United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Paper

RMRS-RP-96

September 2012

Predicting Fire 

Severity Using Surface 

Fuels and Moisture

Pamela G. Sikkink

Robert E. Keane
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Sikkink, Pamela G.; Keane, Robert E. 2012. Predicting fire severity using surface 

fuels and moisture. Res. Pap. RMRS-RP-96. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 37 p.

Abstract

Fire severity classifications have been used extensively in fire management 

over the last 30 years to describe specific environmental or ecological impacts 

of fire on fuels, vegetation, wildlife, and soils in recently burned areas. New fire 

severity classifications need to be more objective, predictive, and ultimately more 

useful to fire management and planning. Our objectives were to (1) quantify the 

relationships between fuel loading and moisture characteristics of surface fuels 

and the temperature and energy produced during combustion, and (2) to produce 

a classification that summarized these relationships into unique, realistic classes 

of fire severity. Using computer simulation, we created 115,280 synthetic fuel 

beds with diverse compositions and moisture conditions and burned them using 

computer simulation with the First Order Fire Effects Model (FOFEM). Using 

average fire intensity, fire residence time, total fuel consumed, depth of soil heating, 

and temperature in the top 1 cm of soil, we created a nine-group classification that 

separated fire severity classes based first on soil heating, second on intensity and 

fire time, and third on fuel consumed. Fuel beds were correctly placed into the nine 

fire severity classes 98% of the time using subsets of the synthetic fuel beds.

Keywords: Computer simulation, fire effects, FOFEM, fuelbed, hierarchical 

clustering

Acknowledgments

This project was funded by the Joint Fire Sciences Program project #09-1-07-4. 

We thank Larry Gangi, Systems for Environmental Management, for customizing 

outputs of FOFEM and troubleshooting outputs from the synthetic dataset runs. 

Kevin Ryan, Dan Jimenez, Jim Reardon, Duncan Lutes, L. Scott Baggett, and 

Elizabeth Reinhardt, all of the U.S. Forest Service Rocky Mountain Research 

Station, provided technical assistance, advice on running FOFEM, and many other 

discussions throughout the duration of the study. We thank the fire managers 

and technicians who provided input and suggestions on the moisture scenarios, 

including Dave Brownlie, Allen Carter, and Rick Vollick, U.S. Fish and Wildlife 

Service; Tom Remus, Bureau of Indian Affairs; Cody Wienk, National Park Service; 

and Eugene Watkins and Zack Riggs, U.S. Forest Service. Art Sikkink provided 

assistance with the Visual Basic macro; and L. Scott Baggett, RMRS, programmed 

the cluster analysis procedure in SAS. Benjamin Bird, RMRS, created the figure 

showing class membership within the moisture scenarios. The cover photograph 

was taken by Michael Harrington, RMRS Missoula Fire Sciences Laboratory, and is 

used with permission. We thank reviewers Kevin Ryan and Theresa Jain, RMRS, 

and Penelope Morgan, University of Idaho, whose comments and suggestions 

greatly improved this manuscript.
Contents

Introduction..............................................................................................................1

Background....................................................................................................2

Methods....................................................................................................................4

Fire simulation ...............................................................................................4

Datasets.........................................................................................................5

Synthetic Dataset...................................................................................5

Actual Dataset .......................................................................................7

Data Analyses................................................................................................8

Creating Groups ............................................................................................9

Verifying Groups ..........................................................................................10

Results....................................................................................................................11

Data Exploration ..........................................................................................11

Cluster Analysis ...........................................................................................13

Class Verification .........................................................................................17

Verification Using an Actual Dataset....................................................17

Verification with Classification and Regression Tree Analysis (CART) 19

Verification with Discriminant Analysis.................................................21

Summary of Validation Results............................................................22

Discussion .............................................................................................................22

Evaluation of the Fire Severity Classification Process.................................22

Limitations of the Classification and Suggestions to Improve Predictability 24

Summary .....................................................................................................25

References .............................................................................................................26

Appendix A—Ranges of fuel components and moisture characteristics 

for each class in the synthetic data set (N = 115,280)....................................32

USDA Forest Service RMRS-RP-96. 2012. 1

Predicting Fire Severity Using Surface 

Fuels and Moisture

Pamela G. Sikkink and Robert E. Keane

Introduction

Fire severity classifications have been used extensively in fire management over 

the last 30 years to address specific environmental or ecological impacts of fire on 

fuels, vegetation, wildlife, and soils in recently burned areas (Azuma et al. 2004; 

Boby et al. 2010; Carey et al. 2003; Jain et al. 2012). For example, managers and 

researchers have used fire severity classifications to evaluate prescribed fire success (Ryan and Noste 1985), stratify post-fire vegetation and soil response (Parsons 

et al. 2010), and describe burn patterns (Carey et al. 2003). Severity classifications have also been used to assess rehabilitation potential (Kuenzi et al. 2008; 

Robichaud et al. 2003) and decide whether burn impacts can be mitigated on a 

landscape (Beschta et al. 2004; Robichaud et al. 2007a). They have been correlated 

with remotely sensed images to develop maps that delineate areas to target efforts 

to reduce erosion, plant seedlings, and restore native plants (Beschta et al. 2004; 

Miller et al. 2003; Parsons 2003). Patterns of fire or burn severity have also been 

used to delineate fire regimes (Heyerdahl et al. 2012; Morgan et al. 2001), link 

landscape patterns and scales of disturbance processes (Chuvieco 1999; Dillon et 

al. 2011; Hudak et al. 2007; Turner et al. 1994), assess post-fire vegetation recovery and reestablishment (Díaz-Delgado et al. 2003; Lentile et al. 2007; Turner et 

al. 1999), and evaluate disturbance of wildlife habitat (Zarriello et al. 1995) and 

the effects of fire on species of concern (Kotliar et al. 2003). All of these fire and 

burn severity classifications are post-fire assessments that have some relationship 

to the response of interest, but little predictive power because they are (1) limited 

in scope; (2) inconsistent; (3) not directly tied to fire characteristics and/or (4) lack 

a distinct quantitative basis for each class.

The main problems with most current fire severity classifications are that they 

are tailored to meet the needs of a specific application and the data used to create them have been reduced to highly subjective, simplistic classes. Specialized 

classifications collapse complex interactions between fire, fuels, biota, and the 

biophysical environment into an over-simplified and over-generalized three- to 

six-group ordinal scale classification. Most of the classifications are ultimately 

reduced to classes of “low,” “moderate,” or “high” that represent a synthesis of the 

complex and interacting effects of a fire (Ryan and Noste 1985). The advantage of 

these simple indices is that they integrate a variety of information and summarize 

it into succinct, intuitive categories useful to management. The disadvantage is 

that they are overly simplistic and rarely address all possible management concerns that require an estimate of severity (Lutes et al. 2006). Subjective, simplistic 

classifications are difficult to teach. They are also difficult to use consistently in 

the field because individuals assess severity using qualitative or semi-quantitative 

evaluation criteria differently based on their previous experiences evaluating fire 

and fuels. Ultimately, most of these fire severity classifications have little predictive power to describe potential fire severity before a site actually burns because 
2 USDA Forest Service RMRS-RP-96. 2012.

an explicit cause-and-effect relationship has not been developed between the fuels 

and the fires that consume them.

A fire severity classification should directly relate the interaction of fire, fuels, 

biota, and the biophysical environment. Ideally, empirical data should include a 

wide variety of fuel beds and moisture scenarios that, when burned, lead to values 

for heat production, burn time, consumption, and other fire effects that form an objective fire-severity continuum. Through robust statistical analysis, the continuum 

is partitioned into distinct classes based on fuel bed and moisture parameters. As 

new technologies become available and are adopted (e.g., high resolution remote 

sensing products, mechanistic simulation modeling, and innovative sampling techniques), burn severity classifications will increasingly need to describe complex 

fire effects based on physical characteristics of a fire and quantitative assessment 

variables, much like those used by fire behavior and many other fields of ecology.

This study was conducted to (1) demonstrate a technique for developing future 

fire severity classifications that are compatible with developing technologies in 

fuels research and (2) to produce a classification comprised of unique, realistic 

classes that objectively relate fire and fuel characteristics to their associated fire 

effects. To meet these objectives, we used computer-simulated burning of real and 

synthetic fuel beds and examined the quantitative relationships between fuel characteristics, moisture, fire intensity, and first-order fire effects. Our results were 

somewhat constrained by the underlying assumptions of our simulation model, but 

using simulation burning was the only practical method to effectively treat the full 

range of fuels and moistures required to build an effective classification. We defined our fire severity classes by the degree of changes in the direct first-order fire 

effects, including the amount of fuels consumed, degree of change in vegetation 

and soil biota due to soil heating, and smoke production. Although the proposed 

classification is based solely on surface fuels, it can be used in fire planning or fuel 

treatments efforts with some simple precautions. We also suggest several ways to 

refine and improve this classification for fire managers.

Background

The terms burn severity, fire severity, and fire intensity have been confused 

among scientists and managers for many years (Jain 2004; Lentile et al. 2006). 

Burn severity and fire severity are often used interchangeably to describe the degree of above- and below-ground organic matter consumption during a fire (Jain 

2004; Keeley 2009; National Wildfire Coordinating Group 2006). Fire or burn 

severity are also used as general terms for the degree of environmental change 

caused by fires or as a specific loss of organic matter or biomass above and below ground that is caused by the burning process (Keeley 2009; National Wildfire 

Coordinating Group 2006). In contrast, the term fire intensity is used for units 

of heat and describes the physical combustion process (Byram and Nelson 1952; 

Rothermel 1972). This report uses the terms fire intensity in its traditional sense. 

We reserve burn severity to denote classifications created for remote sensing applications because the term has a long history of use in the remote-sensing field 

(Dillon et al. 2011; Lentile et al. 2006; Lentile et al. 2007). We use fire severity to 

denote the magnitude of fire-caused damage to vegetation and fuels and general 

descriptions of fire impacts, such as fire effects on soil (Simard 1991). We use the 

term surface fuels to encompass all fuels that lay above the mineral soil A horizon. 

