1

Research Note RMRS-RN-51WWW. 2013

Rocky Mountain Research Station

Research Note

United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

Research Note RMRS-RN-51WWW

May 2013

Predicted Down Woody Fuel Consumption in the 

Burnup Model: Sensitivity to Four User Inputs

Duncan C. Lutes

Citation:

Lutes, D. C. 2013. Predicted fuel consumption in the Burnup 

model: sensitivity to four user inputs. Res. Note RMRS-RN51WWW. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 11 p.

Author:

Duncan C. Lutes is a Fire Ecologist with the USDA Forest Service, 

Rocky Mountain Research Station, Fire Sciences Lab, Missoula, 

MT.

Introduction

Fuelbeds consist of a number of combustible components that are consumed during 

a fire, including duff, litter, vegetation (herbs, shrub, foliage, and branches) and down 

dead woody material (DWM). Combustion of DWM during a fire has a well-documented role in determining fire effects and fire behavior impacts such as emissions 

(Sandberg and others 2002), vegetative changes (Brown and Smith 2000), soil heating (Neary and others 2005), and fire intensity (Pyne and others 1996). Inventory 

programs and research studies, such as Brown and See 1981, note that on forested 

sites in the United States DWM is often the second most abundant fuel component 

by mass (after duff), so an accurate prediction of DWM consumption is important 

for determining fire effects and fire behavior.

A common method for predicting the amount of DWM consumed during the combustion process is through the development of empirical relationships. Empirical 

equations for DWM consumption are created by collecting prefire and postfire 

DWM measurements and then using important variables such as fuel condition, 

burning conditions, site, and ignition pattern to predict the amount of DWM consumed by the fire. Empirical equations are often quite accurate when used to predict 

DWM consumption with input values similar to the experimental data the equations 

were developed from. However, because of the numerous input variables needed for 

empirical DWM consumption calculations, it is often difficult to know if all inputs 

fall within the range of the experimental data or, when the inputs are known to be 

outside the range of the experimental data, the magnitude, if any, the influence those 

variables have on the results seen in any specific simulation.
2

Research Note RMRS-RN-51WWW. 2013

Burnup is a research-based mechanistic fuel consumption model that has been developed, tested, and calibrated in previous work (Albini 1994, Albini and Reinhardt 1995, 

Albini and others 1995, Albini and Reinhardt 1997). Data are brought into Burnup

using input files that describe size class (surface area to volume ratio [S/V ratio]), biomass, fuel moisture, wood density, mineral ash fraction, heat of combustion, heat 

capacity, thermal conductivity, ignition temperature, and char temperature for each 

fuel component. The basic premise of the model is that adjacency of pieces of woody 

material primarily determines consumption through local fire intensity. Burnup uses a 

hierarchical sorting routine to order DWM input data records by increasing diameter 

(decreasing S/V ratio), increasing moisture content, and increasing wood density. After 

an initial ignition pulse to ignite fuel components, Burnup logic limits simulated burning interaction so each size class is influenced only by its own and smaller size classes as 

defined by S/V ratio in the data input file. The ratio of interaction is proportional to the 

ratio of the diameters of the interacting fuel elements so the closer in size two elements 

are the more burning interaction there is between the elements (this process is described 

in detail in Albini 1994 and Albini and others 1995). Fire intensity and fuel consumption are simulated at 15-second time steps from ignition to the end of combustion. The 

temporal component of Burnup is a significant benefit as the model is able to simulate 

smoldering and flaming combustion, and intensity, over time.

In this study, factorial analysis was used to identify the main effects and interaction(s) 

of four independent factors—DWM size classification, load, moisture, and rot—on 

percent DWM consumption simulated in the Burnup model. In standard fuel surveys, 

dead and down woody material greater than 7.62 cm diameter is typically recorded 

in 2.54 cm classes but building Burnup input files at that resolution would be tedious 

and time consuming. On the other hand it would be much simpler to build Burnup

input files where fuel load of DWM greater than 7.62 cm was grouped into one class; 

however, model behavior at that resolution is uncertain. The DWM size classification 

factor was used in this study to test the accuracy of the model with DWM greater than 

