Download - Brushfire Technical Report
Malheur County, Oregon
King, Aaron J
In order to better assess potential damage by wildfires, GIS modeling can be used to predict risk factors and find potential areas of high burning. By using these risk factors,
better decisions can be made in fire risk safety.
December 12, 2014 [ ]
Summary
This report presents a model of brushfire risk in Malheur County, Oregon. This model using the ESRI® program ArcGIS. All models in ArcGIS were created using the software ArcMap and ArcCatalog. This model uses various factors which include vegetation classification and topology to assess risk across the county. Once combined, a new map is made from the outputs showing the areas in Malheur County that are of the highest risk of wildfire.
Background
The county that is being examined is Malheur County located in the south west corner in Oregon as shown in figure 1. This map shows a shaded relief of Malheur County, depicting peaks and valleys in the county. The Owhyee
and Malheur River are the primary rivers that run through the county. Ontario is the most populated city in the county with a population of 11,366 according to the 2012 United States census bureau. Other cities include Nyssa, Vale, Adrian, and Jordan Valley. There are 15 villages located in the county. Interstate I84 runs through the north
portion. 3 US highways 20, 26 and 201 also run through it with a state highway 78 running through the south. There is a Lava field located in the middle of the county to the left of the Owhyee River.
Introduction
Wildfires are a natural, necessary, and often frequent process in many areas around the world, including in the American North West, which includes states like Oregon, Idaho, Washington, and California. These states can also
stretch down in Colorado, Utah, Nevada and even Arizona. One of the problems is, many communities are not equipped to deal with brushfires. Due to changing ecosystems through anthropogenic means as well as poor
planning can lead to severe damage as a result of wildfires.
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Figure 1 is a general reference map of a shaded relief of Malheur County, OR
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Vegetation-Fuel Risk
Various vegetation fuels fires differently. In order to run models on the risk potential, various vegetation type were ranked according to their potential fire risk. 2 primary factors were how well it will burn and how long it will burn
(type and fuel). The different vegetation types can be seen in. They include:
Rocky Mountain and Great Basin Flooded & Swamp Forest Rocky Mountain Cliff, Scree & Rock Vegetation Rocky Mountain Subalpine & High Montane Conifer Forest Rocky Mountain Subalpine & Montane Fen Vancouverian Flooded & Swamp Forest Warm Desert Freshwater Shrubland, Meadow & Marsh Western North American Montane Wet Meadow & Low Shrubland Western North American Vernal Pool Cool Semi-Desert Alkali-Saline Wetland Developed & Urban Great Basin & Intermountain Dry Shrubland & Grassland Great Basin & Intermountain Dwarf Sage Shrubland & Steppe Great Basin & Intermountain Tall Sagebrush Shrubland & Steppe Great Basin Saltbrush Scrub Herbaceous Agricultural Vegetation Intermountain Basin Cliff, Scree & Rock Vegetation Intermountain Singleleaf Pinyon - Western Juniper Woodland Introduced & Semi Natural Vegetation Northern Rocky Mountain Lower Montane & Foothill Forest Open Water Quarries, Mines, Gravel Pits and Oil Wells Recently Disturbed or Modified Rocky Mountain Alpine Cliff, Scree & Rock Vegetation Rocky Mountain Alpine Scrub, Forb Meadow & Grassland Rocky Mountain-Vancouverian Subalpine & High Montane Mesic Grass & Forb Meadow Northern Rocky Mountain-Vancouverian Montane & FoothillGrassland & Shrub
From there, the classes were given a value according to their risk. A value of 1 was given to the lowest risk where a value of 10 was given to the highest risk. This can be seen in table 1. Other features were given special value such
as water, lakes, Mines, quarries, Urban, etc., because of their unique or lack of ability to catch or retain fire.
Vegetation classifications are broken up roughly by type. In general, the break up on vegetation is shown in table 1. Within these types are various sub types. For example, there are various types of conifer forest, e.g. Northern
Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Northern Rocky Mountain Mesic Montane Mixed Conifer Forest. Both are coniferous (mixed) but they have a rating of 7 and 6 respectively, due to their specific
qualities and characteristics as forests.
