United States

Department of

Agriculture

Forest

Service

North Central

Research Station

General Technical

Report NC-256

Sampling Protocol,

Estimation, and Analysis

Procedures for the Down

Woody Materials Indicator

of the FIA Progam

Christopher Woodall and Michael S. Williams
Abstract

The Forest Inventory and Analysis (FIA) program of the USDA Forest Service conducts a national

inventory of forests of the United States. A subset of FIA permanent inventory plots are sampled

every year for numerous indicators of forest health ranging from soils to understory vegetation. Down

woody material (DWM) is an FIA indicator that provides estimates of forest structural diversity, forest

area fuel loadings, and national carbon sources. DWM comprises fine woody debris, coarse woody

debris, slash piles, duff, litter, and shrub/herbs. Components of DWM are sampled using the lineintersect method and fixed-radius sampling. DWM data analyses serve as integral parts of national

inventory reporting requirements, regional/national forest health reports, wildlife habitat assessments,

and fuel loading maps. The DWM inventory began in 2001 and is currently implemented in 38

States.

The goal of this document is to provide the rationale and context for a national inventory of down

woody material; document the various woody material components sampled by the DWM indicator,

the sampling protocol used to measure the DWM components, and estimation procedures; and

provide guidance on managing and processing DWM data and incorporating that data into pertinent

inventory analyses and research projects.

Published by:

North Central Research Station

Forest Service U.S. Department of Agriculture

1992 Folwell Avenue

St. Paul, MN 55108

2005

Web site: www.ncrs.fs.fed.us

The U.S. Department of Agriculture (USDA) prohibits

discrimination in all its programs and activities on the basis of

race, color, national origin, sex, religion, age, disability, political

beliefs, sexual orientation, and marital or family status. (Not all

prohibited bases apply to all programs.) Persons with disabilities

who require alternative means for communication of program

information (Braille, large print, audiotape, etc.) should contact

USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director,

Office of Civil Rights, Room 326-W, Whitten Building,

1400 Independence Avenue, SW, Washington, DC 20250-9410

or call (202) 720-5964 (voice and TDD). USDA is an equal

opportunity provider and employer.

Cover photos (clockwise): Burnt ponderosa pine log near Prescott, AZ; snail on coarse woody debris

in Great Smoky Mountains, NC; fine woody debris near Salinas, CA; and riparian coarse woody debris

in Pisgah National Forest, NC.
February 2007

ERRATA ERRATA ERRATA ERRATA ERRATA

Please note the following changes to the publication:

Woodall, C.W.; Williams, M.S. 2005. Sampling protocol, estimation, and analysis procedures

for down woody materials indicator of the FIA program. Gen. Tech. Rep. NC-256. St. Paul,

MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 47 p.

Overview:

This General Technical Report (GTR-NC-256) broadly describes the down woody materials

inventory of the Forest Inventory and Analysis program of the USDA Forest Service. In addition to describing the inventory, it lists estimation procedures/formulae. Some of these formulae have been found to be in error. Based on user/reader feedback, the following notes both

correct and clarify some of the formulae in this report. A full revision of the report is currently

underway to produce a new edition that follows the structure of inventory estimation procedures documented in SRS-GTR-80 (see Note 11 below for citation) and currently used in

national estimation procedures. The revision will be published as a Northern Research Station

publication.

Note 1: Formula 3.1, page 16.

where y is the volume per unit area, k is a constant that accounts for unit conversions (table

3.1), a is the nonhorizontal lean angle correction factor for the piece of FWD, c is the slope

correction factor, L is the total length of transect, di is the diameter of the FWD piece at the

point of intersection, and n is the number of pieces intersected by the transect. In table 3.1 use

only volume constants (V).

Note 2: Formula 3.2, page 16.

where is the FWD dry weight per unit area, G is the specific gravity of the FWD piece.

In table 3.1 use only weight constants (W) for k.

Note 3: Formula 3.5, page 18.

where y is the volume per unit area, di is the diameter of the CWD piece at the point of intersection. In table 3.1 use only volume constants (V) for k. If you desire a weight estimate, then

use weight constants for k in table 3.1 and G (specific gravity) in the numerator.

