Evaluation of Techniques 

for Determining the Density 

of Fine Woody Debris

Becky Fasth

Mark E. Harmon

Christopher W. Woodall

Jay Sexton

United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

Research Paper NRS-11
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Abstract

Evaluated various techniques for determining the density (i.e., bulk density) of fi ne woody 

debris during forest inventory activities. It was found that only experts in dead wood 

inventory may be able to identify fi ne woody debris stages of decay. Suggests various future 

research directions such as development of a 2-class fi ne woody debris decay class system.

Cover Description

Pine (Pinus spp.) fi ne woody debris along California coast. Photograph by Chris Woodall, 

U.S. Forest Service.

Manuscript received for publication 22 October 2009
1

INTRODUCTION

For this study, fi ne woody debris (FWD) was defi ned as downed and dead 

woody debris less than 7.62 cm in diameter (Woodall and Monleon 2008). 

FWD is one component of forest detritus that is critical to numerous 

scientifi c/ecological fi elds such as carbon accounting (Woodall et al. 2008), 

wildlife habitat assessment (for examples, see Maser et al. 1979, Harmon et 

al. 1986, Bull et al. 1997), and fuel loading estimation (Lutes et al. 2006, 

Rollins et al. 2004, Van Wagner 1968, Woodall and Monleon 2008). 

Detritus provides a diversity (stages of decay, size classes, and species) of 

habitat for fauna ranging from large mammals to invertebrates (Bull et al. 

1997, Harmon et al. 1986, Maser et al. 1979). Plants use the microclimate 

of moisture, shade, and nutrients provided by deadwood to establish and 

regenerate (Harmon et al. 1986). Due to the possibility of dwindling 

deadwood habitat for native species and increasing fuel loadings across the 

United States, comprehensive large-scale inventories of downed deadwood 

including FWD have been established for habitat assessments/wildlife 

conservation eff orts and fi re hazard mitigation eff orts (for examples, see 

Marshall et al. 2000, Ohmann and Waddell 2002, Rollins et al. 2004, 

Tietje et al. 2002, Woodall and Monleon 2008). Worldwide, there has 

been increased eff ort during past years to inventory deadwood resources to 

address greenhouse gas off set accounting and biodiversity concerns (Kukeuv 

et al. 1997, Woldendorp et al. 2004, Woodall et al. 2009). Th erefore, 

refi ning estimates of FWD population attributes (e.g., biomass) is a critical 

component of nationwide eff orts to quantify carbon stocks and wildfi re 

hazards.

FWD is a forest ecosystem attribute that can be rather diffi cult to assess 

due to its relatively small size, ephemeral nature, states of decomposition/

fracture, and heterogeneous spatial orientation within forest sites. FWD 

may be inventoried by areal sample plots where all FWD is collected 

and weighed to determine a fairly accurate population estimate. Such 

sample methods are very time-consuming given the sample collection and 

lab-processing methods. Additionally, such collection methods disturb 

permanent sample plots confounding future remeasurement activities. An 

alternative to the aerial sampling of FWD is the use of line-intersect sampling 

(LIS) methods where FWD population attributes are estimated based on 

the probability of FWD pieces intersecting sampling transects. FWD volume 

may be estimated using LIS estimators; however, coeffi cients representing 

the bulk density and carbon content for FWD must be incorporated into 

FWD LIS estimators to estimate such attributes. Downed dead wood pieces 

in advanced stages of decay may have lost most of their original density 

(Harmon et al. 2008). Given that most national downed dead wood 

inventory programs lack sample methods for determining FWD decay status 

(Woodall et al. 2009), development of rapid fi eld techniques for determining 

such attributes could refi ne FWD biomass estimation procedures.

Th e Authors

BECKY FASTH is a senior faculty 

research assistant with the College of 

Forestry, Oregon State University, 

Corvallis, OR 97331.

MARK E. HARMON is a professor 

and Richardson Chair with the 

College of Forestry, Oregon State 

University, Corvallis, OR 97331.

CHRISTOPHER W. WOODALL is 

a research forester with the Northern 

Research Station, U.S. Forest Service, 

St, Paul MN 55108.

JAY SEXTON is a senior faculty 

research assistant with the College 

of Forestry Science, Oregon State 

University, Corvallis, OR 97331.
2

Fine woody debris is inventoried nationwide by the Forest Inventory and Analysis (FIA) 

program of the U.S. Forest Service in order to determine FWD biomass within particular 

forest conditions or domains (e.g., an entire state or forest type) (Woodall and Monleon 

2008). Current fi eld data procedures consist of tallying FWD by size class along line-intersect 

sampling transects within FIA plots (for details, refer to Woodall and Monleon 2008). Th ese 

counts of individual FWD pieces are then summed and an estimate of volume is determined 

using quadratic mean diameters. Biomass conversion constants published in the literature 

are currently used to convert volume to biomass (see Harmon et al. 2008). For downed dead 

wood with a transect diameter over 7.62 cm (coarse woody debris, CWD), a decay class 

is qualitatively determined for every piece. Two constants are used during CWD biomass 

estimation: (1) the species-specifi c initial density (i.e., specifi c gravity in g/cm3

) and (2) a decay 

reduction factor for each decay class that accounts for the decline in density as this form of 

detritus decomposes (Harmon et al. 2008). Because FIA does not determine the decay class 

or the species of each FWD piece, the initial FWD bulk density is based on a mean of bulk 

densities for the constituent tree species in the plot’s forest type (see Appendix 7.4 in Woodall 

and Monleon 2008). Additionally, only one decay reduction factor is used for all FIA plots 

across the U.S. Previous analyses have indicated that when the FWD decay reduction factor 

is actually measured for a species the uncertainty of biomass is 1 to 3 percent (Harmon et al. 

