</westBL>
<eastBL Sync="TRUE">-122.409510</eastBL>
<northBL Sync="TRUE">38.448803</northBL>
<southBL Sync="TRUE">38.226890</southBL>
</GeoBndBox>
</geoEle>
<exDesc>Sonoma Creek Topobathymetric Lidar</exDesc>
<tempEle>
<TempExtent>
<exTemp>
<TM_Period>
<tmBegin>2021-02-04</tmBegin>
<tmEnd>2021-02-06</tmEnd>
</TM_Period>
</exTemp>
</TempExtent>
</tempEle>
</dataExt>
<idPurp>Provide intensity images of Sonoma Creek Topo bathymetric Lidar data.</idPurp>
<idAbs>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;This .tif file represents the intensity values of the NIR Lidar laser returns from the Sonoma Creek Topo bathymetric dataset. The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Sonoma Creek Topo bathymetric Lidar data for Sonoma Water between 02/04/2021 and 02/06/2021.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
<idCredit>Sonoma Water</idCredit>
<searchKeys>
<keyword>Light Detection and Ranging</keyword>
<keyword>lidar</keyword>
<keyword>intensity</keyword>
<keyword>intensity image</keyword>
<keyword>Sonoma County</keyword>
<keyword>California</keyword>
<keyword>Environmental Science Associates</keyword>
</searchKeys>
<themeKeys>
<keyword>Light Detection and Ranging</keyword>
<keyword>lidar</keyword>
<keyword>intensity</keyword>
<keyword>intensity image</keyword>
</themeKeys>
<placeKeys>
<keyword>Sonoma County</keyword>
<keyword>California</keyword>
<keyword>Environmental Science Associates</keyword>
</placeKeys>
<dataChar>
<CharSetCd value="004"/>
</dataChar>
<dataScale>
<scaleDist>
<value uom="ft_us">1.5</value>
</scaleDist>
</dataScale>
<suppInfo>This data is assembled by AOI and projected in California State Plane, Zone 2.</suppInfo>
<idStatus>
<ProgCd value="001"/>
</idStatus>
<resMaint>
<maintFreq>
<MaintFreqCd value="011"/>
</maintFreq>
</resMaint>
<resConst>
<LegConsts>
<useLimit>Please contact Sonoma Water for information regarding the use of this data.</useLimit>
</LegConsts>
</resConst>
<idPoC>
<rpIndName>Carlos Diaz</rpIndName>
<rpOrgName>Sonoma Water</rpOrgName>
<role>
<RoleCd value="007"/>
</role>
<rpCntInfo>
<cntAddress addressType="postal">
<delPoint>404 Aviation Blvd</delPoint>
<city>Santa Rosa</city>
<adminArea>California</adminArea>
<postCode>95403</postCode>
<eMailAdd>carlos.diaz@scwa.ca.gov</eMailAdd>
<country>US</country>
</cntAddress>
<cntPhone>
<voiceNum tddtty="">707-547-1956</voiceNum>
</cntPhone>
</rpCntInfo>
</idPoC>
<resConst>
<Consts>
<useLimit>&lt;div style='text-align:Left;'&gt;&lt;p style='font-size:16ptmargin:7 0 7 0;'&gt;&lt;span style='font-weight:bold;'&gt;SUBJECT: PUBLIC INFORMATION REQUEST –LiDAR Data&lt;/span&gt;&lt;/p&gt;&lt;p style='font-size:16ptmargin:7 0 7 0;'&gt;&lt;span&gt;&lt;span&gt;The attached information was retrieved from Sonoma County Water Agency CAD-Drafting/GIS Records in connection with a Request for Public Information. These records and the drawings upon which they are based, were produced or conducted solely for the technical and/or functional needs and purposes of the Sonoma County Water Agency and/or other entities which it now acts, or may have acted on behalf of in the past. &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;p style='font-size:16ptmargin:7 0 7 0;'&gt;&lt;span&gt;&lt;span&gt;The Sonoma County Water Agency makes no assertions or representations regarding the sufficiency, accuracy or applicability of the information and/or records provided for any purpose beyond that for which they were originally produced.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;p style='font-size:16ptmargin:7 0 7 0;'&gt;&lt;span&gt;The information contained herein is for the use and information of the party to whom it was directly sent. The redistribution or “sharing”of this information is strictly prohibited.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;</useLimit>
</Consts>
</resConst>
<tpCat>
<TopicCatCd value="010"/>
</tpCat>
</dataIdInfo>
<mdLang>
<languageCode Sync="TRUE" value="eng"/>