∑=

=

n

i

di L

kac

y

1

2

2 ( )

1

n

w

i

i

Gkac y Gy L d =

= = ∑

= ∑di L

kc

y

2

y(w)
Note 4: Formula 3.6, page 18.

where N is the total number of pieces in the population and A is the area of the study region

(DeVries 1986, page 252).

Note 5: Formula 3.7, page 18.

Note 6: Formula 3.11, page 19.

Integrated fuel depth =

Warning: Although the estimator provided in the original publication may provide an adequate estimate of integrated fuelbed depth in some fuel arrangements, it will not accurately

reflect the fuel complex in many situations across the United States. To date, no estimator

has been forwarded that can adequately estimate the fuel complex in the diverse forest

ecosystems of the US.

Note 7: Table 3.4, Volume equations for shape code 3, replaces 3b and 3c equations

where l, h, and w are residue pile dimension measurements.

Note 8. Formula 3.16, page 20

where is the weight of the duff, litter or fuelbed layer in condition c, BD

the bulk density, k a unit conversion constant, nc the number of points falling in condition

class c, and δi = 1 if the i-th point falls in condition class c and δi = 0 otherwise.

Note 9: Formulae 3.18, 3.19, 3.20, pages 21-22.

As currently proposed, these formulae will not be used for national estimation of DWM

attributes by the FIA program, although these estimators may be appropriate for other down

woody inventories in different situations.

∑=

=

N

i i

i

l

y

AL

f Var y

1

2

2 ( ) π

∑=















 



 



 =

n

i i

i

l

y

L

f Var y

1

2 2

2 ( ) π

∑ ∑ = =

n

i

i i

n

i

i h c c

1 1

( ) 1 1 1 1 2 2 2 2 12

1 V = πl h w + h w h w + h w

( ) ( )( )( ) ( ) ( )( ) 1

n

i i

i

DLF

c

y

Y c BD k y c BD k

n

δ

= = =

∑

Y c DLF ( )
Note 10: Incorrect references to formulae:

Page 18, column 1, text line 16: should be equation 3.1

Page 19, column 1, text line 17: should be equations 3.8 and 3.9.

Page 20, column 1, text line 36: should be equation 3.6

Page 20, column 2, text line 30: should be equation 3.8

Page 37, column 1, text line 9: should be equation 3.15

Page 37, column 2, text line 23: should be equation 3.19

Page 38, column 1, text line 9: should be equation 3.20

Note 11: References, pages 32-33.

Revised: Bechtold, W.A.; Patterson, P.L., eds. 2005. 

Forest Inventory and Analysis national sample design and estimation procedures. Gen. Tech.

Rep. SRS-80. Knoxville, TN: U.S. Department of Agriculture, Forest Service, Southern

Research Station. 106 p.

Revised: Harmon, M.E.; Franklin, J.F.; Swanson, F.J.; et al. 1986. 

Ecology of coarse woody debris in temperate ecosystems. Advances in Ecological Research.

15: 133-302.

Removed: Stahl, G.; Ringvall, A.; Fridman, J. 2001. 

Assessment of coarse woody debris—a methodological overview. Ecology Bulletin. 49: 57-70.

Revised: Van Wagner, C.E. 1968. 

The line intersect method in forest fuel sampling. Forest Science 14: 20-26

Revised: Waddell, K.L. 2002. 

Sampling coarse woody debris for multiple attributes in extensive resource inventories.

Ecological Indicators. 1: 139-153.

Note 12: Appendix 8.4

Any references to appendix 8.4 including PL-SQL code are incorrect. Appendix 8.4 is strictly

for processing constants.
Acknowledgments

The Down Woody Materials indicator began as a joint pilot program by the USDA Forest Service Forest

Health and Monitoring and Forest Inventory and Analysis programs in the summers of 1999 and 2000.