Technically, duff would be a ground fuel (Anderson 1982; O’Brien 2004), which 

burns differently during the fire combustion process than litter and larger fuels; but 

for simplicity in writing, we chose to refer to all fuels in this study as surface fuels.
USDA Forest Service RMRS-RP-96. 2012. 3

Many fire and burn severity classification systems have been developed to 

describe specific fire effects using general categories of fire damage. However, 

these classifications are limited to qualitative descriptions of specific environmental effects associated with burning. For example, fire severity classifications have 

been created to describe fire-caused damage to coniferous forests (Bradley et al. 

1992; Schimmel and Granstrom 1996), aspen (Brown and DeByle 1987), and taiga 

(Foote 1983). Ryan and Noste (1985) created a general classification for fire effects focused on fire intensity and duration that was expanded to include spatial 

and temporal effects in boreal forests (Ryan 2002). Some describe fire effects specifically on tree crowns (Jain and Graham 2007); others specifically classify fire 

effects on soil (Huffman et al. 2001; Hungerford 1996; Jain et al. 2006; Lewis et 

al. 2006; Parsons 2003; Parsons et al. 2010; Tarrant 1956; Wells et al. 1979). Other 

severity classifications have been created for fire effects in vegetation and vegetation recovery after wildfire (Moreno and Oechel 1989; USDA 2010). Perhaps 

the most commonly used fire severity classification is the Composite Burn Index 

(CBI) that quantifies severity from soil color, vegetation reduction, and fuel consumption (Key 2005; Key and Benson 1999).

Normalized Burn Ratio (NBR), and the related differenced NBR (dNBR) (Key 

and Benson 2006) and relativized dNBR (RdNBR) (Miller and Thode 2007), are 

commonly used to infer burn severity from remotely sensed images (Singh 1989). 

Often differences in reflectivity between pre-burn and post-burn landscapes are 

classified for severity based on how great those differences are and this has been 

related to specific vegetation responses (Brumby et al. 2001; Epting et al. 2005; 

Redmond and Winne 2001; Wang and Glenn 2009; White et al. 1996).

The sensitivity of CBI and NBR to severity measures have been assessed by 

Key (2006), Kasischke et al. (2008), Murphy et al. (2008), and De Santis and 

Chuvieco (2007), among others. CBI and NBR have been adjusted for new burn 

severity indices that include leaf area index (Boer et al. 2008), relativization of the 

NBR (Miller and Thode 2007), and the use of additional imagery including preand post-fire images (Norton et al. 2009; Robichaud et al. 2007b; van Wagendonk 

et al. 2004). While each of these tools are useful for obtaining a relative assessment 

of fire effects on a specific environmental feature or landscape, each is also limited 

in application because a physical measure of fire behavior (e.g., fire intensity) is 

not objectively related to a quantified fire effect caused by combustion of a specific 

fuel load.

In contrast to fire and burn severity classifications based on subjective, categorical, post-burn assessments described in the examples above, three severity 

classifications have been created using quantitative measures. Two were developed in British Columbia using duff plus slash consumption and/or depth of burn 

to assign fire severity (Feller 1998; Trowbridge et al. 1989). The third classification 

was developed in Spain; it used experimental burns to quantify soil temperatures 

and loss of biomass during burning of shrub ecosystems (Perez and Moreno 1998). 

Although all of these classifications have a quantitative component to their development, none are based on the full range of fuels that would commonly be found 

in forest, shrub, and grassland ecosystems. None relate the fire intensity achieved 

during a burn directly to resulting fire effects, and none can be used to predict fire 

severity before a fire occurs.

Burning the hundreds of thousands of different fuel bed compositions needed 

to create an adequate classification that can predict burn severity is expensive and 

time consuming, and a major reason why few comprehensive fire severity classifications based on fuels exist. Linking the heat produced by a fuel bed with its 

fire effects requires instrumentation and manpower to gather pre-burn, concurrent, 
4 USDA Forest Service RMRS-RP-96. 2012.

and post-burn information on fuel biomass and moisture values, fire heat and residence time, and fire effects. Capturing the range of fire severity possible from 

the variation within fuels and fire effects requires many such instrumented burns. 

Before an objective fire severity classification could be developed, alternatives to 

actual burning had to be developed that could “burn” fuel beds using controlled 

inputs and conditions and that could output relevant characteristics of the fire and 

resulting fire effects. Fire models (i.e., algorithms that represent different burn 

conditions) have greatly improved our ability to understand the burning process 

(Albini and Reinhardt 1995; Linn et al. 2005; Rothermel 1972). Computer programs that model fuel combustion and fire effects, which are based on Albini’s 

(1976; 1994) pioneering work on the relationship between fire intensity and fuels, 

created the means for linking fire intensity, fuel characteristics, and fire effects. 

Because they are computer models, they can also be manipulated to output the 

specific variables required to evaluate the range of fuel bed compositions that are 

needed to create a comprehensive fire severity classification.

Methods

Fire Simulation

The First Order Fire Effects Model (FOFEM) was used to simulate fire behavior 

and effects for our two datasets (Keane et al. 2008; Reinhardt et al. 1997). FOFEM 

was selected over other models that predict fire effects because it (1) calculates soil 

heating to 13 cm (5 in) deep, (2) is limited to surface burns (i.e., does not simulate crown fires), and (3) uses a batch input file to simulate combustion in a large 

number of fuel beds at one time. In addition, we worked with the FOFEM programmers to modify the tool and output several customized variables, including 

four measures of fire intensity (mean intensity, median intensity, sum of intensity 

during a burn, and maximum intensity reached during a burn), the temperatures 

and durations of heating in each 1-cm thick soil layer, and the depth of the deepest 

soil layer reaching 60 oC and 275 oC (see Keane et al. 2008, http://frames.nbii.gov/

portal/server.pt/community/fofem/613).

Several assumptions were made for the simulation burns. Some were inherent 

to the FOFEM simulation program; others were made during data preparation. 

FOFEM assumes that all fuels within a fuel bed will burn; that is, there is no 

patchiness or variability to the burn (Reinhardt et al. 1997). FOFEM has no spatial 

or landscape component to its burns as would be inherent in wildfire or prescribed 

burns. FOFEM also allocates proportions of each fuel component to flaming and 

smoldering combustion depending on a moisture description assigned by the user; 

we assumed that our moisture conditions, which were assigned “wet” to “very 

dry” based on moisture scenario, were within the ranges assigned in FOFEM and 

were handled appropriately for each combustion type. As modelers, we assumed 

that FOFEM would pick the most appropriate algorithms for burning the fuels and 

that each fuel bed we created would burn (i.e., no fuel beds or moisture scenarios 

would be outside the range of FOFEM’s predictive algorithms). We assumed that 

FOFEM contained predictive algorithms that could realistically burn fuel beds for 

the full range of duff in our study although evidence has been found to the contrary 

(Hood et al. 2007). We know that the ranges of duff thickness and biomass are 

biased in FOFEM algorithms toward fuels of the interior west (Brown et al. 1985) 

and that many of our duff measures extend beyond these ranges. This probably 
USDA Forest Service RMRS-RP-96. 2012. 5

affected some class characteristics, but we do not know to what degree. Finally, 

we assumed that assigning the same moisture distribution for all fuel beds, and 

assigning rot percent to the 1000-hr fuels using only five categories instead of a 

continuum, would not adversely affect the burning or classification process.

FOFEM requires specific inputs on several climatic, soil, and moisture distribution factors that were not an integral part of the datasets created or compiled 

for this study so we assigned the default values from FOFEM to these factors. In 

addition to the actual and synthetic fuel loadings and moistures by component, 

we added values within the program for burning season (summer), duff moisture 

distribution (even), region (interior west), soil type (coarse-silt), and fuel type (natural). FOFEM was run in batch mode and fires were simulated for 8,000 seconds 

to calculate the median. Settings for the FOFEM runs included a limit of 10% fire 

intensity under zero duff conditions and use of the original burn up time if duff 

was present.

Datasets

Two datasets were used to create and verify this fire severity classification for 

surface fuels. One dataset, the “synthetic dataset,” was computer generated and 

used exclusively to develop the classification; the simulated fuel beds that were 

generated for it are referred to as the “synthetic fuel beds.” The second dataset 

consisted of actual field data, which provided realistic limits on fuel loads for each 

of the synthetic fuel components and served as an independent dataset to verify 

the uniqueness of the classes created in the classification process. This dataset is 

referred to as the “actual dataset” and its fuel beds are called “actual fuel beds.” 

The fuel components comprising both the synthetic and actual fuel beds include 

the following commonly accepted U.S. size classes (Fosberg 1970):

• Duff

• Litter

• Fine woody debris (FWD)

• 1-hr fuels: particles with diameters <0.64 cm (<0.25 in) in diameter (1 hr 

refers to the number of hours it takes debris of this size to dry enough to 

reach equilibrium moisture content.)

• 10-hr fuels: particles between 0.64 and 2.54 cm (0.25-1.00 in) in diameter

• 100-hr fuels: particles 2.54 to 7.62 cm (1-3 in) in diameter

• Coarse woody debris (CWD)

• 1000-hr fuels consisted of fuel components >7.62 cm (>3 inches) in diameter. This class included all logs. Rot categories were assigned to CWD 

during the fuel-bed creation process.

• Live shrubs

• Herbs

The following paragraphs describe the development of the synthetic and actual 

datasets, including a description of the variables added to each dataset to do the 

simulation burning.

Synthetic Dataset

The synthetic dataset was created by randomly assigning fuel loadings to each 

fuel component and systematically combining the eight fuel components into a 

synthetic fuel bed. All loadings and assignments were made using Visual Basic 

for Applications in Microsoft Access ©. Loading values were randomly assigned to 
6 USDA Forest Service RMRS-RP-96. 2012.

each fuel component from their respective loading ranges as described in Table 1. 

