United States

Department of

Agriculture

Forest Service 

Pacific Northwest

Research Station

General Technical 

Report

PNW-GTR-840 

June 2011

Evaluating Soil Risks 

Associated With Severe 

Wildfire and Ground-Based 

Logging

Keith M. Reynolds, Paul F. Hessburg, Richard E. Miller, 

and Robert T. Meurisse DEPAR TMENT OF AGRICULT URE

HAZARD

+

CONSEQUENCE

RISK 

Low Moderate High
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water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

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grasslands, it strives—as directed by Congress—to provide increasingly greater service 

to a growing Nation.

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Authors

Keith M. Reynolds is a research forester, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200 

SW Jefferson Way, Corvallis, OR 97333; Paul F. Hessburg is a research ecologist, 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station, Forestry Sciences Laboratory, 1133 N. Western Ave., Wenatchee, WA 

98801; Richard E. Miller is a retired soil scientist, 3625 93rd Ave. SW, Olympia, 

WA 98512-1101; Robert T. Meurisse is a retired soil program leader, Tigard, OR 

97224.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

c

Abstract 

Reynolds, Keith M.; Hessburg, Paul F.; Miller, Richard E.; Meurisse, Robert 

T. 2011. Evaluating soil risks associated with severe wildfire and ground-based 

logging. Gen. Tech. Rep. PNW-GTR-840. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 27 p.

Rehabilitation and timber-salvage activities after wildfire require rapid planning 

and rational decisions. Identifying areas with high risk for erosion and soil productivity losses is important. Moreover, allocation of corrective and mitigative efforts 

must be rational and prioritized. Our logic-based analysis of forested soil polygons 

on the Okanogan-Wenatchee National Forest was designed and implemented with 

the Ecosystem Management Decision Support (EMDS) system to evaluate risks 

to soil properties and productivity associated with moderate to severe wildfire and 

unmitigated use of ground-based logging equipment. Soil and related data are from 

standard National Cooperative Soil Surveys. We present results from one national 

forest management unit, encompassing 6,889 soil polygons and 69 438 ha. In the 

example area, 36.1 percent and 46.0 percent of the area were classified as sensitive 

to impacts from severe wildfire and unmitigated use of logging equipment, respectively, and there was a high degree of correspondence between the map of units 

sensitive to wildfire and the map of units sensitive to heavy equipment. We discuss 

options for extending the current model and considerations for validating key model 

components.

Keywords: Decision support, wildfire, logging equipment, risk, logic models, 

forest management, soil surveys.
GENERAL TECHNICAL REPORT PNW-GTR-840

d
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

1

Introduction

Wildfire and ground-based logging equipment (hereafter, logging equipment) both 

can change soil properties in ways that may adversely affect subsequent plant growth. 

Here, we describe results of a decision-support application that evaluates risk of both 

agents to forest soils and their functions. This tool is especially useful for guiding 

decisions about rehabilitation and timber salvage after wildfires.

Use of decision-support systems in management of natural resources has 

steadily increased over the past 20+ years. Conceptual models that verbally 

describe ecological risk can be translated to quantitative, site-specific models 

(O’Laughlin 2005). An increasing benefit to natural management agencies of such 

systems is capturing the local knowledge of resource specialists and field practitioners. This paper presents a knowledge-based approach for interpreting risks to 

soil from fire and logging equipment. In the next two sections, we summarize the 

scientific rationale for this application. 

Effects of Wildfire on Forest Soils 

Fuels have accumulated in many dry forests of the inland Northwest owing to human 

settlement and forest management activities (Agee 1998, Hessburg and Agee, 2003, 

Huff et al. 1995). Consequently, many recent fires in dry forests have been extensive, 

high-intensity, stand-replacing events (Agee 1998, Hessburg et al. 2005). Such events 

represent a significant change in fire regime, particularly in the ponderosa pine (Pinus 

ponderosa Dougl. ex Laws.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and 

grand fir (Abies grandis (Dougl. ex D. Don) Lindl.) zones (Franklin and Dyrness 1988), 

where relatively frequent, variably sized, low- and mixed-severity fires naturally 

occurred (Agee 1994, Hessburg et al. 2007a). 

In recent wildfires, fuel consumption is often more complete than under historical fire regimes, and large fuel loads release considerable energy (Huff et al. 1995). 

This fire intensity and energy release can result in significant alterations to soil 

properties and processes, and hence to soil quality (Harvey et al. 1999). Increasingly, forest managers are treating landscapes to reduce fuel loads and potential 

impacts of wildfires on soils, forest resources, human life, and property. Constrained by funding limitations, managers of public lands must prioritize vegetation and fuel treatments. Thus, assessing risk to soils from severe fires may be an 

important consideration for setting treatment priorities. 

Effects of fires on soils are variable, depending on fire intensity, fire duration, 

and amount of heat transferred (Harvey et al. 1994). Large accumulations of fine 

to coarse fuels on the soil surface increase risk that fires may consume nutrients 

and organic matter in aboveground and surface soil layers. This increases risk 

An application that 

evaluates risk to forest 

soils of both wildfire 

and logging equipment 

is useful for guiding 

decisions about 

rehabilitation and 

timber salvage.
GENERAL TECHNICAL REPORT PNW-GTR-840

2

of nutrient loss and decreased productivity. Fire effects on productivity may also 

depend on resiliency—the relative ability of a soil to recover from a stress. Shallow 

and moderately deep soils, in which moisture is limited for significant periods of 

time, and in which amounts of organic carbon and total nitrogen are relatively low, 

tend to display low resilience; on such soils, fire effects are likely to be more severe 

and long lasting (Meurisse 1999). Conversely, deep soils, in which moisture is less 

limited and organic carbon (C) and total nitrogen (N) are more plentiful, are less 

affected and more resilient.

Fire affects soil properties by altering organic matter on or near the surface 

(Neary et al. 2005). Soil physical properties dependent on organic matter, such 

as soil structure, porosity, and aggregation, are affected by heating during fire. 

Another important physical effect of fires is to reduce water infiltration because of 

repellency (DeBano 1991), which can contribute to soil erosion. The extent of fireinduced erosion also depends on fire severity, slope, storm size, and rainfall (Miller 

et al. 2003). For example, sediment yield from areas in a ponderosa pine forest 

after low-severity fire recovered to normal levels within 3 years, after a moderateseverity fire within 7 years, and after high-severity fire within 14 years (DeBano 

et al. 1996). First-year erosion rates after a wildfire in a mixed ponderosa pine/

Douglas-fir forest in eastern Oregon were more than twice as great on 60-percent 

slopes as on 20-percent slopes (Robichaud and Brown 1999). 