Rick Busing, K. Rimar, Ken Stolte, T. Stohlgreen, and Karen Waddell implemented this initial work. In

2001, the Forest Inventory and Analysis program began to implement a national DWM indicator

program. Karen Waddell served as the first national indicator advisor for DWM through the summer of

2002. It is through the pioneering work of these individuals that the DWM indicator exists today.

The authors would like to thank regional trainers, field crews, QA staff, database programmers, and

analysts who collect, manage, and disseminate DWM inventory data and products. Finally, the authors

would like to thank the following individuals for being instrumental in the development and

documentation of the DWM indicator’s sample protocols and estimation procedures: Ken Lautsen, Larry

DeBlander, Karen Waddell, Olaf Kuegler, and Vicente Monleon.
Table of Contents

Page

1. INDICATOR OVERVIEW .................................................................................................................. 1

1.1 FIA Inventory and Forest Health Indicators ............................................................................... 1

1.2 The Down Woody Materials Indicator ....................................................................................... 2

1.2.1 DWM as it Relates to Fire, Wildlife, and Carbon Modeling Sciences................................ 2

1.2.2 Detection and Evaluation Monitoring .............................................................................. 3

1.2.3 Current and Expected Outputs ....................................................................................... 3

2. Plot-Based Sampling Protocol ............................................................................................................ 9

2.1 Introduction and Literature Review ........................................................................................... 9

2.2 Fine Woody Debris .................................................................................................................. 12

2.3 Coarse Woody Debris .............................................................................................................. 12

2.4 Duff/Litter/Fuelbed .................................................................................................................. 13

2.5 Shrubs and Herbs .................................................................................................................... 13

2.6 Slash Piles................................................................................................................................ 13

2.7 Transect Segmenting ................................................................................................................ 14

3. ESTIMATION .................................................................................................................................. 15

3.1 Estimation Methods ................................................................................................................. 15

3.1.1 The approach to Inference for Fine and Coarse Woody Debris ...................................... 15

3.1.1.1 Fine Woody Debris Estimators ........................................................................ 15

3.1.1.2 Coarse Woody Debris Estimators .................................................................... 17

3.1.2 Duff, Litter, and Fuelbed ............................................................................................... 18

3.1.3 Shrubs and Herbs.......................................................................................................... 19

3.1.4 Slash ............................................................................................................................. 19

3.2 Estimation for Plot Classification ............................................................................................. 19

3.2.1 FWD and CWD Classification Methods for a Single Plot ............................................... 20

3.2.2 Duff, Litter, and Fuelbed Classification Methods for a Single Plot .................................. 20

3.2.3 Slash Pile Classification Methods for a Single Plot ......................................................... 21

3.3 Estimation of Population Totals by Combining Information from Multiple Plot Estimates ....... 21

3.4 Estimation for Monitoring and in Conjunction with other FIA inventories .............................. 22

3.5 DWM Data Processing ............................................................................................................. 23

4. CHANGE ESTIMATION .................................................................................................................. 24

5. DWM ANALYTICAL GUIDELINES .................................................................................................. 25

5.1 Field Data ................................................................................................................................ 25

5.2 Plot Summary Core Tables ....................................................................................................... 25

5.3 Population-Level Core Tables .................................................................................................. 27

5.4 Tabular and Graphical Summaries ........................................................................................... 27

5.5 Maps ....................................................................................................................................... 29

6. REFERENCES .................................................................................................................................. 32

7. GLOSSARY ...................................................................................................................................... 35

8. APPENDICES .................................................................................................................................. 36

8.1 Example: Classifying Plots based on the Level of CWD .......................................................... 36

8.2 Example: Estimation of Population Totals for CWD................................................................ 37

8.3 Example: Estimation of Mean Fuel Loadings by Forest Type for Large Area ............................ 38

8.4 DWM Processing Constants ..................................................................................................... 40

8.5 Historical DWM Sample Designs, 1999-2001 .......................................................................... 47
Sampling Protocol, Estimation, and Analysis

