20170127 12380800 1.0 FALSE extract_raster 002 1355205.659767 1415085.659767 11843361.482575 11868501.482575 1 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_Lambert_Conformal_Conic <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/10.4'><WKT>PROJCS[&quot;NAD_1983_Lambert_Conformal_Conic&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Conformal_Conic&quot;],PARAMETER[&quot;False_Easting&quot;,930000.0],PARAMETER[&quot;False_Northing&quot;,6430000.0],PARAMETER[&quot;Central_Meridian&quot;,-85.0],PARAMETER[&quot;Standard_Parallel_1&quot;,44.5],PARAMETER[&quot;Standard_Parallel_2&quot;,53.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-35121300</XOrigin><YOrigin>-18032700</YOrigin><XYScale>124921975.83361751</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem> 20170127 12381000 20170127 12381000 Version 6.2 (Build 9200) ; Esri ArcGIS 10.4.1.5686 extract_raster 1 -79.786261 -79.030649 43.300539 43.035702 DEM of Niagara Subappellations. Niagara subappellations DEM of Niagara Subappellations. File Geodatabase Raster Dataset dataset Pixel 838 1996 30.000000 30.000000 32 1 Upper Left FALSE NONE 1 pixel codes FGDBR TRUE row and column 30.000000 30.000000 20170127 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