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General Technical Report

RMRS-GTR-225

May 2009
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Sikkink, Pamela G.; Lutes, Duncan C.; Keane, Robert E. 2009. Field guide for identifying fuel loading models. 

Gen. Tech. Rep. RMRS-GTR-225. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 33 p.

Abstract

This report details a procedure for identifying fuel loading models (FLMs) in the field. FLMs are a new classification 

system for predicting fire effects from on-site fuels. Each FLM class represents fuel beds that have similar fuel loadings 

and produce similar emissions and soil surface heating when burned using computer simulations. We describe how to 

estimate fuel load in the field, match the load estimates to an appropriate FLM, and use the FLMs to predict the smoke 

or soil heating that could result from burning those loads. The FLM names can also be used as fuel descriptors in other 

applications, including inputs into fire models for predicting fire effects, data layers for mapping fuel conditions, and supplements to vegetation data for more complete environmental descriptions to use in restoration or wildlife habitat planning.

Keywords: First-order fire effects, fuel classification, fuel loading, fuel mapping, fuel classification key

The Authors

Pamela G. Sikkink is a Research Ecologist with Systems for 

Environmental Management, Missoula, MT (email: pgsikkink@

fs.fed.us). Her recent research focuses on comparing the 

effectiveness of fuel-sampling methods used in western coniferous forests to measure fuels and on investigating how fire 

intensity affects surface-fuel consumption and soil characteristics. She has been involved in the integration of FIREMON 

and the Fire Ecology Assessment Tool, which produced a tool 

to store and organize inventory and monitoring data from many 

different vegetation sampling methods. She has also sampled 

grassland and shrubland ecosystems in Yellowstone National 

Park and in western Montana since 2002 as a volunteer, collecting vegetation data for select multiple-decade monitoring 

programs. She received a B.S. degree in biology and geology 

from Bemidji State University, MN. She earned M.S. degrees in 

geology and forestry and a Ph.D in forestry from the University 

of Montana, Missoula.

Duncan C. Lutes is a Fire Ecologist with the U.S. Forest 

Service, Rocky Mountain Research Station at the Missoula 

Fire Sciences Laboratory, Missoula, MT. He has been involved 

in the development of the First Order Fire Effects Model, the 

Fire and Fuels Extension to the Forest Vegetation Simulator, 

the FIREMON fire effects monitoring system, Fuel Calc, and 

the integration of FIREMON and the Fire Ecology Assessment 

Tool. Duncan has B.S. and M.S. degrees from the University 

of Montana, Missoula.

Robert E. Keane is a Research Ecologist with the U.S. Forest 

Service, Rocky Mountain Research Station at the Missoula 

Fire Sciences Laboratory, Missoula, MT. Since 1985, he has 

developed various ecological computer models for the Fire Effects Project for research and management applications. His 

most recent research includes the synthesis of a First Order 

Fire Effects Model; construction of mechanistic ecosystem 

process models that integrate fire behavior and fire effects 

into succession simulation; restoration of whitebark pine in the 

Northern Rocky Mountains; spatial simulation of successional 

communities on the landscape using GIS and satellite imagery; 

and the mapping of fuels and fire regimes for fire behavior 

prediction and hazard analysis. He received a B.S. degree 

in forest engineering from the University of Maine, Orono; an 

M.S. degree in forest ecology from the University of Montana, 

Missoula; and a Ph.D in forest ecology from the University of 

Idaho, Moscow.

Acknowledgments

Partial funding for this project was provided by the U.S. Forest 

Service Fire and Aviation Management in conjunction with the 

LANDFIRE Prototype Project. We thank Wendel Hann, U.S. 

Forest Service FAM, for financial support; and Roger Ottmar, 

U.S. Forest Service Pacific Wildfire Sciences Lab, for granting 

use of his photo series photographs to depict fuel loads in our 

appendices. We thank Matt Reeves, USFS; Laura Hudson, National Park Service; and Ben Butler of the Student Conservation 

Association for their suggestions to improve the manuscript.
i

Contents

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

What is an FLM? ..........................................................................................................2

How Were the FLM Classes Developed?.....................................................................2

Forested areas ................................................................................................2

Non-forested areas..........................................................................................5

How Can Managers Use FLMs? ..................................................................................7

Identification of Fuel Loading Models in the Field .....................................................................7

Identifying an FLM in Forests ....................................................................................................8

Identifying an FLM in Grasslands, Shrublands, or Chaparral.......................................8

Management Advice..................................................................................................................9

References ..............................................................................................................................10

Appendix A—Field Form for Recording FLM Data ..................................................................12

Appendix B—Tables for Calculating Biomass of Duff and Litter in Forested Areas.................13

Appendix C—Fine-Woody Debris Loadings for Forested Areas .............................................15

Appendix D—Photoloads of Coarse Woody Debris Loadings for Forested Areas..................16

Appendix E—FLM Key for Forested Areas (T acre–1) .............................................................21

Appendix F—FLM Key for Forested Areas (kg m–2)................................................................23

Appendix G—Representative Fuel Loads in Sagebrush Areas...............................................25

Appendix H—Representative Fuel Loads in Non-Sagebrush Areas .......................................26

Appendix I—FLM Key for Non-Forested Areas ......................................................................30

Appendix J—Practice Set: Using the FLM Key .......................................................................31

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 1

Introduction ____________________

Historically, fuel classifications used to estimate fire 

effects have been based on the vegetative characteristics 

of a particular site or location (Reinhardt and others 1997; 

Sandberg and others 2001). Vegetation-based classifications generally use cover type, structural stage, and/or 

habitat type (Mueller-Dombois 1964; Pfister and Arno 

1980) as surrogates for describing the type and quantity 

of fuels on the ground and in the forest canopy (Hawkes 

and others 1995; Keane and others 2006; Mark and others 

1995; Shasby and others 1981). The rationale for using 

vegetation characteristics to classify fuels is that fuels 

are ultimately derived from vegetation, so knowing how 

much fuel a particular vegetation type produces should 

provide an acceptable estimate of fuel load on the ground. 

However, vegetation-based fuel classifications fail to 

recognize that (1) fuel beds, or the fuels in the surface fuel 

and litter/lichen/moss strata (Scott 2007), are composed 

of diverse fuel components (for example, a combination 

of downed woody debris, shrubs, and herbs as opposed 

to only shrubs or only litter), (2) each fuel component is 

highly variable in loading across space and time, (3) the 

fuels and the vegetation may have different disturbance 

histories in space and time that affect their correlation 

(Brown and Bevins 1986), and (4) most sampling methods 

are limited in their ability to capture both fuel variability 

and how much fuel is produced by any particular vegetation type (Brown and See 1981; Lutes 1999, 2002). 

Alternatives to vegetation-based classifications have 

been developed to classify fuels that are input into fire 

behavior computer models such as BEHAVE and FARSITE (Andrews 1986; Andrews and Bevins 1999; Finney 

2004). These fuel classifications, which are also known 

as Fire Behavior Fuel Models (FBFMs), include only 

fuel bed components that are important for predicting fire 

behavior. They consist of a limited number of fuel beds 

that, in turn, have limited load values for fine fuels and 

live herb and woody material (Anderson 1982; Burgan 

1987; Scott and Burgan 2005). They do not include fuel 

greater than 3 inches (7.6 cm) in diameter because this 

material does not substantially contribute to fire spread. 

Field Guide for Identifying 

Fuel Loading Models

Pamela G. Sikkink, Duncan C. Lutes, and Robert E. Keane

In a sense, the fuel beds used within these FBFMs are 

artificial, or stylized, because individual fuel components 

were manually adjusted within each FBFM class to produce expected fire behaviors that follow the fire spread 

model of Rothermel (1972). Unfortunately, classifications that use artificial fuel beds, or exclude important 

fuel components, are inappropriate for computing fire 

effects like fuel consumption, smoke production, and tree 

mortality. For accurate simulation of these fire effects, 

most fire effects computer models, such as CONSUME 

and FOFEM (Ottmar and others 2008; Reinhardt and 

others 1997), require actual fuel loadings across all of 

the major surface fuel components.