Soil chemical properties may also change with surface heating because organic 

C is volatilized at relatively low temperatures (280 °C, DeBano 1991). Nitrogen 

and sulfur are also sensitive because they volatilize between 200 and 375 °C, whereas 

potassium and phosphorus are moderately sensitive, volatilizing near 774 °C. 

Soil micro-organisms may be killed directly by heating or be indirectly affected 

by organic matter alteration. Among the more sensitive micro-organisms are 

mycorrhizae (DeBano 1991, Neary et al. 2005); in fact, intense wildfire affects 

mycorrhizal community composition and structure more adversely than does 

clear-cutting (Visser and Parkinson 1999). Soil micro-organism activity and related 

nutrients are more concentrated and vulnerable near the soil surface where fire 

exclusion has created higher accumulation of organic debris than in the past when 

there were frequent fires (Harvey et al. 1999). 

Several studies of fire effects on soils were conducted in our study area, the 

Wenatchee-Okanogan National Forest. For example, Helvey et al. (1985) reported 

large increases in stream sediments 2 years after high-severity fires in the Entiat 

drainage in 1970. They concluded that most sediment was from debris torrents after 

an intense rainfall on heavy snowpack in 1972, and from dry ravel on steep slopes 

near streams. After those same fires in the Entiat Watershed, Grier (1975) measured 

Resiliency is the 

relative ability of 

a soil to recover 

from a stress.

Several studies of 

fire effects on soils 

were conducted in 

our study area, the 

Wenatchee-Okanogan 

National Forest.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

3

nutrient distribution in and leaching from soils that averaged 2 m depth and contained strata of ash and pumice ejecta from Glacier Peak. Large nutrient losses 

occurred from volatilization during the fire; other mineralized cations leached 

rapidly into the soil, where most were retained. Grier reported that only 3 percent 

of the N in the original forest floor remained after the fire, that N of the A1-horizon 

was reduced by one-third, and that a heavy residue of fuel ash on the soil surface 

initially contained high concentrations of base cations, much of which leached into 

the soil after rain and snowmelt. With more intense rains or less porous soils, these 

nutrients would be more susceptible to loss in runoff. 

Baird et al. (1999) examined nutrient pools in soil and aboveground debris in 

ponderosa pine/Douglas-fir forest and lodgepole pine (Pinus contorta Dougl. ex 

Loud.)/Engelmann spruce (Picea engelmannii Parry ex. Engelm.) forests severely 

burned in 1994. In both forest types, fire initially reduced total C in the forest floor 

by 90 percent and total N by 95 percent. One year after the fire, soil C and N in the 

mineral soil remained reduced by 30 and 46 percent, respectively. 

Effects of Logging Equipment on Forest Soils

The National Forest Management Act of 1976 instructs managers to avoid actions that 

significantly and permanently reduce the productive capacity of forest soils (USDA 

Forest Service 1983). Two key questions emerge from this instruction: What types, 

severities, and extents of soil disturbance significantly reduce soil productivity? How 

long must the reduction in capacity persist before it is considered permanent? Although 

tree growth is a common measure of soil productivity (Powers et al. 1999), reduced tree 

growth after disturbance may or may not persist. 

Soil disturbances caused by logging equipment include compaction, churning (incorporating organic debris), topsoil removal and displacement, and topsoil 

mixing with subsoil. For example, compaction and churning reduce amount and 

continuity of macropore space, and thereby reduce exchange of gas and moisture. 

Reductions in volume and continuity of large pores also reduce infiltration rates 

(Greacen and Sands 1980), slow saturated water flux, reduce gaseous flux (Grable 

1971), increase thermal conductivity and diffusivity (Willis and Raney 1971), and 

increase soil resistance to penetration (Sands et al. 1979).

Because soils differ in various properties, they differ both in their initial resistance to these impacts and subsequent rate of recovery. Differences in soil properties are used by soil scientists to predict or to make interpretations about the hazard 

or likelihood of these types of soil disturbance occurring when heavy equipment 

is used. Such interpretations imply consequences for soil functions, and by logical 

extension, consequences for plant growth. In fact, consequences for plant growth 

also depend on interactions with climate and other factors. 

The National Forest 

Management Act of 

1976 instructs 

managers to avoid 

actions that significantly and permanently 

reduce the productive 

capacity of forest soils.
GENERAL TECHNICAL REPORT PNW-GTR-840

4

Compared to visual and measured effects on soil properties, consequences of 

soil disturbance to subsequent tree growth are not as well predicted, researched, 

and experimentally controlled (Greacen and Sands 1980, Miller and Anderson 

2002, Wronski and Murphy 1994). For example, trees planted on skid trails and 

landings are subjected to the most severely disturbed soils, yet these altered soil 

properties do not always result in poorer tree growth or survival (Firth and Murphy 

1989, Greacen and Sands 1980, Miller et al. 1996, Senyk and Craigdallie 1997). In 

some coarse-textured soils, seedling performance can be better on compacted than 

on undisturbed soil because more moisture is retained in the smaller pores created 

by compaction (Gomez et al. 2002). Site-to-site differences in climatic stress also 

weaken generalizations about tree performance on disturbed soil. 

Among inland Northwest forests, we found data for 20 locations for which tree 

growth was measured on logging-disturbed and nondisturbed soils: three locations 

in northern Idaho (Clayton et al. 1987); one location near Bend, Oregon (Cochran 

and Brock 1985); and one location each near Priest River, Idaho, and Diamond Lake 

Ranger District, Oregon (Page-Dumroese, personal communication1); one location 

near Headquarters, Idaho (Roche 1997); 12 locations in the Blue Mountains, Oregon 

(Geist et al. 2008); and one location on the Ochoco National Forest, Oregon (Froehlich 

et al. 1979). Based on site characteristics and tree response at these locations, Miller 

and Anderson (2005) calibrated a model to assign risk to tree growth after heavy 

equipment usage. 