Procedures for the Down Woody Materials

Indicator of the FIA Program

1. INDICATOR OVERVIEW

1.1 FIA Inventory and Forest

Health Indicators

Forest ecosystems are more than just assemblages

of trees. They are associations of flora (i.e., trees,

shrubs, herbs, and mosses), fauna (i.e., mammals,

amphibians, and soil microbes), and abiotic

entities (i.e., decaying organic material, mineral

soils, and water) coexisting in complex unity. In

recognition of this fact, the Forest Inventory and

Analysis (FIA) program of the USDA Forest Service

conducts an inventory of our Nation’s forest

ecosystems that not only estimates tree components, but also inventories numerous nontimber

ecosystem attributes (Gillespie 1999, McRoberts et

al. 2004, USDA 1999). Estimating various forest

ecosystem attributes, instead of focusing solely on

trees, may indicate the status and trends in forest

ecosystem health. Nontimber ecosystem attributes

estimated by the FIA program are collectively

referred to as forest health indicators

and currently comprise tree crown

conditions, ozone injury, tree

damage, lichen communities, down

woody materials, vegetation

structure and diversity, and soil

condition (McRoberts et al. 2004).

The down woody materials (DWM)

indicator is sampled in conjunction

with FIA’s national sampling protocol.

Sampling of DWM occurs on a

subsample of FIA’s regular forest

inventory plots. FIA’s national

sampling protocol, for all phases of

inventory excluding forest health

indicators, is detailed in Bechtold and

Patterson (In press). Briefly, FIA’s

national program consists of three phases of data

collection, although these phases are not

equivalent to three-phase sampling (see Cochran

1977, Chapter 12). During the first phase of

inventory, auxiliary information is collected to

poststratify forest inventory ground plots into a

minimum of two strata—forest and nonforest. This

stratification of inventory sample plots is used to

improve the precision of estimates of population

totals.

In phase 2 of the FIA inventory, information is

gathered from a network of permanent ground

plots, with a spatial sampling intensity of

approximately one plot per 6,000 acres. Each FIA

phase 2 plot consists of four 24-foot fixed-radius

subplots arranged in a clustered formation (fig.

1.1). Additional plots are used in some situations.

The third and final phase of the FIA inventory

involves the sampling of forest health indicators

such as DWM. The DWM sampling protocol is

applied to a subset of phase 2 inventory plots

Figure 1.1.—The Forest Inventory and Analysis program’s (USDA Forest Service) phase

2 sampling design.

About The Authors:

Christopher Woodall is a Research

Forester with the Forest Inventory and

Analysis unit at the North Central

Research Station in St. Paul, MN. He

holds a B.S. in forest resource

management from Clemson University

and a M.S. and Ph.D. in forest science

from the University of Montana. He

joined the North Central Research

Station in 2001 where his research

has focused on individual tree growth/

mortality models, fuel dynamics, and

innovative forest inventory analysis

methodologies. He has served as the

national indicator advisor for DWM

since 2002.

Michael S. Williams is a

mathematical statistician with the

Forest Inventory and Analysis unit in

Ft. Collins, CO. He holds a M.S. in

math, M.S. in statistics, and is

currently working on a Ph.D. in

forestry at Colorado State University).

His most recent research interests

include the development of new

sampling strategies for standing and

downed woody materials, the

integration of remotely sensed and

ground data, comparisons between

design- and model-based inference

and the impacts of model failure, and

the development of efficient sampling

designs for the concurrent estimation

of both sample survey and spatial

estimation problems.
(approximately 1/16 of all phase 2 plots). The

National Sample Design and Estimation

Procedures manual details the first two phases

of the FIA sampling protocol (Bechtold and

Patterson, In press); separate sample design

documents for each indicator collectively

describe the third inventory phase. This

document details the sampling protocol and

estimation procedures for the DWM indicator

in context of FIA’s national sampling protocol.

1.2 The Down Woody Materials

Indicator

The DWM indicator estimates dead organic

materials (resulting from plant mortality and

leaf turnover) and fuel complexes of live shrubs

and herbs. Specifically, components estimated

by the DWM indicator are fine woody debris,

coarse woody debris, litter, duff, fuelbed,

slash piles, live/dead shrubs, and live/dead

herbs. Definitions, sample designs, and

estimation procedures for each DWM

component will be described in subsequent

chapters.