Lutes and others (in press) recently created a new classification, called Fuel Loading Models (FLMs) specifically 

developed to predict fire effects from on-site surface fuels. 

Their FLM classification is one of the first classifications 

that categorize fuel beds into readily identifiable classes 

based on their predicted fire effects. It is unique because 

the FLM classes are readily identifiable in the field using 

on-site fuels. Over 4,000 actual fuel beds from across the 

United States were used to create the new classification 

and the individual groups within it are distinguished by 

two important fire effects—the amount of smoke that 

is produced upon combustion (specifically, the 2.5 μ 

particulate emissions) and the amount of soil heating. 

Both of these fire effects are important indicators of the 

physical and chemical changes that will occur on a site 

when fuels are burned. Tools, such as FLMs, that aid in 

predicting these fire effects are critical to fire management.

Unlike the vegetation-based approaches used to classify fire effects, FLMs use computer models to balance 

the high variability of fuel beds across a stand with the 

resolution needed to broadly describe unique fuel classes 

for the continental United States. Therefore, FLMs can be 

used to capture the variability of individual fuel components within a fuel bed, as well as describe differences in 

those fuel components across many spatial and temporal 

scales. FLMs are not designed to replace existing fuel 

classifications, such as the Fuel Characteristics Classification System (Ottmar and others 2007; Sandberg 

and others 2001), nor are they designed to eliminate the 
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

need for extensive fuel inventories using planar intersect 

techniques (Brown 1974; Lutes and others 2006). FLMs 

are solely intended to be an additional tool for managers to describe fuels for fire management. This report 

presents a quick and easy method for identifying a FLM 

so that its fuel information can be integrated with other 

applications, including computer predictions of fire 

effects.

What is an FLM?

Fuel Loading Models (FLMs) is a new classification 

system for predicting fire effects from on-site fuels. In 

this context, the word “model” denotes both the classification itself and the specific sets of fuel loadings and 

fire effects that define each class within it. Fuel loadings 

include the quantities of duff, litter, fine-woody debris, 

and coarse woody debris (logs) in tons per acre (T acre–1) 

or kilograms per meter2 (kg m–2). Fire effects include the 

type and amount of surface fuels consumed, the quantity 

of PM2.5 emissions (smoke), and the maximum soil 

heating obtained during combustion at 0.8-inch (2-cm) 

soil depth.

Like other classification systems, such as the National 

Vegetation Classification System (http://biology.usgs.

gov/npsveg/nvcs.html), there is a hierarchy within the 

FLMs. The most basic unit, or class, is the fuel loading 

model. Each FLM class differs significantly from every 

other FLM class when its fuel load composition is compared statistically (p<0.05) (Lutes and others, in press). 

When an FLM class is assigned to a particular location, it 

describes both the on-site fuels and the range of consumed 

fuels, particulate emissions (smoke), and maximum soil 

heating that may be expected from burning those fuels. 

For example, FLM 71 represents distinct ranges of smoke 

and soil heating that result when moderate to heavy logs 

and light duff are consumed during computer-simulated 

combustion (table 1). FLM 14, however, has very different predictions for smoke and soil heating because 

its main fuels consist of sagebrush (Artemisia spp.) at 

loadings of <12 T acre–1 (6.2 kg m–2). Because some 

FLM units produce emissions and soil heating effects 

that are similar, the classes can be grouped together at 

a higher hierarchical level. Groups of FLM classes that 

produce similar ranges of particulate emissions and soil 

heating are designated “Effects Groups.” The number used 

to name each FLM indicates (1) its Effects Group and 

(2) its class within an Effect Group. For example, FLM 

62 identifies the FLM as a member of Effects Group 6, 

but has the fuels, smoke, and soil heating characteristics 

of Class 2 within Effects Group 6. In general, increasing Effect Group numbers indicate higher particulate 

emissions and increasing maximum soil temperatures. 

The 10 Effects Groups and their associated fuels and fire 

effects are described in table 1.

How Were the FLM Classes Developed?

The FLMs were developed using slightly different 

methods for forested and non-forested areas. In the following sections, we provide an overview of how the classification was created for forested areas, which is taken 

from Lutes and others (in press). We also summarize how 

the classification was created for non-forested areas by 

D. Lutes. The FLM classification for non-forested areas 

and its development process have not been published 

elsewhere. A complete description of FLM development 

for forested areas is provided in Lutes and others (in press).

Forested areas—We define forested areas as having 

greater than 10% tree cover or having a tree species 

name for the cover type classification within the sample 

data. Lutes and others (in press) developed the FLMs 

classification for the forested areas using the following 

procedures:

1. An extensive database of plot-level fuel loadings 

was compiled from sampled fuel beds located across 

the United States. Initially, data were compiled using over 11,000 fuel beds, but the data used in the 

final classification of forested areas were ultimately 

reduced to 4,046 fuel beds that met selection criteria.

2. Each fuel bed was “burned” using computer-aided 

simulation. The First Order Fire Effects Model 

(FOFEM) (Reinhardt and others 1997) was used to 

simulate combustion and obtain predictions of fuel 

consumption, smoke emissions, and soil heating 

for each fuel bed.

3. The fire effects’ predictions were grouped into statistically unique groups of fuel beds using cluster 

analysis. The unique groups were called Effects 

Groups.

4. Unique fuel beds were determined using classification tree analysis (Breiman and others 1984) with 

the Effects Groups as the independent variable. 

These unique fuel beds of duff, litter, fine woody 

debris, and logs became the FLM classes.

5. The classification error was determined and validated using two different methods.

6. A key to the FLMs was created using the classification results.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

3

Table 1—The Effects Groups and their associated Fuel Loading Model (FLM) classes.

Effects groupnumber Effects group characteristics a

Fuel load characteristics of FLM classes within the Effects Group (forested sites only)

Abbreviated description of the Effects Group (in bold) b, cand its associated FLM classes

1 Low PM2.5 particulate emissions 

(<0.22 T acre–1)

<212 °F surface soil temperature

Light to no duff or litter (<2.23 T acre–1)

Fine woody debris > 2. 23 T acre–1

Wispy-Cool-SparseFLM 11: Light FWD, light to no duff d, eFLM 12: Moderate FWD, light litterFLM 13: Moderate FWD, light to moderate litter, light duffFLM 14: Shrub-Sagebrush with low total loadFLM 15: Shrub-Non-sagebrush with low total load

2 Low PM2.5 particulate emissions 

(<0.33 T acre–1)

212 to 392 °F surface soil temperature

2.23 to 4.46 T acre–1 duff

< 8.92 T acre–1 logs

Wispy-Warm-LightFLM 21: Light logs, light duff

3 Low PM2.5 particle emissions

(<0.33 T acre–1)

392 to 662 °F surface soil temperature

<8.92 T acre–1each of duff and litter

<8.92 T acre–1logs

Wispy-Hot-ModerateFLM 31: Moderate litter, light duff, light logs

4 Low PM2.5 particulate emissions(<0.33 T acre–1)

752 to 1112 °F surface soil 

temperature

Light to no duff, but litter and fine 

woody debris (>2.23 T acre–1) 

present

Wispy-Very Hot-LightFLM 41: Moderate FWD, light to moderate litter

5 Low PM2.5 particulate emissions 

(0.22 to 0.45 T acre–1)

<302 °F surface soil temperature

Heavy concentration of duff (13.4 to 17.80 T acre–1)

<4.46 T acre–1 logs

Wispy-Cool-ModerateFLM 51: Moderate duff, light logs f

FLM 53: Shrub – Sagebrush with medium total loadFLM 54: Shrub – Non-sagebrush with medium total load

