20220524
08102900
1.0
FALSE
PCop_Maks_vanndybde_33N.tif
file://\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\PCop_Maks_vanndybde_33N.tif
Local Area Network
002
262013.118006
267566.118006
6626512.158490
6639624.158490
1
Projected
GCS_ETRS_1989
Linear Unit: Meter (1.000000)
ETRS_1989_UTM_Zone_33N
<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/2.9.0'><WKT>PROJCS["ETRS_1989_UTM_Zone_33N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",15.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",25833]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>450445547.3910538</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><WKID>25833</WKID><LatestWKID>25833</LatestWKID></ProjectedCoordinateSystem>
32
FALSE
LZW
1
TIFF
TRUE
continuous
floating point
inf
Clip Resultater_Samlet\PCop\Maks_vanndybde\PCop_Maks_vanndybde.tif "599471.637734344 6620642.49111455 603885.158733054 6633399.78411094" "\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\PCop_Maks_vanndybde.tif" Resultater_Samlet\Klippefil_Samlet_uten_merge inf ClippingGeometry NO_MAINTAIN_EXTENT
ProjectRaster "Dybde (klippet)\Raster 32N\PCop_Maks_vanndybde.tif" "\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\PCop_Maks_vanndybde_33N.tif" PROJCS["ETRS_1989_UTM_Zone_33N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",15.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] "Nearest neighbor" "1 1" # # PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] NO_VERTICAL
20220524
08141500
20220524
08141500
Microsoft Windows 10 Version 10.0 (Build 19042) ; Esri ArcGIS 12.9.0.32739
PCop_Maks_vanndybde_33N_1
1
10.753684
10.867021
59.828809
59.708236
PCop_maksD
Maksimal vanndybde ved København-regnet (2011)
Raster Dataset
dataset
EPSG
4.3(4.0.0)
2
1
0
262013.118006 6626512.158490
262013.118006 6639624.158490
267566.118006 6639624.158490
267566.118006 6626512.158490
264789.618006 6633068.158490
5553
1.000000
13112
1.000000
Band_1
1528169899229200.000000
0.000500
32
20220524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