7.62 cm grouped into a number of classes based on the piece diameter measured where 

pieces intersected the sample plane (Brown 1974). Fuel load is typically correlated with 

piece density—as the fuel load increases, there is greater likelihood of DWM being 

adjacent or crossing. The interaction of burning DWM is a fundamental component of 

the Burnup simulation model, so diverse fuel load classes were included in this study to 

test model sensitivity to variable fuel load and the correlated interaction of burning fuel 

particles. Fuel moisture affects the amount of oxygen available for combustion and heat 

flux, and it is important for determining duration of flaming and smoldering (Albini 

and Reinhardt 1995). The moisture classes used in this study were similar to those used 

in the First Order Fire Effects Model (FOFEM) (Reinhardt 2003). Consumption of 

DWM is directly related to the amount of rot present in burned pieces (Albini and 

Reinhardt1995, Brown and others 1991, Hyde and others 2011). Percent of rot was 

included to test the sensitivity of Burnup to the presence of rot in DWM.

The analysis contained in this research note was undertaken to assist with the incorporation of Burnup in three fire simulation models: FOFEM, FARSITE Fire Area Simulator 

(Finney and Andrews 1998), and the BehavePlus Fire Modeling System (Heinsch and 

Andrews 2010).
3

Research Note RMRS-RN-51WWW. 2013

The objectives of the analysis were to: (1) identify the importance of four input variables in estimating the percent total DWM fuel consumption, and (2) identify any 

interaction between the four tested factors.

Methods

Data for this study were collected as part of the Gradient Modeling and Remote Sensing 

(GMRS) study (Keane and others 2002) and consisted of 922 plots sampled on the 

Kootenai National Forest in northwest Montana and Nez Perce National Forest in 

central Idaho. The inventory data from the GMRS study were collected along ecological gradients and used as baseline data for mapping important landscape characteristics. 

The GMRS data were used in this sensitivity analysis because they were collected with 

consistent field sampling procedures and from diverse ecological types. Specifically, the 

GMRS dataset provided duff and DWM loading, and duff depth values for the Burnup

sensitivity analysis. Of the 922 plots sampled, 141 (15 percent) had no DWM greater 

than 7.62 cm in diameter and were removed from consideration for this study. The 

remaining 781 plots made up the study dataset. For simplification of model inputs, 

duff, litter, and woody fuels less than 7.62 cm diameter were used as found in the study 

dataset. Herbaceous and live woody fuels were not analyzed.

Four Burnup input variables were tested: (1) classification of 7.62 cm and greater 

DWM, (2) total DWM fuel load, (3) moisture content, and (4) percent of DWM 

biomass that was considered rotten. The study used a balanced factorial experiment 

with 20 observations per cell to identify effects on the dependent variable: percent total 

DWM consumed. “Percent total DWM consumed” in this study was based on the 

summed total biomass per unit area of pieces 0 to 0.635 cm (1-hr timelag fuels), 0.635 

to 2.54 cm (10-hr timelag fuels), 2.54 to 7.62 cm (100-hr timelag fuels), and 7.62 cm 

and greater DWM components of the fuel complex.

The GMRS data were analyzed at three levels of DWM fuel load. Load for each factor 

level was established as the median total DWM biomass of 20 samples around the 10th, 

50th, and 90th percentiles (10 samples either side of the percentile) of the 781 plots in 

the study dataset. These 60 samples provided the fuel load data used in this study.

The class factor described how the load of DWM greater than 7.62 cm in diameter 

was grouped for input to the model. For each of the 60 GMRS study plots identified 

above, the fuel load of DWM greater 7.62 cm was calculated using standard equations 

(Brown 1974) by grouping log diameters into the five classes listed in table 1. Each of 

the five classes was considered a factor level in this study. The 4-class factor level closely 

corresponded to the classification used by Albini and others (1995) in validation tests.

Fuel size within Burnup is actually defined by S/V ratio, not diameter. Diameter was 

used to describe fuel classes only because it is more intuitive to most readers than S/V 

ratio. S/V ratio for each class was calculated with the formula:

S/V ratio = d d

2

4 4 +

l u where,

dl is the diameter at the lower class limit, in meters and

du is the diameter at the upper class limit, in meters.
4

Research Note RMRS-RN-51WWW. 2013

Moisture content was tested at the four factor levels presented in table 2. The levels 

used for DWM fuel moisture were similar to the default fuel moisture classes used 

in FOFEM, which were developed with input from researchers and managers in the 

western United States.