6 PM2.5 particulate emissions 0.45 

to 0.81 T acre–1

<392 °F surface soil temperature

Duff highly variableLogs highly variable

Hazy-Warm-Moderate to HeavyFLM 61: Moderate to very heavy logs, light duffFLM 62: Moderate duff, light logs, light litter f

FLM 63: Moderate duff, light to heavy logs, light litterFLM 64: Moderate to heavy duff, light to heavy logsFLM 65: Shrub-Sagebrush with high total loadFLM 66: Shrub-Non-sagebrush with high total load

7 Low PM2.5 particulate emissions 

(0.22 to 0.56T acre–1)

347 to 572 °F surface soil temperature

4.46 to 22.30 T acre–1 duff; litter 

presentLogs highly variable

Wispy to Hazy-Hot-ModerateFLM 71: Moderate to heavy logs, light duffFLM 72: Moderate duff, light to moderate logs, moderate litter

(continued)
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

8 PM2.5 particulate emissions 0.56 

to 0.89 T acre–1

212 to 752 °F surface soil temperature

Up to 26.76 T acre–1 duff;

litter variableLogs 4.46 to 35.69 T acre–1 

Hazy-Warm to Hot-HeavyFLM 81: Very heavy logs, light duffFLM 82: Moderate duff, light to heavy logs, moderate litterFLM 83: Heavy to very heavy logs, moderate duff

9 PM2.5 particulate emissions 0.89 

to 2.23 T acre–1

<572 °F surface soil temperature

Duff, litter, logs and FWD biomass variable but relatively heavy biomass for each

Smokey-Cool to Hot-Very HeavyFLM 91: Heavy duff, light to heavy logsFLM 92: Very heavy logsFLM 93: Heavy duff, heavy to very heavy logs

10 PM2.5 particulate emissions 

>2.23 T acre–1

Most < 212 °F soil temp

Very heavy duff ORLight duff but heavy litter and logs <8.2 T acre–1

Smokey-Cool-Very HeavyFLM 101: Very heavy duffFLM 102: Heavy litter, light duff, light logs

 a To convert Effects Groups characteristics to metric:

For metric emissions: Multiply T acre–1 by 0.2242 to get Mg km–2For metric temperature: Multiply (°F – 32) by (5/9) to get °CFor metric fuel load: Multiply T acre–1 by 0.22417 to get kg m–2

 b Effects Group descriptions are arranged as Emissions-Soil Surface Temperature-Total Fuel Loads (e.g., Smokey-Cool-Very Heavy). Within each Effects Group, 

individual FLM classes are distinguished by differences in proportions of duff, litter, fine woody debris, and logs.

 c Ranges for the abbreviated Effects Group descriptions:

PM2.5 Emissions (smoke particles ≤2.5 mμ in diameter): Wispy <0.33 T acre–1 (<75 Mg km–2); Hazy 0.33 to 0.89 T acre–1 (75 to 200 Mg km–2); 

Smokey >0.89T acre–1 (>200 Mg km–2)

Temperature: Cool <212 °F (100 °C); Warm 212 to 392 °F (100 to 200 °C); Hot 392 to 752 °F (200 to 400 °C); Very Hot >752 °F (>400 °C)Total Fuel Loads : Sparse Very little duff or litter; usually less than 2.75 T acre–1 (0.62 kg m–2) of total fuel load; Light Mostly duff; total load is less than 

15 T acre–1 (3.4 kg m–2) even if coarse woody debris is present; Moderate Duff, litter and coarse woody debris present; total load rarely exceeds 

30 T acre–1 (6.7 kg m–2); Heavy Abundant biomass both in duff and logs; total load can vary between 35 T acre–1 and 60 T acre–1 (7.8 to 13.5 kg m–2); Very 

Heavy Abundant duff and very abundant biomass for logs; total load usually exceeds 45 T acre–1 (10 kg

m–2)

 d Ranges for fuel load components in abbreviated fuel load descriptions of FLM classes (forested sites only):

Duff: Light <12.9 T acre–1 (<2.9 kg m–2 ); Moderate 12.9 to 35.6 T acre–1 (2.9 to 7.9 kg m–2 ) Heavy 35.7 to 58 T acre–1 (8.0 to 13.0 kg m–2); 

Very heavy >58 T acre–1 (>13 kg m–2 )

Litter: Light ≤2.7 T acre–1 (≤0.6 kg m–2 ); Moderate 2.7 to 8.92 T acre–1 (0.7 to 2.0 kg m–2 ); Heavy >8.92 T acre–1 (>2.0 kg m–2)

Fine woody debris (FWD): Light <2.2 T acre–1 (<0.5 kg m-2); Moderate ≥2.2 T acre–1 (≥0.5 kg m–2)

Logs: Light <8.9 T acre–1 (<2 kg m–2 ); Moderate 8.9 to 12.9 T acre–1 (2.0 to 2.9 kg m–2 ); Heavy 13.0 to 26.8 T acre–1 (3.0 to 6.0 kg m–2 ); Very heavy >26.8

T acre–1

(>6 kg m–2 )

Note: Ranges for the fuel components described above are based on the FLM development rules of Lutes and others (in press). Some adjustments to those rules 

have been made so that they correspond to breaks in their dichotomous key and the FLM key developed for this paper.

e Unless otherwise noted, descriptions for FLMs are for forested sites.f FLM classes 51 and 62 have identical descriptions according to the criteria listed above (that is, Moderate duff, light logs, light litter). FLM 51 has approximately 2.23

T acre–1

(0.5 kg m–2) more duff load than FLM 62, which produces unique fire effects during computer simulations but does not produce short, unique fuel descriptions using the 

criteria above. We eliminated the light litter descriptor from FLM 51 to ensure all FLM descriptions were unique.

Effects groupnumber Effects group characteristics a

Fuel load characteristics of FLM classes within the Effects Group (forested sites only)

Abbreviated description of the Effects Group (in bold) b, cand its associated FLM classes

Table 1 (Continued).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 5

Data were selected for statistical analysis by Lutes and 

others (in press) based on the completeness of the data 

for surface fuel components and their spatial distribution 

across the United States. Each sample had to include load 

estimates of fine-woody fuels (i.e., the 1 hr, 10 hr, and 

100 hr fuel-moisture classes), coarse-woody fuels (logs 

>3 inches or 7.62 cm in diameter), and depths of the litter 

and duff. If any one of these six components was missing, 

the sample was eliminated from further consideration for 

the FLM study. Plot-level data quality was maintained 

by using only datasets that (1) were collected on plots 

not greater than 0.25 acres (0.1 ha); (2) did not include 

subjective assessments of loading; and (3) included all 

six fuel components needed for the FOFEM simulations 

(Lutes and others, in press). All of the selected datasets 

contained estimates of downed woody debris loads derived 

from using the planar intersect method (Brown 1971; 

van Wagner 1968; Warren and Olsen 1964). Duff and 

litter load was (a) estimated by averaging multiple depth 

measurements and multiplying by a predetermined bulk 

density or (b) calculated from dried samples. To insure 

that the classification was pertinent to many regions of 

the United States, the data were also selected based on 

regional distribution. Most data came from recent research 

and large inventory or monitoring projects conducted by 

the Department of Defense, Bureau of Land Management, 

Bureau of Indian Affairs, U.S. Forest Service, and the 

Student Conservation Association. Their projects were 

spread throughout the contiguous United States.

FOFEM was used to simulate fuel bed burning. FOFEM provided outputs for many fire effects; however, 

only two outputs were used to create the FLMs. These 

included 1) the smoke estimates measured in lb acre–1

or kg m–2 for the 2.5 mμ particulate (PM2.5) emissions 

and 2) the maximum temperature measured at the soil 

surface. These two estimates represented important effects resulting from a real burn. They were also poorly 

correlated, which made them good variables to include in 

the cluster analysis used to develop the Effects Groups.