Coarse woody debris (CWD) values were assigned a random percent rot value 

within the systematic process to satisfy FOFEM input requirements (Reinhardt et 

al. 1997) and simulate more realistic field conditions for CWD. Foliage or branches from tree sources were not included in the loading assignment process because 

they were not considered surface fuels. The values in Table 1 were initially derived 

for the fuel loading models (FLMs) developed by Lutes (2009) and they are currently used to map national fuel layers (U.S. Department of the Interior 2011). 

FLMs were developed from actual field data so they were ecologically based and 

resulted in realistic ranges for the fuel components. Because the fuel bed data 

used to create the FLMs came from a limited number of sample areas across the 

United States, however, we increased the range of FLM fuel loadings for our study 

by adding two to three additional ranges to each fuel component at the top end 

of each FLM range. This expanded our range of variability and at least doubled 

maximum loadings in the actual data set to include fuel beds that may not have yet 

been sampled. We did not evaluate whether all of the synthetic fuel beds that were 

created for this study actually existed in nature.

Distributing the fuels systematically into beds resulted in 11,528 individual 

fuel beds that contained fuel loading values for eight fuel components, a range of 

duff depths, and a variety of rot values for the CWD component. The fuel loads 

ranged from minimal to heavy loadings and the systematic assignment of variable 

values allowed us to limit the total number of fuel beds for analysis. We initially 

experimented with systematically increasing biomass for each component using 

a set loading for each fuel type instead of assigning random loadings within intervals for each; however, many fuel beds with low biomass were duplicated and 

Table 1—Ranges of increasing biomass (kg m-2) and percent rot used to systematically create the fuel components of each 

synthetic fuel bed.

We created 11,528 simulated fuel beds using nested loop routines within Visual Basic for Applications (VBA). We defined 

upper and lower limits for each of the four major fuel components and percent rot according to study objectives; then we 

created intervals within those limits from which we generated random numbers for the biomass of each fuel component. 

Components were nested in order of: (1) litter biomass, 6 intervals; (2) coarse woody debris biomass, 9 intervals; 

(3) percent rot of coarse woody debris, 5 intervals; (4) duff biomass, 11 intervals; and (5) fine woody debris biomass, 4 

intervals. Herb and shrubs were randomly assigned biomass values for each fuel bed from within their respective interval. 

Fine woody debris was further subdivided into biomass values for 1-hr, 10-hr, and 100-hr fuels. To fit the input parameters 

of FOFEM, duff biomass was converted to duff depth using a duff density value. Several checks were made after the fuel 

components were combined into a fuel bed to eliminate combinations of fuels or characteristics that could not exist; for 

example, no rot percentage was assigned if there was no coarse woody debris.

Biomass intervals for each surface fuel component (kg m-2)

 Coarse woody

Total fine Coarse decay/rot

Increment Duff Litter woody debris woody debris Herb Shrub (%)

 1 0.00-0.01 0-0.20 0-0.53 0-1.01 0-2.02 0-8.97 0.00

 2 0.01-0.42 0.21-0.57 0.54-6.74 1.02-1.83 20.00

 3 0.43-1.11 0.58-2.41 6.75-22.42 1.84-2.25 40.00

 4 1.12-2.97 2.42-11.22 22.43-44.84 2.26-3.56 60.00

 5 2.98-4.23 11.23-33.64 3.57-6.34 80.00

 6 4.24-4.86 33.65-67.25 6.35-7.86

 7 4.87-5.89 7.87-22.42

 8 5.90-8.44 22.43-44.84

 9 8.45-13.40 44.85-89.67

 10 13.41-22.42

 11 22.43-78.47

 12 78.48-156.92
USDA Forest Service RMRS-RP-96. 2012. 7

so many fuel beds were generated that computational times for statistical analysis 

and graphing were impractical. Alternately, assigning loadings or biomass by true 

random methods did not guarantee a wide range of fuel bed compositions as did 

the assignment of loadings within intervals. Assigning fuel loads by systematic increases within defined intervals was ultimately the most practical method to insure 

a wide range of fuel loadings and a reasonable number of fuel beds for analysis 

and classification. The same rationale was used to limit the ranges of the synthetic 

fuel components to double those of the actual fuel beds. If we used a wider range of 

values for each component, more fuel beds would be created using the systematic 

process.

The two exceptions to assigning loadings using the FLM intervals were in the 

assignments of the fine-woody debris and shrub/herb components. Fine woody debris (FWD) was not separated into individual components (i.e., 1-, 10-, and 100-hr 

fuels) within the FLMs, but the individual components were required by FOFEM 

for the simulation burn. To make these assignments, we randomly picked a total 

for FWD from the appropriate FWD intervals in Table 1; we then divided that 

number by two and selected random values for 1-hr and 10-hr components within 

this new limit. New 1-hr and 10-hr components were then subtracted from the total 

FWD with the remainder assigned to the 100-hr loading. We assigned shrub and 

herb loadings to each fuel bed using random values over a single range that was 

twice the loading values for these components that occurred in the actual data set 

(described in the next section).

Moisture values for the duff, 10-hr, 1000-hr, and soil were assigned to each of 

the 11,528 fuel beds using 10 different moisture scenarios (Table 2). The moisture 

scenarios were designed by managers from the western and northeastern United 

States, the Midwest, and Florida; they emulated different conditions that are used 

to guide wildfire and prescription-burn decisions around the country. By using scenarios with vastly different moisture criteria, we ensured that a range of fire effects 

were simulated. Ultimately, we generated 115,280 synthetic fuel beds (11,528 fuel 

beds x 10 moisture scenarios) for the classification process.

Actual Dataset

The actual data set was comprised of 4,046 fuel beds sampled from field sites 

throughout the contiguous United States. The field sites were all sampled using 

established sampling protocols. The data set was compiled by Lutes et al. (2009), 

Table 2—Moisture values (%) assigned to the synthetic and actual datasets before simulation burning with the First Order Fire Effects 

Model (FOFEM).

Scenarios were constructed for regions or areas based on input from fire managers in the western United States (West), northeastern 

United States (NE), Florida and West Virginia (FL), and South Dakota (SD) for prescribed burns (Prescribed) and wildfires (see 

Applicable areas column). Scenario G includes the settings used to create the original fuel loading models (FLMs) of Lutes et al. (2009). 

All moisture scenarios should be applicable across the United States and internationally.

10-H 1000-H Duff Soil Examples of

Scenario moisture moisture moisture moisture Condition applicable areas

A 4 10 20 5 Very dry fuels, duff, and soil Wildfire West & NE

B 4 20 125 5 Dry fuels and soil with wet duff Prescribed West

 C 6 15 50 10 Dry fuels with moderate duff moisture West, very dry in FL

D 8 20 100 15 Moderately dry fuels with wet duff Dry FL, prescribed NE

E 8 75 75 20 Moderately dry fuels and duff, wet soil Prescribed West, FL, & NE

F 8 25 175 25 Dry fuels, wet duff and wet soil Prescribed FL & NE

 G 10 15 40 10 Dry fuels , moderate duff moisture FLM burn scenario

H 10 20 100 15 Moderately wet fuel and soil, wet duff West, FL, NE; prescribed NE,FL

I 10 20 175 25 Wet fuels, duff, and soil Prescribed FL

J 12 40 35 20 Wet fuels and soil, dry duff Averages for prescribed in SD
8 USDA Forest Service RMRS-RP-96. 2012.

who used the fuel beds to create a fuels classification that would predict smoke 

particulate and soil heating when surface fuels burned. Fuel beds from the Fuel 

Characteristic Classification System (Ottmar et al. 2007; Prichard et al. 2006) were 

also embedded within the FLM analyses (Lutes et al. 2009). Each fuel bed consisted of biomass values for litter, duff, 1-hr, 10-hr, 100-hr, and 1000-hr fuels; but 

a constant value was assigned to herb and shrub components. Like the synthetic 

dataset, fire behavior and effects were simulated for each of the fuel beds under 

the 10 moisture scenarios, resulting in a data set with 40,460 total fuel beds. For 

this study, the actual dataset fulfilled two functions: (1) it provided field values 

for the loads of each fuel component and (2) it served as an independent dataset 

to verify the severity classes developed with the synthetic dataset and validate the 

classification.

Data Analyses

Before classification analysis, we verified that we met two important objectives 

for the synthetic fuel beds, which were to (1) double the range of load values in 

the actual data set, and (2) create combinations of fuels that did not exist within 

the actual data set. Box plots were used to evaluate the ranges of each fuel type in 

the actual and synthetic data sets. The range of compositions and fuel ratios within 

the actual and synthetic fuel beds (objective 2) were tested using a piper diagram 

within Sigma Plot (Systat Software Inc. 2008).

Twenty-eight independent variables were available for statistical and classification analysis from the FOFEM outputs including 14 fire effects (fuel consumption 

and soil heating) and 14 fire behavior variables (Table 3). These outputs were stored 

in each of the two datasets using a fuel-bed identification number. The identification number contained a code letter for moisture scenario and a number relating 

Table 3—Outputs for each fuel bed after simulation burning using First Order Fire Effects Model (FOFEM). Variables used 

in cluster analysis are shown in bold.

Type Output Units Abbreviation for output 

Fire effect Fuel consumption for nine fuel components kg m-2 DuffC, LitC, DW1C, DW10C, DW100C,

(e.g., Duff consumed = DuffC) HerC, ShrC.