2008). In contrast, when the decay reduction factor has to be estimated, the uncertainty of 

FWD biomass estimation may range from 12 to 19 percent (Harmon et al. 2008). A more 

systematic sampling of FWD density (i.e., decay status) would be optimal to refi ne FWD 

biomass estimation and subsequent carbon stocks nationwide, especially to increase the power 

of detecting FWD biomass loss and subsequent carbon fl ux.

When decay stage-specifi c density is coupled with the volume estimates in each decay class, 

one can potentially estimate the biomass of woody debris with more certainty. Th e work that 

has been done with CWD has shown that species have diff erent patterns of density decline 

associated with decay classes (Yatskov et al. 1992). Th e change in density of FWD over time 

due to decomposition might be accounted for by establishing various decay classes of FWD 

and determining the density of each decay class. Classes can be defi ned by the presence of 

criteria that tend to be associated with certain decay stages. An important caveat is that there 

can be a great deal of variation within and between pieces of decomposing wood that may 

cause misclassifi cation by some individuals and introduce additional uncertainty. Th ere is also 

the question of whether FWD can be inventoried on permanent sample plots in a way that will 

allow the use of decay class specifi c densities without excessively increasing fi eld crew eff ort. If 

using empirically derived FWD decay classes can reduce the uncertainty of FWD population 

estimates (e.g., biomass), the evaluation of FWD decay measurement techniques is warranted.

OBJECTIVES

Th e goal of this study was to evaluate sample techniques for determining the density of FWD 

within the context of FIA’s national inventory of downed woody materials. Specifi c objectives 

were to (1) determine whether qualitative diff erences in FWD states of decay have resulting 

signifi cant diff erences in wood density, (2) identify characteristics of FWD that could enable a 

FWD decay class system, and (3) determine to what extent fi eld crews are able to consistently 

identify decay classes of FWD and the eff ects of decay class measurement repeatability on 

subsequent FWD population estimates.
3

METHODS

Study Sites

Two stands were chosen at Oregon State University’s McDonald-Dunn Research Forest. 

Th e fi rst was harvested in 2007 (Fig. 1), and the second was a second-growth mixed conifer 

stand, roughly 40 years old (Fig. 2). Th e McDonald-Dunn Research Forest consists of about 

11,250 acres of predominantly forested land on the western edge of the Willamette Valley 

in Oregon and on the eastern foothills of the Coast Range. Th e forest is somewhat isolated 

topographically from the rest of the Oregon Coast Range, residing in the rain shadow created 

by it. Th e forest is dominated by Douglas-fi r, western hemlock, and western redcedar.

Initial FWD Decay Class System

A system currently exists for determining decay classes of CWD (Harmon et al. 1986, Maser 

et al. 1979), but none yet exists for decay classes of FWD other than green versus decayed. 

Based on characteristics typically used to ascertain classes of CWD decay, an extensive list of 

FWD characteristics (e.g., presence/absence and visual characteristics of the leaves, bark, and 

wood) was used to develop an initial FWD decay class system:

Decay Class 1 (DC1): Fresh FWD with leaves or twigs present; appearance of recent 

fall, complete bark cover that is usually shiny in appearance

Decay Class 2 (DC2): Not quite fresh FWD with no leaves but sometimes twigs 

present, complete bark cover that is usually dull in appearance

Decay Class 3 (DC3): FWD that seems to have been on forest fl oor for some time, 

may be incorporated into the forest fl oor and moss layer, with signifi cant bark loss 

and possibly wood loss (starting to decompose)

Decay Class 4 (DC4): Sample very decomposed and incorporated into the forest 

fl oor with no bark and surface wood eroded from decomposition

Figure 1.—First sample site (approximately 40 years old) 

harvested in 2007, Oregon State University’s McDonaldDunn Research Forest, Willamette Valley, Oregon.

Figure 2.—Second sample site, a second-growth mixed 

conifer stand (approximately 40 years old), Oregon State 

University’s McDonald-Dunn Research Forest, Willamette 

Valley, Oregon.