Evaluating Consequences of Wildfire and Logging

Equipment Impacts on Soils

In this study, we used the Ecosystem Management Decision Support (EMDS) 

system (Reynolds et al. 2003) to evaluate anticipated risks to soils associated with 

moderate to severe wildfire and with heavy equipment used for harvesting and site 

preparation. The EMDS is an extension to ArcMapTM (Environmental Systems 

Research Institute, Redlands, CA) that integrates logic-based modeling into the 

geographic information system (GIS) environment. Since 1997, this modeling 

system has been used around the world for a variety of applications in natural 

resource management. For example, applications of EMDS in the Western United 

States include decision support for severe wildfire danger evaluation and treatment 

planning in central Utah (Hessburg et al. 2007b), landscape evaluation and restoration planning in eastern Washington (Reynolds and Hessburg 2005), biodiversity 

conservation in the Sierra Nevada (White et al. 2005), watershed assessment in 

1 D. Page-Dumroese. 2004. Personal communication. Research soil scientist. Rocky Mountain 

Research Station, 1221 South Main, Moscow, ID 83843.

The Ecosystem 

Management Decision 

Support system 

integrates logic-based 

modeling into the 

geographic information 

system environment.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

5

northern California (Bleier et al. 2003), landscape-change analysis in eastern 

Washington (Hessburg et al. 2004), and watershed monitoring of the Northwest Plan 

Aquatic Conservation Strategy (Reeves et al. 2003). 

In addition, PBS Engineering and Environmental (2003) developed an EMDS 

application to evaluate wildfire risk to soils that encapsulated many of the considerations discussed above. Here, we revise that PBS application to allow easier updating of key data elements such as fire regime and fuel condition class, and add an 

evaluation of risk associated with using logging equipment, based on an equipmentuse model developed by Miller and Anderson (2005). Similar to the PBS application, 

the equipment-use model encapsulates many considerations presented above. 

In the remaining text, we (1) describe logic models used to evaluate risks associated with wildfire and logging equipment, (2) present results for an example soil 

survey area, and (3) discuss considerations for model validation and for extending 

the current model to incorporate other threats. 

Materials and Methods

Study Area

The study area is located within the Okanogan-Wenatchee National Forest. Soils 

within the forest are dominantly Andisols, Inceptisols, and Alfisols with cryic, 

frigid, and mesic temperature regimes and a xeric moisture regime. Spodosols occur 

at higher elevations. We evaluated soil risks in all soil polygons classified as forest 

land within an area of about 888 000 ha (fig. 1). All forested soils in this area are 

influenced by or are dominantly from volcanic ash, cinders, and pumice. Volcanic 

ash and tephra deposits are common on or near the mineral surface of most forested 

soils throughout the interior Columbia River basin (ICRB). These materials, originating from Mount Mazama and Glacier Peak eruptions, impart unique properties 

that influence vegetation communities of the ICRB (Harvey et al. 1994, Meurisse 

1987).

Great variety and complexity of soil properties and external features exist 

throughout the study area. For purposes of illustration, we present an example that 

is limited to the Okanogan East soil survey area (fig. 1). Within this example area of 

about 69 438 ha, soils are forming in volcanic ash over glacial till or granitic bedrock 

and are moderately deep to very deep (50 cm or more). Most soils are on slopes less 

than 45 percent, and are distributed across xeric moisture regimes and frigid 

to mesic temperature regimes. Soil textures are mostly fine sandy loams, loams, and 

silt loams with coarse fragment (>2 mm) content ranging from few to more than 

35 percent. Organic matter content in the top 30 to 40 cm usually ranges from 1.5 

to 3 percent by weight. 

Volcanic ash and 

tephra deposits are 

common on or near 

the mineral surface 

of most forested soils 

throughout the interior 

Columbia River basin.
GENERAL TECHNICAL REPORT PNW-GTR-840

6

Logic Models

Logic models for evaluating risk to soils from severe wildfire (fig. 2) and logging 

equipment (fig. 3) were implemented with the NetWeaver Developer system (Miller 

and Saunders 2002). A NetWeaver model represents an evaluation as a network of 

topics, each of which evaluates the strength-of-evidence for a proposition associated 

with the topic. The two logic models (figs. 2 and 3) are presented here in reduced 

form, in which three logic operators (and, or, union) have been omitted for simplicity. 

Elementary topics (those occurring at the lowest level of the network, tables 

1 and 2), evaluate observed data (tables 3 and 4) against functions (hereafter, 

membership functions) that map the observed value into a measure of strength-ofevidence for the elementary topic. Evidence from elementary topics is combined by 

using logic operators to evaluate higher order topics that depend on those elementary topics. If one thinks of a higher order topic as testing a conclusion, then the 

propositions of its elementary topics represent its premises. For example, the two 

elementary topics, fire hazard and soil resilience, represent premises of the higherorder topic, nutrients volatilized (fig. 2). 

Figure 1—General location of project area in eastern Washington. Labeled features are soil survey 

areas on the Okanogan-Wenatchee National Forest. Results are presented for the Okanogan East unit. 

The feature labeled “Detail area” indicates the portion of Okanogan East displayed in figure 6.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

7

Figure 2—Logic tree for risk to soils from moderate to severe wildfire. Each topic evaluates a proposition (table 1). Topics on the far 

right are elementary topics and evaluate data. Elementary topics are indicated in bold in table 1. Data requirements for each elementary 

topic are listed in table 1 and defined in table 3.

Figure 3—Logic tree for risk 

to soils from ground-based 

equipment. Topics on the far 

right are elementary topics that 

evaluate data. Elementary topics 

are indicated in bold in table 

2. Data requirements for each 

elementary topic are listed in 

table 2 and defined in table 4.

Higher order topics may themselves serve as premises of still higher order 

topics. So, for example, the topics surface erosion and dry ravel are premises of the

 soil loss topic. The complete logic structures for wildfire risk (fig. 2) and logging 

equipment risk (fig. 3) can thus each be construed as a formal logical argument 

leading to a conclusion about risk from wilfire or logging equipment. Although 

detailed specifications of elementary topics have been omitted for brevity, the 

complete model specification may be downloaded from ftp://ftp2.fs.fed.us/

incoming/pnw/CFSL/Reynolds/soil%20risk/soilImpact.nw.

Erosion hazard

Fire hazard

Landtype association runoff

Root strength

Soil climate

Soil cohesion

Soil resilience

Surface erosion

Dry ravel

Nutrients volatilized

Nutrient runoff

Wood recruitment

Soil loss

Fire impact

Soil quality

Microclimate

Macroclimate

Soil horizons

AplusB depth

Rooting depth

Slope class

Tephra

Equipment impact

Higher order topics 

may themselves serve 

as premises of still 

higher order topics. 
GENERAL TECHNICAL REPORT PNW-GTR-840

8

Table 1—Logic topics for risk of soil impacts from moderate to severe wildfirea

Topicb Proposition Data inputsc

Dry ravel Soil lost to dry ravel is low.

