20210226
09132900
1.0
FALSE
RasterCalculator ""scplidar_2017.tif" * 3.2808399" R:\LIDAR\2017\scplidar_2017_FT.tif
ProjectRaster scplidar_2017_FT.tif R:\LIDAR\2017\LiDAR2017.tif PROJCS['NAD_1983_StatePlane_Louisiana_South_FIPS_1702_Feet',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',3280833.333333333],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-91.33333333333333],PARAMETER['Standard_Parallel_1',29.3],PARAMETER['Standard_Parallel_2',30.7],PARAMETER['Latitude_Of_Origin',28.5],UNIT['Foot_US',0.3048006096012192]],VERTCS['NAVD_1988',VDATUM['North_American_Vertical_Datum_1988'],PARAMETER['Vertical_Shift',0.0],PARAMETER['Direction',1.0],UNIT['Meter',1.0]] NEAREST "3.28083333333333 3.28083333333333" 'WGS_1984_(ITRF08)_To_NAD_1983_2011 + WGS_1984_(ITRF00)_To_NAD_1983' # PROJCS['NAD_1983_2011_UTM_Zone_15N',GEOGCS['GCS_NAD_1983_2011',DATUM['D_NAD_1983_2011',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',-93.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]] NO_VERTICAL
ProjectRaster LiDAR2017FT.tif \\geo-imageserver\lidar\2017\LiDAR2017FT_WebMercator.tif PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0]] "Nearest neighbor" "0.355193326012942 0.355193326012942" WGS_1984_(ITRF00)_To_NAD_1983 # PROJCS["NAD_1983_StatePlane_Louisiana_South_FIPS_1702_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",3280833.333333333],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-91.33333333333333],PARAMETER["Standard_Parallel_1",29.3],PARAMETER["Standard_Parallel_2",30.7],PARAMETER["Latitude_Of_Origin",28.5],UNIT["Foot_US",0.3048006096012192]],VERTCS["NAVD_1988",VDATUM["North_American_Vertical_Datum_1988"],PARAMETER["Vertical_Shift",0.0],PARAMETER["Direction",1.0],UNIT["Meter",1.0]] NO_VERTICAL
LiDAR2017FT_WebMercator.tif
file://\\geo-imageserver\lidar\2017\LiDAR2017FT_WebMercator.tif
Local Area Network
002
-10082369.346014
-10036254.951691
3462265.267037
3515400.767835
1
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
WGS_1984_Web_Mercator_Auxiliary_Sphere
<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/3.1.0'><WKT>PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>148923141.92838538</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>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem>
32
FALSE
LZW
1
TIFF
TRUE
continuous
floating point
-3.4028235e+38
20241031
11543700
20241031
11543700
Microsoft Windows Server 2016 Technical Preview Version 10.0 (Build 20348) ; Esri ArcGIS 13.1.2.41833
LiDAR 2017
1
-90.571465
-90.157212
30.092153
29.678302
LiDAR_2017
MapServer
LiDAR
Raster Dataset
dataset
EPSG
6.18.3(9.3.1.2)
2
1
0
-10082369.346014 3462265.267037
-10082369.346014 3515400.767835
-10036254.951691 3515400.767835
-10036254.951691 3462265.267037
-10059312.148853 3488833.017436
129829
0.355193
149596
0.355193
Band_1
72.703415
-21.256561
32
20241031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