Courtesy of Becky Fasth, Oregon State UniversityCourtesy of Becky Fasth, Oregon State University
4

Collection of FWD Samples

Th e two separate study sites allowed limited evaluation of FWD decay class effi cacy between 

and within stands. Th ree experts in identifying CWD decay classes entered each study site 

and collected samples of FWD according to the study’s FWD decay class system. Samples 

of FWD were randomly collected throughout each study site and placed by decay class into 

individual 5-gallon buckets for transport back to the laboratory. We tried to collect at least 

20 samples of each decay class from each forest stand, but this was not always possible. For 

example, we were unable to fi nd enough of decay class 4 in either stand, and decay classes 

one and two were diffi cult to locate in the mixed conifer stand. A total of 62 samples were 

collected from the clearcut harvested stand and 46 samples were collected from the mixed 

conifer stand (Table 1). Th e FWD samples were cut to a length of 35 cm or less so that they 

would fi t lengthwise into the sample bucket. Th e physical characteristics of each sample were 

recorded (Table 2). Random pieces of each decay class of FWD were also broken by hand to 

determine if the decay classes broke in a characteristic way and how the wood appeared once 

broken (e.g., splintered break, clean break, bending break, dark or light wood, rotten core).

Determination of Individual FWD Piece Densities

Th e species (Table 3), mean diameter, and total length were recorded for each sample upon 

return to the laboratory. Th e green volume for each FWD piece was determined, assuming 

the piece was a cylinder, given the total lack of FWD volume equations in the literature. We 

acknowledge the FWD volume equation knowledge gap and suggest it be studied further. 

Following “green” measurements, the FWD pieces were individually weighed, put into 

paper bags, and oven-dried at 55 oC. When the weight of the samples had stabilized (i.e., the 

Table 1.—Number of samples collected and classifi ed by 

three experts on each sample site

Decay class Harvested Mixed conifer

1 22 6

2 16 10

3 20 25

4 45

Total 62 46

Table 2.—Characteristics used to classify the decay for 

each fi ne woody debris sample

Characteristics

Green needles present Brown rot present

Moss and Lichen present White rot present

Fungi present Bright wood

Shiny bark Dull wood

Dull bark Sapwood fragmenting

Bark loss Solid core remains

Chipping bark Sample dark and rotten

No bark

Table 3.—Number of fi ne woody debris samples by species and study site

Species Harvested Mixed conifer

Acer circinatum 1

Acer macrophyllum 8 19

Prunus emarginata 1

Pseudotsuga menziesii 28 21

Quercus garryana 6

Unknown hardwood 4

Unknown 14 6
5

samples were no longer losing moisture), they were weighed again to obtain the dry weight. 

Density was calculated as dry mass divided by green volume for each individual FWD sample 

for a total of 108 density measurements. Sample collection and processing time (drying time 

excluded) was cumulatively ≈ 3 days.

Determination of FWD Density by Decay Class Using 

Water Displacement

Using the FWD samples from the FWD piece density analysis, the next step in the lab 

was to lightly clean the FWD pieces of lichen and moss (Fig. 3). Th e FWD pieces were 

then placed in a sample bucket whose weight had already been tared on a scale. Th e bucket 

and branches were weighed together to determine the total fi eld wet weight of all of the 

samples within the bucket. A hardware cloth circle of known weight was placed across the 

top of the branches (to avoid loss of FWD materials during water displacement procedures) 

and the top of the bucket was sealed down. Th e bucket was then fi lled with water and the 

branches were soaked for 15 minutes to decrease the error in volume created when the 

samples absorb instead of displace the water. Th e water was then poured off ; the bucket 

was opened; and with the mesh circle held in place, the bucket was tipped to pour off any 

remaining water. Th e bucket, top, mesh, and samples were re-weighed after soaking and the 

weight was recorded. A second identical sealed bucket was fi lled with water until the water 

level produced a meniscus at the middle of the open pour hole located on the bucket lid. A 

length of tubing was used to siphon the water from this water supply bucket into the sealed 

sample bucket. When the water level was near the top in the sample bucket, smaller diameter 

tubing was used to siphon the last water needed to fi ll the sample bucket to the same level 

to which the water supply bucket had been fi lled. A pipette was used to fi ne-tune the water 

level so that the meniscus fell precisely at the middle of the open pour hole. Th e water supply 

bucket was then weighed to determine how much water remained, which was equal to the 

volume displaced by the FWD in the sample bucket. Th e weight of the bucket and top was 

subtracted from the weight of the entire water-fi lled bucket to determine the weight of the 

water displaced by FWD. To reduce the error due to water absorption into the branches 

during the volume determination, this amount was measured by emptying the sample bucket 

in the same way as was done after the pre-soaking. Th e bucket, top, mesh, and branches were 

then remeasured, and the diff erence between this new weight and the weight recorded before 

the volume determination was added to the weight of the displaced water to correct for 

absorption. Th e volume measurement was done three times in succession, and the average of 

the three determinations was recorded as the volume of the FWD samples. Th e process was 

repeated for each decay class.