The study dataset was analyzed to determine levels of the proportion of rot factor. 

The condition of DWM greater than 7.62 cm was recorded in the GMRS study as 

one of five condition classes (Maser 1979). In this study condition classes 1, 2, and 3 

were considered sound DWM, and classes 4 and 5 were considered rotten DWM. In 

the Burnup model, two characteristics differentiate rotten wood from sound wood: 

(1) rotten wood has lower wood density (224 Kg/m3 for rotten fuel vs. 513 Kg/m3

for sound fuel) and (2) rotten wood has a lower ignition temperature than sound 

wood (302 degrees C vs. 327 degrees C). These figures come from Albini (1994) and 

were used in the Albini and others (1995) calibration study. In simulation input files 

the fuel moisture for rotten DWM greater than 7.62 cm was set at 250 percent that 

of the sound fuels. This multiplier was found in the original Burnup development 

files and may be suspect. For example, Brown and others (1991) noted fuel moisture 

of rotten DWM greater than 7.62 cm was 175 percent and 140 percent greater than 

sound material in spring and fall, respectively.

Two factorial analyses were used to examine data. The first was used to identify the 

factors that significantly influenced the percent total DWM consumed. Then those 

results were used to reduce the number of factors and/or levels where appropriate. 

The second analysis was again used to look for important factors in the reduced 

model but also to identify significant interactions between levels. Heterogeneous 

variance and non-normality were present in the analysis. Alternative analyses (arcsin transformation, trimmed mean and Welch-type analysis of variance for multiple 

factors [Wilcox 1997]; Kruskal-Wallis analysis for single factors) were computed 

to assess sensitivity of results to violation of homogeneous variance and normality 

Table 1—Biomass of DWM greater than 7.62cm was distributed into these five levels based on piece diameter at point of intersection with 

sampling plane.

Fuel factor levels Load factor classes (cm) Load factor classes (in.)

19-class >7.62 to 10.16, >10.16 to 12.70, >12.70 to 15.24, >50.80 >3 to 4, >4 to 5, >5 to 6...>20

6-class >7.62 to 15.23, >15.23 to 22.85, >22.85 to 30.47, >3 to 6, >6 to 9, >9 to 12, >12 to 15, >15 to 20, >20

>30.47 to 38.09, >38.09 to 50.79, >50.8

4-class >7.62 to 15.23, >15.23 to 22.85, >22.85 to 50.80, >50.80 >3 to 6, >6 to 9, >9 to 20, >20

3-class >7.62 to 22.85, >22.85 to 50.79, >50.80 >3 to 9, >9 to 20, >20

1-class >7.62 >3

Table 2—Four fuel moisture levels were tested as inputs to Burnup to 

identify significant differences in the model’s output.

Percent moisture

Fuel size Very dry Dry Moderate Wet

0 to 0.635 cm 3 6 9 11

0.635 to 2.54 cm 4 8 11 14

2.54 to 7.62 cm 6 10 15 18

7.62+ cm 13 15 20 35

Duff 40 75 120 195
5

Research Note RMRS-RN-51WWW. 2013

Table 3—Minimum, median, and maximum total DWM biomass for 

each load level.

Load level Minimum Median Maximum

--------------------------kg m-2 (t ac-1)---------------------------

Low 0.43 (1.91) 0.49 (2.19) 0.56 (2.49)

Medium 3.09 (13.79) 3.16 (14.10) 3.26 (14.54)

High 9.10 (40.58) 9.52 (42.49) 9.91 (44.20)

assumptions of the factorial analyses. No concerns were identified in these secondary 

analyses. Therefore, only the factorial results for the untransformed data are reported 

here. Variation within individual factors was interpreted using the Tukey-Kramer 

HSD multiple comparison procedure.

A significance value of 0.05 was used to identify significant factors throughout this 

study.

Results and Discussion

The median load of the low, medium, and high total DWM load levels corresponded 

to 0.49 kg m-2 (2.19 tons ac-1), 3.16 kg m-2 (14.10 tons ac-1), and 9.52 kg m-2 

(42.49 tons ac-1), respectively (table 3).