Plots were grouped by cluster analysis based on the 

particulate and soil heating effects using agglomerative 

hierarchical clustering (Lance and Williams 1967). In this 

type of clustering, a plot is located in two-dimensional 

space based on the soil heating (x-axis) and emissions 

(y-axis). Each plot starts out as its own group; however, 

during an iterative process, plots are added to groups or 

groups are recombined until all of the plots are members 

of one cluster. At each iteration of the clustering process, 

there will be between 1 and n (for FLMs, n=4,046) clusters, 

and the plots are grouped in a way that minimizes the 

increases in the overall sum of the squared within-cluster 

differences. In the FLM study, the final number of clusters 

was set at 10 because it was found, through a number 

of exploratory analyses, that classification rules applied 

during the FLMs process could not uniquely identify 

differences between clusters when more than 10 clusters 

were used. The cluster analysis, and a complementary 

classification tree procedure used to verify the groupings, 

produced 21 forest-type FLMs in 10 Effects Groups. The 

accuracy of the FLM key developed from this process 

was tested with cross-validation and contingency table 

analyses, which estimated the misclassification error as 

34% and <30% respectively (Lutes and others, in press). 

Each FLM had a range of loading values for each fuel 

component that, when consumed, produced a respective 

smoke and heating effect. The median values for each of 

these fuel components are summarized for forest FLMs 

in table 2.

Non-forested areas—Non-forested areas have <10% 

tree cover and fuels that originate primarily from grass, 

herbs, or shrubs. The main difference between the process 

used to create the FLM classification for forested areas and 

the process used for the non-forested areas concerned how 

fuel loading was used. In forested areas, loadings of the 

six individual fuel components were entered directly into 

FOFEM and emissions and soil heating were calculated 

automatically. In shrub and grassland areas, fuel data was 

often collected as TOTAL biomass without distinction as 

to how much fuel was in each of the six fuel components. 

Therefore, fire effects had to be estimated using the total 

biomass for these areas. To use total biomass, a correlation had to be established between total fuel load and the 

amount of emissions that might result from burning that 

load so that fire effects were comparable for both forest 

and non-forest areas. The correlations between load and 

emissions were established using published emission 

factors for sagebrush and chaparral (DeBano and others 

2005; Fahnestock and Agee 1983; Frandsen 1987; Ottmar 

and others 1996; Sandberg and others 2002; Taylor and 

Sherman 1996).

Several assumptions were made to assign maximum soil 

temperatures as a fire effect in non-forested areas. In general, 

the heat pulse was considered short in these ecosystems 

and burn severities were considered minimal (Molina and 

Llinares 2001; Ryan 2002), thus maximum soil temperatures were also assumed to be low. Soil-temperatures in the 

non-forested areas were considered to be equivalent to, or 

less than, the lowest temperatures obtained by burning the 

forest fuel beds, which were obtained in Effects Groups 1, 

5, and 6. None of the temperatures for these Effects Groups 

exceeded 400 °F (200 °C).
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Six FLM classes were created for non-forest areas using 

the following procedure:

1. We used the PM2.5 emissions at the upper and lower 

boundaries of each Effects Group (from the forest 

classification) as upper and lower emission limits 

in the non-forest classification.

2. We calculated the amount of total plot fuel load 

needed to produce the emission values at the upper 

and lower boundaries of the selected Effects Group 

using published emission factors. The formula used 

for calculating total fuel load from emissions at the 

Effects Group boundaries was:

, where

L is the total plot fuel load in T acre–1 (kg m–2),

Epm2.5 is the PM2.5 emissions in lb ac–1

(MG km–2), and

EF is either (a) the PM2.5 emissions factor for 

sagebrush (26.7 lb ton–1 or 13.35 kg per metric 

megagram [MG–1]) OR (b) the PM2.5 emissions 

factor for chaparral or herbaceous (17.3 lb ton–1 

or 8.65 kg MG–1), depending on whether you 

are calculating sagebrush load or non-sagebrush 

and using English or metric measures.

(Note: The emissions factors were taken from 

the Smoke Management Guide [National Wildfire Coordinating Group 2001]. The equation 

assumes 90 percent consumption of the shrub 

and herbaceous fuel beds. Fuel consumption 

in herb and shrub systems is highly variable 

but 90 percent consumption was used to limit 

complexity and to represent a typical “worst 

case” scenario for emissions production. According to Green [1970], even heavily loaded 

fuel beds such as chaparral can approach 

consumption levels of 90%. Emissions factors 

for grassland dominated fuels are similar or 

slightly lower than in shrub dominated systems 

[National Wildfire Coordinating Group 1985] 

so we estimated grassland emissions using the 

chaparral emissions factors).

3. We selected all non-forested plots from the data 

set. Most of these plots were from the grasslands 

and shrublands of the western United States.

4. We compared the total fuel load of each individual 

plot to the upper and lower fuel load limits that 

were calculated for each Effects Group in step 2. 

Table 2—Median loadings for each forested FLM by fuel component in tons per acre (T acre–1) and kilograms per meter 

squared (kg m–2).

Litter Duff 1-hour 10-hour 100-hour Logs

Effects T T T T T T

FLM group acre–1 kg m–2 acre–1 kg m–2 acre–1 kg m–2 acre–1 kg m–2 acre–1 kg m–2 acre–1 kg m–2

011 01 0.18 0.04 0.00 0.00 0.07 0.01 0.08 0.02 0.04 0.01 0.00 0.00

012 01 0.27 0.06 0.00 0.00 0.27 0.06 1.56 0.35 2.68 0.60 2.59 0.58

013 01 2.50 0.56 1.20 0.27 0.23 0.05 1.52 0.34 2.05 0.46 2.23 0.50

021 02 1.16 0.26 3.30 0.74 0.20 0.04 0.64 0.14 0.67 0.15 0.94 0.21

031 03 1.87 0.42 7.31 1.64 0.27 0.06 0.89 0.20 1.07 0.24 1.52 0.34

041 04 2.41 0.54 0.00 0.00 0.27 0.06 1.67 0.37 2.57 0.58 2.59 0.58

051 05 1.52 0.34 15.83 3.55 0.16 0.04 1.10 0.25 1.42 0.32 1.43 0.32

061 06 0.89 0.20 3.61 0.81 0.29 0.06 1.31 0.29 2.46 0.55 16.73 3.75

062 06 1.34 0.30 20.56 4.61 0.14 0.03 0.93 0.21 1.25 0.28 1.61 0.36

063 06 1.52 0.34 17.04 3.82 0.20 0.04 1.39 0.31 2.38 0.53 7.76 1.74

064 06 2.90 0.65 25.87 5.80 0.31 0.07 1.11 0.25 1.66 0.37 3.35 0.75

071 07 2.19 0.49 9.37 2.10 0.45 0.10 1.42 0.32 2.19 0.49 11.51 2.58

072 07 3.79 0.85 16.77 3.76 0.38 0.09 1.01 0.23 1.33 0.30 2.85 0.64

081 08 0.89 0.20 3.97 0.89 0.33 0.07 1.16 0.26 2.65 0.60 36.35 8.15

082 08 3.79 0.85 17.71 3.97 0.53 0.12 1.58 0.35 3.16 0.71 12.04 2.70

083 08 2.50 0.56 11.91 2.67 0.50 0.11 1.58 0.36 2.87 0.64 22.43 5.03

091 09 1.16 0.26 45.94 10.30 0.31 0.07 1.42 0.32 1.66 0.37 2.90 0.65

092 09 3.03 0.68 14.85 3.33 0.55 0.12 1.60 0.36 3.32 0.74 46.12 10.34

093 09 6.20 1.39 32.83 7.36 0.57 0.13 1.40 0.31 2.40 0.54 21.50 4.82

101 10 9.99 2.24 99.99 22.42 0.39 0.09 0.94 0.21 1.08 0.24 1.61 0.36

102 10 17.97 4.03 10.48 2.35 0.14 0.03 0.49 0.11 1.05 0.24 4.59 1.03
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 7

We then assigned plots to the Effects Group with 

the appropriate range of total load and calculated 

the median emissions value for each Effects Group.