Th e water displacement process was also performed with 250-ml square Nalgene plastic 

bottles of known volume to ascertain the precision and repeatability of the volume 

determinations. Th ree volume determinations were done with 10 bottles, 3 with 15 bottles, 

11 with 20 bottles, and 1 with zero bottles. To test the precision and repeatability of the 

whole procedure including the volume of water absorbed by wood, 35 wood blocks of 

known volume were put through the same process as the FWD. Volume determination with 

the wood blocks was repeated 10 times in the same buckets used for FWD analysis. Precision 

of the volume determination ranged from 96 to 99 percent of the true volume.
6

Figure 3.—Steps in measuring fi ne woody debris water displacement: (a) Field collection of 

a single decay class in a sample bucket, (b) fi eld wet weight recorded, (c) fi ne woody debris 

wetted for 15 minutes then sample bucket completely emptied and re-weighed, (d) 20L 

water supply bucket fi lled until meniscus at mid-point of pour hole, (e) water siphoned 

from water supply bucket to lower sample bucket, (f) volume adjusted by pipette to proper 

meniscus location, (g) water supply bucket containing water displaced by branch volume 

re-weighed, (h) branches split to facilitate drying, and (i) branches dried to stable weight.

abc

def

ghi

All photos courtesy of Becky Fasth, Oregon State University

After volume determination, the branch samples were removed from the bucket and split 

with chisels, clippers, or an axe to facilitate drying in an oven. Care was taken to avoid loss 

of biomass due to violent fracturing and the scattering of wood dust. Th e samples were dried 

in the oven at 55 oC for a week, until their weight stabilized. At this point the oven-dry 

weight of the entire bulk sample was recorded for one FWD decay class. Total dry weight 

divided by total volume was used to calculate an average density of all the pieces in the 

bucket. Additional samples from each decay class present in the stand were processed in the 

same manner. Sample collection and processing time (drying time not included) per stand 

was cumulatively ≈ 2 days.
7

Field Crew Identifi cation of FWD Decay 

Classes

Th e fi nal study objective was to determine if people 

could consistently identify pieces of FWD by decay class 

in the fi eld. Th is objective was accomplished in three 

parts starting with the potentially most precise and time 

consuming and fi nishing with the potentially least precise 

and least time consuming:

1) Assessment of decay class of individual pieces along 

a transect

2) Assessment of percentage of pieces in each decay 

class on a 2 by 2 m quadrat

3) Assessment of the percentage of pieces in each decay 

class in a visual circle around the fi eld crew

In part one, CWD decay class experts located 80 pieces 

(samples) of FWD in varying stages of decomposition in 

the mixed conifer stand and placed them along a transect 

(200 m). All of the samples were fl agged with ribbon and 

labeled with a pin fl ag numbered 1 through 80 (Fig. 4). 

Twenty participants were recruited to traverse the transect 

and record a decay class for each sample. Each participant 

repeated the entire transect after performing parts two 

and three of this investigation. We hoped that by not 

repeating the transect immediately a participant would 

have fewer chances to remember the decay class recorded on the fi rst walkthrough. Th e 

repetition was done to determine repeatability and variation within a single observer. Of the 

20 participants, 3 were experts at CWD decay class identifi cation, 2 had previous experience 

in CWD decay class identifi cation, 10 had previously worked in forests, and 5 were random 

high school students. Th e approximate time spent by a participant in assessing and recording 

a decay class was from 2 to 5 seconds per sample. Th e following information was given to 

test participants about decay class diff erentiation:

DC1: Fresh, fi ne twigs, possibly with needles or leaves; bark intact, no sign of rot

DC2: Not fresh, some loss of twigs or bark, no loss from rot, some changes due to age

DC3: Most fi ne twigs gone, signifi cant bark loss, some outer surface erosion, still 

strong, may be incorporated in the forest fl oor

DC4: Little bark, fully colonized by rot, loss of strength, incorporated in the forest 

fl oor

After all participants had completed the transect, the samples were collected and returned 

to the lab where mean diameter, length, and oven-dry weight were recorded and used 

to determine the true density of each sample. Th e “true” decay class for each sample was 

Figure 4.—Individual tagged pieces of fi ne woody debris.

Courtesy of Becky Fasth, Oregon State University
8

determined to be the consensus decay class of the expert determinations, when two or 

three of the three experts recorded the same decay class for a piece. A mean wood density 

by expert consensus decay class was then calculated and termed the mean consensus 

wood density for that decay class. Th e overall mean stand wood density was calculated 

by determining the mean wood density of all 80 samples. We should note that the FWD 

pieces collected for this study objective were separate from FWD pieces collected for study 

objectives one and two.

For the second part of the study, we were interested in determining whether our 20 

participants could consistently record decay class abundance (percent of each decay class) 

within a fi xed-area plot. Eight 2 by 2 m plots were established in the mixed conifer stand 

and the corners were marked with pin fl ags. Participants were asked to record the percentage 

of each of four decay classes that they believed were present within each of the eight plots. 

Th e time spent by each participant at each plot was about 1 minute. Samples were collected 

from this location and treated as in the previous section on determination of individual 

FWD piece densities to determine the actual density for these plots.

In the third part of the study, participants were asked to stand in three diff erent spots in 

the mixed conifer stand and record percentages of decay classes of FWD they believed were 

present for everything visible (visual circle) within the forest stand. As with the fi xed-area 

plots, the time spent by each participant at each circle was about 1 minute. Samples were 

collected from this general location and treated as in the previous section on determination 

of individual FWD piece densities to determine the actual density for this stand.

To estimate a weighted average wood density of each observer for the transect, small plots, 

or stand, the percentage of pieces in each decay class was multiplied by the average wood 

density for that decay class. Th ese products were then summed for all decay classes and 

divided by 100 to calculate the average wood density for each observer. Th e density of the 

decay classes was an average from two sources: the individual pieces collected and bulk 

density as determined by water displacement.