Characteristics that were taken into account were included type of “fuel”, which would fuels capacity to burn, and amount of fuel. A map of the various types of fuel can be seen in figure 2. The type of fuel is essential do fire risk rating. Certain plants and trees have natural adaptations against fire, which would, in effect, protect them against
fire .An example of this would be Walnut Tree (a deciduous tree). Jack Pines have a thick xylem, which makes it difficult to for the fire to catch onto the thin dry material underneath that would light on fire easily. In contrast thin vegetation will catch fire much quicker and easier. A map depicting the corresponding fire risk to vegetation can be
seen in figure 3.
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December 12, 2014 [ ]
Table 1 shows fire risk classification for various vegetation types.
Types Risk Value
Barren 1Wetland
Vegetation2
Short Grass Prairie 3Tall Grass Prairie 4
Scrub 5Shrub 6
Deciduous Forest 7Conifer Forest 8
Chaparral 9Conifer Clear
Cut/Slash10
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Figure 2 shows a map of the different vegetation classes in Malheur County
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Figure 3 shows the fire risk classifications for Malheur County, OR
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Slope/Aspect
Slope and Aspect are two more factors that contribute to wildfire risk rating. Different slope and aspects contribute differently based on positioning, and can allow the fire to burn or spread more effectively. The slope of the area helps determine flowing patterns to the fire. For example, an area with a high slope runs a higher risk of catching fire. Fire, of course, burns up, so if there is flammable substance
located above where the area is burning, there will be a higher chance or risk that that object will catch on fire. Table 2 shows how the degrees of the slope have been broken up.
The degrees range from 0-90⁰ and were broken up and given a risk rating from 1-10, with 10 being the highest and 1 being the lowest. Each risk has a different amount of degrees to it. This is because not
every 5 degree jump is a jump has the same level of risk associated with it. From 0 to 10 degrees, there is not much associated change because at that low of slope change, the changes are not as drastic. A 3⁰ slope will have a very similar if not identical as a 5⁰-6⁰ because it is so low. The same theory applies for very high numbers i.e. above 50⁰to 90⁰, the risk rating goes from 8 to 9. Even though that is a drastic change in degree measure, the slope is so steep that a change from 50⁰ compare to 70⁰ will result in
almost the same degree of being lit on fire. The middle degree slope is broken up because after 25⁰ -35⁰, there is a drastic pick up in flammability and thus a greater risk, allowing for a greater jump from 25⁰ -50⁰. Figure 4 and 5 show two slope maps, showing the degree and the risk rating with the associated
degree.
Table 2 shows the corresponding risk value with degree of slope. Risk values ranged from 1-10.
Slope Degree Risk Value
0-5 1
5-10 2
10-25 3
25-35 5
35-50 7
50-65 8
65-80 9
80-90 10
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Figure 4 depicts a map of the slope of Malheur County. The slope is measured in degrees.
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Figure 5 depicts the associated risk values with slope. Each range of degrees was given a risk value. Values range from 1-10.
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December 12, 2014 [ ]
Aspect is determined by using cardinal directions and their associated degrees. This gives land forms orientation, i.e. facing south, south by southwest, North, etc. Due to the position of the Earth to the sun,
certain directions receive different amounts of sunlight. This, in turn, leads to different moisture contents, temperature, wind factor, and other things. These features can affect how susceptible an area
is to wild fire risk as well.