Of 781 plots in the study dataset, 7 percent of the plots had 100 percent of the 

DWM 7.62 cm and greater diameter recorded as sound and 27 percent of the plots 

had 100 percent of the DWM 7.62 cm and greater diameter recorded as rotten. The 

10th, 50th, and 90th percentiles of the proportion of rotten fuels calculated from the 

study dataset were used as the three levels of rot: low=8 percent rotten total DWM 

load, moderate=77 percent, and high=100 percent. At each level the same proportion 

of rot was applied to all 1-hr, 10-hr, 100-hr, and the 7.62 cm and greater DWM.

First Analysis

Analysis of variance found the model as a whole was significant (p < 0.001). Percent total 

predicted DWM consumption was significantly influenced by each of the four factors 

(p < 0.001). Two interesting relationships emerged while examining the within-factor 

significance tests. First, within the fuel class factor the only significant difference 

from the grand mean of consumption was for the 1-class level (p < 0.001). Tests using Tukey-Kramer HSD confirmed this significance. Predicted percent consumption 

of the other class factor levels was not significantly different than the grand mean 

or each other. The greater consumption simulated with the 1-class level was due to 

having all the 7.62 cm and greater fuel represented by one S/V ratio in the Burnup

input file, thus all simulated interaction was within the same size class rather than 

across several size classes. Because Burnup was calibrated with the 7.62 cm and greater 

fuels separated in multiple elements and, in this study, all the fuel class factor levels 

with multiple elements had similar simulated consumption, the 1-class factor was 

deemed to be the unusual case and removed. Second, Tukey-Kramer analysis found 

an insignificant difference between the dry and very dry levels of the moisture class 

(p = 0.082). The value was marginal relative to the other p-values so both levels were 

retained.
6

Research Note RMRS-RN-51WWW. 2013

Second Analysis

The 4X3X4X3 balanced factorial analysis examined the significance of main effects, 

the two-, three-, and four-way interactions, and the significance of each level in determining percent total DWM consumption (table 4).

Model Test

The second factorial experiment resulted in a significant but poorly explained relationship (table 5). The low R-square is not surprising because the analysis only 

examined four user inputs so other important model inputs were held constant and 

not tested. The shortcoming of testing the model this way was that unaccounted 

variation remained in the residual error which desensitized the F-tests, increasing the 

chance of a Type II error. Factor profiles for class, load, moisture, and rot are shown 

in figure 1.

Class Factor Test

With the 1-class factor level removed from the analysis, the class factor was not a significant 

effect in the model (F = 2.06, p(F) = 0.1034). Tukey-Kramer tests found a relatively high 

p-value for the 19-class vs. 3-class factor level (p = 0.068). Other factor levels had 

lower p-values.

The following tabulation includes the differences of mean predicted percent total 

DWM consumption and (p-value) for all Tukey-Kramer comparisons of the class 

factor:

Level 19-Class 6-Class 4-Class

6-Class 1.2 (0.831)

4-Class 1.4 (0.714) 0.3 (0.997)

3-Class 3.3 (0.068) 2.2 (0.379) 1.9 (0.507)

Table 4—Factors and levels used in the final factorial analysis were 

selected after initial analysis determined that the 1-class factor 

level was not significant.

Factor Level

Class 3-Class

4-Class

6-Class

19-Class

Load Low

 Medium

 High

Moisture Very Dry

 Dry

 Moderate

 Wet

Rot Sound

 Moderate

 Rot

Table 5—Results of the final factorial analysis noted a significant but 

poorly explained relationship.

Root mean R-square

n square error (adjusted) F p(F)

2880 25.86 0.253 6.48 <0.0001
7

Research Note RMRS-RN-51WWW. 2013

Load Factor Test

The Load factor significantly influenced percent total DWM consumption (F = 29.4, 

p(F) < 0.001); however, there was no significant difference in percent total DWM 

consumption estimated at the high and low factor levels (p = 0.092) when tested using Tukey-Kramer HSD. Tests of low vs. medium and medium vs. high levels found 

significantly different percent total DWM consumption (p < 0.001). The relationship 

of percent consumption at these levels was not immediately intuitive as percent consumption was higher at the moderate load level than at the low or high levels (fig. 1). 