5. We calculated the total on-site fuel load required 

to obtain those emissions for each of the three Effects Groups using the median emissions value and 

the equation above (see table 3). We established 

the range of loadings for each Effects Group by 

plotting each Effects Group’s loading distribution 

(Appendix I).

The non-forest FLMs should not be used for tall 

(>6 ft) shrub communities (for example, California’s tall 

chaparral communities). Tall shrub communities have 

more biomass than the shrub communities analyzed for 

this classification, which results in higher fuel loads. 

They also have a more open structure than short shrub 

communities, which affects fuel bulk density. With the 

total fuel load higher, vegetation more volatile, and bulk 

densities structurally optimal for fuel consumption, the 

fuels in these systems can burn more completely and 

at higher intensities. Intense burning can lead to substantially higher emissions and soil heating compared 

to the minimal-fuel, low-severity fires typical of the 

non-forested areas classified for this study. Developing 

FLMs that are appropriate for these shrub communities 

will require further research.

How Can Managers Use FLMs?

Managers can use FLMs to quickly estimate the fuel 

loads of six fuel components while in the field. FLMs 

can be an economical alternative for fuels sampling 

because sampling can be done quickly or without visiting an area. FLMs can also be easily integrated with 

other types of plot-level data, such as stand structure 

or vegetation cover, to create a more comprehensive 

description of a plot’s characteristics with little additional 

field sampling. Because FLMs can be consistently and 

accurately identified in the field, they can also be used 

as map units of fuel loadings. The map units can be 

used to quantify fire effects and plan, prioritize, and 

implement fuel treatments. Map accuracy can be easily 

assessed because the map units can be checked in the 

field using the FLM identification key presented in 

this report. The FLM classification also allows users to 

quickly enter fuel loading data into simulation models 

to compute fire effects. For example, fuel descriptions 

can be input into FOFEM using a FLM number instead 

of inputting detailed information on six separate fuel 

components.

Many ecosystems were not represented in the FLM fuel 

bed data so we do not recommend that this classification 

be applied in some rare ecosystems, such as pocosin bogs, 

boreal forests, deserts, and some hardwood forest types. 

More targeted sampling and additional analysis will be 

required to extend this FLM classification system to these 

special systems.

Identification of Fuel Loading 

Models in the Field ______________

FLMs are identified using the tools provided in this 

field guide, including (1) a field form that outlines which 

fuel components must be sampled and which FLM key 

to use (Appendix A); (2) FLM keys to forested and nonforested vegetation types (Appendices E, F, and I); and 

(3) photographic examples of threshold load values that 

are specified in the FLM keys (Appendices C, D, G, and 

H). The field form has space to record all the fuel load 

information collected in the field and to assign the FLM 

that summarizes the plot’s fuel beds.

During the identification of FLMs, users must make 

some coarse measurements or visual estimates of fuel 

load components within their sample area. For users 

who lack experience visually estimating fuel load, we 

provide photographs of known fuel loads in this field 

guide to compare with plot conditions (Appendices C, 

D, G, and H). Users only need to decide whether the 

Table 3—Median of plot loadings for non-forested FLMs.

Median plot load

FLM Effects group System T acre–1 kg m–2

 014 01 Sagebrush 0.75 0.17

 053 05 Sagebrush 21.0 4.70

 065 06 Sagebrush 40.9 9.20

 015 01 Chaparral and herbaceous 1.15 0.26

 054 05 Chaparral and herbaceous 32.5 7.30

 066 06 Chaparral and herbaceous 63.2 14.2
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

load for a specific fuel component on their plot appears 

greater or less than the load shown in the photographs. 

We also provide three problem sets to practice identifying FLMs from known fuel loads (Appendix J). Each 

example in the problem set has a photograph of on-site 

fuels, gives the estimated on-site fuel components, and 

provides step-by-step instructions to key the data to a 

specific FLM class.

To key FLMs on your plot, first determine if you are in 

a forested or non-forested area, then follow the process 

detailed in the appropriate section below or the steps 

outlined in the plot form presented in Appendix A. To be 

considered a forested area, canopy cover of trees must 

be greater than 10% or the habitat type should key to a 

forest type. Non-forested areas can consist of sagebrush 

canopy cover, which have at least 10% canopy cover of 

Artemisia spp., or non-sagebrush canopy cover, whose 

fuels are derived from other shrubs or grasses that are 

<6 ft (2 m) tall.

Identifying an FLM in Forests______

Step 1: Estimate duff and litter depths. Do this at 

several points within the sampling unit or plot and 

take an average of your measurements. Methods 

for defining and measuring duff and litter can be 

found in Lutes and others (2006). Record the average depth in the appropriate place on the plot form 

(Appendix A).

Step 2: Pick an appropriate duff and litter bulk 

density value. Consult the scientific literature or local experts before you go into the field so that you 

choose the most appropriate bulk density value for 

your area. Typical bulk density values are provided 

in Appendix B, but they may not be realistic for all 

parts of the United States. You can interpolate between 

these bulk density values to get values that are more 

appropriate for your area.

Step 3: Calculate the duff and litter biomass. Biomass is calculated as thickness (depth) multiplied by 

bulk density (T acre–1 in–1 or g cm–3). Examples of 

duff and litter biomasses that have been calculated 

using common bulk densities are provided in Appendix B. Record the biomasses in the appropriate 

places in Appendix A.

Step 4: Estimate the fine-woody debris loading. 

Use Appendix C to determine if the fine-woody debris 

load is less than 2.4 T acre–1 (0.5 kg m–2). Record the 

load as greater or less than 2.4 T acre–1 (0.5 kg m–2) 

on the field form. If you need to record more precise 

estimates of the fine-woody component for project objectives, measure the 1 hr, 10 hr, and 100 hr fuels using 

standard fuel sampling procedures or estimate loads 

using the methods described in Keane and Dickinson 

(2007). Record values on the plot form in Appendix A.

Step 5: Estimate log loadings (1000 hr fuels). 

Precision is important for this fuel component, so 

we have provided photographs of known log loads 

(photoloads) to help users estimate this component 

more accurately in the field. Use the photoloads (Appendix D) to decide whether you have the minimum 

fuel loads required in the key. Record the value on 

the plot form in Appendix A.

Step 6: Key to the appropriate FLM. Using the 

values that you recorded on the plot form, follow the 

directions through the FLM key for forested areas. 

Use Appendix E if you have measured the loads in 

tons per acre and Appendix F if you have measured 

loads in kilograms per meter squared. Stop at the 

FLM that best fits your estimates. Watch the greater 

than or equal to (≥) and less than (<) signs in the key 

to be sure you are making the correct decisions for 

the pathways.

Step 7: Record the FLM number on the field 

form. If you need fuel load values for individual load 

components to input into a software application, the 

median loads for each FLM are summarized in table 2.

Identifying an FLM in Grasslands, 

Shrublands, or Chaparral

Non-forested areas can be keyed to an FLM in the 

field using several methods, including (a) making visual 

estimates of the total fuel load using photo series guides, 

(b) calculating total fuel load from counts or measures 

of separate fuel components, or (c) clipping and weighing all on-site fuels. The FLM key for the non-forested 

areas requires an estimate or measurement of total site 

fuel load that includes FWD, coarse woody debris, duff, 

litter, and shrub and herbaceous cover. To help users 

visualize total plot loads, we provide examples of measured site loads using photographs from several photo 

series guides (Ottmar and Vihnanek 2000; Ottmar and 

others 2000; Wright and others 2002). The photo series 

examples contain total load values for each picture plus 

a list of the individual fuel components that comprise the 

total load. Any photo series guide can be used to estimate 

site load. The photographs and data used in this guide 

are from the Digital Photo Series developed by the U.S. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 9

Forest Service, Pacific Northwest Research Station, Fire 

and Environmental Research Applications Team (http://

depts.washington.edu/nwfire/dps/). However, users may 

want to use a photo series guide that is specific to their 

ecosystem or landscape. As long as the photo series has 

measures of total site load and can be compared with 

the values required in the non-forest FLM key, it can be 

used to estimate loads and identify FLMs. Users should 

visually compare the total loads on their plot with the 

photo series photographs and decide if their plot looks 

like it has more or less load than the photos. When total 

fuel load is determined, the non-forest FLMs are easily 

identified. The identification process is as follows:

Step 1: Determine the cover type. In non-forested 

environments, the FLM key is based on whether the 

fuel bed is created by sagebrush or non-sagebrush 

vegetation. Sagebrush plots must have at least 10% 

canopy cover of Artemisia spp. Remember, the FLMs 

were not developed for areas with shrubs greater than 

6 ft tall.