To model effectively, certain degree ranges where given a certain risk degree. Figure 6 shows a picture of orientation on a compass. Table 3 shows how those numbers were broken up and given risk factors. The range is 1 to 10 with 1 being the lowest and 10 being the highest. Areas that were facing north and
east were given low numbers. This is because, generally speaking, north/east facing slopes get more sun and thus will be warmer and thus, be wetter. This is not conducive to wildfires. On the contrary, south
and west sides of slopes tend to be cooler and drier. This allows for this to catch fire much easier. Thus, they had a higher risk rating. The highest risk values were directed towards the south and west, making south to south by south west the highest risk. 0 to 90⁰ faces north to north by north east, so they were
given lower values. Figure 7 and 8 show the aspect degrees and associated risk rating. A value of -1 accounts for areas that are completely flat. It was given a value of 3 due to the fact that it has properties
of both, but mostly affected by northern air.
Figure 6 shows a compass. This is a representation of orientation for slope. 0 degrees faces north, 180 degrees is directing south. 90 degrees is east, and 270 degrees is west. A value of -1 was given to flat surfaces that do not rest on slope.
Table 3 shows the corresponding risk values to orientation for aspect.
Aspect Orientation Risk Value
0-45 145-90 3
90-135 5135-180 8180-225 10225-270 10270-315 6315-360 4
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Figure 7 shows the orientation of the aspect
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Figure 8 shows the corresponding risk values as the aspect.
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Cartographic Model of Risk Rating for Risk Factors
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GAP Vegetation Raster file
DEM (Downloaded from USGS)
Mosaic Raster
Extract by mask
Mask Raster
Reclassed Raster
Vegetation Risk
Mask DEM
Slope Risk
Malheur County
Shapefile
Mask Slope
Extract by mask
Reclass Raster
Reclass Raster
Reclass Raster
Slope
Mosaic to raster
Aspect Mask
Aspect Risk Risk
Aspect
December 12, 2014 [ ]
Cartographic Model Continued
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Reclassed Raster
Vegetation Risk
Slope Risk Aspect Risk
Weighted Overlay
Wildfire Risk Map
Reclass
Reclass
Wildfire Risk Map
December 12, 2014 [ ]
Brushfire Risk
In order to create the brushfire risk, these 3 layers (vegetation risk, slope risk, and aspect risk) are combined. The output of the combination will give values that correspond with the combining numbers.
However, vegetation risk, slope risk, and aspect risk do not contribute equally to the risk of brushfire. Vegetation will have a bigger overall effect on whether or not an area will be less of greater risk for a
fire. This is why a weighted overlay option was used to combine these layers over the weighted sum. A weighed sum uses floating point numbers to combine values to create a different raster. The raster that
was in use was reclassed to include only integers so a weighted overlay had to be used. A weighted overlay raster file, which are filled with a certain integer, combines them to create a new raster. What makes this option special is that layers can be weighted due to their importance to outcome map. For
the brushfire risk map, as seen in Table 4, the layers were weighted as such:
Table 4 shows the weighted values of 3 layers to the Brushfire risk map.
WeightedVegetation 60%
Slope 25%Aspect 15%
Vegetation has the most important role in fire risk analysis. Without any land cover, there could be no fire, much less varying degrees of risk associated with fire. Still important but stressed less was slope.
Slope is important to a point, however it is not a driving feature in brushfire risk. Slope, especially intense slopes, can contribute to the dispersal and spread of fire, but have less importance as compared to the vegetation. Aspect was weighted the least. This is because the orientation can contribute to the
fire, but much like slope, the face it is positioned on will not drastically affect the fire. A fire that is occurring in steep slopes with highly volatile vegetation will burn regardless if the slope face is facing north or south. It does become important however, in areas that close to being medium or high risk
values. In a medium risk area, the aspect may be enough to push it into a high risk category because it was facing farther south. The brush fire risk map can be seen in figure 8.
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December 12, 2014 [ ]
Figure 9 is the brushfire risk map. This map depicts all the areas within Malheur and their associated risk of fire.