An untested explanation for this has to do with the interaction between burning 

pieces as modeled in Burnup. At the low load level, the sparse spatial distribution of 

pieces did not allow the energy from one burning piece to greatly effect the burning 

of adjacent pieces. At the high load level pieces interacted substantially but, because 

absolute DWM load was high, the percent DWM consumption was lower than for 

the moderate fuel load level. Also, in the study dataset, median piece diameter at the 

high fuel load level was 17.8 cm (7 in.) compared to 127 cm (5 in.) at the low and 

medium load levels and large pieces with low surface area to volume ratio may not 

have burned as completely as DWM in the low and medium levels. At the moderate 

level the spatial distribution of pieces and modest fuel load allowed the simulated 

interaction of elements to effectively increase the percent DWM consumed above 

the other two levels.

The following tabulation includes the differences of mean percent total DWM consumption and (p-value) for all Tukey-Kramer comparisons of the load factor:

Level Low Medium

Medium 8.8 (<0.001)

High -2.5 (0.092) -6.3 (<0.001)

Moisture Factor Test

Moisture factor significantly influenced percent total DWM consumption (F = 197.3, 

p(F) < 0.001). Tukey–Kramer comparison of the dry vs. very dry factor found an 

insignificant difference in consumption (p = 0.137) at those two levels. Other withinfactor significance tests indicated highly significant relationships (p < 0.001). The 

moisture factor explained more model variation than any of the other factors in the 

model, with the wet level responsible for much of that variation.

Figure 1—Factor profiles for class, load, moisture, and rot. Bars represent 95% confidence intervals.
8

Research Note RMRS-RN-51WWW. 2013

The following tabulation includes the differences of mean percent total DWM consumption and (p-value) for all Tukey-Kramer comparisons of the moisture factor:

Level Wet Moist Dry

Moist 20.1 (<0.001)

Dry 27.1 (<0.001) 6.2 (<0.001)

Very Dry 30.0 (<0.001) 9.2 (<0.001) 2.9 (0.137)

Rot Factor Test

Proportion of rotten DWM significantly influenced the percent total DWM consumed (F = 109.2, p(F) < 0.001). Each of the levels significantly affected the percent 

DWM consumed and fuel consumption between levels behaved in an intuitive 

way—percent consumption increased as percent rotten fuel load increased (fig. 1). 

Albini and others (1995) and, more recently, Hyde and others (2012) confirmed this 

relationship.

The following tabulation includes the differences of mean percent total DWM consumption and (p-value) for all Tukey-Kramer comparisons of the rot factor:

Level Sound Moderate

Moderate 12.5 (<0.001)

Rotten 16.8 (<0.001) 4.3 (0.001)

Factor Interaction

There was little interaction noted in this analysis, perhaps because the moisture factor explained much of the model variation, relative to the other levels (table 6). The 

only significant interaction was between load and rot, with increasing consumption 

as load and proportion of rot increased (fig. 2). This interaction is a combination of 

two model processes previously discussed: (1) other factors being equal, proportionally less total DWM biomass was consumed at the high fuel level because there was 

proportionally less consumption of the large diameter DWM and (2) percent rotten 

fuel load increases total DWM consumption. Thus, the interaction is due to proportionally more rotten DWM being consumed at the high loading level because the 

large DWM did not burn as completely when sound as it did when rotten.

Table 6—Of eleven interactions tested, only the load vs. rot 

interaction was significant. One of the remaining 10 interactions, 

load vs. moisture, showed a moderate significance.

Source F Ratio Prob>F

Class-Load 0.11 0.999

Class-Moisture 0.003 1.000

Class-Rot 0.029 0.999

Load-Moisture 1.94 0.0710

Load-Rot 8.87 <0.0001

Moisture-Rot 0.05 0.999

Class-Load-Moisture 0.027 1.000

Class-Load-Rot 0.061 1.000

Class-Moisture-Rot 0.006 1.000

Load-Moisture-Rot 0.135 0.999

Class-Load-Moisture-Rot 0.003 1.000
9

Research Note RMRS-RN-51WWW. 2013

Conclusion

There are two specific recommendations that result from the analyses. First, when 

organizing Burnup input data, avoid distributing log diameters for DWM 7.62 cm 

(3 in.) and greater into classes more coarsely than the 4-class distribution: >7.62 cm 

to 15.23 cm, >15.23 cm to 22.85 cm, >22.85 cm to 50.79 cm, and >50.79 cm (3 in. 