Erosion hazard Sheet and rill erosion are low. ehrSoil

Fire hazard Vegetation cover on slopes remains good. fireReg 

 fcc

Fire impact Risk of soil degradation is low.

Landtype association (LTA) runoff Runoff associated with landtype association is low. slopeClass

 hydro

Nutrient runoff Ash loss (from burned organic matter) with runoff is low.

Nutrient volatilization Loss of soil nutrients and organic matter is low.

Root strength Root strength is not impaired. slopeClass

 aspect 

 rtst

Soil climate Soil climate is mild. slopeClass

 aspect

climt

Soil cohesion Soil cohesiveness is not impaired. slopClass

 cohe

Soil loss Soil loss owing to physical removal from the site is low.

Soil quality Risk to soil quality from biochemical effects is low.

Soil resilience Soil resilience is high. slopeClass

 aspect

resil

Surface erosion Loss of soil to sheet and rill erosion is low. 

Wood recruitment Recruitment of coarse woody material is high.

a The structure of the logic model is displayed in figure 2.

b Topics displayed in bold text are elementary topics in figure 2.

c Only elementary topics evaluate data. Data elements listed in this column are defined in table 2. 

 Data elements displayed in bold indicate data used to switch the logic path according to context.

Table 2—Logic topics for risk of reduced tree growth after worst-case equipment operationsa

Topicb Proposition Data inputsc

AplusB depth Thickness of A and B horizons indicates low risk. depthSoil

Equipment impact Thickness and tephra size indicate low risk. tephraPres

Macroclimate Macroclimate is mild, indicating low risk. pvt

Microclimate Microclimate is mild, indicating low risk. aspect

slopeClass

Rooting depth Deep depth of fine roots indicates low risk. depthRoot

Soil horizons Characteristics of soil horizons indicate low risk. depthA

 depthSoil

fragClassA

fragClassB

 textureA

textureB

Slope class Slope of soil unit does not contribute to risk. slopeClass

Tephra Condition of soil tephra indicates low risk. tephraPres

a The structure of the logic model is displayed in figure 3.

b Topics displayed in bold text are elementary topics in figure 3.

c Only elementary topics evaluate data. Data elements listed in this column are defined in table 4.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

9

Table 3—Definition of data elements used by the logic model to assess wildfire impacta

Data input Definition Source

aspect Indicator for north versus south slopes. LCITb

climt An indicator for rate of vegetation recovery following wildfire, interpreted from seven broad

vegetation groups associated with the soils. NCSSc

cohe Soil cohesion classified as low, moderate, or high, based on clay content of soil (< 18 percent,

18–35 percent, and >35 percent, respectively). NASISd

ehrSoil Erosion hazard classified as low, moderate, high, or very high, based on inherent erodibility of

the soil (Kw) and slope classes. NASIS

fireReg Modal fire regime of a soil map unit (polygon). LCIT

frcc Proportion of map unit with fire regime condition class rating > 1. LCIT

hydro Surface runoff potential classified as slow, moderate, or flashy, based on an interpretation of LTAse

landtype association geomorphology, stream density and pattern, soil material, and slope.

resil Soil resilience classified as low, moderate, or high, based on thickness of surface layer, soil

depth, and organic matter content. NASIS

rtst Root strength classification, interpreted from seven broad vegetation groups associated with

the soils. NCSS

slopeClass Classes 1–4 represent slope intervals of 15 percent from 0 to 60 percent slopes. Class 5 was

assigned to slopes > 60 percent. LCIT

a Definitions of data elements in table 1.

b Landscape and Climate Interactions Team (Forestry Sciences Laboratory, Pacific Northwest Research Station, 

USDA Forest Service, Wenatchee, WA).

c National Cooperative Soil Surveys.

d National Soil Information System.

e Landtype Associations of North Central Washington, preliminary report (1/12/2000).

Table 4—Definition of data elements used by the logic model to assess equipment riska

Data input Definition Source

aspect Indicator for north versus south slopes. LCITb

depthA Depth of A (or AB) horizon (inches). NASISc

depthRoot Depth of fine and very fine roots (inches) at which intensity is ≥ 6 roots in-2. NCSSd

depthSoil Depth of A and B horizons (inches). NASIS

fragClassA Coarse fragments in A horizon classified as low (0–35 percent), moderate (36–60 percent), NASIS

high (> 60 percent).

fragClassB Coarse fragments in B horizon classified as low (0–35 percent), moderate (36–60 percent),

high (> 60 percent). NASIS

pvt Potential vegetation type. LCIT

slopeClass Slope of soil map unit classified into one of five classes (0–15, 16–30, 31–45, 46–60, LCIT

> 60 percent).

tephraPres Thickness and type of tephra present (ash mantle, mixed ash-soil, mixed pumice-soil, NASIS and

pumice mantle, or none). NCSS

textureA Texture of A (or AB) horizon classified as sandy, loamy, or clayey. NASIS

textureB Texture of B (or BC) horizon classified as sandy, loamy, or clayey. NASIS

a Definitions of data elements in table 3.

b Landscape and Climate Interactions Team (Forestry Sciences Laboratory, Pacific Northwest Research Station, 

USDA Forest Service, Wenatchee, WA).

c National Soil Information System.

d National Cooperative Soil Surveys. 
GENERAL TECHNICAL REPORT PNW-GTR-840

10

Submodel for wildfire risk— 

This model (fig. 2, tables 1 and 3) was adapted from a version designed by PBS 

Engineering and Environmental (2003) that provided the logic operators and 

membership functions. Changes to that original version include (1) allowing for 

aspect adjustment in the soil-climate topic, (2) revising the wildfire risk topic by 

replacing the forest cover type look-up table with one based on fire regime and fuel 

class (table 3), and (3) splitting the original 30–60 percent slope class interval into 

intervals of 30–45 and 45–60 percent. 