Fuel consumption for downed woody 1000-hr kg m-2 DW1kSndC, DW1kRotC

fuels (DW1k), solid (Snd) and rotten(Rot)

Fuel consumption for crown canopy foliage kg m-2 FolC, BraC

and branches (not used in this study)

Total biomass of all fuels consumed during kg m-2 TotCon

combustion

Maximum temperature reached for various oC S0 (temperate at surface), S1...... to

soil depths S13 (temperature at 1 to 13 cm below 

 surface)

Maximum depth to reach 60oC or 275oC cm SL60, SL275

Mineral soil exposure after burn % MSEPer

Reduction in duff depth cm DDRed

Physical properties Fire intensity measures (mean, median, Kw/m2 FIAvg, FIMed, FIMax, FISum

of fire during burn maximum, and sum)

Fire intensity attributed to vegetation Kw/m2 FISHFB

(S=shrub; H=herb; F=foliage; B=branch)

Duration of fire sec FTime

Duration of flames sec FlaDur

Duration of smoke sec SmoDur
USDA Forest Service RMRS-RP-96. 2012. 9

to the fuel bed (e.g., E11575 was fuel bed 11575 burned using moisture scenario 

E). All original fuel-bed compositions, moisture values, and FOFEM outputs were 

stored in the relational database to export the different combinations of variables 

selected for statistical analysis or classification procedures.

Several assumptions were made related to the classification process that affected the final product, but they were assumptions that we felt were normal to 

make. We assumed that creating widely diverse fuel beds would produce the greatest range of fire behavior and effects and represent the entire set of fuel beds in 

the United States. We assumed that current burning models (specifically FOFEM) 

were rigorous and progressive enough to provide relative, unbiased predictions; 

and we assumed that clustering the variability in fire characteristics generated from 

diverse fuel beds into a small number of classes would reduce redundancy across 

classes and provide an objective way to describe fire effects.

A variety of data exploration techniques were used to decide which FOFEM 

output variables would be most useful to create groups from the synthetic and 

actual data sets. Descriptive statistics were run within SAS (SAS Institute Inc. 

2008) on each of the moisture scenarios to explore means and standard deviations 

for output variables. Histograms and box plots were constructed to explore the 

fuel-bed distributions. QQ tests were performed on individual variables to test for 

normal distribution of the data and subjected to common transformations when 

found to be non-normally distributed. If data were not normally distributed, nonparametric statistics were used to test differences in variables between moisture 

scenarios and between cluster groups. Correlation coefficients were computed in 

SAS using the Spearman’s rank and Pearson’s correlations on the input variables 

for the various run scenarios and the output variables from FOFEM used in cluster analysis. Cutoff values for correlations using these two tests were 0.80 with 

those variables exceeding the cutoff eliminated from further analysis. Eliminated 

variables included flame duration and several measures of fire intensity among 

others. Kruskal-Wallis non-parametric one-way ANOVA tests were run in SAS to 

determine if each variable differed in means between moisture scenarios. Those 

variables that had significant differences between scenarios were considered more 

likely to show significant differences when all the moisture scenarios were combined for the classification analysis. Differences were considered significant if 

p<0.05.

Creating Groups

Classification of the fuel beds was accomplished through agglomerative hierarchical cluster analysis using PROC CLUSTER and the Lance-Williams Flexible 

Beta method in the SAS statistical package (SAS Institute Inc. 2008). Eight 

FOFEM output variables were suitable to use for clustering each data set because 

they showed significant differences in FOFEM runs across all moisture scenarios: average fire intensity (FIAvg in kw m-2), fire residence time (FTime, sec), 

total fuels consumed (TotCon, kg m-2), smoke duration (SmoDur, sec), maximum 

temperature reached at one centimeter below the surface (S1, oC), deepest soil 

depth to reach 60 oC (SL60, cm), duff depth (DuffDep, cm), and 1000-hr moisture 

(CWDMoist, %). Values for each of these outputs were standardized within SAS 

and the standardized values were used in all statistical analyses and clustering procedures. Raw values corresponding to these standardized values were only used to 

illustrate points on the cluster dendrograms and tables to make the diagrams easier 

to understand.
10 USDA Forest Service RMRS-RP-96. 2012.

Cluster analysis trials were run using three, four, and five of the eight potential variables in different combinations. Five variables (FIAvg, TotCon, FTime, 

S1, and SL60) were ultimately selected for hierarchical clustering because they 

(1) showed significant differences between moisture scenario runs using KruskalWallis comparisons (p-value <0.05); (2) represented a range of factors, including 

time, intensity, depth of burn, and consumption; and (3) could be consistently estimated in some way by managers on site after a fire. Group characteristics were 

tested for six, seven, and nine groups before the final cluster procedure was run to 

completion. For each group, means and standard deviations of the five variables 

were summarized to determine what the differences were between groups. Cubic 

Clustering Criterion, Pseudo-F and Pseudo-t-squared tests were also used within 

SAS to help assess the optimal number of clusters. In this paper, we use the terms 

clusters, classes, and groups to refer to the groups created during this hierarchical 

clustering process.

Verifying Groups

We tested burn severity class compositions using three methods to determine 

whether the classes created by the clustering procedure with the synthetic data 

were distinct enough to group the same way using different data or grouping techniques. First, we analyzed the actual dataset using the same procedure used to 

cluster the synthetic data. Because clustering results are often different if data 

within a single data set are changed or removed (McCune and Grace 2002), we 

used an entirely new dataset as a strong test of whether the classes were unique 

and repeatable. Hierarchical cluster analysis of the actual data set was conducted 

with the same five variables and pruned to the same number of groups as the synthetic data set to determine if the same variables were controlling the clustering 

of each (i.e., similar variables were controlling branching in each branch of the 

cluster dendrogram). Next, the same five variables used in cluster analysis were 

analyzed using classification and regression tree analysis (CART) on both the synthetic and actual data sets. We compared the characteristics of classes created in 

the CART with the classes created in cluster analysis to see if the same variables 

were important to differentiating classes in both the synthetic and actual datasets. CART results provided an independent evaluation of the cohesiveness of each 

class’s characteristics, as well as an alternative measure of classification error, because CART uses different procedures to create groups than hierarchical clustering 

(McCune and Grace 2002; Nisbet et al. 2009; Venables and Ripley 2002). Finally, 

we tested the repeatability of the classes with non-parametric discriminant analysis 

(DA) on the synthetic data set. Discriminant analysis compared clustered classes 

made using a subset of the synthetic data to the classification made using the entire 

synthetic dataset to give estimates of the percentages of misclassifications within 

each group (McCune and Grace 2002). We conducted discriminant analysis using 

the five standardized variables and cluster as the grouping variable. We used nonparametric discriminant analysis in the SAS set with kernel = normal, r =0.3, and 

pool = yes. The value for the smoothing parameter r was computed as a weighted 

group mean based on the number of fuel beds in each cluster and on using five 

variables for the analysis as instructed in SAS.
USDA Forest Service RMRS-RP-96. 2012. 11

Results

Data Exploration

To create an objective fire severity classification that would be applicable to the 

wide variety of fuels and fuel-bed compositions found across the United States, 

we needed to create a dataset that expanded the known limits of fuel loadings 

and fuel-component combinations of previously sampled fuel-beds in the United 

States. Our goal was to double the range of each fuel component and examine the 

combustion effects over the expanded range. In the actual data set, the loads falling 

between the 25th to 75th percentiles on the box plot were less than 2.2 kg m-2 (10 T 

ac-1) for all components. Maximum duff load was less than 90 kg m-2 (400 T ac-1) 

(Fig. 1a). For our synthetic data set, the 25th to 75th percentiles fell between 3.4 to 

15.5 kg m-2 (15 and 70 T ac-1) (Fig. 1b). Maximum duff load was identical to the 

actual dataset but the value was considered unimportant because FOFEM treats 

extremely high duff loads identically during the simulation burns. All other fuel 

components met the objective of double the actual fuel biomass.

Figure 1—Range of biomass values in kg 

m-2 for each of the fuel types in (A) the 

actual data set, n = 4,046 (Lutes et al. 

2009); and (B) the synthetic data set, n 

= 11,528. CWD = Coarse-woody debris 

(1000-hr) fuels. Vegetation = total for 

shrub and herb loadings combined. 

Conversion factor for biomass in tons 

acre-1 = kg m-2 * 4.4609.
12 USDA Forest Service RMRS-RP-96. 2012.

In addition to expanding the load limits of each individual fuel component 

(Fig. 1), combining those components within the fuel beds into new proportions 

was also important to creating a fire severity classification that encompassed a wide 

variety of fuel beds, combustion characteristics, and fire effects. For example, a fuel 

bed consisting of 100% duff measured at 150 kg m2 creates different fire intensities and fire effects during simulated combustion than a fuel bed with 150 kg m-2

of fuel comprised of 65 km2 duff, 10 kg m-2 FWD, and 75 kg m-2 CWD, for a ratio 

of 64%: 4%: 32% on the fuel bed, respectively. If our classification process lacked 

fuel beds with certain ratios of fuel components (e.g., there were no fuel beds with a 

25%, 50%, and 25% duff-FWD-CWD combination), our fire severity classification 

would not adequately represent fire effects that would result from fuel beds having 

this combination. Filling in missing proportions of fuel components during our data 

creation phase was important to ensuring that the maximum possible combinations 

of fuel-bed compositions were included in our burn simulations. As a result of the 

data creation process, gaps in the ratios of fuel components in the actual data (Fig. 

2a) appeared to be adequately covered in the synthetic data set (Fig. 2b). Many new 

proportions of fuel components were created to fill in the white (gap) areas of the 

actual dataset and only a small area exists in the high proportions of 10-hr and low 

proportions of 100-hr fuels that remain unfilled (Fig. 2, lower right corner).