to 6 in., 6 in. to 9 in., 9 in. to 20 in., and diameters greater than 20 in.), or something that closely approximates that classification. This classification appeared to give 

robust answers with a minimum of DWM classification effort. These results should 

not be interpreted to suggest that large size DWM only need be tallied by size class 

in the field. Large DWM is an important component for wildlife, nutrient cycling, 

and carbon processes (Harmon and others 1986) so the diameter should be recorded 

to, at least, the nearest 2 cm for accurate estimates of large DWM, then grouped 

into classes for Burnup simulations. Second, most of the variation in the model was 

explained by one factor—moisture. Fuel moisture is not often calculated from fuel 

samples prior to a prescribed fire; instead, it is inferred from fire danger rating or 

assigned based on the manager’s experience. The results suggest measured fuel moisture may produce better DWM consumption (and associated emissions) predictions. 

Likewise, field measurement of wood condition (decay) class is sometimes bypassed 

to reduce sampling time; however, the results of this study indicate that accurate assessments are important for estimating percent consumption.

Figure 2—Interaction profiles for all factor levels. The only significant interaction was between 

load and rot.
10

Research Note RMRS-RN-51WWW. 2013

Acknowledgments

I would like to thank Rudy King, Forest Service, RMRS statistician, for his assistance 

with the statistical analysis, interpretation, and review of this paper, and Bob Keane, 

Roger Ottmar, and Maureen Kennedy for their helpful review comments.

References

Albini, Frank A. 1994. Program Burnup: A simulation model of the burning of 

large woody natural fuels. Unpublished final report for Research Grant INT92754-GR. On file at the USDA, Forest Service, RMRS, Missoula Fire Science 

Laboratory, Missoula, MT.

Albini, Frank A.; Reinhardt, Elizabeth D. 1995. Modeling ignition and burning rate 

of large woody natural fuels. International Journal of Wildland Fire. 5(2): 81-91.

Albini, Frank A.; Brown, James K.; Reinhardt, Elizabeth D.; Ottmar, Roger D. 1995. 

Calibration of a large fuel burnout model. International Journal of Wildland Fire. 

5(3): 173-192.

Albini, Frank A.; Reinhardt, E.D. 1997. Improved calibration of a large fuel burnout 

model. International Journal of Wildland Fire. 7(1): 21-28.

Brown, James K. 1974. Handbook for inventorying downed woody material. Gen. 

Tech. Rep. INT-GTR-16. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment Station. 24 p.

Brown, James K.; See, Thomas E. 1981. Downed and dead woody fuel and biomass 

in the northern Rocky Mountains. Gen. Tech. Rep. INT-GTR-117. Ogden, UT: 

USDA Forest Service Intermountain Forest and Range Experiment Station. 48 p.

Brown, James K.; Reinhardt, Elizabeth D.; Fischer, William C. 1991. Predicting duff 

and woody fuel consumption in northern Idaho prescribed fires. Forest Science. 

37: 1550-1566.

Brown, James K.; Smith, Jane K. eds. 2000. Wildland fire in ecosystems: effects of fire 

on flora. Gen. Tech. Rep. RMRS-GTR-42-vol. 2. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 257 p.

Finney, Mark A.; Andrews, Patricia L. 1998. FARSITE: Fire Area Simulator—A 

model for fire growth simulation. Fire management notes. 59(2): 13-15.

Heinsch, Faith Ann; Andrews, Patricia L. 2010. BehavePlus fire modeling system, 

version 5.0: Design and Features. Gen. Tech. Rep. RMRS-GTR-249. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 111 p.

Harmon, Mark E.; Franklin, J.F.; Swanson, F.J.; Sollins, P.; Gregory, S.V.; Lattin, 

J.D.; Anderson, N.H.; Cline, S.P.; Aumen, N.G.; Sedell, J.R.; Lienkaemper, G.W.; 

Cromack, K.; Cummins, K.W. 1986. Ecology of coarse woody debris in temperate 

ecosystems. Advances in Ecological Research. 15: 133-302.