<countryCode Sync="TRUE" value="USA"/>
</mdLang>
<mdChar>
<CharSetCd Sync="TRUE" value="004"/>
</mdChar>
<distInfo>
<distFormat>
<formatName Sync="TRUE">Raster Dataset</formatName>
<formatVer>1</formatVer>
</distFormat>
<distributor>
<distorCont>
<rpIndName>Carlos Diaz</rpIndName>
<rpOrgName>Sonoma Water</rpOrgName>
<role>
<RoleCd value="005"/>
</role>
<rpCntInfo>
<cntAddress addressType="postal">
<delPoint>404 Aviation Blvd</delPoint>
<city>Santa Rosa</city>
<adminArea>California</adminArea>
<postCode>95403</postCode>
<eMailAdd>carlos.diaz@scwa.ca.gov</eMailAdd>
<country>US</country>
</cntAddress>
<cntPhone>
<voiceNum tddtty="">707-547-1956</voiceNum>
</cntPhone>
</rpCntInfo>
</distorCont>
</distributor>
</distInfo>
<mdHrLv>
<ScopeCd Sync="TRUE" value="005"/>
</mdHrLv>
<mdHrLvName Sync="TRUE">dataset</mdHrLvName>
<refSysInfo>
<RefSystem>
<refSysID>
<identCode Sync="TRUE" code="6418"/>
<idCodeSpace Sync="TRUE">EPSG</idCodeSpace>
<idVersion Sync="TRUE">8.9.3(10.3.1)</idVersion>
</refSysID>
</RefSystem>
</refSysInfo>
<spatRepInfo>
<Georect>
<cellGeo>
<CellGeoCd Sync="TRUE" value="002"/>
</cellGeo>
<numDims Sync="TRUE">2</numDims>
<tranParaAv Sync="TRUE">1</tranParaAv>
<chkPtAv Sync="TRUE">0</chkPtAv>
<cornerPts>
<pos Sync="TRUE">6392154.000000 1845000.000000</pos>
</cornerPts>
<cornerPts>
<pos Sync="TRUE">6392154.000000 1925532.000000</pos>
</cornerPts>
<cornerPts>
<pos Sync="TRUE">6444024.000000 1925532.000000</pos>
</cornerPts>
<cornerPts>
<pos Sync="TRUE">6444024.000000 1845000.000000</pos>
</cornerPts>
<centerPt>
<pos Sync="TRUE">6418089.000000 1885266.000000</pos>
</centerPt>
<axisDimension type="002">
<dimSize Sync="TRUE">34580</dimSize>
<dimResol>
<value Sync="TRUE" uom="ftUS">1.500000</value>
</dimResol>
</axisDimension>
<axisDimension type="001">
<dimSize Sync="TRUE">53688</dimSize>
<dimResol>
<value Sync="TRUE" uom="ftUS">1.500000</value>
</dimResol>
</axisDimension>
<ptInPixel>
<PixOrientCd Sync="TRUE" value="001"/>
</ptInPixel>
</Georect>
</spatRepInfo>
<contInfo>
<ImgDesc>
<contentTyp>
<ContentTypCd Sync="TRUE" value="001"/>
</contentTyp>
<covDim>
<Band>
<dimDescrp Sync="TRUE">Band_1</dimDescrp>
<maxVal Sync="TRUE">255.000000</maxVal>
<minVal Sync="TRUE">1.000000</minVal>
<bitsPerVal Sync="TRUE">8</bitsPerVal>
<valUnit>
<UOM gmlID="" type="length">
<unitSymbol>[arb'U]</unitSymbol>
</UOM>
</valUnit>
<seqID>
<aName>NIR Intensity</aName>
<attributeType>
<aName>NIR_INT</aName>
</attributeType>
</seqID>
</Band>
</covDim>
<trianInd>False</trianInd>
<radCalDatAv>False</radCalDatAv>
<camCalInAv>False</camCalInAv>
<filmDistInAv>False</filmDistInAv>
<lensDistInAv>False</lensDistInAv>
<attDesc>Intensity</attDesc>
</ImgDesc>
</contInfo>
<mdDateSt Sync="FALSE">2021-04-21</mdDateSt>
<mdContact>
<rpOrgName>NV5 Geospatial</rpOrgName>
<rpCntInfo>
<cntAddress addressType="both">
<delPoint>1100 Circle Blvd. Suite 126</delPoint>
<city>Corvallis</city>
<adminArea>OR</adminArea>
<postCode>97330</postCode>
<country>US</country>
</cntAddress>
<cntPhone>
<voiceNum tddtty="">541-752-1204</voiceNum>
</cntPhone>
<cntOnlineRes>
<linkage>http://www.nv5.com/geospatial</linkage>
</cntOnlineRes>
</rpCntInfo>
<displayName>NV5 Geospatial</displayName>
<role>
<RoleCd value="006"/>
</role>
<displayName>NV5 Geospatial</displayName>
</mdContact>
<mdMaint>
<maintFreq>
<MaintFreqCd value="011"/>
</maintFreq>
</mdMaint>
<dqInfo>
<dqScope>
<scpLvl>
<ScopeCd value="005"/>
</scpLvl>
</dqScope>
<report dimension="" type="DQCompOm">
<measDesc>Lidar data has been collected and processed for all areas within the project study area.</measDesc>
<evalMethDesc>Flight plans are designed with sufficient sidelap to ensure there are no gaps between flightlines. Shaded relief images have been visually inspected for gaps.</evalMethDesc>
</report>
<report dimension="" type="DQConcConsis">
