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.

1

December 12, 2014 [ ]

Figure 1 is a general reference map of a shaded relief of Malheur County, OR

2

December 12, 2014 [ ]

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.

3

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

4

December 12, 2014 [ ]

Figure 2 shows a map of the different vegetation classes in Malheur County

5

December 12, 2014 [ ]

Figure 3 shows the fire risk classifications for Malheur County, OR

6

December 12, 2014 [ ]

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

7

December 12, 2014 [ ]

8

December 12, 2014 [ ]

Figure 4 depicts a map of the slope of Malheur County. The slope is measured in degrees.

9

December 12, 2014 [ ]

Figure 5 depicts the associated risk values with slope. Each range of degrees was given a risk value. Values range from 1-10.

10

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

11

December 12, 2014 [ ]

Figure 7 shows the orientation of the aspect

12

December 12, 2014 [ ]

Figure 8 shows the corresponding risk values as the aspect.

13

December 12, 2014 [ ]

Cartographic Model of Risk Rating for Risk Factors

14

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

15

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.

16

December 12, 2014 [ ]

Figure 9 is the brushfire risk map. This map depicts all the areas within Malheur and their associated risk of fire.

17

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.

18

December 12, 2014 [ ]

Table 5 shows the associated risks per spatial buffer per each of the 6 test addresses

19

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.

21

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.

22