Methodology
The following section outlines the methods use to produce the maps and results that will be presented in the 'results' section.
In this context GIS refers to ArcGIS, the program used to produce the maps.
Controlling Parameters
PC1. Minimum coldest month temperature higher than -40ºC
PC2. Minimum 833 degree-days above 5ºC
PC3. Average maximum August temperature above 18.3ºC
PC4. Total spring precipitation lower than long term average.
PC: PC1 ∧ PC2 ∧ PC3 ∧ PC4 = 1. [1]
PC is true (suitable, = 1) if, and only if, PC1, PC2, PC3 and PC4 are all true, and is false (unsuitable, = 0) otherwise (Carrol et al., 2006: 2).
Forest parameters:
PF1: Species (Lodgepole pine)
PF2: Age (80-160 years)
PF: PF1 ∧ PF2 = 1. [2]
S: PS ∧ PF = 1. [3]
That is, S is true (the area is suitable, = 1) if, and only if, PS is true, (PC1, PC2, PC3 and PC4 are all true), and PF is true (PF1 and PF2 are both true) and false otherwise (i.e. the area is unsuitable for MPB, = 0).
GIS method
In order to
limit the amounts of data as well as the calculation time, a smaller
part of BC
was chosen as our study area. It was decided to extend from
900000E-1350000E
and 1150000N-1450000N in the BC Albers Projection. This area was chosen
due to
its location at the northernmost outskirts of the MPB range, as well as
its
relatively high frequency of susceptible, but not yet infested trees as
well as
some areas already infested.
Practically, the layers were limited to the study area by setting the processing extend to the coordinates mentioned above in geoprocessing environments.
See map of study area.
Practically, the layers were limited to the study area by setting the processing extend to the coordinates mentioned above in geoprocessing environments.
See map of study area.
Forest
data
Raster data
with forest
age and leading species was downloaded from HectaresBC as ASCII-files. The
ASCII-files were converted
into raster-files in ESRI ArcGIS (GIS) 10 using the ‘from
ASCII to Raster’
conversion tool.
Using the ‘define projection’ tool, the raster layer was then assigned a spatial reference, 1983 NAD CSRS BC Environment Albers.
Using the ’raster calculation’ tool, the two reclassed layers were multiplied to create a new layer were cells with the value 1 contain trees in the susceptible age and species. The rest of cells with the value 0 were discarded off.
Using the ‘define projection’ tool, the raster layer was then assigned a spatial reference, 1983 NAD CSRS BC Environment Albers.
Using the ’raster calculation’ tool, the two reclassed layers were multiplied to create a new layer were cells with the value 1 contain trees in the susceptible age and species. The rest of cells with the value 0 were discarded off.
Flowchart, Forest
Climate
data
The
ClimateBC software was downloaded from Climate BC. To
generate data to fit the
ClimateBC multiple location format, a DEM (1x1 km cells) was
reprojected to the
CSRS BC Environment Albers projection (define projection)
and multiplied
by 1 (raster calculator) to limit it to the study
area.
Secondly, the DEM containing the study area was converted into a point file and reprojected to decimal degrees in NAD1983 using the ’feature project’ tool in the ’data management toolbox’.
Two float-fields with the header x and y, respectively, were added in the attribute table of the new DEM point layer. The x- and y-values were calculated in these new fields using the ’calculate geometry’ tool. The attribute table was exported as a dBASE-file (.dbf), opened in Microsoft Office Excel, formatted to the ClimateBC standard and saved as a comma delimited file (.csv). The necessary climate data was then extracted with the ClimateBC software using the ’multiple location’ function.
To create raster layers from the climate data it was imported back into GIS as X, Y coordinates (decimal degrees) in NAD1983 with the climate parameter of interest as z-value. This was performed by right-clicking on the climate data table in ArcCatalog and choosing ’create feature class from X,Y table’. These layers were then reprojected back to CSRS BC Environment Albers projection and converted from a point layer to a raster layer using ‘feature to raster’ tool. As the climate data field is imported in double format we need to multiply it by 10 and use the ’int([layer])’ function in the raster calculator to turn it into integer data and create an attribute table.
Finally, these layers were reclassified so all cells with the appropriate climate conditions are assigned the value 1 and all other cells were assigned the value 0 (see equation 1).
Secondly, the DEM containing the study area was converted into a point file and reprojected to decimal degrees in NAD1983 using the ’feature project’ tool in the ’data management toolbox’.
Two float-fields with the header x and y, respectively, were added in the attribute table of the new DEM point layer. The x- and y-values were calculated in these new fields using the ’calculate geometry’ tool. The attribute table was exported as a dBASE-file (.dbf), opened in Microsoft Office Excel, formatted to the ClimateBC standard and saved as a comma delimited file (.csv). The necessary climate data was then extracted with the ClimateBC software using the ’multiple location’ function.
To create raster layers from the climate data it was imported back into GIS as X, Y coordinates (decimal degrees) in NAD1983 with the climate parameter of interest as z-value. This was performed by right-clicking on the climate data table in ArcCatalog and choosing ’create feature class from X,Y table’. These layers were then reprojected back to CSRS BC Environment Albers projection and converted from a point layer to a raster layer using ‘feature to raster’ tool. As the climate data field is imported in double format we need to multiply it by 10 and use the ’int([layer])’ function in the raster calculator to turn it into integer data and create an attribute table.
Finally, these layers were reclassified so all cells with the appropriate climate conditions are assigned the value 1 and all other cells were assigned the value 0 (see equation 1).
Flowchart, Climate Data