Figure 2—Piper diagram showing proportion of each fuel 

component within individual fuel beds. (A) actual data set 

(n = 4046); (B) synthetic data set (n = 11528). Each dot on 

the piper diagram shows the makeup of one fuel bed; its 

position determined by percentage of each fuel type that 

is found within the fuel bed. The lower left triangle consists 

of the smallest fuels; the lower right, the largest. Each 

corner of the small triangles = 100% of the respective fuel 

component. The 1-hr and 10-hr fine fuels are combined 

on the lower left triangle so that the percentage of 

vegetation biomass (herbs and shrubs) can be included 

with the small fuels. The placement of a fuel bed on the 

upper diamond depends on where the composition falls 

within each of the two lower triangles. If all fuel within the 

bed consists of only duff, the dot for the bed falls on the 

extreme left of the lower triangle and is projected to the 

upper portion of the diamond along with the proportion 

of litter. Scale = 0 to 100% along each edge of the lower 

triangles and along each edge of the upper diamond.
USDA Forest Service RMRS-RP-96. 2012. 13

None of the fuel variables (Table 3) in the actual or synthetic data set were 

normally distributed, nor could they be made normally distributed with common 

transformations. Histograms for all eight fuel components were skewed to the left 

(i.e., concentrations of small values) and QQ plots were sigmoid in shape with 

long tails on each end. Histograms of data from the FOFEM outputs were also 

skewed to the left, although some had bimodal tendencies, and the QQ plots were 

sigmoid in shape with long tails on one or both ends. Correlation tests on the input 

data showed duff depth (DuffDep) and duff biomass (DuffBio) highly correlated 

with the 10-hr fuels and with each other. Correlations using the Spearman correlation were 0.87, 0.88, and 1.0 (p < 0.0001). Pearson’s correlation tests on the 

standardized output from FOFEM simulation burning showed that S1 and SL60 

were highly correlated (0.87) as were S5 and SL60 (0.86, p < 0.0001) and S1 and 

S5 (0.91, p < 0.0001). These variables showed significant differences, however, 

when tested among the individual moisture scenario runs using Kruskal-Wallis 

Chi-squared tests (p < 0.0001).

Of the ten FOFEM outputs used to measure intensity, time, and total consumption, almost all showed significant differences among the 10 moisture scenarios in 

both the actual and synthetic data sets (p < 0.0001). Only FIMax was not significantly different (p < 0.55). Some moisture scenarios had similar burn characteristics 

for the soil variables (SL60 and S1), but they were still different enough to be 

highly significant using the non-parametric ANOVA tests (p < 0.001).

Cluster Analysis

Based on distinct divisions within the dendrogram, we found that a classification of the synthetic dataset could contain two to 24 groups (Fig. 3). We found 

that the optimal number of groups ranged from four to 18 based on the Cubic 

Clustering Criterion, Pseudo-F, and Pseudo-t-squared tests. The clearest indication 

of the optimal number of groups came from the Pseudo-t-squared test, which indicated seven to nine groups would be most appropriate, but we experimented with 

classifications ranging from six to 14 groups. Ultimately, we chose nine groups to 

use for the surface fuels classification (see Fig. 3, line indicating 9-group level), 

because they separated well on the dendrogram’s normal semi-partial R-squared 

value and could be differentiated on the basis of field characteristics for the fire 

and fuel beds. By determining why the dendrogram branched at each point (i.e., 

using means and ranges for each of the five analysis variables), we easily assigned 

differences in burn characteristics to nine groups; but found subsequent attempts 

at assigning surface fuel criteria to 14 groups using means and ranges was difficult 

(Fig. 3).

Means for the five FOFEM output variables used in the agglomerative clustering are shown by class in Table 4. Significant differences in the fire variables were 

found among the classes using Kruskal-Wallis non-parametric tests (p < 0.0001). 

The relationships between dendrogram branching and the five main fire effects 

used for the cluster process are shown in Figure 3. Along each branch of the dendrogram, the dominant pre-burn fuel characteristics for each of the nine classes are 

shown in Figure 4.

The characteristics for each of the nine cluster classes are summarized in Tables 

5 and 6. The tables also include characteristics for additional fire effects from 

FOFEM outputs that were not used in the clustering process. The most important variables to class formation from the synthetic dataset were soil heating, fire 

intensity and burn time, and a combination of burn time and total consumption. 

Temperature in the top 1 cm (0.4 in) of the soil horizon and the total depth of heat 
14 USDA Forest Service RMRS-RP-96. 2012.

penetration that reached lethal limits for living tissue (i.e., 60 oC/140 oF) define 

classes early in the process, but they were also highly correlated with each other 

because both reflect heat conducted in the same soil horizon. Classes 1, 7, and 8 

represented fuel beds that, when burned, resulted in an average predicted temperature >100 o

C (212 oF) in the upper 1 cm of the soil surface and that reached lethal 

temperature for living tissue from 2 to 7 cm (0.75 to 2.75 in) deep (i.e., “deep 

burns”). Classes 2, 4, 5, 6 and 9 resulted from burns that did not reach lethal limits 

Table 4—Mean values (by class) of fire effects used in agglomerative clustering. The standard error for each mean is 

shown in parentheses. Differences among classes using the five variables were significant using Kruskal-Wallis tests 

(p < 0.0001).

Total fuels Average fire Soil depth Fire residence Soil temp

consumed intensity reaching 60 oC time @ 1cm 

CLUSTER (TotCon) (FIAvg) (SL60) (FTime) (S1)

or Class n kg m-2 Kw m-2 cm sec oC

 1 12,664 40.29 (0.21) 156.81 (1.44) 5.46 (0.01) 3120.09 (12.49) 207.37 (0.54)

 2 20,936 21.38 (0.12) 199.24 (2.18) -0.44 (0.01) 1542.38 (7.02) 37.07 (0.15)

 3 469 37.89 (1.01) 4518.43 (68.58) 0.89 (0.11) 74.87 (0.79) 75.59 (3.46)

 4 264 55.16 (1.03) 13199.96 (237.60) -0.02 (0.11) 62.16 (0.32) 47.19 (2.92)

 5 22,468 86.15(0.19) 131.10 (0.33) -0.18 (0.01) 7853.45 (2.55) 42.01 (0.22)

 6 31,092 33.36 (0.08) 69.31 (0.27) -0.68 (0.00) 5192.68 (9.30) 31.54 (0.10)

 7 9,206 72.19 (0.33) 123.42 (0.52) 6.25 (0.02) 7438.45 (9.86) 215.44 (0.61)

 8 9,877 31.70 (0.21) 94.00 (0.85) 2.53 (0.01) 3672.49 (16.09) 104.62 (0.29)

 9 8,304 78.93 (0.18) 196.19 (1.47) -0.84 (0.00) 3664.84 (17.81) 26.76 (0.13)

Figure 3—Dendrogram showing the groupings of synthetic fuel beds based on outputs from the simulation burns in 

FOFEM. The cluster was run using Ward’s method and standardized variables for total fuels consumed (TotCon), 

average fire intensity (FIAvg), duration of fire (FTime), deepest soil layer to reach 60 oC (SL60), and maximum 

temperature reached at 1 cm below the surface (S1). Additional variables that distinguish groups include temperature 

of soil layer 5 centimeters below the surface (S5) and flame duration (FlaDur). Each branch is labeled with its dominant 

characteristic(s). Numerical values are means for each division. The 9-group and 14-group divisions are designated by 

horizontal lines. N = 115,280 fuel beds.
USDA Forest Service RMRS-RP-96. 2012. 15

for organics at more than 2 cm deep and did not create temperatures >75 oC (167 o

F) in the upper 1 cm of soil (i.e., “shallow burns”). Classes 5 and 7 burned with 

relatively long duration (i.e., “Slow burns”) while Classes 3 and 4 were characteristic of very short,”flashy” burns (Fig. 3, Table 5).

The mean fuel loads for each fuel type and the moisture conditions from the 

original fuel beds that correspond to each cluster class are summarized in Table 6. 

The range of loading values by fuel component and percent moisture values for 

each class are shown in Appendix A. Significant differences in fuel moistures and 

fuel compositions were found among all the cluster groups using Kruskal-Wallis 

non-parametric tests (p < 0.0001).

Visual examination of fuel bed distributions for three key fire variables shows 

that the clusters divide fairly well in three- dimensional space, although the sheer 

number of plots makes the distribution of some groups difficult to assess. Most 

groups are distinct (Fig. 5), but locating the full extent of Group 6 in the scatterplot is difficult. The scatterplot also shows that several of the groups span a range 

from shallow to deep burns within the same group, but others like Group 2 have 

a narrow range of burn depth (Fig. 5, lower center). Statistical summaries showed 

significant differences between the means of the five fire variables using Wilk’s 

Lambda (p < 0.000) and highly correlated values for SL60 and S1 (>0.86).

The effects of moisture on the classification process were complex (Fig. 6). 

Each cluster class contained fuel beds from all moisture scenarios. Classes 1 and 

Figure 4—Dendrogram showing the groupings of synthetic fuel beds based on the original fuel characteristics of the cluster 

groups. The cluster was run using Ward’s method and standardized variables for total fuels consumed (TotCon), average 

fire intensity (FIAvg), duration of fire (FTime), deepest soil layer to reach 60 oC (SL60), and maximum temperature 

reached at 1 cm below the surface (S1). Original fuels and moisture characteristics include: duff biomass (DuffBio), duff 

depth (DuffDep), coarse woody debris biomass (CWDBio), coarse woody debris moisture (CWD moisture), 1-hr + 10-hr 

+ 100-hr grouped (Fine fuels), litter, and soil moisture. Each branch is labeled with the original fuel characteristic that 

is important to branching. Numerical values are based on means and range of values for each group. The nine-group 

division is designated by the horizontal line. N = 115,280.
16 USDA Forest Service RMRS-RP-96. 2012.

Table 5—Median values for characteristics in each burn severity class based hierarchical clustering of the synthetic dataset 

using all moisture scenarios. Cluster classes are arranged in order of decreased soil heating. -1 = surface heating only.