Hyde, Joshua C.; Smith, Alistair M.S.; Ottmar, Roger D.; Alvarado, Ernesto C.; 

Morgan, Penelope. 2011. The combustion of sound and rotten coarse woody debris: a review. International Journal of Wildland Fire. 20: 163-174.
11

Research Note RMRS-RN-51WWW. 2013

Hyde, Joshua C.; Smith, Alistair M.S.; Ottmar, Roger D. 2012. Properties affecting 

the consumption of sound and rotten coarse woody debris in northern Idaho: a 

preliminary investigation using laboratory fires. International Journal of Wildland 

Fire. 21: 596-608.

Keane, Robert E.; Rollins, Matthew G.; McNicoll, Cecilia H.; Parsons, Russell A. 

2002. Integrating ecosystem sampling, gradient modeling, remote sensing, and 

ecosystem simulation to create spatially explicit landscape inventories. Gen. Tech. 

Rep. RMRS-GTR-92. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 99 p.

Maser, Chris; Anderson, Ralph G.; Cromack, Kermit, Jr.; Williams, Jerry T.; Martin, 

Robert E. 1979. Dead and down woody material. Chap. 6, pp. 79-95. In: Thomas, 

J.W. (Ed.), Wildlife Habitats in Managed Forests, the Blue Mountains of Oregon 

and Washington, USDA For. Serv. Agric. Handb. 553, Washington, DC.

Neary, Daniel G.; Ryan, Kevin C.; DeBano, Leonard F. eds. 2005. (revised 2008). 

Wildland fire in ecosystems: effects of fire on soils and water. Gen. Tech. Rep. 

RMRS-GTR-42-vol. 4. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 250 p.

Pyne, Stephen J.; Andrews, Patricia L.; Laven, Richard D. 1996. Introduction to 

Wildland Fire. Second Edition. New York; Wiley.

Reinhardt, Elizabeth. 2003. Using FOFEM 5.0 to estimate tree mortality, fuel consumption, smoke production and soil heating from wildland fire. Presentation 

at the 2nd International Wildland Fire Ecology and Fire Management Congress, 

16-20 November 2003, Orlando, FL. 6 p. Available online at: http://www.firelab.org/ScienceApps_Files/downloads/FOFEM/FOFEM5Using.pdf. [Verified 

8/9//2012]

Sandberg, David V.; Ottmar, Roger D.; Peterson, Janice L.; Core, John. 2002. 

Wildland fire in ecosystems: effects of fire on air. Gen. Tech. Rep. RMRS-GTR42-vol. 5. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 79 p.

Wilcox, Rand R. 1997. Introduction to robust estimation and hypothesis testing. 

San Diego: Academic Press. 296 p.
The Rocky Mountain Research Station develops scientific information 

and technology to improve management, protection, and use of the 

forests and rangelands. Research is designed to meet the needs of 

the National Forest managers, Federal and State agencies, public and 

private organizations, academic institutions, industry, and individuals. 

Studies accelerate solutions to problems involving ecosystems, range, 

forests, water, recreation, fire, resource inventory, land reclamation, 

community sustainability, forest engineering technology, multiple use 

economics, wildlife and fish habitat, and forest insects and diseases. 

Studies are conducted cooperatively, and applications may be found 

worldwide. For more information, please visit the RMRS web site at: 

www.fs.fed.us/rmrs.

Station Headquarters

Rocky Mountain Research Station

240 W Prospect Road

Fort Collins, CO 80526

(970) 498-1100

Research Locations

The U.S. Department of Agriculture (USDA) prohibits discrimination against its 

customers, employees, and applicants for employment on the bases of race, 

color, national origin, age, disability, sex, gender identity, religion, reprisal, and 

where applicable, political beliefs, marital status, familial or parental status, 

sexual orientation, or all or part of an individual’s income is derived from any 

public assistance program, or protected genetic information in employment 

or in any program or activity conducted or funded by the Department. (Not all 

prohibited bases will apply to all programs and/or employment activities.) For 

more information, please visit the USDA web site at: www.usda.gov and click on 

the Non-Discrimination Statement link at the bottom of the page.

Reno, Nevada

Albuquerque, New Mexico

Rapid City, South Dakota

Logan, Utah

Ogden, Utah

Provo, Utah

Flagstaff, Arizona

Fort Collins, Colorado

Boise, Idaho

Moscow, Idaho

Bozeman, Montana

Missoula, Montana

To learn more about RMRS publications or search our online titles:

www.fs.fed.us/rm/publications

www.treesearch.fs.fed.us

Federal Recycling Program Printed on Recycled Paper