1.2.1 DWM as it Relates to Fire, Wildlife,

and Carbon Modeling Sciences

Scientists from many disciplines may use DWM

data to quantify numerous aspects of forest

ecosystems. These scientists include but are not

limited to wildlife biologists, ecologists, fuels

specialists, foresters, carbon/climate change

modelers, and criterion/indicator analysts. The

DWM indicator, coupled with the entire integrated FIA program, can provide information

on the fuels, carbon pools, and wildlife habitat

of our Nation’s forest ecosystems to policymakers, scientists, States, and concerned

citizens as a whole.

The fine and coarse woody components of the

DWM indicator were specifically designed to match

the components defined by the National Fire

Danger Rating System (NFDRS). This system

divides fine and coarse woody debris into size

classes that are equivalent to the fuel-hour class

system (1-hour, 10-hour, and 100-hour) used by

many fire scientists (table 1.1) (Burgan 1988,

Deeming et al. 1978). Additionally, numerous other

components of the DWM indicator may aid the fire

sciences. Litter and duff depths are sampled for

estimates of fuel loadings, while estimates of

fuelbed depths and microplot vegetative structures

(both dead and living) can provide estimates of the

spatial aspects of forest fuel complexes. Coupled

with the entire FIA inventory, the DWM inventory

can be used to estimate fuel loads at strategic scales

across the United States.

The United States, mirroring efforts by other

countries (Woldendorp et al. 2002), is attempting

to quantify forest ecosystem carbon pools (criterion

5, criteria and indicators for the conservation and

sustainable management of temperate and boreal

forest) (Anonymous 1995, 1997; McRoberts et al.

2004). This quantification of the carbon budget can

aid efforts to better understand climate change and

the role of forest carbon dynamics in climate

change scenarios. The DWM indicator may help in

increasing the precision of carbon pool estimates

across the United States. Estimates of DWM

components, such as coarse and fine woody debris,

may be coupled with phase 3 estimates of soil

organic carbon content and phase 2 estimates of

tree carbon to create comprehensive forest

ecosystem carbon estimates (O’Neill et al. 2004,

2005). The FIA program, with all phases

contributing toward a comprehensive assessment of

total forest carbon, is the only national public

database that can estimate and provide continuous

monitoring of forest carbon pools in the U.S.

(Heath and Birdsey 1997).

Table 1.1.—Fuel-hour and FIA fine/coarse woody debris diameter classes

Transect diameter (in.) DWM class name Fuel-hour class (hr)

0.00-0.24 Small FWD 1

0.25-0.99 Medium FWD 10

1.00-2.99 Large FWD 100

3.00+ CWD 1,000+

2
DWM components, such as coarse woody debris,

serve as critical habitat for numerous flora and

fauna. From “nurse logs” in the Pacific Northwest

to black bear dens in the Southeast, many species

find their ecological niche in the shelter that DWM

provides. Flora use the microclimate of moisture,

shade, and nutrients provided by CWD to

establish regeneration (Harmon et al. 1986). CWD

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). Due to the

possibility of dwindling habitat for many native

species across our Nation, inventories of DWM are

important for habitat assessments and wildlife

conservation efforts.

1.2.2 Detection and Evaluation Monitoring

Besides providing estimates and associated

variances for various DWM components at various

spatial scales, the DWM indicator serves in the

broader context as a monitoring tool of forest

ecosystem health. Data from other phases and

forest health indicators of the FIA program have

been used to evaluate the status, changes, and

trends in forest health conditions on an annual

basis across all ownerships (Keyes et al. 2003).

Forest heath monitoring typically involves detection, evaluation, and intensive site monitoring

(Keyes et al. 2003). The baseline down woody

inventory, provided by the DWM indicator, may be

used to detect regional disparities in wildlife

habitats or prominent fire hazards. Once possible

forest health hazards are detected, these areas may

be further evaluated with additional phase 3 plots

(plot intensification) or through the implementation of additional studies (intensive site monitoring) (Tkacz 2002). For example, through analysis

of baseline DWM inventory for Minnesota, it may

be observed that fuel loadings for the Boundary

Waters Canoe Area are abnormally high (e.g., fig.