Fire effect 7 1 8 3 2 5 4 6 9

Fuel consumed 65.93 38.15 25.53 32.59 17.21 80.90 54.51 33.87 75.03

kg m-2 (t ac-1) (294.1) (170.2) (113.9) (145.4) (76.8) (360.9) (243.2) (151.1) (334.7)

Soil depth 

that reached 

60oC (cm) 6.0 5.0 3.0 -1 -1 -1 -1 -1 -1

Fire characteristic

Soil heating

upper 1 cm (oC) 209 201 101 41 28 22 20 20 20

Soil heating

at 5cm (oC) 70 65 49 27 23 21 20 20 20

Fire intensity 116 115 72 4373 88 125 12839 58 153

Burn tme (sec) 7995 3225 3825 75 1485 7995 60 4875 3885

Flame duration (sec) 3180 1140 1095 60 300 3810 60 1185 1245

Smoke duration (sec) 24225 9975 10110 3795 7365 29430 4088 13890 17040

Table 6—Median values for original (pre-burn) fuel loads and moisture percentages by cluster group.

Fuel component 7 1 8 3 2 5 4 6 9

Duff biomass 0.78 1.23 0.78 3.34 4.75 5.83 4.89 6.28 12.02

kg m-2 (t ac-1) (3.5) (5.48) (3.5) (14.9) (21.2) (26.0) (21.8) (28.0) (53.6)

Duff depth 0.34 0.54 0.34 1.50 2.10 2.60 2.20 2.80 5.40

cm (in) (0.13) (0.21) (0.13) (0.59) (0.83) (1.02) (0.87) (1.10) (2.13)

Litter biomass 4.46 5.85 1.95 21.86 1.39 8.14 46.52 1.43 41.31

kg m-2 (t ac-1) (19.9) (26.1) (8.7) (97.5) (6.2) (36.3) (207.5) (6.4) (184.3)

Fine fuels biomass 9.46 8.79 5.38 0.04 1.23 9.89 0.02 4.60 16.63

kg m-2 (t ac-1) (42.2) (39.2) (24.0) (0.17) (5.5) (44.1) (0.1) (20.5) (74.2)

CWD biomass 33.8 2.20 2.98 3.81 1.66 40.1 3.65 5.76 2.68

kg m-2 (t ac-1) (150.8) (9.8) (13.3) (17.0) (7.4) (178.9) (16.3) (25.7) (11.9)

Moisture (%)

Duff 40 40 50 75 100 100 75 100 100

10H 8 6 8 8 8 8 8 8 8

CWD 15 15 20 40 20 20 40 20 20

Soil 10 10 10 20 20 15 20 15 1

7 had the most fuel beds burned under the driest moisture scenario (A). The most 

fuel beds from wettest scenarios (F and I) were found in clusters 2, 5, and 6. Class 

5 and 9 had approximately equal representation of all moisture scenarios; whereas 

the number of fuel beds burned under each scenario varied considerably within 

classes 1, 2, 6, and 7. Of the 10 moisture scenarios created for this study, two 

were almost identical in their maximum, minimum, and mean values for all of the 

FOFEM outputs (i.e., scenarios D and H). Two other pairs (G and J; and F and I) 

were so similar that we considered eliminating one of each pair to run subsequent 

statistical analyses. However, Kruskal-Wallis comparisons showed significant differences among all the scenarios so we retained all of them.

The effect of fuel and soil moistures on depth of burn, fuel consumed, and smoke 

duration is shown in Fig. 6. Moisture scenario A, which is the driest scenario, 

had the deepest burns and high fuel consumption. The wettest scenarios (H and I) 
USDA Forest Service RMRS-RP-96. 2012. 17

predicted shallow burns and longer smoke durations for those fuels that could burn 

(i.e., depth >-1). Clusters 2 and 6 consistently plotted with shallow burn depths, 

low fuel consumption, and moderate smoke. Clusters 1, 7, 8, which predicted long, 

deep burns in cluster analysis (Fig. 3), consistently occurred in the upper portion 

of each moisture scenario but the amount of fuel consumed resulted in different 

spreads for the groups (Fig. 6 above 2 cm depth of burn). Scenarios with moist 

fuels and soil predicted little consumption but longer smoke if more fuels were 

consumed. Scenario E was perhaps the most interesting because all cluster groups 

were spread out along all three axes.

Class Verification

The uniqueness and consistency of the nine classes produced from the synthetic 

dataset was verified using three methods: cluster analysis of an independent dataset (i.e., the actual dataset), classification and regression tree analysis (CART), and 

discriminant analysis (DA).

Verification Using an Actual Dataset

Agglomerative hierarchical clustering of the actual data set showed the same 

general branching patterns in the dendrogram as the synthetic data set. Both the 

synthetic and actual data sets had classes at the far right and left sides of the dendrogram to create the difference between six and nine groups (Fig. 7). Initial 

clusters were divided on the same soil temperatures and burn depth variables as the 

synthetic data set. Subsequent divisions were also similar to synthetic data set, but 

the actual data set had much lower average fire intensities and total consumption 

Figure 5—Scatterplot of group distributions for the three variables explaining the most variation in the synthetic data set 

(FIAvg = 46.9%; FTime = 27.6%; SL60 = 19.4%). Groups were created by agglomerative clustering. The most variation 

in fire intensity occurs within groups 2, 3, and 4. Plot shows results from simulation burning of all fuel beds in all moisture 

scenarios (n = 115,280). FIAvg = average fire intensity (KW m-2), SL60 = soil depth (cm) that reaches 60°C, FTime = fire 

duration (sec).
18 USDA Forest Service RMRS-RP-96. 2012.

Figure 6—Scatterplots showing the variation in total consumed (kg m-2), depth of burn (cm), and smoke duration (seconds) 

for the 10 moisture scenarios described in Table 2 (n = 11528 fuel beds for each scenario). Each cluster is color coded 

to show the variation in abundance of fuelbeds from each group. Each moisture scenario contains members from all 

clusters. Kruskal-Wallis comparisons showed the moisture scenarios to be significantly different (p < 0.0001). For the 

three variables shown in these scatterplots, all were also significantly different among the scenarios (Kruskal-Wallis 

p < 0.0001). Surface burns are at 0 on the depth of burn axes and -1 indicates no burn.
USDA Forest Service RMRS-RP-96. 2012. 19

values because the ranges of fuel loading were more restricted. The main differences between the classification created with the actual data set and the synthetic 

data set occurred within the “deep burn” groups (right side of dendrogram, Fig. 3) 

where the actual field data contained only 1/10 of the surface fuels for total consumption and fire times differed by 1⁄2 to 3⁄4 that of the synthetic data set. Smoke 

duration was also important to dendrogram branching earlier in the actual data set 

than in the synthetic data set.

Verification with Classification and Regression Tree Analysis 

(CART)

CART split the burned fuel beds based on the standardized fire effect outputs 

differently than cluster analysis. Where depth of soil heating was most important 

in cluster analysis, fire time (FTime) was most important in CART for dividing the 

actual and synthetic data sets. For the synthetic data set, the difference between 

short- and long-burning fires created the initial split in classes (Fig. 8b); soil heating, either in the top 1 cm (0.4 in) of the soil horizon (Z_S1, right branch, Fig. 8) 

or in the depth of the soil horizon that reached lethal temperatures (Z-SL60, left 

branch, Fig. 8), was of secondary importance. In CART analysis of the synthetic 

data set, Classes 3 and 4 were not distinguished; both were characterized by few 

fuel beds in comparison to other groups and very high average fire intensities. 

CART analysis of both data sets also resulted in several classes (Classes 2,3,5,6 

and 7) being duplicated on opposite branches of the regression tree (Fig. 8). The 

Figure 7—Dendrogram showing the groupings of the actual fuel beds based on outputs from the simulation burns in 

FOFEM. The cluster was run using Ward’s method and standardized variables for total fuels consumed (TotCon), 

average fire intensity (FIAvg), duration of fire (FTime), deepest soil layer to reach 60 oC (SL60), and maximum 

temperature reached at 1 cm below the surface (S1). Additional variables that distinguish groups include temperature of 

soil layer 5 centimeters below the surface (S5) and smoke duration (SmoDur). Each branch is labeled with its dominant 

characteristic(s). Numerical values are means for each division. The 9-group and 14-group divisions are designated by 

the horizontal lines. N = 115,280.
20 USDA Forest Service RMRS-RP-96. 2012.

Figure 8—Classification and 

regression trees based on the 

nine classes created during 

hierarchical cluster analysis (A) 

actual data set (B) synthetic data 

set. Variables included total fuels 

consumed (TotCon) in kg m-2, fire 

time (FTime) in sec, temperature 1 

cm (0.4 in) below the soil surface 

(S1) in oC, deepest soil depth 

(SL60) to reach 60 oC (140 oF) in 

cm, and the average fire intensity 

achieved during the burn (FIAvg) 

in Kw m-2. Standardized values 

of each variable were used in the 

analysis but the corresponding 

raw values were used as labels 

in this illustration. Terminal nodes 

are labeled with the class value 

from hierarchical agglomerative 

clustering.
USDA Forest Service RMRS-RP-96. 2012. 21

splits based on FTime failed to uniquely assign individual results to one single 

class within the synthetic data set because fuel beds on both sides of the first CART 

split burned for longer than 7,042 sec. Within R, the pruning rules for the CART 

tree required that the minimum number of observations in a node be 30 before a 

split is attempted and the split must decrease the overall lack of fit by 0.001. All of 

the classes from the cluster analysis had over 30 observations (Table 4).

Verification with Discriminant Analysis

Statistical summaries showed significant differences between the means of the 

five fire variables using Wilk’s Lambda (p < 0.0001) and highly correlated values 

for SL60 and S1 (>0.86). Five canonical discriminant functions showed how much 

variation in the data was explained by each of the fire variables. The first canonical 

discriminant function, and the most important, related to FIAvg. It had a canonical correlation (CC) of 0.935 and explained 46.9% of the variation in the data. 