<measDesc>Shaded relief images have been visually inspected for data errors such as pits, border artifacts, and shifting. Lidar flightlines have been examined to ensure consistent elevation values across overlapping flightlines. The Root Mean Square Error (RMSE) of line to line relative accuracy for this dataset is 0.068 ft (0.021 m). Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Data was examined at a 1:2000 scale. Relative accuracy of the flightlines was assessed in Microstation using TerraMatch.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>RMSE</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.068 ft (0.021 m)</quanVal>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQAbsExtPosAcc">
<measDesc>The Non-vegetated Vertical Accuracy (NVA) of this dataset, tested at 95% confidence level is 0.122 ft (0.037 m) versus the classified point cloud. Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Non-vegetated Vertical Accuracy was assessed using 8 ground check points. These check points were not used in the calibration or post processing of the lidar point cloud data.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>NVA</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.122 ft (0.037 m)</quanVal>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQAbsExtPosAcc">
<measDesc>The Non-vegetated Vertical Accuracy (NVA) of this dataset, tested at 95% confidence level is 0.115 ft (0.035 m) versus the bare earth digital elevation model. Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Non-vegetated Vertical Accuracy was assessed using 8 ground check points. These check points were not used in the calibration or post processing of the lidar point cloud data.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>NVA</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.115 ft (0.035 m)</quanVal>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQAbsExtPosAcc">
<measDesc>The Vegetated Vertical Accuracy (VVA) of this dataset, evaluated at the 95th percentile is 0.588 ft (0.179 m) versus the classified point cloud. Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Vegetated Vertical Accuracy was assessed using 24 VVA check points. These check points were not used in the calibration or post processing of the lidar point cloud data.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>VVA</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.588 ft (0.179 m)</quanVal>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQAbsExtPosAcc">
<measDesc>The Vegetated Vertical Accuracy (VVA) of this dataset, evaluated at the 95th percentile is 0.611 ft (0.186 m) versus the bare earth digital elevation model. Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Vegetated Vertical Accuracy was assessed using 24 VVA check points. These check points were not used in the calibration or post processing of the lidar point cloud data.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>VVA</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.611 ft (0.186 m)</quanVal>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQAbsExtPosAcc">
<measDesc>Vertical accuracy was also assessed using ground control points that were used in the calibration and post processing of the lidar point cloud as they still serve as a good indication of the overall accuracy of the lidar dataset. The Root Mean Square Error (RMSE) of the vertical accuracy of the lidar dataset as compared to ground control points is 0.068 ft (0.021 m). Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>12 ground control points were collected and utilized in the calibration and post processing of the lidar data point cloud.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>RMSE</quanValType>
<quanVal>0.068 ft (0.021 m)</quanVal>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQQuanAttAcc">