5.4). Because this may be a forest health hazard,

phase 3 plots may be intensified to refine both

loading estimates and mapping efforts. Therefore,

the DWM indicator serves as a baseline inventory

for detecting and evaluating forest health hazards.

1.2.3 Current and Expected Outputs

DWM plays a varied role in many ecosystem

processes. As such, the data analysis techniques

vary depending on the specific issue being

addressed. The DWM inventory may be used to

address three primary issues:

• Classification of individual plots into various

categories. Reasons for classifying individual

plots can be quite varied. For example, an

analyst might want to determine whether the

amount of CWD on a plot meets the requirements for suitable habitat or an analyst may

want to search for relationships between the

shrub/forb fuel ladders and the overstory of

the plot.

• Estimation of per/acre or per/hectare values

for plots. These values may be mapped to

determine spatial patterns in DWM or

monitored over time to indicate changes in the

DWM resource.

• Estimation of population totals, which could

be used for assessments of the amount of

carbon sequestered in DWM.

Estimation techniques for classifying plots and

estimating population totals are identical in many

cases. However, the techniques differ when specific

subpopulations are estimated. FIA recognizes two

different types of subpopulations: membership in a

subpopulation is determined by either characteristics of individual pieces of DWM or characteristics of the land on which the DWM is located.

The term domain is used to refer to subpopulations

determined by the status of individual pieces of

DWM (e.g., species, decay status, fuel class). The

term condition class is used to denote area-based

subpopulations, which can be mapped on the

ground (e.g., forest type, ownership).

Current outputs from the DWM indicator can be

broadly grouped into the following categories: raw

field data, core tables, tabular/graphical summarizations of core tables, and maps. Raw field data are

organized into tables according to the DWM

component (e.g., fine or coarse woody debris) and

the sampling protocol that facilitates its estimation.

The six tables containing the DWM field data are

coarse woody debris (table 1.2), fine woody debris

(table 1.3), fuelbed (table 1.4), transect (table 1.5),

piles (table 1.6), and shrub/herbs (table 1.7).

3
Table 1.2.—Coarse woody debris field data

Plot Sub- Tran. CWD Spp Tran Small Large Length Decay Hollow CWD

plot dist. dia. dia. dia. class hist.

xxxx x xxx xxx.y (ft) xxx xxx (in.) xxx (in.) xxx (in.) xxx (ft) x x x

1 1 030 3.5 317 3 3 5 6 2 N 1

1 1 030 4.0 316 11 5 12 22 3 N 1

1 2 030 22.7 316 7 5 8 19 4 N 1

1 3 150 11.1 317 5 4 5 12 1 N 1

1 3 150 2.2 802 9 7 11 37 3 N 1

1 3 270 7.0 316 5 5 6 28 5 N 1

1 3 270 15.6 802 3 3 4 24 2 N 1

1 4 270 21.0 802 17 15 18 57 3 Y 1

1 4 270 2.9 202 7 5 7 42 2 N 1

2 1 30 17.1 317 14 13 18 40 2 N 1

2 2 30 19.9 317 12 10 15 35 1 N 1

2 2 150 10.3 802 8 7 9 6 3 Y 1

2 2 270 8.7 317 5 4 6 7 2 N 1

2 3 270 3.1 316 8 5 9 31 2 N 1

2 3 270 11.0 316 7 4 11 29 2 Y 1

2 4 30 7.5 317 5 4 9 31 2 Y 1

2 4 150 17.1 317 7 4 9 31 2 Y 1

2 4 270 16.4 316 6 4 10 32 3 N 1

4

Table 1.3.—Fine woody debris field data

Plot Sub- Cond. Small Medium Large Reason Residue

plot class FWD FWD FWD high pile

xxxx x x xxx xxx xxx x x

1 1 10 0 2 0 0

1 2 18 6 3 0 0

1 3 13 1 1 0 0

1 4 10 3 1 0 0

2 1 15 4 1 0 0

2 2 11 1 2 0 0

2 3 10 2 1 0 0

2 4 20 1 4 0 0

3 1 16 2 1 0 0
5

Table 1.4.—Fuelbed field data

Plot Sub- Transect Sample Duff Litter Fuelbed

plot taken depth depth depth

xxxx x xxx x xx.y (in.) xx.y (in.) xx.y (ft)