The other significant canonical discrimination functions, in decreasing explanatory order, were FTime (CC 0.896 explaining 27.6% of the variation), SL60 (CC 

0.860, 19.4%), TotCon (CC 0.687, 6.1%), and S1 (CC 0.058, 0%). Of the original 

groupings found in cluster analysis, 97.9% were classified in the same groups using discriminant analysis resubstitution; 97.7% of fuel beds classified in the same 

groups as cluster analysis using discriminant analysis cross-validation (Table 7). 

The class with the greatest error in assignments was Class 2 (8.82%); classes 3, 4, 

7, and 9 all classified with <1% error compared to the cluster groups.

Table 7—Classification results from non-parametric discriminant analysis using standardized values for total consumed 

(TotCon), average fire intensity (FIAvg), fire time (FTime), maximum temperature in the upper 1-cm soil horizon (S1), and 

depth that reached 60 oC in subsurface (SL60).

Tests of equality of the group means for these five variables using Wilkes Lambda showed the means were significantly 

different (p < 0.000). Eigenvalues for the first five canonical discriminant functions were 6.9 (explaining 46.9% of variance), 

4.0 (27.6%), 2.85 (19.4%), 0.895 (6.1%) and 0.003 (0%), respectively.

Predicted group membership using non-parametric discriminant analysis (percent)

CLUSTER 1 2 3 4 5 6 7 8 9 Total

Re-substituted a 1 98.18 0 0 0 0 0 0.43 1.39 0 100

2 0 97.13 0.02 0 0 1.04 0 0.91 0.89 100

3 0 0 100 0 0 0 0 0 0 100

4 0 0 0 100 0 0 0 0 0 100

5 0.06 0 0 0 97.42 0.22 0.47 0.64 1.19 100

6 0 3.12 0 0 1.62 91.23 0 0.84 3.19 100

7 0.26 0 0 0 0 0 99.73 0.01 0 100

8 0.35 0.06 0 0 0.05 0 0.12 99.09 0.32 100

9 0 0.04 0 0 0.58 0.01 0 0.19 99.18 100

Total 10.85 18.49 0.41 0.23 19.47 24.84 8.11 9.18 8.43 100

Errord 1.82 2.87 0 0 2.58 8.77 0.27 0.91 0.82 2.01

Cross-validated b, c 1 97.58 0.01 0 0 0.02 0 0.74 1.65 0 100

2 0.03 96.99 0.04 0 0 1.05 0 0.96 0.93 100

3 0 0.64 99.15 0.21 0 0 0 0 0 100

4 0 0 0 100 0 0 0 0 0 100

5 0.07 0 0 0 97.25 0.23 0.53 0.71 1.22 100

6 0 3.12 0 0 1.64 91.18 0 0.86 3.19 100

7 0.51 0 0 0 0.04 0 99.44 0.01 0 100

8 0.57 0.09 0 0 0.05 0 0.13 98.79 0.37 100

9 0 0.06 0 0 0.58 0.01 0 0.29 99.06 100

Total 10.83 18.47 0.41 0.23 19.45 24.83 8.14 9.21 8.44 100

Errord 2.42 3.01 0.85 0 2.75 8.82 0.56 1.21 0.94 2.29

a

 97.99% of re-substituted cases correctly classified.

b

 97.71% of cross-validated grouped cases correctly classified.

c

 Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all 

cases other than that case. d

 Error count estimates for classification variable (rate) expressed as percent.
22 USDA Forest Service RMRS-RP-96. 2012.

Summary of Validation Results

The results of the three validation methods can be summarized as follows:

1) The synthetic and actual datasets produced the same number of classes (Figs. 

3, 7).

2) Even though the actual dataset had fuel beds with less than half the biomass 

for each fuel component than the synthetic dataset, the clustering dendrogram 

showed essentially that the same fire variables were important to each major 

branch and at similar levels in the dendrogram. The limited ranges in biomass 

values resulted in lower temperatures and burn times that mainly affected the 

labeling of split branches more than the actual order or differences variable 

importance (Fig. 7).

3) CART created classes of fire severity based first on fire time (FTIME), which 

made comparison with methods that split first on soil heating (S60 and S1) 

difficult (Figs. 3, 8).

4) CART created 10 (11 in actual dataset) classes, but some of the classes were 

duplicated on opposing branches of the regression tree (i.e., Class 6) and 

Classes 3 and 4 were missing altogether (Fig. 8). Separation of Classes 1, 8, 

and 9 was incomplete because each of these groups contained fuel beds with 

fire times greater than 7,042 seconds but the groups reside only on the left side 

of the diagrams where fire times were less than 7,042 sec.

5) Discriminant analysis produced similar results to hierarchical clustering, 

but Class 6 had 8.82% classification error and Classes 2 and 5 each had 

approximately 3% of their total fuel beds misclassified (Table 7).

Discussion

We successfully created a fire severity classification that merged fire effects 

from surface fuel loadings with associated fire behavior for a wide range of 

moisture conditions. Using a classification process based on empirical data, the 

variance inherent in over 115,000 very different fuel beds was objectively distributed among nine unique, identifiable groups. Fuel beds were correctly placed into 

the nine fire severity classes 98% of the time using subsets of the synthetic fuel 

beds (Table 7) and independently verified using fuel loading data from real fuel 

beds (Fig. 7). More importantly, the classification seems to uniquely identify these 

classes in ways that are important to planning and management (Tables 5, 6) and 

presents groups of fire effects and fire behaviors that are realistically different and 

meaningful (Fig. 3, 4). Even though the classification was created with computer 

simulation, the creation of unique, realistic, and reproducible classes gave us confidence that the classes would be useful predictors of fire severity from on-site fuels.

Evaluation of the Fire Severity Classification Process

Our main objective in this study was to present a methodology to create an 

objective fire severity classification based on fire behavior and effects that would 

represent loadings and combustion across a wide diversity of fuel beds and improve 

predictability of fire effects. We found this process to be quite straightforward: (1) 

find or create a large number of fuel beds, (2) find a mechanism to burn them, (3) 
USDA Forest Service RMRS-RP-96. 2012. 23

group the fuel beds together by the outputs they have in common, and (4) find a 

method to verify that the classes are uniquely identified and consistent.

Burning the real and synthetic fuel beds was the most problematic step in the 

classification process because the actual burning of so many diverse fuel beds on 

the landscape is impractical. Measuring effects that result from burning different 

vegetation types can also be problematic because each effect requires a different 

measurement during an actual burn. For example, measuring how deep a fire affects in the soil horizon during burning would require one set of instrumentation 

but measuring fire residence time in the different fuel components would require 

quite another. For most fires, collecting these measurements and relating them to 

the physical properties of the fire is time consuming, costly, and labor intensive. 

During the past four decades, significant progress has been made in the development of fire models that more realistically describe the physical aspects of fire and 

predict how different types of fuel beds burn (Anderson et al. 2006; Andrews 1986; 

Finney 2004; Parsons et al. 2011; Reinhardt and Keane 1998). These models make 

the simulation of fuel bed combustion more practical than using actual fire on the 

landscape and have paved the way for creating a basic classification based solely 

on fuels.

The main problems with the current burn simulation models is that (1) they have 

no spatial context that would vary fire effects based on the spatial arrangement of 

fuels on the ground (Reinhardt et al. 1997); (2) they cannot incorporate topography 

or wind, both of which are crucial to realistic fire severity values on the surface 

and in the soil; and (3) FOFEM, in particular, has been shown to underestimate 

duff consumption and fireline intensity in some fuel types (Hood et al. 2007). That 

said, it is more practical to simulate fuel bed combustion with these limited models 

than to use fire on the landscape to burn the large number of fuel beds needed to 

create an adequate classification that covers numerous possibilities of fuel components and moisture combinations. Moreover, our severity classification is “tuned” 

to FOFEM, which means that we can approximate likely fire severity prior to burning using FOFEM if we can know or estimate likely fuel and soil moistures.

Grouping fire behavior and fire effects variables from burn simulations under 

different moisture scenarios was straightforward using the agglomerative hierarchical clustering. It was also the most objective method for grouping because it 

assured that the number of groups was determined by the similarities in the data 

and not by operator bias. The number of classes used for this study could easily 

have ranged from six to 20 based on the cluster optimization tests, but our analyses 

ultimately led us to a nine-group surface fuel classification that best represented 

several different types of fires and was also relevant to fire effects that can be observed in the field. Constructing a classification based on more than nine classes 

was much more difficult because unique fire effects, intensities, and fire times were 

difficult if not impossible to distinguish between groups (Figs. 3, 4). Plotting the 

fuel beds by fire effect showed the same groups identified with cluster analysis 

plotting together on the scatterplot (Fig. 6) and the fire effects predicted within 

each scenario seemed reasonable. For example, very dry scenario A burned deeper 

and consumed more fuel than the wetter I or J scenarios, which had much less fuel 

consumed and often did not burn (i.e., depth of burn = -1).

Validating the classes proved to be challenging because the three different methods produced three different results, although the differences among techniques 

were not as great as expected. Some of the differences in verification results can 

be explained by the way the individual classification processes run. CART and 

DA use different approaches to dividing groups (McCune and Grace 2002). The 

missing classes 3 and 4 in CART and the arrangement of the tree could be due 
24 USDA Forest Service RMRS-RP-96. 2012.

to either the stopping rules (clusters 3 and 4 have small numbers of samples) or 

pruning rules that are based on the variability within and between groups or how 

much classification error remains. Why CART separated the fuel beds on fire time 

instead of depth of burned like the hierarchical clustering is not known for certain (order of variables was not a factor). Theoretically, fire time and depth of 

burn should be highly correlated, and we did find them significantly correlated 

with correlation analysis (Pearson correlation coefficient = 0.04, p < 0.0001). We 

also found FTime highly correlated with fire temperatures at depth (S1 and S5, 

p < 0.0001). Because soil heating and depth of burn are rate-limited processes, 

duration of burning or fire time should be much more important to creating fire 

effects than fire intensity, which make the CART classes additively informative to 

our fire severity classification.