Mean stand wood density generated from each of the methods in this study was compared 

to the mean stand wood density generated using the methods sometimes employed by the 

U.S. Forest Service where two values for density (“fresh” and “decayed”) are currently used 

to calculate mass of FWD from the volume data collected on the plots. For this comparison, 

the “fresh” materials were those assigned to decay class one and the “decayed” pieces were 

those assigned to decay classes two, three, and four. Th e fresh and decayed values for 

Pseudotsuga menziesii (0.57 and 0.43 g/cm3

, respectively) from Harmon et al. (2008) were 

used because it is the dominant species in the mixed conifer stand examined. Th e expert 

consensus decay classes were used to generate a mean stand wood density, and the nonexpert decay class determinations were used to calculate the range of potential values.

Where appropriate, ANOVA (GLM procedure in SAS) was used to test signifi cant 

diff erences between the density of FWD decay classes.
9

RESULTS

Physically Identifi able Characteristics of Decay Classes

Th e presence of needles/leaves and shiny bark were fairly consistent indicators of decay class 

one in FWD in both the harvested and mixed conifer stands (Tables 4 and 5). Bright wood 

at breaks was also a strong indicator of decay class one in the harvested stand, but not in the 

mixed conifer stand. Decay class two showed no clear and decisive physical characteristics 

except that the bark was falling off ; instead, it shared many of the same characteristics of 

both decay class one and decay class three. In the mixed conifer stand, the presence of moss/

lichen, bark loss, dull bark, and dull wood was consistent throughout most samples for decay 

class three. In the harvested stand, however, bark loss and sapwood fragmentation were the 

main characteristics for decay class three. In both forest stands, decay class four shared many 

characteristics with decay class three, yet could be distinguished by having dark rotten wood, 

a solid core, and no bark.

Table 4.—Number of samples in harvested study site listed 

by the physical characteristics used to classify decay

Decay class

Characteristic 1 2 3 4

Shiny bark 22 4 

Bright wood 22 11 

No bark 1 3 4

Bark chipping 1 

Moss and lichen 9 10 

Brown rot 1 4

Bark loss 17 

Sapwood fragmenting 20 

Solid core remains 4

Table 5.—Number of samples in mixed conifer study site 

listed by the physical characteristics used to classify decay

Decay class

Characteristic 1 2 3 4

Green needles 1 

Moss and lichen 4 20 1

Shiny bark 5 

Bark loss 1 16 

No bark 1 5

Dull bark 3 23 

Bark chipping 7 

Solid core remains 1 3

Brown rot 1 

White rot 1 

Fungi 2 1

Dark/rotten 2 5

Bright wood 2 

Sapwood fragmenting 5 5

Dull wood 15 
10

Wood Density—Decay Class Relationships

Densities by decay class of multiple individual samples of FWD collected by experts

Wood density values decreased signifi cantly (p-value 0.0130) as decay class increased based 

on the mean of samples from the mixed conifer stand (Fig. 5). Th is was not the case in the 

harvested stand where wood density values stayed level and even increased as decay class 

increased. Th e p-value 0.0613 is suggestive of a diff erence, but not as signifi cant as in the 

mixed conifer stand. Th ere was weak statistical diff erence (p-value = 0.0993) between the 

mean wood density by decay class of the harvested and the mixed conifer stands. All wood 

density values, particularly decay class three, demonstrated overlap with adjacent decay 

classes in both the harvested and mixed conifer stands. When all samples from both stands 

(mixed conifer and harvested sites) were combined to create an overall mean wood density 

by decay class, there was a signifi cant diff erence (p-value = 0.0008) between FWD decay 

classes with decreasing wood density with increasing decay class until class four, in which the 

wood density increased slightly. Th e mean overall wood density combining stands and decay 

classes was calculated to be 0.457 g/cm3

 (n=108). Th e unbalanced distribution of FWD 

decay class samples suggests that this overall mean could be slightly biased toward fresher 

decay classes; however, given the lack of highly decayed FWD pieces in forest ecosystems, 

this may represent reality.

Densities of bulk samples of FWD using water displacement

When water displacement was used to determine a mean bulk density for each decay class, 

there was a clear trend of decreasing density with increasing decay class (Fig. 6) (p-value 

0.0447 moderately suggestive that the means are diff erent). Th e mean overall stand density 

for this method of density determination was calculated to be 0.468 g/cm3

.

Figure 5.—Mean density of fi ne woody debris by decay class in separate stands and 

combined.

0.52

0.48

0.41 0.43

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1234

Density (g/cm3)

Decay class

Combined Stands Harvested Stand Mixed Conifer Stand
11

Field Crew Identifi cation of Decay Classes

Decay class identifi cation and density along a transect

Th ere was a noticeable diff erence between an expert’s consistency in identifying decay classes 

of FWD and that of the non-experts. Although the three experts did not agree on every 

sample of FWD along the transect, they did not vary by more than one decay class from the 

mean (Fig. 7). Th e non-expert observers, however, ranged one to two decay classes diff erent 

Figure 6.—Mean density and associated standard errors of fi ne woody debris by decay class 

for water displacement method.