Submodel for equipment risk— 

The original model for logging equipment risk was implemented as a spreadsheet 

application by the third author. The model considered the two components of risk: (1) 

hazard, if rubber-tired skidders were used in unrestrained scheduling and access, and 

(2) consequences for subsequent tree growth over a range of climatic stress created 

by macro- and microclimate. Recognizing the wide variation among soils and their 

inherent characteristics, we assumed that potentially high-hazard equipment usage is 

mitigated by soil characteristics that resist initial compressive and shearing forces of 

heavy equipment; these include properties promoting rapid infiltration and percolation 

of water, and large content of coarse fragments in the top foot (0.305 m) or more of soil 

that enhance load-bearing capacity. 

In assessing consequences for tree growth, we used two major assumptions. 

First, each soil was considered like a bank account; small withdrawals from a deep, 

fertile soil account had small effects, and conversely, large withdrawals from a 

shallow, infertile soil account had large consequences. In short, soils having many 

characteristics that supported vigorous plant growth were assumed to be less 

affected by soil compaction or displacement than soils with few supportive characteristics. For example, the productivity of soils with thick volcanic ash or pumice 

mantles, or thick A- or AC-horizons were assumed to be less likely to lose productive capacity than those with shallow tephra mantles or topsoils. Further, soils 

with few rock fragments were assumed to be less affected by soil degradation than 

shallow, rocky (skeletal) soils. The second assumption about consequences was that 

impacts on soil properties and processes were more likely to reduce subsequent tree 

growth in stressful climatic conditions than in favorable moisture and temperature 

environments. A priori, we assumed more risk of reduced vegetative growth in 

climatically harsher areas because: (1) vegetative growth is more dependent on soil 

conditions, which could be degraded by fire or heavy equipment and (2) more time 

may be needed for soil and vegetation to recover. Specific to the ICRB, equipment 

impacts to soils on north aspects (favorable microclimate) or in macroclimates with 

more favorable temperature and precipitation have least consequence for subsequent 

The original model 

considered the two 

components of risk: 

(1) hazard, and (2) 

consequences for 

subsequent tree 

growth.

Soils having many 

characteristics that 

supported vigorous 

plant growth were 

assumed to be 

less affected by 

soil compaction or 

displacement than 

soils with few 

supportive 

characteristics. 
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

11

tree growth. Such assumptions based on general knowledge will remain necessary 

until long-term tree response to soil disturbance is reliably measured and reported 

for a wide range of soil types and climatic conditions. 

The original spreadsheet application (Miller and Anderson 2005) was translated 

into a logic model to enable complementary evaluation with the fire-risk model. 

Although the two risk models were designed independently, they shared several 

data inputs and similar assumptions about soil and plant response to fire and equipment. Thus, the two original models were revised and integrated as submodels into 

a single model.

Examples of Logic Processing

Figures 2 and 3 and tables 1 through 4 provide a broad overview of model logic in 

terms of topics treated and data inputs required. A comprehensive description of the 

logic is beyond the scope of this paper, but full documentation can be found at ftp://

ftp2.fs.fed.us/incoming/pnw/CFSL/Reynolds/soil%20risk/html.zip. However, here we 

illustrate some basic methods of logic processing using examples from the wildfire 

impact submodel. In the following, keep in mind that the logic is always testing for 

a condition (strength-of-evidence) of low risk.

Fire hazard is evaluated in terms of fireReg and frcc (tables 1 and 3). The 

contribution of fireReg (table 3) to fire hazard is evaluated in a lookup table, in 

which values of fireReg (1, 2, and 3, corresponding to low, moderate, and strong 

departure from historical fire regime, respectively) are assigned values for strengthof-evidence of 1, 0, and -1, respectively. The lookup table is a part of the model 

specification (ftp://ftp2.fs.fed.us/incoming/pnw/CFSL/Reynolds/soil%20risk/

soilImpact.nw), and can be edited by users in NetWeaver. The contribution of frcc

(table 3) to fire hazard is evaluated by a membership function, in which frcc = 0 

evaluates to 1, frcc = 1 evaluates to -1, and intermediate values are interpolated by 

a linear function. The membership function for frcc also represents a model specification that can be edited by users if desired. The aggregate contribution of fireReg

and frcc to fire hazard is represented by a union operator, which in NetWeaver semantics means that fireReg and frcc incrementally contribute to the overall prediction 

of fire hazard. An alternative way to think about the union operation is that its 

arguments (in this case fireReg and frcc) are additive and compensatory, meaning 

that a negative result on one argument can be offset by a positive result on the other.

Predictions of landtype association (LTA) runoff, root strength, and soil climate 

(table 1) are slightly more complex than the first two examples. In each of these 

three evaluations, one of four or five possible logic pathways is selected, depending 

on slopeClass. Each logic pathway corresponding to a slopeClass is then evaluated 

Impacts on soil 

properties and 

processes were 

more likely to reduce 

subsequent tree 

growth in stressful 

climatic conditions 

than in favorable 

moisture and 

temperature 

environments.
GENERAL TECHNICAL REPORT PNW-GTR-840

12

with a look-up table. For example, similar to fireReg, values for hydro, rtst, and 

climt (tables 1 and 3) are translated into strength-of-evidence, but now there are 

separate look-up tables depending on slopeClass. Predictions for root strength and 

soil climate are both slightly more complex than that for LTA runoff, because both 

include an additive adjustment for slope aspect (table 1), with south aspects evaluating as less favorable than north aspects.

Data Sources 

For wildfire-risk evaluation (table 3)— 

Four data inputs were provided by the Landscape and Climate Interactions Team 

(Pacific Northwest Research Station, Forestry Sciences Laboratory, Wenatchee, 

Washington); another six were developed from interpretations of National Soil 

Information System (NASIS) data by PBS Engineering and Environmental (2003). 

Seven vegetation groups were obtained from the woodland interpretations table 

and map unit descriptions associated with each soil survey. Erosion hazard was 

derived from the K-factor (from NASIS) and soil properties including percentage 

silt and very fine sand, percentage organic matter, soil structure, and permeability. 

The amount of rock in the soil modified the K-factor; for example, high rock content 

reduced the value of K. Runoff rates were derived from a map of Landtype Associations (USDA FS 2004) that were interpreted from stream density, geomorphology, 

soil materials, type of bedrock, and precipitation characteristics. 

Soil resiliency was assigned to reflect the ability of the soil to recover its 

functions after disturbance. Low resilience values were assigned to shallow soils 

with thin surface layers and organic matter contents < 1.5 percent, and high values 

were assigned to deep and very deep soils with thick surface layers and organic 

matter contents > 4 percent. Moderate values were assigned to intermediate cases. 