Limitations of the Classification and Suggestions to 

Improve Predictability

Our classification can be compared only generally to other fire severity classifications that quantitatively describe fire effects in the soil horizon. Feller’s (1998) 

low fire severity classes from slash burning would correspond to the depths of 

burn <2 cm (0.75 in) on the left side of our dendrogram (Fig. 3). All of Feller’s 

other classes (moderate to very high) fall within the deeper burns on the right 

side of the dendrogram (classes 1, 7, and 8). There was no one-to-one correspondence between his classification and ours. The classes developed by Trowbridge 

et al. (1989) using duff depth and total consumption of slash fuels are not readily comparable to the biomass and consumption values for each fuel bed in our 

classification. Trowbridge et al. (1989) used a maximum of duff consumed; our 

classification focuses on the depth of the original duff and what fire effects burning 

that thickness of duff would cause. Both Trowbridge et al. (1989) and Feller (1988) 

focused on fire effects from burning slash piles. Our classification is based on a 

much broader range of fuel components and focuses on the diverse fuel types that 

would be found throughout the United States.

While the presented classification can be used in the field in its current form, 

there are several limitations that must be addressed when it is used. First, most forest and non-forest surface fuels are represented within this classification by size or 

origins, not by specific genera or species composition (e.g., feather moss, lichen, 

pine-needles or grass are not specifically incorporated in the fuel beds but may be 

considered as part of the herb or, when dried, litter classes). The classification does 

not, incorporate any tree canopy fuels or their associated fire effects into its design 

and it should not be used to predict fire severity within the canopy or tree strata. 

Second, although we feel that the fire variables used to construct the classification 

span the vital characteristics in fuel consumption—fire time and soil effects—we 

may have limited its usefulness by using only five fire variables and excluding 

other fire effects in its construction. Our rationale for excluding several FOFEM 

outputs, which we did explore during this process, was that these five showed the 

most promise for field applications and providing ecological differences. The effect of this decision can be tested in future work by classifying the datasets using 

predictors other than these five and comparing the results. Third, the BURNUP 

model in FOFEM used to simulate fuel bed combustion may inaccurately portray 

some combustion dynamics for simulated data because the fuel characteristics may 

be outside the limits of its algorithms or the model assumptions. The duff consumption model is particularly problematic and needs refinement in all fire models that 
USDA Forest Service RMRS-RP-96. 2012. 25

use its algorithms. Carefully planned experiments in the lab or field to solve the 

thin-duff/heat insulator problem and the deep duff effects are critical to creating a 

better classification. Finally, managers who use this classification should be aware 

that the class characteristics will greatly benefit from verification. Verification 

will require measuring pre-burn fuel beds; then recording temperatures during the 

burn, fuel consumption, and fire effects at the exact points where the fuels were 

measured. Although it is impractical to observe the large number of fuel beds and 

range of fires represented within this classification due to economic, logistical, and 

methodological constraints, a class or subset of each group could be tested in the 

laboratory or field to verify our class characteristics. Because this classification 

is based on physical properties of fire, it can be explored or tested with carefully 

planned experiments and, to a limited extent, in the field to determine if the classes 

created with computer simulation reasonably reflect the fire effects for different 

intensities of fire or if they need refinement to match real situations. Such experiments would also inform development of improved models linking fire behavior to 

fire effects, particularly if observations were spatially explicit and captured spatial 

heterogeneity in fuels and fires.

Ultimately, this classification represents the most complete, objective, empirical integration of fuels, moisture effects, and fire characteristics to date. It is most 

useful as a tool for predicting fire severity from pre-burn fuels and moisture conditions. Exceptions to its use may be fuel beds with extremely thin or extremely thick 

duff that FOFEM may not model adequately at this time. Furthermore, this classification is not meant to be used in a post-burn environment where burning has 

destroyed surface fuels. Sometimes pre-fire conditions can be assessed from unburned areas within the burn, or areas adjacent to the burn, to provide insight into 

fuel consumption, soil heating, and fire intensity. Other times, indices from other 

studies can be used in conjunction with this classification to estimate how severely 

fire has affected parts of the landscape. Jain et al. (2012) have summarized some 

of the indices that can be used in a post-burn environment to estimate fire severity 

effects in the soil and on trees. Additional work will be needed to tie predicted with 

actual post-burn effects using experiments or prescribed burns as described above.

Summary

The future of fire severity work will lie in quantitative studies that extend the 

same classification procedure used with the surface fuels (i.e., create fuels, burn 

them, classify the effects, and verify classes) to other biotic and abiotic components to form a complete picture of fire severity. In addition to the surface fuels, 

tree canopy consumption is an important fuel source that will need to be quantified. 

Physical effects due to the combustion process, such as ash production or nutrient 

changes, will also need to be quantified to create another piece of the fire severity 

picture. The physical properties of each of these types of fuels (e.g., their moisture 

content, depth, biomass, size and shape), biotic and abiotic characteristics, and the 

physical properties of the fire resulting from combinations of these fuels and biota 

burning on the landscape, have relationships that are observable, testable, and, ultimately, predictable. Exploring these relationships in quantitative terms presents 

one direction for future work in burn severity classification. Just as fire intensity, 

spread rates, and flame lengths are physical measures that have made fire behavior 

models more realistic, we feel that fire ecology critically needs a set of quantitative variables that describe fire effects in terms of the physical characteristics 
26 USDA Forest Service RMRS-RP-96. 2012.

of a fire’s effect to make fire severity classifications more realistic and versatile. 

Consider, for example, how much more versatile percent tree mortality is to relate 

to fire characteristics like intensity or flame duration than a fire severity category of 

low, medium, or high. Quantitative values more effectively link fire effects to the 

flaming and smoldering combustion processes represented in models that link fire 

behavior and effects. Likewise, we need variables that are ecologically meaningful 

and that we can readily measure at multiple scales remotely and in the field (Hudak 

et al. 2007; Lentile et al. 2006, 2007). Addressing the issue of variability of burns 

and how that affects fire severity classes across those spatial scales will take an 

integrated approach by many fields of science, including soils, geology, forestry, 

and remote sensing, among others. The proposed classification is an important step 

toward describing the mechanisms of fire severity that can provide the flexibility 

needed for use across multiple scales, applications, and projects.

According to Ryan and Noste (1985), a fire severity classification should possess 

several key attributes. It should be (1) a meaningful index of ecological change; 

(2) broadly applicable; (3) useful for predicting fire effects moderately accurately; 

(4) applicable to all types of fires; (5) applicable to many vegetation types; (6) 

easy to use; (7) safely implemented (i.e., with post-fire observations); and (8) easy 

for managers, scientists, and others to compare and evaluate field observations 

with predicted results. The classification created in this study meets most of these 

criteria, with the possible exception of being applicable to all types of fires. It is 

not meant to apply to crown fires in forests, but it could be expanded to do so in 

the future using a similar developmental process to this surface fuel fire severity classification. Like the fire severity rating system created by Ryan and Noste 

(1985), this surface-fuel classification integrates pre-fire conditions, fire behavior, 

and fire effects. However, it adds new dimension to the classifications previously 

developed by Ryan and Noste (1985), Feller (1998), and Trowbridge et al. (1989) 

because it places quantitative values on the fuels, moistures, and fire characteristics needed to produce specific fire effects. Its real strength is in the assessment 

of potential fire effects from pre-burn fuel variability and exploring the physical 

relationships with fire. Ultimately, it is just one step toward a more quantitative, 

objective, scalable approach to predicting fire severity from very accessible data, 

that is, the surface fuels and other on-site parameters.

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32 USDA Forest Service RMRS-RP-96. 2012.

Appendix A—Ranges of fuel components and moisture 

characteristics for each class in the synthetic data set 

(N = 115,280).

Range of duff biomass within each of nine classes. Classes created by agglomerative clustering of 115,280 fuel beds and five variables. Box and whisker plots 

show the 25th percentile (lower edge of box), median (line within box), the 75th

percentile (upper edge of box, extreme upper and lower values [whiskers from 

box], and outliers (circles).

A. Range of duff within each of the nine classes.

B. Range of litter within each of the nine classes. 

C. Range of 1-hr fuels within each of the nine classes.

D. Range of 10-hr fuels within each of the nine classes.

E. Range of 100-hr fuels within each of the nine classes.

F. Range of coarse woody debris values within each of the nine classes.

G. Range of duff moisture values within each of the nine classes for duff layers 

comprising each fuel bed.

H. Range of moisture values within each of nine classes for the 10-hr fuels component of each fuel bed.

I. Range of moisture values within each of nine classes for the coarse woody debris 

fuels component of each fuel bed.

J. Range of moisture values within each of the nine classes for the soil moisture 

component of each fuel bed.
USDA Forest Service RMRS-RP-96. 2012. 33

A. Range of duff within each of the nine classes. 

B. Range of litter fuels within each of the nine classes.
34 USDA Forest Service RMRS-RP-96. 2012.

C. Range of 1-hr fuels within each of the nine classes.

D. Range of 10-hr fuels within each of the nine classes.
USDA Forest Service RMRS-RP-96. 2012. 35

E. Range of 100-hr fuels values within each of the nine classes.

F. Range of coarse woody debris within each of the nine classes for duff layers 

comprising each fuel bed.
36 USDA Forest Service RMRS-RP-96. 2012.

H. Range of moisture values within each of nine classes for the 10-hr fuels 

component of each fuel bed.

G. Range of duff moisture values within each of the nine classes for duff layers 

comprising each fuel bed.
USDA Forest Service RMRS-RP-96. 2012. 37

J. Range of moisture values within each of the nine classes for the soil moisture 

component of each fuel bed.

I. Range of moisture values within each of nine classes for the coarse woody 

debris fuels component of each fuel bed.
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