20240226
14421400
1.0
FALSE
SampleDepZoneWidth
002
Projected
GCS_North_American_1983
Linear Unit: Meter (1.000000)
NAD_1983_BC_Environment_Albers
<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["NAD_1983_BC_Environment_Albers",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["Albers"],PARAMETER["False_Easting",1000000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-126.0],PARAMETER["Standard_Parallel_1",50.0],PARAMETER["Standard_Parallel_2",58.5],PARAMETER["Latitude_Of_Origin",45.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3005]]</WKT><XOrigin>-13239300</XOrigin><YOrigin>-8610100</YOrigin><XYScale>316279566.22660488</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>102190</WKID><LatestWKID>3005</LatestWKID></ProjectedCoordinateSystem>
20240324
14351500
20240324
14351500
Esri ArcGIS 13.1.0.41833
SampleDepZoneWidth
Riparian
Sample lakes depression zone width
File Geodatabase Feature Class
dataset
EPSG
6.11(9.0.0)
0
Simple
FALSE
0
TRUE
FALSE
SampleDepZoneWidth
Feature Class
0
OBJECTID_12
OBJECTID_12
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
OBJECTID
OBJECTID
Integer
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
Join_Count
Join_Count
Integer
4
0
0
Width
Width
Double
8
0
0
TARGET_FID
TARGET_FID
Integer
4
0
0
ORIG_FID
ORIG_FID
Integer
4
0
0
WATERBODY_POLY_ID
WATERBODY_POLY_ID
Double
8
0
0
WATERSHED_GROUP_ID
WATERSHED_GROUP_ID
Double
8
0
0
WATERBODY_TYPE
WATERBODY_TYPE
String
1
0
0
WATERBODY_KEY
WATERBODY_KEY
Integer
4
0
0
AREA_HA
AREA_HA
Double
8
0
0
GNIS_ID_1
GNIS_ID_1
Integer
4
0
0
GNIS_NAME_1
GNIS_NAME_1
String
80
0
0
GNIS_ID_2
GNIS_ID_2
Integer
4
0
0
GNIS_NAME_2
GNIS_NAME_2
String
80
0
0
GNIS_ID_3
GNIS_ID_3
Integer
4
0
0
GNIS_NAME_3
GNIS_NAME_3
String
80
0
0
BLUE_LINE_KEY
BLUE_LINE_KEY
Integer
4
0
0
WATERSHED_KEY
WATERSHED_KEY
Integer
4
0
0
FWA_WATERSHED_CODE
FWA_WATERSHED_CODE
String
143
0
0
LOCAL_WATERSHED_CODE
LOCAL_WATERSHED_CODE
String
143
0
0
WATERSHED_GROUP_CODE
WATERSHED_GROUP_CODE
String
4
0
0
LEFT_RIGHT_TRIBUTARY
LEFT_RIGHT_TRIBUTARY
String
7
0
0
WATERBODY_KEY_50K
WATERBODY_KEY_50K
Integer
4
0
0
WATERSHED_GROUP_CODE_50K
WATERSHED_GROUP_CODE_50K
String
4
0
0
WATERBODY_KEY_GROUP_CODE_50K
WATERBODY_KEY_GROUP_CODE_50K
String
55
0
0
WATERSHED_CODE_50K
WATERSHED_CODE_50K
String
45
0
0
FEATURE_CODE
FEATURE_CODE
String
10
0
0
SE_ANNO_CAD_DATA
SE_ANNO_CAD_DATA
Blob
0
0
0
FEATURE_AREA_SQM
FEATURE_AREA_SQM
Double
8
0
0
FEATURE_LENGTH_M
FEATURE_LENGTH_M
Double
8
0
0
AREA
AREA
Double
8
0
0
LEN
LEN
Double
8
0
0
OBJECTID_1
OBJECTID
Integer
4
0
0
GEOMETRY_AREA
GEOMETRY_AREA
Double
8
0
0
GEOMETRY_LEN
GEOMETRY_LEN
Double
8
0
0
fme_feature_type
fme_feature_type
String
50
0
0
WATERBODY_POLY_ID_1
WATERBODY_POLY_ID
Double
8
0
0
WATERSHED_GROUP_ID_1
WATERSHED_GROUP_ID
Double
8
0
0
WATERBODY_TYPE_1
WATERBODY_TYPE
String
1
0
0
WATERBODY_KEY_1
WATERBODY_KEY
Integer
4
0
0
AREA_HA_1
AREA_HA
Double
8
0
0
GNIS_ID_12
GNIS_ID_1
Integer
4
0
0
GNIS_NAME_12
GNIS_NAME_1
String
80
0
0
GNIS_ID_23
GNIS_ID_2
Integer
4
0
0
GNIS_NAME_23
GNIS_NAME_2
String
80
0
0
GNIS_ID_34
GNIS_ID_3
Integer
4
0
0
GNIS_NAME_34
GNIS_NAME_3
String
80
0
0
BLUE_LINE_KEY_1
BLUE_LINE_KEY
Integer
4
0
0
WATERSHED_KEY_1
WATERSHED_KEY
Integer
4
0
0
FWA_WATERSHED_CODE_1
FWA_WATERSHED_CODE
String
143
0
0
LOCAL_WATERSHED_CODE_1
LOCAL_WATERSHED_CODE
String
143
0
0
WATERSHED_GROUP_CODE_1
WATERSHED_GROUP_CODE
String
4
0
0
LEFT_RIGHT_TRIBUTARY_1
LEFT_RIGHT_TRIBUTARY
String
7
0
0
WATERBODY_KEY_50K_1
WATERBODY_KEY_50K
Integer
4
0
0
WATERSHED_GROUP_CODE_50K_1
WATERSHED_GROUP_CODE_50K
String
4
0
0
WATERBODY_KEY_GROUP_CODE_50K_1
WATERBODY_KEY_GROUP_CODE_50K
String
55
0
0
WATERSHED_CODE_50K_1
WATERSHED_CODE_50K
String
45
0
0
FEATURE_CODE_1
FEATURE_CODE
String
10
0
0
SE_ANNO_CAD_DATA_1
SE_ANNO_CAD_DATA
Blob
0
0
0
FEATURE_AREA_SQM_1
FEATURE_AREA_SQM
Double
8
0
0
FEATURE_LENGTH_M_1
FEATURE_LENGTH_M
Double
8
0
0
AREA_1
AREA
Double
8
0
0
LEN_1
LEN
Double
8
0
0
Shape_Length_1
Shape_Length
Double
8
0
0
Elevation_m
Elevation_m
Double
8
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
20240324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