0.516

0.474

0.427

0.377

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1234

Density (g/cm3)

Decay class

Figure 7.—Expert consensus fi ne woody debris decay class and recorded decay class for 80 

pieces along transect.

0

1

2

3

4

5

0 10 20 30 40 50 60 70 80

Decay class assigned

FWD piece number

Expert Fonsensus Gecay Flass Expert Rbservations
12

from the expert consensus decay class (Fig. 8). For decay class two and three, non-expert 

observers recorded any one of the four decay classes. Th e mean wood density by expert 

consensus decay class had a trend of decreasing density with increasing decay class (Fig. 9). 

Th e mean overall stand wood density for all of the samples on the transect was 0.433 g/cm3

.

Figure 8.—Expert consensus decay class and all other observer recorded decay class 

for 80 pieces along transect.

0

1

2

3

4

5

0 10 20 30 40 50 60 70 80

Decay class assigned

FWD piece number

Expert Fonsensus Gecay Flass All other Rbservers

0.58

0.456

0.408

0.296

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

01234

Density (g/cm3)

Expert consensus decay class

Figure 9.—Density and mean of transect samples by expert consensus decay class.
13

Decay class abundance identifi cation and wood density within fi xed-area 

plots and visual circles

Th ere was no consistent pattern or agreement between the 27 observations made within the 

fi xed-area plots and visual circles to determine decay class abundance. Multiple observers 

were unable to look at an area and determine percentages of each decay class with any sort of 

agreement (Figs. 10 and 11). Although there was no agreement among the observers, some 

minor patterns emerged within the stand overall. Th ere was a tendency to record a higher 

Figure 10.—Percent of decay class noted for all observers in fi xed-area plot 1.

Figure 11.—Percent of decay class noted for all observers in visual circle 1.

0

10

20

30

40

50

60

70

80

90

100

Percent of decay class noted by observer

Individual observation within same fixedͲarea plot

dc1 dc2 dc3 dc4

0

10

20

30

40

50

60

70

80

90

100

Percent of each decay class noted by observer

Individual observation within same visual circle

dc1 dc2 dc3 dc4
14

percentage for decay class two than other decay classes and a much lower percentage for decay 

class four in both the fi xed-area plot and visual area circle. It is doubtful that this pattern 

would be detectable with fewer than 10 observations using either method.

When the wood densities by decay class from individually collected samples, water 

displacement samples, and currently accepted method (Harmon et al. 2008) were used to 

determine a total stand mean FWD density for all observers along the transect sample, the 

mean stand density ranged from 0.42 to 0.47 g/cm3

. Th e actual measured wood density of 

all samples on the transects was 0.43 g/cm3

 (Fig. 12). When the same densities were applied 

to the percentage of decay class abundance for each of the fi xed area plots, there was a wider 

spread of possible densities ranging from 0.30 to 0.54 g/cm3

 depending upon which fi xed area 

plot and which observer are being considered (Fig. 12). Th e actual mean measured density 

of samples from the fi xed area plots was 0.453 g/ cm3

. Again, when the same densities were 

applied to the percentages of decay classes in visual area circles, the densities ranged from 0.36 

to 0.53 g/cm3 (Fig. 12). Th e actual mean measured density of samples from the visual area 

circles was 0.43 g/cm3

.

1 2 3

Density measurement technique

A

B

C

0.30

0.40

0.50

0.60

Actual mean 

density of transect 

samples

0.3

0.4

0.5

0.6

Density (g/cm3

)

Actual mean 

density of 

samples

0.30

0.40

0.50

0.60

Actual mean 

density of 

samples

Figure 12.—Density by decay class from (1) 

individual samples, (2) water displacement, 

and (3) Harmon et al. (2008) applied to decay 

class abundance recorded on (A) the transect, 

(B) fi xed-area plots, and (C) visual circles. 

Solid black line shows range of results, blue 

box is mean of all observations, and dashed 

line is measured stand mean for that area.
15

DISCUSSION

Th e implications of this study’s results are restricted due to the limited spatial scope of 

this study (Pacifi c Northwest of the United States) and lack of replication among the two 

disparate study sites (harvested versus mature mixed conifer). Th e numerous inconclusive 

results of this study suggest that a large number of replicated study sites across the diverse 

forest ecosystems of the U.S. are necessary to reach a level of certainty for aff ecting the 

national FWD sampling scheme used by FIA. Th is important caveat aside, the results of this 

study can be used as an indicator regarding future study designs and the possible benefi ts of 

refi ned FWD sampling procedures.