Root strength was derived from the seven broad vegetation groups. Ponderosa pine 

was considered low risk because of normally abundant, well-distributed roots, and 

a strong taproot. Grand fir and lodgepole pine were assigned high risk because of 

many fine roots near the surface that could be injured or killed by fire. 

For the equipment risk evaluation— 

Data sources included soil and site factors that were available as modal profile characteristics or descriptions from National Cooperative Soil Surveys (table 4). 

We used potential vegetation types (PVT) as surrogates for macroclimate. 

A PVT indicates the most shade-tolerant conifer that would occur in the absence 

of disturbance (Arno et al. 1985, Steele and Geier-Hayes 1989). In a broad-scale 

assessment of the ICRB, 88 PVTs were mapped across the basin (Hann et al. 1997). 

Hessburg et al. (2000) combined this map of PVTs along with other GIS layers 

We used potential 

vegetation types 

as surrogates 

for macroclimate.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

13

to develop their ecoregions. The additional layers represented mean annual temperature (°C), total annual precipitation (mm), and averaged annual daylight solar 

radiative flux (W/m2) from modeled raster maps for the 1989 weather year, which 

was considered an average weather year (Thornton et al. 1997). 

We used the PVT and climate data from the Hessburg et al. (2000) ecoregionalization to calculate the percentage area of 30 forested PVTs in each of the 

precipitation, temperature, and solar radiation classes. For most PVTs, ≥ 60 percent 

of their area was associated with a single, combined precipitation-temperature-solar 

flux class; a few PVTs tolerated a wider range of solar flux values. Based on these 

classes, a climate value was assigned to each PVT in the equipment risk submodel. 

Data Processing and Analysis

Most data for evaluating risk to soil from wildfire were assembled from the NASIS 

database and associated to soil polygons of the Okanogan-Wenatchee National Forest 

(PBS Engineering and Environmental 2003). Polygons not classified as commercial 

forest by Miller and Anderson (2005) were deleted from the analysis to simplify and 

focus interpretations for subsequent analysis. Data for evaluating risks from logging 

equipment were tabulated by soil type either as a single mapping unit or as a component within soil complexes that contained two or more soil types. At least 50 percent of 

the original polygons consisted of complexes with two or more soil types that were 

not mapped individually. For each complex, we assigned the risk rating of the most 

extensive component (soil type). In most complexes, this was also the highest risk 

component, so our ratings for equipment may have a conservative bias. To produce 

more environmentally homogeneous polygons, soil polygon delineations were 

refined by intersecting that soil map with a map of slope and aspect (north versus 

south) polygons derived from a digital elevation model. With this refinement, we 

assessed those portions of the original soil polygon that had least- vs. most-favorable microclimate for soil development, and soil recovery and tree growth after 

equipment usage. 

Results

Of the 69 438 ha evaluated in the Okanogan East soil survey area, almost all measures 

of strength-of-evidence for low wildfire impact fell within the range of -0.6 to 0.2 

(fig. 4). Note that the potential range lies between -1 (weak strength-of-evidence) 

and +1 (strong evidence). Consequently, the five intervals on the abscissa (fig. 4) can 

be interpreted as classes corresponding to very high, high, moderate, low, and very 

low risk associated with wildfire, progressing from left to right. Assuming the very 

high and high risk classes to be generally indicative of wildfire sensitivity, 21 196 ha 

At least 50 percent 

of the original polygons consisted 

of complexes with 

two or more soil types.
GENERAL TECHNICAL REPORT PNW-GTR-840

14

Figure 5—Area (ha) of expected impacts to tree growth from unmitigated equipment 

operations. The five intervals on the x axis can be interpreted as risk classes corresponding 

to very high, high, moderate, low and very low risk, progressing from left to right. Area (thousand ha)

40

30

20

10

 0

Strength of evidence

Area (thousand ha)

50

40

30

20

10

 0

Strength of evidence

Figure 4—Area (ha) of expected risk to soil quality from wildfire owing to nutrient volatilization, nutrient runoff, and reduced organic matter inputs from recruitment of woody 

debris. The five intervals on the x axis can be interpreted as risk classes corresponding to 

very high, high, moderate, low and very low risk, progressing from left to right.

< -0.6 -0.6 to -0.2 -0.2 to -0.2 -0.2 to -0.6 > 0.6

< -0.6 -0.6 to -0.2 -0.2 to -0.2 -0.2 to -0.6 > 0.6

(30.5 percent) can be considered sensitive to wildfire in the Okanogan East soil 

survey area.

The range of response in strength-of-evidence for low risk of logging equipment was similar (fig. 5). Following the same classification scheme for strengthof-evidence as above, 31 913 ha (46.0 percent) were predicted to be sensitive to 

reduced tree growth after worst-case (unmitigated) use of logging equipment. A 

modest proportion of soil polygons evaluated as relatively insensitive (risk classes 

low and very low) to logging equipment.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

15

Beyond this simple aspatial summarization of results, there is the obvious question: “Where are the most sensitive soil polygons within the landscape of the East 

Okanogan soil survey area?” Because the analysis area contained 6,889 relatively 

fine-scale polygons, it was not feasible to present a readable map of the spatial 

dispersion of sensitive soils. To illustrate the power of spatial information, however, 

we focused on a small example area, indicated as “Detail area” in figure 1, which 

includes 656 soil polygons with forest cover. The two maps of soil polygons sensitive to wildfire (fig. 6A) and logging equipment (fig. 6B) appear remarkably similar; 

indeed the reader may need to carefully compare the two maps to be convinced 

that they are different. The degree of similarity in the two maps was not anticipated, 

especially considering that each is derived from a submodel that was developed 

mostly independent of the other. Although the two submodels share a few variables 

in common, most of their data inputs are distinctly different (see tables 1 and 2).

Figure 6—(A) Detail area from figure 1, indicating soil units sensitive to wildfire. (B) Detail area 

from figure 1, indicating soil units sensitive to ground-based equipment. A map unit was classified 

as sensitive if the strength-of-evidence for low impact was ≤ -0.2.