<measDesc>Vertical accuracy was also assessed using bathymetric bottom check points collected over submerged bathymetric terrain. The bathymetric check point accuracy of this dataset, tested at 95% confidence level is 0.544 ft (0.166 m). Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Bathymetric bottom vertical accuracy was assessed using 84 bathymetric bottom check points. These check points were not used in the calibration or post processing of the lidar point cloud data.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>95% Confidence</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.544 ft (0.166 m)</quanVal>
</QuanResult>
</measResult>
</report>
<report dimension="vertical" type="DQQuanAttAcc">
<measDesc>Vertical accuracy was also assessed using wetted edge check points collected at the interface between water and terrain. The wetted edge check point accuracy of this dataset, tested at 95% confidence level is 0.369 ft (0.112 m). Please see the lidar data report for a discussion of the statistics related to this dataset.</measDesc>
<evalMethDesc>Wetted edge vertical accuracy was assessed using 16 wetted edge check points. These check points were not used in the calibration or post processing of the lidar point cloud data.</evalMethDesc>
<measResult>
<QuanResult>
<quanValType>95% Confidence</quanValType>
<quanValUnit>
<UOM gmlID="" type="">
<unitSymbol>ft_us</unitSymbol>
</UOM>
</quanValUnit>
<quanVal>0.369 ft (0.112 m)</quanVal>
</QuanResult>
</measResult>
</report>
<dataLineage>
<prcStep>
<stepDesc>Acquisition. NV5 Geospatial collected the Sonoma Creek Topobathymetric Lidar data between 02/04/2021 and 02/06/2021. The survey used a Riegl VQ-880-GII laser system mounted in a Cessna Caravan. Ground level GPS and aircraft IMU were collected during the flight. Sensor: Riegl VQ-880-GII
Maximum returns: 15
Nominal pulse density: 15 pulses/m^2
Nominal pulse spacing: 0.26 m
AGL: 450 m
Speed: 145 knots
FOV: 40°
Scan frequency: 80 Lines Per Second
Pulse rate: 200 kHz
Pulse duration: 1.5 ns
Pulse footprint: 31.5 cm
Wavelength: 532 nm
Pulses in air mode: Multiple Times Around
Beam divergence: 0.7 mrads
Swath width: 328 m
Overlap: 30%
Sensor: Riegl VQ-880-GII-IR
Maximum returns: 15
Nominal pulse density: 15 pulses/m^2
Nominal pulse spacing: 0.26 m
AGL: 450 m
Speed: 145 knots
FOV: 42°
Scan frequency: Uniform Point Spacing
Pulse rate: 300 kHz
Pulse duration: 3 ns
Pulse footprint: 9 cm
Wavelength: 1064 nm
Pulses in air mode: Multiple Times Around
Beam divergence: 0.2 mrads
Swath width: 345 m
Overlap: 30%
</stepDesc>
<stepDateTm>2021-02-06T00:00:00</stepDateTm>
</prcStep>
<prcStep>
<stepDesc>1. Flightlines and data were reviewed to ensure complete coverage of the study area and positional accuracy of the laser points. 2. Laser point return coordinates were computed using RiProcess V1.8.5 and POSPac MMS V.8.5 software based on independent data from the lidar system, IMU, and aircraft. 3. The raw lidar file was assembled into flightlines per return with each point having an associated x, y, and z coordinate. 4. Visual inspection of swath to swath laser point consistencies within the study area were used to perform manual refinements of system alignment. 5. Custom algorithms were designed to evaluate points between adjacent flightlines. Automated system alignment was computed based upon randomly selected swath to swath accuracy measurements that consider elevation, slope, and intensities. Specifically, refinement in the combination of system pitch, roll, and yaw offset parameters optimize internal consistency. 6. Noise (e.g., pits and birds) was filtered using post-processing software, based on known elevation ranges and included the removal of any cycle slips. 7. Using TerraScan and Microstation, ground classifications utilized custom settings appropriate to the study area. 8. The corrected and filtered return points were compared to the ground survey points collected to verify the vertical accuracy. 9. Points were output as TIFFS colored by intensity values (0-255).</stepDesc>
<stepDateTm>2021-04-21</stepDateTm>
</prcStep>
</dataLineage>
</dqInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
</Thumbnail>
</Binary>
</metadata>