Can a FWD decay system, besides undecayed versus decayed, be developed? Th e results 

from this study suggest that a comprehensive list of numerous decay attributes can be 

developed and readily trained/employed in the fi eld. Th ere is abundant literature on CWD 

decay attributes of classifi cation schemes. Is the FWD decay classifi cation scheme repeatable 

with distinct diff erences between stages of decay? Th is study indicates that the FWD decay 

classifi cation scheme is probably only repeatable among highly trained forest detritus 

experts. Th e amount of uncertainty of the mean stand density estimate based on the decay 

class determination of a non-experienced crew member was greater than the measurement 

repeatability tolerance (± one decay class) of the national CWD inventory conducted by 

FIA (Westfall and Woodall 2007). Unless substantial training is undertaken, the reliability 

of the decay class determination would be highly questionable. Th ere is so much overlap in 

FWD decay characteristics among the classifi cation scheme that novices or even typical forest 

inventory fi eld crews would not be able to repeat FWD decay class assessments. Th ere was 

a tendency for FWD decay classes one and two to diff er in attributes compared to FWD in 

decay classes three and four. Th ere is a slim possibility that forest inventory fi eld crews could 

be trained to identify proportions in undecayed (decay classes one and two) and decayed 

(decay classes three and four) FWD.

If FWD could be accurately assigned to decay classes, what diff erence would it make in 

eventual population estimates? Th e answer to this question hinges on the premise that the 

expert CWD scientists in this study accurately assigned FWD into correct classes of decay. 

Th is is the ultimate confounding factor when developing any FWD decay class study. With 

this caveat aside, expert opinion was used in this study because if an expert couldn’t separate 

FWD pieces into decay classes with signifi cant diff erences in density then there is little 

chance that fi eld crews could. Th ere was signifi cant diff erence in FWD density, with density 

decreasing as stage of decay increased. Does this signifi cant diff erence aff ect population 

estimates? Consider this example: if the volume of FWD on a particular FIA plot is 5 m3

/ha, 

then the resulting biomass estimate using the most precise overall stand density (individually 

measured FWD) would be 22.8 Kg/ha. If we used the same scenario and the density values 

from water displacement, the calculated stand FWD biomass would be 23.4 Kg/ha. When 

FWD density is based on currently used techniques (FWD forest type table, Harmon et al. 

2008, Woodall and Monleon 2008), the calculated stand biomass is 22.5 Kg/ha. Given the 

additional measurement errors incurred by having fi eld crews assess FWD states of decay and 

lab analysis of FWD density, it would be diffi cult to suggest that a FWD decay class system 

would substantially refi ne eventual FWD population estimates.
16

Given the limited possibility of improving the precision of FWD biomass estimates using 

a FWD decay class system, did this study’s techniques require a lot of time? Th e range of 

density was much narrower when the decay class of each piece of FWD was estimated, but 

this took up to 15 seconds per FWD piece. It took 15 to 20 minutes to complete a piece 

by piece classifi cation along a transect with 80 FWD pieces. Results were not signifi cantly 

diff erent between a fi xed-area plot and a total visual area circle when percentages of FWD 

decay class abundance were recorded. Each fi xed-area plot or visual circle took roughly 1 

minute to complete. Th e main diff erence was a higher percentage of decomposed FWD 

recorded in the fi xed-area plots as compared to the visual circles. Although not a timeconsuming endeavor for visual circles, this study’s objectives and scope would have to be 

substantially expanded to confi rm increases in the precision of FWD biomass estimates 

before even a few minutes per plot would be added to FIA fi eld crew time. Th e collection of 

spurious FWD decay class information, despite just a few minutes per plot, might impart 

unjustifi ed additional certainty to FWD estimates.

Finally, is there a superior lab method for determining FWD density? Two lab methods 

were explored in this study: individual piece measurement and water displacement. Neither 

method had results that indicated supremacy. Both methods required the diligent collection 

of FWD pieces, enough to fi ll relatively large buckets. Th e water displacement methodology 

required less individual handling of FWD pieces, so it might be optimal in that particular 

context. However, the water displacement of highly decayed FWD pieces introduces the 

possible measurement error of siphoning off of FWD decayed wood dust and chunks. Th e 

eff ort required to estimate FWD density using lab methods on an individual inventory plot 

is beyond the time limitations of most fi eld crews.

CONCLUSIONS

What can be suggested for the future? First, there appears to be no FWD sample protocols 

readily available that would refi ne the FWD biomass estimation procedures currently used 

by FIA. Th e results of this study indicated through time-consuming work that there are 

signifi cant diff erences in density across stages of FWD decay. Only an expert can identify 

these diff erences that don’t result in a substantial diff erence in estimates of FWD biomass. 

Second, expanded research in the area of developing a decayed versus undecayed FWD 

classifi cation scheme is suggested, especially if non-expert fi eld crews could be easily trained 

to repeat such a measurement. Perhaps the sample technique of visual circle assessments of 

FWD decayed versus undecayed proportions should be explored. Th ird, only a limited set 

of tree species on two sites was explored in this study. Future studies should focus on more 

diverse tree species in various stages of stand development/disturbance. Finally, there was 

an indication that a recently harvested site might have substantially more undecayed FWD 

pieces. Perhaps the stand disturbance/management history collected by FIA fi eld crews 

could be used as a FWD density reduction factor. More focus could be given to increasing 

the precision of tallying FWD pieces as opposed to quantifying decay class given the poor 

repeatability of FWD piece counts (Westfall and Woodall 2007) that might introduce far 

greater errors in FWD estimates than density assumptions. Overall, given the fractured/

decayed state of FWD that is often an ephemeral forest entity, FIA’s current methodology of 

using a mean bulk density for each forest type as the default FWD density appears justifi ed 

until expanded research indicates a superior methodology.
17

ACKNOWLEDGMENTS

Th e authors would like to thank Duncan Lutes and Pamela Sikkink at the Missoula Fire 

Sciences Lab for conscientious critiques of this study.