Discussion

Our decision-support application could help assess the environmental consequences of 

postwildfire rehabilitation and timber salvage as well as guide management of forest 

fuels (McCaffrey and Graham 2007). Several recently developed methods help managers evaluate prescribed burning and thinning options for reducing current and future 

fire hazard (for example, Johnson et al. 2007). However, risk of these prescriptions to 

soil productive capacity and subsequent tree growth was not considered, despite these 

productivity considerations being required by environmental impact assessments.
GENERAL TECHNICAL REPORT PNW-GTR-840

16

Risk Associated With Wildfire

The majority of the Okanogan East analysis area is rated moderate risk for moderate and severe fires (fig. 4). About 30 percent of the analysis area is high or very 

high risk. Only a minor amount is rated low or very low risk. Although some soils 

have total depths >50 cm to >150 cm, 84 percent of the example area has soils with 

<50 cm of developed soil (A- and B- horizons) (table 5). Generally, soils in this area 

have a surface layer of volcanic ash that is about 20- to >35-cm thick. Such soils 

often have organic matter contents >1.5 percent in the surface layer. Also, more 

than 70 percent of the area is on slopes of ≤30 percent (table 5). These properties 

contribute to moderately resilient soils, and to moderate or lower erosion hazards. 

When these favorable soil conditions are combined with current fire regime and 

fuel condition classes that are within historical ranges or are only moderately 

altered from historical conditions, there is a strong likelihood that fire risk would 

be moderate or low, as our results demonstrate. 

More than 60 percent of the area has a southerly aspect (90 to 270 degrees, 

table 5). Where soils are shallow (<50 cm depth), have high coarse-fragment 

contents, and are on slopes exceeding 30 percent, this combination of factors 

contributes to high and very high risk for moderate and severe fires. This is 

especially true if they also happen to have fire regime and fuel condition classes 

that are moderately or significantly altered from historical ranges.

Table 5—Distribution of soil polygons and area, by aspect, slope, and soil depth

 

Aspect Slope Thickness of A+B horizons Polygonsa Area

b

Percent Centimeters Percent Percent

North ≤ 30 ≤ 50 20.32 18.79

> 50 4.72 5.04

30–60 ≤ 50 8.52 8.52

> 50 1.74 2.31

> 60 ≤ 50 0.44 0.59

> 50 0.19 0.17

South ≤ 30 ≤ 50 39.63 42.29

> 50 6.47 6.96

30–60 ≤ 50 14.33 12.72

> 50 2.64 1.94

> 60 ≤ 50 0.45 0.30

> 50 0.55 0.39

 Total 100.00 100.00

a Total count of soil polygons in the study area was 6,889.

b Total study area was 69 438 ha.

The majority of 

the Okanogan East 

analysis area is rated 

moderate risk for 

moderate and 

severe fires.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

17

Risk Associated With Logging Equipment

About 70 percent of the analysis area is on slopes ≤30 percent and therefore accessible for ground-based logging equipment. Yet nearly half of the example area is 

rated high or very high risk for reduced growth after unmitigated use of logging 

equipment (fig. 5). Risk ratings for unmitigated or “worst-case” use of logging 

equipment can alert users about the relative need for mitigative measures, such as 

avoiding use of rubber-tired skidders, scheduling logging when soils are dry or 

covered with deep snow, stopping equipment operations where soil is wet or very 

moist, placing a protective layer of logging slash before trafficking, designating 

skid trails or optimizing the yarding pattern. Risk-rating models should also help 

to implement project plans and assist in addressing soil issues during the NEPA 

(National Environmental Policy Act) process.

As previously stated, there is considerable overlap of risk to soil functions from 

fire and logging equipment in this area. Several factors could cause this overlap. 

Nearly two-thirds of the example area has south aspect (table 5), which indicates 

less favorable microclimate for soil development and plant growth and greater fire 

hazard and consequences than on north aspects. Depth of soil development, which 

we defined as the combined depths of the A- or AC- plus the B-horizon, is 50 cm 

or less on about 84 percent of the area. Based on propositions and premises of our 

modeling, these characteristics support an inference of high risk for both severe 

fire and unmitigated use of heavy equipment. Although we gauged consequences 

simply in terms of potential impacts on growth of commercial tree species, we 

assume that most other plants or soil organisms would respond in a similar direction and magnitude.

A Recent Application of Our Model

We applied our model after the 2006 Tripod Fire that burned more than 70 800 ha 

(175,000 acres) near Winthrop, Washington. The model displayed several classes of 

risk, by contrasting colors, on two separate maps. One map provided an assessment 

of risk for erosion and loss of soil productivity within the burned area; the other 

map identified estimated risk of reduced tree growth after salvage logging with 

rubber-tired skidders and without mitigative practices to reduce impacts. 

Soil-Climate Interaction Affects Vegetative Response

to Disturbance

We assume that risk of growth reductions should differ by climatic areas or ecoregions. A priori, we assume more risk to vegetative growth in climatically harsher 

areas because (1) longer periods may be needed for soil and vegetation to recover 

About 70 percent of 

the analysis area is on 

slopes ≤30 percent and 

therefore accessible 

for ground-based 

logging equipment.

Risk ratings for 

unmitigated or 

“worst-case” use of 

logging equipment 

can alert users about 

the relative need for 

mitigative measures.

We assume that risk 

of growth reductions 

should differ by 

climatic areas 

or ecoregions, 

with more risk to 

vegetative growth in 

climatically harsher 

areas.
GENERAL TECHNICAL REPORT PNW-GTR-840

18

and (2) vegetative growth is more dependent on soil conditions, which could be 

degraded by heavy equipment. Important differences in climate exist within and 

among soil survey areas, as indicated by differences in soil temperature and moisture classes that are used to classify soils taxonomically. Although soil taxonomic 

information (e.g., xeric vs. udic moisture or cryic vs. frigid temperatures) could be 

added to our database, we did not do so because of resource limitations. Perhaps 

climatic differences are sufficiently captured by differences in modal soil characteristics, e.g., shallow, low-fertility soils in which harsh climate slows natural soil 

development and resilience after disturbance, and by the potential vegetation types 

that we used as a surrogate for gross climate.

Our ratings are based on modal characteristics within soil polygons. Most 

polygons include two or more taxonomically different soil series or types. In some 

polygons, taxonomic differences may have little significance for predicting the 

consequence of interest (erosion, dry ravel, resilience, tree response). Before using 

risk ratings or other interpretations derived from soil surveys, one should verify by 

field inspection the location and actual characteristics of these taxonomically different soils. Note also that our stratifying the original soil polygons by slope-aspect 

classes was intended to identify strata of differing microclimate that can affect soil 

development and tree growth response.

