20250930
21011300
1.0
FALSE
risk_current
002
Projected
GCS_North_American_1983
Linear Unit: Meter (1.000000)
NAD_1983_Alaska_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/3.4.0'><WKT>PROJCS["NAD_1983_Alaska_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",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-154.0],PARAMETER["Standard_Parallel_1",55.0],PARAMETER["Standard_Parallel_2",65.0],PARAMETER["Latitude_Of_Origin",50.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3338]]</WKT><XOrigin>-13752200</XOrigin><YOrigin>-8948200</YOrigin><XYScale>10000</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>102006</WKID><LatestWKID>3338</LatestWKID></ProjectedCoordinateSystem>
20260727
20014500
20260727
20014500
Esri ArcGIS 13.4.0.55405
risk_current
Current FIREWALL wildfire risk output.
FIREWALL
wildfire
Alaska
Railbelt
risk
Current FIREWALL wildfire risk result.
File Geodatabase Feature Class
dataset
EPSG
6.12(9.3.0.0)
0
Simple
FALSE
0
TRUE
FALSE
risk_current
Feature Class
0
OBJECTID
OBJECTID
OID
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.
GRID_ID
GRID_ID
String
12
0
0
SUM_W_PL_Exp
SUM_W_PL_Exp
Double
8
0
0
PowerLine_Norm
PowerLine_Norm
Double
8
0
0
DATE
DATE
DateOnly
8
0
0
TEMP
TEMP
Double
8
0
0
WIND
WIND
Double
8
0
0
PRCP
PRCP
Double
8
0
0
FFMC
FFMC
Double
8
0
0
BUI
BUI
Double
8
0
0
ISI
ISI
Double
8
0
0
ffmc_cat
ffmc_cat
Double
8
0
0
bui_cat
bui_cat
Double
8
0
0
isi_cat
isi_cat
Double
8
0
0
temp_cat
temp_cat
Double
8
0
0
wind_cat
wind_cat
Double
8
0
0
prcp_cat
prcp_cat
Double
8
0
0
road_cat
road_cat
Double
8
0
0
slope_cat
slope_cat
Double
8
0
0
elev_cat
elev_cat
Double
8
0
0
lc_cat
lc_cat
Double
8
0
0
power_cat
power_cat
Double
8
0
0
water_cat
water_cat
Double
8
0
0
conifer_cat
conifer_cat
Double
8
0
0
developed_cat
developed_cat
Double
8
0
0
mixed_forest_cat
mixed_forest_cat
Double
8
0
0
low_veg_cat
low_veg_cat
Double
8
0
0
lc_no_fuel
lc_no_fuel
Double
8
0
0
CENTRAL_IGNITION_COUNT
CENTRAL_IGNITION_COUNT
Integer
4
0
0
NEIGHBOR_COUNT
NEIGHBOR_COUNT
Integer
4
0
0
NEIGHBOR_IGNITION_SUM
NEIGHBOR_IGNITION_SUM
Integer
4
0
0
SMOOTHED_IGNITION_COUNT
SMOOTHED_IGNITION_COUNT
Integer
4
0
0
SMOOTHED_IGNITION_NORM
SMOOTHED_IGNITION_NORM
Double
8
0
0
hist_ign_cat
hist_ign_cat
Double
8
0
0
IGN_SCORE
IGN_SCORE
Double
8
0
0
IGN_CLASS
IGN_CLASS
String
8000
0
0
SPR_SCORE
SPR_SCORE
Double
8
0
0
SPR_CLASS
SPR_CLASS
String
8000
0
0
HAZ_SCORE
HAZ_SCORE
Double
8
0
0
HAZ_CLASS
HAZ_CLASS
String
8000
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
20260727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