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December 12, 2014 [ ]
Application
With the fire risks determined over the county of Malheur, now addresses and houses can be overlayed to see what the fire risk is for neighboring areas. This can be done with every address in the county of
Malheur now. For purpose of demonstration, 8 addresses were used and geocoded into the map. Only 6 locations worked. Further analysis and evidence will be needed to determine why this is. In order to
geocode, lists of addresses are compiled in a spreadsheet. Once put into the, 400 foot buffers, 1000 foot buffers, and 5000 foot buffers were drawn. Within those buffers, percentages of risk were calculated. So for every address, each buffer has a percent of the total land per risk category. A table for the 6 address can be seen in table 5. This includes 6 addresses as well as the total county of Malheur. Table 6 shows
one individual address that is isolated, 5199 Central Oregon Hwy. A map of that area can be seen in figure 9.
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December 12, 2014 [ ]
Table 5 shows the associated risks per spatial buffer per each of the 6 test addresses
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December 12, 2014 [ ]
20
Fire
Ris
k:N
umbe
r of A
cres
Perc
enta
geM
alhe
ur C
ount
yLo
w24
3322
8.88
938
.22%
Med
ium
2557
444.
789
40.1
7%Hi
gh10
4742
3.02
816
.45%
Very
Hig
h93
91.6
7279
0.15
%W
ater
2726
9.15
921
0.43
%Cr
opla
nd/A
gric
ultu
re26
0475
.925
44.
09%
Deve
lope
d La
nd28
606.
3244
50.
45%
Qua
rrie
s/M
ines
106.
5246
70.
00%
Bedr
ock/
Mou
natin
2459
.697
450.
04%
400
ft. b
uffer
1000
ft. b
uffer
5000
ft. b
uffer
Fire
Ris
k:N
umbe
r of A
cres
Perc
enta
geFi
re R
isk:
Num
ber o
f Acr
esPe
rcen
tage
Fire
Ris
k:N
umbe
r of A
cres
Perc
enta
geAd
dres
s:Lo
w0.
1424
171.
24%
Low
6.41
3803
8.90
%Lo
w41
5.06
7665
423
.02%
5199
Cen
tral
Ore
gon
Hwy
Med
ium
1.36
0895
11.8
1%M
ediu
m22
.178
906
30.7
7%M
ediu
m70
9.58
3464
39.3
6%Hi
gh3.
8989
3233
.82%
High
15.9
2076
922
.08%
High
309.
3973
1417
.16%
Crop
land
/Agr
icul
ture
1.40
3306
12.1
7%Cr
opla
nd/A
gric
ultu
re18
.118
306
25.1
3%W
ater
0.14
2417
0.20
%De
velo
ped
Land
4.72
1387
40.9
6%De
velo
ped
Land
9.31
577
12.9
2%Cr
opla
nd/A
gric
ultu
re29
2.11
5992
16.2
0%De
velo
ped
Land
67.6
1400
33.
75%
Addr
ess:
Low
1.81
6924
15.7
6%Lo
w21
.310
496
29.5
6%Lo
w41
4.09
6193
22.9
7%10
27 M
oore
s Hol
low
Rd
Med
ium
9.69
3158
84.0
9%M
ediu
m41
.297
923
57.2
9%M
ediu
m11
31.0
7260
662
.74%
High
0.01
6856
0.15
%Hi
gh8.
5805
6911
.90%
High
256.
8066
5114
.24%
Crop
land
/Agr
icul
ture
0.90
0981
1.25
%Cr
opla
nd/A
gric
ultu
re0.
9009
810.
05%
Addr
ess:
Low
3.11
4195
27.0
2%Lo
w20
.393
603
28.2
9%Lo
w55
8.85
0211
31.0
0%20
01 A
irpor
t Rd
Med
ium
1.81
0195
15.7
0%M
ediu
m12
.355
607
17.1
4%M
ediu
m69
9.00
9812
38.7
7%Hi
gh6.
4502
7755
.96%
High
38.5
2346
553
.44%
High
486.
6314
1926
.99%
Wat
er0.
1522
711.
32%
Wat
er0.
8172
951.
13%
Wat
er6.
3950
430.
35%
Crop
land
/Agr
icul
ture
0.82
7404
0.05
%De
velo
ped
Land
51.1
6254
12.