Practical Issues

The EMDS application for evaluation of soil risks from wildfire and use of heavy 

equipment requires Microsoft Windows 2000® or later, ArcGIS 9.0®, and the EMDS 

3.12 extension to ArcMap (http://www.institute.redlands.edu/emds/). The ease with 

which this decision-support technology can be transferred to potential users is context dependent. Forest managers on the Okanogan-Wenatchee National Forest can 

easily learn to use this application with a few training hours. Additionally, most data 

fields rarely need changing, but a few will need to be updated sometime in the next 

few years (for example, fire-regime condition class, table 2). Updating data fields will 

require a few days of geoprocessing by a GIS technical specialist. 

Managers on the forest also may want to modify components of the logic model 

as knowledge or data quality increase. Training in the design of logic models 

requires about 2 days, thus representing a slightly more substantial effort to transfer 

technology and improve predictions based on new information. Although this application of EMDS can be extended to new geographic areas within the ICRB, the 

effort to do this is substantial, because of the need to build databases and perform 

2 The use of trade or firm names in this publication is for reader information and does not imply 

endorsement by the U.S. Department of Agriculture of any product or service.
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

19

the required geoprocessing. Fortunately, the existing application provides a detailed, 

documented template for new applications because the logic model already exists, 

and data requirements are defined and can be readily adapted.

Integrating Knowledge and Communicating Results 

Applications that translate the reasoning, knowledge, and field experiences of subjectmatter experts into formally specified logic models serve several important purposes 

in resource management. First, they make large, abstract, and relatively complex problems solvable by nonexperts. We note that technical staff with high levels of expertise 

in specific subject-matter areas are often in short supply. Second, logic models provide 

a means of capturing relatively rare knowledge, and making it available as institutional 

knowledge that can be readily shared. Third, in contrast to many modeling applications 

that operate as “black boxes,” logic-based systems provide a documented interface 

to a solution that transparently shows the derivation of model outputs. This is true of 

the logic component in EMDS, although space does permit us to illustrate this point. 

Access to intuitive explanation of results is important to model users who need to 

understand the basis for model results, and to detect when models are misbehaving. 

Particularly for land managers, this capacity for intuitive explanation also provides a 

basis for effective communication with partners. Continuing assessments of watershed 

conditions in areas covered by the Northwest Forest Plan illustrate the methods and 

benefits of an interactive approach (Gallo et al. 2005).

A Related Tool

Detailed soil surveys are unavailable for parts of the interior Columbia Basin. This 

potentially limits the utility of our application. Recognizing this limitation, we developed a companion application that interactively prompts the user for data rather than 

batch-processing database tables. In contrast to our GIS application that was designed 

to handle tens of thousands of soil map records, the interactive version was designed 

to evaluate individual sites in the field when used by field personnel equipped with a 

laptop PC. The interactive version is available upon request to the first author.

Extending the Model

Another virtue of model implementation in EMDS is that the current soil-risk application can be extended to include additional topics such as of the likelihood of a severe 

wildfire (as opposed to risk of impacts from a wildfire) or likelihood of introductions 

of exotic species. Hessburg et al. (2007b) have previously presented an EMDS application for evaluation of severe wildfire danger based on national data layers delivered 

by the LANDFIRE program (http://www.landfire.gov), and this latter model could be 

integrated with the present application with relative ease. Similarly, a new submodel 

Logic-based systems 

provide a documented 

interface to a solution 

that transparently 

shows the derivation 

of model outputs. 

The interactive version 

of our application was 

designed to evaluate 

individual sites in the 

field when used by field 

personnel equipped 

with a laptop PC.
GENERAL TECHNICAL REPORT PNW-GTR-840

20

relating to invasive species could be developed based on current research supported by 

the Western Wildland Environmental Threat Assessment Center (Prineville, Oregon). 

There is a clear need for models that simultaneously can evaluate multiple, interacting 

threats. Moreover, significant economies in application development can be gained 

from common data requirements across model components.

The current model could also be extended to provide priorities for various management activities across soil polygons. For example, the decision-model component 

of EMDS can be used to rate soil polygons for remedial treatments after wildfire—

based on expected impacts from wildfire as well as practical considerations related 

to feasibility and effectiveness of restoration measures (Reynolds et al. 2003)—or 

for timber salvage using ground-based equipment without mitigative practices.

Verification and Validation

An important part of model development is verifying that the model accurately 

represents observations of a subject-matter expert. This is similar to goodness-of-fit 

testing in regression analysis. Two authors are the subject-matter experts for the 

risk submodels (figs. 2 and 3); both have carefully reviewed a representative set of 

polygons to verify model performance. 

In contrast, model validation is a more demanding task that requires model 

predictions to be confirmed with new, independent data. The submodel for risk 

of impacts from wildfire was partially validated during initial development by 

PBS Engineering and Environmental (2003), but more extensive validation testing 

is warranted. The submodel for risk from equipment usage was not validated. 

Validation would entail measurement of tree growth after controlled or operationally created soil disturbances across a representative sample of soil types and 

climates. 

Conclusions

Our risk-rating scheme can aid in prescribing, planning, and scheduling harvest and 

restoration activities. Ratings identify and locate sensitive areas where mitigative or 

restorative efforts could be focused to maximize benefits and minimize costs and 

ecological consequences.

Acknowledgments

This project was funded by National Fire Plan project #01.PNW.C.2 (Forestry Sciences 

Laboratory, Wenatchee, Washington), and by the Western Wildland Environmental 

Threat Assessment Center (Prineville, Oregon). The Pacific Northwest Region of 

Model validation is a 

more demanding task 

that requires model 

predictions to be 

confirmed with new, 

independent data. 
Evaluating Soil Risks Associated With Severe Wildfire and Ground-Based Logging

21

the U.S. Forest Service funded final publication. The GIS processing was provided 

by R. Brion Salter (Forestry Sciences Laboratory, Wenatchee, Washington). We 

thank the Natural Resources Conservation Service (NRCS) for helping us obtain 

electronic files from official soil survey areas. We also thank Duane Lammers, 

Mark Jensen, Steve Howes, Annie Greene (USDA Forest Service), and Steve 

Campbell (NRCS) for their thoughtful reviews of the manuscript.

English Equivalents

When you know: Multiply by: To find:

Millimeters (mm) 0.0394 Inches

Centimeters (cm) .394 Inches

Meters (m) 3.28 Feet

Hectares (ha) 2.47 Acres

Degrees Celsius (°C) 1.8 °C + 32 Degrees Fahrenheit

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