Step 2: Estimate the total fuel load on site. Compare 

your site conditions with the photographs of known 

fuel loads in Appendix G (sagebrush sites) or Appendix H (non-sagebrush sites). Note that each photo 

series already includes duff and litter in its total load 

value so you need only to match the picture with your 

plot conditions to assign an approximate load. In the 

appropriate boxes on the field form (Appendix A), 

record the number and total load of the photo series 

that most closely matches load on your plot.

Step 3: Key to the appropriate FLM. Use the total 

load determined in step 2 and match it with the load 

ranges in the non-forested area key (Appendix I) to 

pick the FLM that best describes the cover type and 

load estimate for your plot.

Step 4: Record the FLM number on the field 

form. If you need fuel-load values for individual load 

components to input into a software application, the 

median loads for each FLM are summarized in table 3.

Management Advice _____________

As users begin to apply this classification, we would 

like to stress several points about its use and applicability. First, make this classification as convenient for your 

work situation as possible. You can use this classification without using the field form provided in this paper. 

Simply add fields on your normal field form if it makes 

recording the FLM data more convenient. By using the 

key regularly, we are confident that you will soon be able 

to recognize fuel load thresholds and identify FLMs in 

your area very quickly so you should make recording 

the data as convenient as possible. Second, there may 

be times when the fuels on your site may not seem to fit 

into this classification. In these situations, first ensure 

that you are in an appropriate vegetation type for the 

FLM key. As stated previously in this paper, do not use 

this key in rare ecosystems such as pocosin swamps or 

on sites with tall shrub vegetation (>6 ft tall). If you are 

in an applicable vegetation type and are unable to make 

the key fit, make sure that you are reading the greater 

than or equal to (≥) and less than (<) symbols correctly, 

as they can be confusing when you start out. If one column does not seem to fit your fuel load data, then step 

back a row and try again, paying close attention to these 

symbols. This key is designed to help you make estimates 

accurately and quickly. Estimating fuels takes practice, 

so recheck your field estimates to make sure that you 

have accurately portrayed the on-site fuels. Some of the 

FLMs in this classification have very small differences 

in fuel components, such as duff thickness, which may 

require measurement instead of estimation of their values. 

When you begin your identification of FLMs, pull out the 

ruler and measure these critical thicknesses, if necessary. 

If you still have trouble fitting a site’s characteristics to 

the FLM key after checking all of these items, remember 

that this key misclassified plots 34% of the time during 

its development and this could be what is affecting your 

ability to correctly identify an FLM (Lutes and others, in 

press). New classification systems invariably need adjustments as they are applied to new locations and used by 

more people and programs. As more plots and data are 

collected and analyzed across ecosystems, the extent of 

the FLM misclassification problem can be investigated 

and rectified to improve the FLM classification. Finally, 

and perhaps most importantly, we want to stress that this 

classification does have limitations. Do not attempt to 

extrapolate fire effects for FLMs to effects caused by 

canopy fuel consumption. The FLM key is designed to 

predict fire effects only from the surface fuels.

FLMs constitute an important advance in fuel classification because they relate actual on-site fuels to the 

smoke and soil heating that may result from burning 

those fuels. As such, the FLMs can be an important tool 

in many fire studies and management decisions. There 

may be fire effects in addition to smoke and soil heating 

that could be incorporated into the FLM classes in the 

future that would make them more pertinent to some 

wildlife, vegetation, and microbe studies. However, the 

current FLM classes are a positive step in the process 
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

to create a fuels classification that directly relates cause 

to effect in fuels consumption, and they should be an 

improvement over earlier fuel classification methods for 

many applications.

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12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Appendix A—Field Form for Recording FLM Data
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 13

Appendix B—Tables for Calculating Biomass 

of Duff and Litter in Forested Areas

Table B1. Biomass calculation in English units (T acre–1)

Find your duff or litter depth in the left column of each table and pick one density value along the top row. Biomass 

is the value at the intersection of these columns. Use these tables for English units. Use the tables on the next page 

for metric units.

If you chose a different bulk density than is listed in these tables, multiply your duff depth or litter depth by bulk 

density to get biomass in T acre–1.

 a Examples of duff bulk densities from common stand types are: Sierra Nevada ponderosa pine, 2 to 8 T acre–1 in–1; white fir, 5 to 

16 T acre–1in–1 (Stephens and others 2004); northern Rocky Mountain subalpine fir, 8 to12 T acre–1 in–1; Douglas-fir, 7 to 17 T acre–1 in–1; 

western red cedar, 10 to 15 T acre–1 in–1; western hemlock, 10 to 15 T acre–1 in–1 (Brown 1981). 

 b Examples of litter bulk densities from common stand types are: northern Rocky Mountain ponderosa pine, 1.2 to 5 T acre–1 in–1; low elevation subalpine fir, 0.2 to 19 T acre–1 in–1; high elevation subalpine fir, 5 to 13 T acre–1 in–1 (Snell 1979); Douglas-fir, 3 to 7 T acre–1 in–1; western 

red cedar, 5 to 9 T acre–1 in–1 (Brown 1981).

 Duff bulk density (T acre–1 in–1)

 a

Duff 4.0 7.0 12.0 15.0

depth (in.) Biomass (T acre–1)

0.10 0.40 0.70 1.20 1.50

0.50 2.00 3.50 6.00 7.50

1.00 4.00 7.00 12.00 15.00

1.50 6.00 10.50 18.00 22.50

2.00 8.00 14.00 24.00 30.00

2.50 10.00 17.50 30.00 37.50

3.00 12.00 21.00 36.00 45.00

4.00 16.00 28.00 48.00 60.00

6.00 24.00 42.00 72.00 90.00

8.00 32.00 56.00 96.00 120.00

10.00 40.00 70.00 120.00 150.00

12.00 48.00 84.00 144.00 180.00

14.00 56.00 98.00 168.00 210.00

 Litter bulk density (T acre–1 in–1)

 b

Litter 1.5 2.2 4.0 6.0

depth (in.) Biomass (T acre–1)

0.10 0.15 0.22 0.40 0.60

0.20 0.30 0.44 0.80 1.20

0.50 0.75 1.10 2.00 3.00

1.00 1.50 2.20 4.00 6.00

1.50 2.25 3.30 6.00 9.00

2.00 3.00 4.40 8.00 12.00
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Table B2. Biomass calculation in metric units (g cm–2)

Find your duff or litter depth in the left column of each table and pick one density value along the top row. Biomass 

is the value at the intersection of these columns.

If you chose a different bulk density than is listed in these tables, multiply your duff depth or litter depth by bulk 

density to get biomass in g cm–2.