1 1 30 Y 0.2 0.9 2.2

1 1 150 Y 0.8 1.9 4.5

1 1 270 N 0 0 0

1 2 30 Y 0.2 2.0 1.9

1 2 150 Y 1.1 1.7 1.0

1 2 270 Y 0.5 0.8 0.5

1 3 30 Y 0.6 1.9 2.0

1 3 150 Y 0.5 3.2 1.9

1 3 270 Y 0.9 3.0 1.7

Table 1.5.—Transect field data

Plot Subplot Transect Cond. class Begin slope dist1 Slope pct End slope dist2

xxxx x xxx x xxx.y (ft) xxx (%) xxx.y (ft)

1 1 30 1 0.0 10 24.0

1 1 150 1 0.0 5 24.0

1 1 270 1 0.0 6 24.0

1 2 30 1 0.0 7 24.0

1 2 150 1 0.0 2 24.0

1 2 270 1 0.0 3 24.0

1 3 30 2 0.0 5 24.0

1 3 150 2 0.0 4 24.0

1 3 270 2 0.0 5 11.5

1 3 270 1 11.6 2 24.0

1 4 30 1 0.0 4 24.0

1 4 150 1 0.0 3 24.0

1 4 270 1 0.0 5 20.5

1 4 270 3 20.6 2 24.0

2 1 30 1 0.0 3 24.0

2 1 150 1 0.0 2 24.0

2 1 270 1 0.0 2 24.0

2 2 30 1 0.0 4 24.0

2 2 150 1 0.0 0 24.0

2 2 270 1 0.0 2 24.0

2 3 30 9 0.0 0 24.0

2 3 150 9 0.0 0 24.0

2 3 270 9 0.0 1 2.1

2 3 270 1 2.2 3 24.0

2 4 30 1 0.0 2 24.0

2 4 150 1 0.0 2 24.0

2 4 270 1 0.0 3 24.0
The FIA program uses the term “core tables” to

refer to standard tabular summaries of

inventory data. For phase 2 of FIA’s inventory,

core tables may consist of per acre estimates of

standing timber volume and number of trees.

Core tables for the DWM indicator will consist

of processed DWM inventory field data. For

example, for one FIA plot there may be a dozen

CWD pieces. A database-processing algorithm

may estimate a single value of CWD tons/acre

for that plot. The resulting output from this and

other database-processing algorithms populates

core tables. Fuel loading-oriented core tables

provide plot-level estimates of forest fuel

tonnage (table 1.8), while carbon or wildlife

core tables contain processed outputs intended

for their respective disciplines.

Tabular and graphical summaries of core tables

provide more user-friendly outputs for

interpreting DWM estimates. Because the phase 3

inventory uses sampling protocols and sample

intensities different from the associated phase 2

inventory, graphing DWM component estimates

and associated variances may better facilitate

inventory dissemination and interpretation (fig.

1.2). The DWM indicator uses a sampling

intensity sufficient to indicate the current status

and trends in DWM components across large

regions of the U.S., thus table summaries should

be at larger scales than typically found in State

phase 2 inventory reports (example: see Schmidt

et al. 2000).

Table 1.7.—Microplot fuel loading field data

Plot Sub- Live Live Dead Dead Live Live Dead Dead Litter

plot shrub shrub shrub shrub herb herb herb herb cover

cover HT cover HT cover HT cover HT

xxxx x xx (%) xx.y (ft) xx (%) xx.y (ft) xx (%) xx.y (ft) xx (%) xx.y (ft) xx (%)

1 1 30 6.1 0 0.0 60 1.2 1 1.2 60

1 2 60 5.7 0 0.0 30 1.9 0 0.0 30

1 3 40 4.0 1 3.1 20 1.5 0 0.0 80

14 0 6.3 0 0.0 1 2.0 0 0.0 100

2 1 70 5.3 10 4.6 0 1.5 0 0.0 90

2 2 90 4.1 0 0.0 60 1.6 5 2.2 85

2 3 10 5.0 1 0.7 0 1.5 1 1.8 95

2 4 50 7.8 20 4.0 10 2.0 0 0.0 82

3 1 60 2.9 0 0.0 20 1.1 0 0.0 90

6

Table 1.6.—Slash pile field data

Plot Sub- Cond. AZ Shape L1 L2 W1 W2 HT1 HT2 Density

plot class

xxxx x x xxx ( ̊) x xx (ft) xx (ft) xx (ft) xx (ft) xx (ft) xx (ft) xx (%)