84%
Addr
ess:
Low
1.13
1314
9.81
%Lo
w3.
7469
185.
20%
Low
151.
4123
418.
40%
3133
Beu
la R
dM
ediu
m8.
0911
7870
.19%
Med
ium
43.4
1508
660
.22%
Med
ium
1080
.107
216
59.9
1%Hi
gh1.
4838
4412
.87%
High
24.9
2796
734
.58%
High
557.
1985
1130
.91%
Very
Hig
h10
.900
399
0.60
%W
ater
00.
00%
Crop
land
/Agr
icul
ture
3.25
7963
0.18
%Ad
dres
s:Lo
w2.
3531
7120
.41%
Low
12.1
6458
716
.87%
Low
525.
3424
5129
.14%
5955
John
Day
Hw
yM
ediu
m0.
9582
788.
31%
Med
ium
17.1
6569
723
.81%
Med
ium
632.
0307
3935
.06%
High
2.08
8668
2.90
%Hi
gh39
.719
354
2.20
%Cr
opla
nd/A
gric
ultu
re3.
8411
2433
.32%
Crop
land
/Agr
icul
ture
29.6
9441
541
.19%
Crop
land
/Agr
icul
ture
546.
2215
1230
.30%
Deve
lope
d La
nd4.
3743
6437
.95%
Deve
lope
d La
nd10
.976
602
15.2
3%De
velo
ped
Land
53.1
0559
72.
95%
Bedr
ock/
Mou
natin
6.45
6778
0.36
%Ad
dres
s:Cr
opla
nd/A
gric
ultu
re10
.306
374
89.4
1%Cr
opla
nd/A
gric
ultu
re68
.338
382
94.8
0%Lo
w74
.161
461
4.11
%99
9 M
endi
ola
RdDe
velo
ped
Land
1.22
0564
10.5
9%De
velo
ped
Land
3.75
1588
5.20
%M
ediu
m18
6.02
0649
10.3
2%Hi
gh52
.208
117
2.90
%Cr
opla
nd/A
gric
ultu
re14
13.3
2616
678
.39%
Deve
lope
d La
nd77
.160
037
4.28
%
December 12, 2014 [ ]
Figure 10 is a map depicting the area of one address in particular, 5199 Central Oregon Hwy.
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December 12, 2014 [ ]
Table 6 shows the indivdual risks per buffer for 5199 Central Oregon Hwy.
400 ft. buffer 1000 ft. buffer 5000 ft. bufferFire Risk: Number of Acres Percentage Fire Risk: Number of Acres Percentage Fire Risk: Number of Acres Percentage
Address: Low 0.142417 1.24% Low 6.413803 8.90% Low 415.0676654 23.02%5199 Central Oregon Hwy Medium 1.360895 11.81% Medium 22.178906 30.77% Medium 709.583464 39.36%
High 3.898932 33.82% High 15.920769 22.08% High 309.397314 17.16%Cropland/Agriculture 1.403306 12.17% Cropland/Agriculture 18.118306 25.13% Water 0.142417 0.20%Developed Land 4.721387 40.96% Developed Land 9.31577 12.92% Cropland/Agriculture 292.115992 16.20%
Developed Land 67.614003 3.75%
This figure shows that within 400 feet of the address, approximately 1.24% of it in the low risk category, 11.81% is in the medium risk, 33.82% is high, 12.17% is cropland and 40.96% is developed. For insurance
sake, this distribution is important. As the buffer expands, the risk slightly changes. Low become 8.9%, medium is 30%, high becomes 22%. These numbers are further changed by the application of the 5000
foot buffer.
Malheur county overall, according to the Model, is mostly in the Medium category for fire risk. Medium makes up roughly 40.17% of the area. Low makes up for 38.22% and High makes up for 16.45. Very High only takes up .15% of the county. According to the map, this only happens in the far north west edge of
the map. The other 5.01% is made up of Cropland, Developed Land, Quarries/Mines, and Bedrock/Mountain.
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