 Litter bulk density (g cm–3)

 d

Litter 0.01 0.02 0.04 0.05

depth (cm) Biomass (g cm–2)

0.20 0.00 0.00 0.01 0.01

0.50 0.01 0.01 0.02 0.03

1.00 0.01 0.02 0.04 0.05

4.00 0.04 0.08 0.16 0.20

5.00 0.05 0.10 0.20 0.25

6.00 0.06 0.12 0.24 0.30

 Duff bulk density (g cm–3)

 c

Duff 0.04 0.06 0.11 0.13

depth (cm) Biomass (g cm–2)

0.50 0.02 0.03 0.06 0.07

1.00 0.04 0.06 0.11 0.13

2.50 0.10 0.15 0.28 0.33

4.00 0.16 0.24 0.44 0.52

5.00 0.20 0.30 0.55 0.65

6.50 0.26 0.39 0.72 0.85

7.50 0.30 0.45 0.83 0.98

10.00 0.40 0.60 1.10 1.30

15.00 0.60 0.90 1.65 1.95

20.00 0.80 1.20 2.20 2.60

25.50 1.02 1.53 2.81 3.32

30.50 1.22 1.83 3.36 3.97

35.50 1.42 2.13 3.91 4.62

 cExamples of duff bulk densities from common stand types are: Sierra Nevada ponderosa pine, 0.01 to 0.07 g cm–3; white fir, 0.04 

to 0.14 g cm–3 (Stephens and others 2004); northern Rocky Mountain subalpine fir, 0.07 to 0.11 g cm–3; Douglas-fir, 0.06 to 0.15 g cm–3; 

western red cedar, 0.09 to 0.13 g cm–3; western hemlock, 0.09 to 0.13 g cm–3 (Brown 1981).

 dExamples of litter bulk densities from common stand types are: northern Rocky Mountain pine, 0.01 to 0.04 g cm–3; low elevation 

subalpine fir, 0.00 to 0.17 g cm–3; high elevation subalpine fir, 0.04 to 0.11 g cm–3 (Snell 1979); Douglas-fir, 0.03 to 0.06 g cm–3; western red 

cedar, 0.04 to 0.08 g cm–3 (Brown 1981).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 15

Appendix C—Fine-Woody Debris Loadings for Forested Areas

Figure C-1—Fine-woody debris, 2.4 T acre–1 (0.53 kg m–2). 

Photograph is a composite of 1 hour, 10 hour, and 100 hour 

photoloads of fuels in approximately a 1:2:1 ratio. Photograph 

covers 11 ft2

 (1 m2). Modified from Keane and Dickinson (2007).

Figure C-2—Fine-woody debris, 2.4 T acre–1 (0.53 kg m–2). 

Photograph uses same fuels as photograph C-1 but in approximately 

a 1:4:6 ratio. Photograph covers 11 ft2

 (1 m2). Modified from Keane 

and Dickinson (2007).
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Appendix D—Photoloads of Coarse Woody Debris 

Loadings for Forested Areas

Figure D-1—Coarse-woody debris loading of 4.5 T acre–1 (1.0 kg m–2) made from 6-inch diameter (15.24-cm) “logs” constructed 

from cardboard tubes. Total log length = 43 ft (13 m). Each yellow square is 1,075 ft2 (100 m2

). White staff is 5.5 ft (1.6 m) tall. 

Contrast with the same loading made from 10-inch (25.4-cm) logs in D-2. Modified from Keane and Dickinson (2007).

Figure D-2—Coarse-woody debris loads of 4.5 T acre–1 (1.0 kg m–2) made from 10-inch diameter (25.4-cm) “logs” constructed 

from cardboard tubes. Total log length = 16 ft (4.8 m). Each yellow square is 1,075 ft2 (100 m2

). White staff is 5.5 ft (1.6 m) tall. 

Modified from Keane and Dickinson (2007).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 17

Figure D-4—Coarse-woody debris loads of 8.2 T acre–1 (1.8 kg m–2) made from 10-inch (25.4-cm) diameter logs. Total log 

length = 28 ft (8.5 m). Modified from Keane and Dickinson (2007).

Figure D-3—Coarse-woody debris loads of 8.2 T acre–1 (1.8 kg m–2) made from 6-inch (15.4-cm) diameter logs. Total log 

length = 78 ft (23.5 m). Contrast with same loading from 10-inch (25.4-cm) diameter debris in D-4. Modified from Keane 

and Dickinson (2007). 
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Figure D-5—Coarse-woody debris loads of 10.1 T acre–1 (2.3 kg m–2) using 6-inch (15.4-cm) diameter logs. Total log length = 

102 ft (31 m). Modified from Keane and Dickinson (2007).

Figure D-6—Coarse-woody debris loads of 10.1 T acre–1 (2.3 kg m–2) using 10-inch (25.4-cm) diameter logs. Total log length = 

36.5 ft (11 m). Modified from Keane and Dickinson (2007).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 19

Figure D-7—Coarse-woody debris loads of 15.9 T acre–1 (3.6 kg m–2) using 6-inch (15.24-cm) diameter logs. Total log 

length = 158 ft (48 m). Modified from Keane and Dickinson (2007).

Figure D-8—Coarse-woody debris loads of 15.9 T acre–1 (3.6 kg m–2) using 10-inch (25.4-cm) diameter logs. Total log length = 

57 ft (17 m). Modified from Keane and Dickinson (2007).
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Figure D-9—Coarse-woody debris loads of 28.3 T acre–1 (6.4 kg m–2) using 10-inch (25.4-cm) diameter logs. Total log length = 

101 ft (30.5 m). A comparison of loading with 6-inch (15.24-cm) diameter logs is not provided, but the total log length using 6-inch 

(15.24-cm) diameter logs would equal 280 feet (85 m). Modified from Keane and Dickinson (2007).

Figure D-10—Coarse-woody debris loads of 35.1 T acre–1 (7.9 kg m–2) using 10-inch (25.4-cm) diameter logs. Total log length = 

125 ft (38 m). A comparison of loading with 6-inch (15.24-cm) diameter logs is not provided, but the total log length using 6-inch 

(15.24-cm) diameter logs would equal 346 feet (105.5 m). Modified from Keane and Dickinson (2007).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 21

Appendix E—FLM Key for Forested Areas (T acre–1)

Step 1: Select the duff biomass range (in orange) that best fits the calculated biomass value that you entered on the 

field form (Appendix A).

Step 2: Follow down the appropriate duff biomass column and match all remaining column criteria to your field 

values for FWD load, litter biomass, or log load as required. Pay particular attention to greater than or equal 

to (≥) and less than (<) signs. All loadings are in tons per acre (T acre–1). Non-critical elements for each section are marked with “≥0”. Examples of how to use the key are found in the main text.

Step 3: Use the photographs of known loadings in Appendix C and D to make critical loading decisions if needed. 

References are placed in this key where these photographs may be required. The pictures are referenced by a 

letter (for the Appendix) and a number (for the picture number within the appendix). For example, C-1 refers 

to Appendix C, photo 1.

Step 4: From the bottom of the column, read the resulting fuel-load model number that matches all of your load 

criteria. If a lower row does not match your observed conditions, step back up one row and select another 

column to the right. 
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 23

Appendix F—FLM Key for Forested Areas (kg m–2)

Step 1: Select the duff biomass range (in orange) that best fits the calculated biomass value that you entered on the 

field form (Appendix A).

Step 2: Follow down the appropriate duff biomass column and match all remaining column criteria to your field 

values for litter biomass, FWD load, or log load as required. Pay particular attention to greater than or equal 

to (≥) and less than (<) signs. All loadings are in kilograms per meter squared (kg m–2). Non-critical elements 

for each section are marked with “>0”. Examples of how to use the key are found in the main text.

Step 3: Use the photographs of known loadings in Appendix C and D to make critical loading decisions if needed. 

References are placed in this key where these photographs may be required. The pictures are referenced by a 

letter (for the Appendix) and a number (for the picture number within the appendix). For example, C-1 refers 

to Appendix C, photo 1.

Step 4: From the bottom of the column, read the resulting fuel-load model number that matches all of your load 

criteria. If a lower row does not match your observed conditions, step back up one row and select another 

column to the right. 
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 25

Figure G-2—Total biomass = 15 T acre–1 (3.4 kg m–2). Photo 

series SWSB 11, Ottmar and others (2000).