LITERATURE CITED

Bull, E.L.; Parks, C.G.; Torgersen, T.R. 1997. Trees and logs important to wildlife in 

the Interior Columbia River Basin. Gen. Tech Rep. PNW-391. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacifi c Northwest Research Station. 55 p.

Harmon, M.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.

Harmon, M.E.; Woodall, C.W.; Fasth, B.; Sexton, J. 2008. Woody detritus density and 

density reduction factors for tree species in the United States: a synthesis. Gen. Tech. 

Rep. NRS-29. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 84 p.

Kukuev, Y.A.; Krankina, O.N.; Harmon, M.E. 1997. Th e forest inventory system in 

Russia. A wealth of data for western researchers. Journal of Forestry. 95: 15-20.

Lutes, D.C.; Keane, R.E.; Caratti, J.F.; Key, C.H.; Benson, N.C.; Sutherland, S.; Gangi, 

L.J. 2006. FIREMON: Th e fi re eff ects monitoring and inventory system. Gen. Tech. 

Rep. RMRS-164-CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.

Marshall, P.L.; Davis, G.; LeMay, V.M. 2000. Using line intersect sampling for coarse 

woody debris. Tech. Rep. TR-003. Vancouver, BC: British Columbia Ministry of 

Forests, Vancouver Forest Region. 34 p.

Maser, C.; Anderson, R.G.; Cromack, K., Jr.; Williams, J.T.; Martin, R.E. 1979. Dead and 

down woody material. In: Th omas, J.W., tech. ed. Wildlife habitats in managed forests: 

the Blue Mountains of Oregon and Washington. Agric. Handbk. 553. Washington, DC: 

U.S. Department of Agriculture: 78-95.

Ohmann, J.L; Waddell, K.L. 2002. Regional patterns of dead wood in forested habitat 

of Oregon and Washington. In: Laudenslayer, W.F.G., Jr.; Shea, P.J.; Valentine, B.E.; 

Weatherspoon, C.P.; Lisle, T.E., tech. cords. Proceedings of the symposium on the 

ecology and management of dead wood in western forests, November 2-4, 1999. Gen. 

Tech. Rep. PSW-181. Albany, CA: U.S. Department of Agriculture, Forest Service, 

Pacifi c Southwest Research Station: 535-560.
18

Rollins, M.G.; Keane, R.E.; Parsons, R.A. 2004. Mapping fuels and fi re regimes using 

remote sensing, ecosystem simulation, and gradient modeling. Ecological Applications. 

14: 75-95.

Tietje, W.D.; Waddell, K.L.; Vreeland, J.K.; Bolsinger, C.L. 2002. Coarse woody debris in 

oak woodlands of California. Western Journal of Applied Forestry. 17: 139-146.

Van Wagner, C.E. 1968. Th e line-intersect method in forest fuel sampling. Forest 

Science. 14: 20-26.

Westfall, J.A.; Woodall, C.W. 2007. Measurement repeatability of a large-scale inventory 

of forest fuels. Forest Ecology and Management. 253: 171-176.

Woldendorp, G.; Keenan, R.J.; Barry, S.; Spencer, R.D. 2004. Analysis of sampling 

methods for coarse woody debris. Forest Ecology and Management. 198: 133-148.

Woodall, C.W.; Monleon, V.J. 2008. Sampling protocols, estimation procedures, 

and analytical guidelines for the down woody materials indicator of the Forest 

Inventory and Analysis Program. Gen. Tech. Rep. NRS-22. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northern Research Station. 68 p.

Woodall, C.W., Heath, L.S., Smith, J.E. 2008. National inventories of dead and downed 

forest carbon stocks in the United States: opportunities and challenges. Forest Ecology 

and Management. 256: 221-228.

Woodall, C.W.; Rondeux, J.; Verkerk, P.J.; Ståhl, G. 2009. Estimating dead wood during 

national forest inventories: a review of inventory methodologies and suggestions for 

harmonization. Environmental Management. 44: 624-631.

Yatskov, M.; Harmon, M.E.; Krankina, O.N. 1993. A chronosequence of wood 

decomposition in the boreal forests of Russia. Canadian Journal of Forest Research. 33: 

1211-1226.
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Fasth, Becky; Harmon, Mark E.; Woodall, Christopher W.; Sexton, Jay. 2010. 

Evaluation of techniques for determining the density of fi ne woody debris. 

Res. Pap. NRS-11. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 18 p.

Evaluated various techniques for determining the density (i.e., bulk density) of fi ne 

woody debris during forest inventory activities. It was found that only experts in 

dead wood inventory may be able to identify fi ne woody debris stages of decay. 

Suggests various future research directions such as development of a 2-class fi ne 

woody debris decay class system.

KEY WORDS: detritus, fi ne woody debris, density, inventory
www.nrs.fs.fed.us