<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20211116</CreaDate>
<CreaTime>11181400</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<DataProperties>
<lineage>
<Process Date="20211116" Time="111814" ToolSource="c:\program files\arcgis\pro\Resources\ArcToolbox\toolboxes\Data Management Tools.tbx\CreateFeatureclass">CreateFeatureclass "C:\Users\SG\Documents\ArcGIS\Projects\PC398 Burnpiles\PC398 Burnpiles.gdb" PFIRS_2018_2021 Point "'PFIRS 2018-2021$'" No No "GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision" # 0 0 0 #</Process>
<Process Date="20211116" Name="XY Table To Point" Time="111815" ToolSource="c:\program files\arcgis\pro\Resources\ArcToolbox\toolboxes\Data Management Tools.tbx\XYTableToPoint">XYTableToPoint 'PFIRS 2018-2021$' "C:\Users\SG\Documents\ArcGIS\Projects\PC398 Burnpiles\PC398 Burnpiles.gdb\PFIRS_2018_2021" LONGITUDE LATITUDE # "GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]];-400 -400 1000000000;-100000 10000;-100000 10000;8.98315284119521E-09;0.001;0.001;IsHighPrecision"</Process>
<Process Date="20211116" Time="112934" ToolSource="c:\program files\arcgis\pro\Resources\ArcToolbox\toolboxes\Data Management Tools.tbx\UpdateSchema">UpdateSchema "CIMDATA=&lt;CIMStandardDataConnection xsi:type='typens:CIMStandardDataConnection' 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/2.8.0'&gt;&lt;WorkspaceConnectionString&gt;DATABASE=C:\Users\SG\Documents\ArcGIS\Projects\PC398 Burnpiles\PC398 Burnpiles.gdb&lt;/WorkspaceConnectionString&gt;&lt;WorkspaceFactory&gt;FileGDB&lt;/WorkspaceFactory&gt;&lt;Dataset&gt;PFIRS_2018_2021&lt;/Dataset&gt;&lt;DatasetType&gt;esriDTFeatureClass&lt;/DatasetType&gt;&lt;/CIMStandardDataConnection&gt;" &lt;operationSequence&gt;&lt;workflow&gt;&lt;DeleteField&gt;&lt;field_name&gt;F16&lt;/field_name&gt;&lt;field_name&gt;F17&lt;/field_name&gt;&lt;field_name&gt;F18&lt;/field_name&gt;&lt;field_name&gt;F19&lt;/field_name&gt;&lt;/DeleteField&gt;&lt;/workflow&gt;&lt;workflow&gt;&lt;AddField&gt;&lt;field_name&gt;Year&lt;/field_name&gt;&lt;field_type&gt;LONG&lt;/field_type&gt;&lt;field_alias&gt;Year&lt;/field_alias&gt;&lt;field_is_nullable&gt;True&lt;/field_is_nullable&gt;&lt;field_is_required&gt;False&lt;/field_is_required&gt;&lt;/AddField&gt;&lt;/workflow&gt;&lt;/operationSequence&gt;</Process>
<Process Date="20211116" Time="113001" ToolSource="c:\program files\arcgis\pro\Resources\ArcToolbox\toolboxes\Data Management Tools.tbx\CalculateField">CalculateField "PFIRS 2018-2021 by Total Tons" Year Year($feature.BURN_DATE) Arcade # Text NO_ENFORCE_DOMAINS</Process>
<Process Date="20211207" Time="112422" ToolSource="c:\program files\arcgis\pro\Resources\ArcToolbox\toolboxes\Data Management Tools.tbx\CalculateField">CalculateField "PFIRS 2018-2021" BURN_TYPE "Landing Pile" "Python 3" # Text NO_ENFORCE_DOMAINS</Process>
</lineage>
</DataProperties>
</Esri>
<dataIdInfo>
<idAbs/>
<searchKeys>
<keyword>PFIRS</keyword>
<keyword>PC398</keyword>
<keyword>Prescribed Fire</keyword>
<keyword>Emissions</keyword>
</searchKeys>
<idPurp>PFIRS 2018-2021</idPurp>
<idCredit/>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
<idCitation>
<resTitle>PFIRS 2018_2021</resTitle>
</idCitation>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
</Thumbnail>
</Binary>
</metadata>