Appendix G—Representative Fuel Loads in Sagebrush Areas

Surface material 1.53 T acre–1 0.34 kg m–2

Downed woody 4.19 0.94

Vegetation biomass 4.94 1.12

Total biomass 10.66 2.34

Figure G-1—Total biomass = 11.0 T acre–1 (2.5 kg m–2). From 

photo series HI-S 03, Wright and others (2002). Note: This 

site is not a sagebrush site, but it is typical of 11 T acre–1 load.

Surface material 4.9 T acre–1 1.10 kg m–2

Downed woody 3.23 0.72

Vegetation biomass 7.17 1.61

Total biomass 15.30 3.43
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Surface material 0.66 T acre–1 0.15 kg m–2

Downed woody -- --

Vegetation biomass 1.66 0.37

Total biomass 2.32 0.52

Surface material 2.39 T acre–1 0.54 kg m–2

Downed woody -- --

Vegetation biomass 10.00 2.24

Total biomass 12.39 2.78

Appendix H—Representative Fuel Loads in Non-Sagebrush Areas

Figure H-1—Total biomass = 2 T acre–1 (0.45 kg m–2). Photo 

series TP 08, Ottmar and others (2000).

Figure H-2—Total biomass approximately 11.0 T acre–1

(2.5 kg m–2). Photo series P-S 04, Ottmar and Vihnanek(2000).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 27

Surface material 2.79 T acre–1 0.63 kg m–2

Downed woody -- --

Vegetation biomass 9.97 2.23

Total biomass 12.76 2.86

Surface material 0.0 T acre–1 0.0 kg m–2

Downed woody -- --

Vegetation biomass 17.6 4.0

Total biomass 17.6 4.0

Figure H-3—Total biomass approximately 11.0 T acre–1

(2.5 kg m–2). Photo series P-S 05, Ottmar and Vihnanek (2000).

Figure H-4—Total biomass = 18 T acre–1 (4.03 kg m–2). Photo 

series CH 09, Ottmar and others (2000).
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Surface material 0.0 T acre–1 0.0 kg m–2

Downed woody -- --

Vegetation biomass 17.6 3.95

Total biomass 17.6 3.95

Surface material 3.85 T acre–1 0.86 kg m–2

Downed woody -- --

Vegetation biomass 19.30 4.33

Total biomass 23.15 5.19

Figure H-5—Total biomass approximately 19.0 T acre–1

(4.26 kg m–2). Photo series HI-S 06, Wright and others (2002).

Figure H-6—Total biomass approximately 19.0 T acre–1

(4.26kgm–2). Photo series P-S 06, Ottmar and Vihnanek (2000).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 29

Figure H-7—Total biomass = 40 T acre–1 (kg m–2). Photo 

series CH 15, Ottmar and others(2000). Note: This photo 

shows how much fuel is needed to approach the 19 to 44 T 

acre-1 requirements for FLM 54, but its fuel bed exceeds the 

6-ft (1.8-m) upper limit for FLMs.

Figure H-8—Total biomass = 52 T acre–1 (11.7 kg m–2). Photo 

series CH 16, Ottmar and others (2000). Note: This photo 

shows how much fuel is needed to approach the 44 to 92 T 

acre-1 requirements for FLM 66, but its fuel bed exceeds the 

6-ft (1.8-m) upper limit for FLMs.

Surface material 0.0 T acre–1 0.0 kg m–2

Downed woody -- --

Vegetation biomass 39.7 8.9

Total biomass 39.7 8.9

Surface material 0.0 T acre–1 0.0 kg m–2

Downed woody -- --

Vegetation biomass 52.2 11.7

Total biomass 52.2 11.7
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Appendix I—FLM Key for Non-Forested Areas 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 31

Problem 1: Identify the FLM that best describes 

the fuel components in this mixed hardwood and 

conifer site in Alaska (Ottmar and Vihnanek 2002). 

Its fuels are distributed as follows:

Duff: 20.78 T acre–1

Litter: 0.81 T acre–1

Fine woody debris (FWD): 3.7 T acre–1

Coarse woody debris (CWD): 3.0 T acre–1

Solution: Notice that the measures of fuel load in 

this example are in tons per acre (T acre–1) so you 

will be using Appendix E to solve this problem. Appendix E is arranged so that you can make selections 

by working downward, row by row. Within each 

row, boxes or columns display different ranges of 

loadings (biomass). Select the columns that best fit 

the field data for this site as follows: 

1. Select the orange box whose duff values best match the 20.78 T acre–1 of duff on this site. 

You should select the box with duff ranging from 18.9 to 21.69 T acre–1.

2. In the yellow litter row, choose the “less than 2.6” column because the litter loading for this 

site is 0.81. 

3. In the CWD load row, choose the “less than 4.5” load. This best fits the 3.0 T acre–1 conditions on the site. 

4. Read the FLM value from the lowest row. The FLM that best describes this site is FLM 62.

Notice that within this range of duff biomass, the fine woody debris values are not needed to identify the FLM.

Appendix J—Practice Set: Using the FLM Key
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009

Problem 2: Identify the FLM that best describes the 

fuel components on this jack pine forest site in northern 

Minnesota (Ottmar and others 2002). The fuel loadings 

are distributed as follows: 

Duff: 9.32 T acre–1

Litter: 1.83 T acre–1

Fine woody debris (FWD): 2.3 T acre–1

Coarse woody debris (CWD): 7.7 T acre–1

Solution: Like the previous problem, the units for this 

problem are in tons per acre (T acre-1) so you will use 

Appendix E to identify the FLM that best describes the 

fuel loads on this site. Use the field data from the site to 

make decisions in the key as follows:

1. The duff value estimated for this site is 9.32 T 

acre–1. The best fit for this value is the orange box with duff between 5.0 and 10.24 T acre–1.

2. Match the litter value from this site (1.83 T acre–1) to values in the litter row (yellow) in the 

key. At this point, you have several possibilities that could fit the site values, including the 

box containing “<10.8” or any of the boxes containing “>0”. The “>0” in the boxes means 

that they could contain any litter value. Since you can not rule out any of the boxes with 

“>0”, you will have to consider all of these boxes simultaneously as you go to the next row 

in the key.

3. Pick the CWD column that best fits the site load (7.7 T acre–1), which is the box containing 

<8.2. With this decision made, the only combination of litter and CWD values that fits this 

site is “<10.8” for the litter and “<8.2” for the CWD.

4. Using these choices, read the FLM number from the lowest row of the key. The FLM that 

best fits this site is FLM 31. Again note that the FWD is not used to determine FLMs for this 

range of duff values.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-225. 2009 33

Problem 3: This ponderosa pine coniferous 

forest site in western Montana (Keane and 

Dickinson 2007) has the following distribution 

of fuel loadings: 

Duff: 0.32 kg m–2

Litter: 0.00 kg m–2

Fine woody debris (FWD): 1.12 kg m–2

Coarse woody debris (CWD): 0.34 kg m–2

Identify the FLM that best describes its fuel 

components.

Solution: Notice that the measures of fuel load in this example are in kilograms per meter squared 

(kg m–2) so you must use Appendix F to solve this problem. 

1. Select the orange box that has a duff loading (biomass) range between 0.01 and 0.42 kg m–2

in Appendix F. This box best fits the field value of 0.32 kg m–2.

2. In the FWD row (gray), choose the “greater than 0.53” columns because the litter loading 

for this site is 1.12 kg m–2. You will have to consider both of these columns as you move to 

the next row.

3. Choose “less than 0.21” column for the litter biomass. This is the only column that fits the 

0.00 kg m–2 conditions on the site. Unlike the previous problems, litter biomass is critical 

for identifying the FLM at sites that have very low duff biomass.

4. Read the FLM value from the lowest row. The FLM that best describes this site is FLM 12.


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