11 1 140 4 10 9 4 10

23 1 285 3 15 7 5 20

5 1 1 39 2 22 11 6 20

92 1 177 3 7 15 4 30

15 3 1 112 1 16 14 9 10

27 3 2 127 2 10 7 11 20

35 4 1 50 3 14 9 10 20

45 1 1 15 3 20 11 9 30

46 3 3 30 2 21 14 8 10
7

Maps of DWM component

estimates give analysts the

ability to estimate not only the

amount, but also the location

of DWM components such as

duff (fig. 1.3). DWM maps are

most successfully created

through full analytical

integration of all three phases

of the FIA inventory. Due to

the relative low sample

intensity of the DWM indicator,

modeling efforts with

associated phase 2 plots, along

with phase 1 remotely sensed

imagery, are often used

(Woodall et al. 2004).

Table 1.8.—Processed DWM data oriented toward fuel loading output, plot-level

State County Plot 1-hr* 10-hr* 100-hr* 1,000-hr* Duff* Litter* Herb/shrub (ft) Total tons*

X 17 385 0.2 0.5 1.9 2.6 5.3 0.7 1.0 11.2

X 31 434 0.4 0.5 0.7 1.0 5.6 1.9 0.3 10.1

X 31 436 0.5 1.1 2.6 5.0 3.5 1.6 1.3 14.3

X 31 439 0.7 1.4 4.1 4.6 9.1 3.3 0.7 23.2

X 31 442 0.1 1.3 2.6 3.0 4.9 2.2 0.5 36.8

X 31 446 1.1 2.6 3.0 5.5 15.6 2.1 5.6 29.9

X 31 109 0.9 1.5 9.3 10.4 3.8 3.3 0.8 29.2

X 31 120 1.3 0.8 5.5 1.8 4.7 1.5 0.4 15.6

X 31 900 0.4 1.4 3.3 3.2 17.8 1.9 2.8 28.0

* Tons/acre

Figure 1.2.—Graphical summary

example of processed FIA DWM data for

certain States.

Figure 1.3.—Preliminary map of duff

tonnage estimates of North Central

States.
Additional outputs of the DWM indicator

involve refining many of the current outputs of

the DWM indicator. Future work may include

development of new mapping methodologies,

more sophisticated data processing algorithms,

seamless integration with other FIA inventory

phases, and integration into State/regional

reports. Other outputs will include population

estimates such as tons of CWD by forest type/

State/region, in addition to mean/acre estimates.

Change estimates will not be provided until

remeasurement of DWM plots begins in 2006.

8
2.1 Introduction and Literature

Review

Note: DWM sampling protocols described in this

manual are for the field protocol used by the FIA

program since 2002. For information on historic

DWM sample designs used by the Forest Health

Monitoring (1999-2000) and the FIA programs

(2001), see appendix 8.5 (figure A, B, and C).

The diversity of ecosystem attributes estimated by

the DWM indicator requires a variety of sampling

techniques (for a full description of field procedures see USDA 2004) (fig. 2.1). Certainly, FIA field

crews cannot efficiently count the number of pine

needles and down twigs and measure the duff

depth across an entire FIA plot. Hence, the DWM

sampling protocol is distinctly different from phase

2 sample techniques used to estimate standing tree

populations. The plot-based sampling protocol for

DWM components includes:

1. line-intersect sampling for fine and coarse

woody debris,

2. simple random sampling for duff, litter, and

fuelbed depths,

3. fixed-area plot sampling for estimating the

coverage and height of shrubs and herbs,

4. shape and packing ratio estimation for slash

piles.

Canfield (1941) introduced the concept of lineintersect sampling (LIS) in reference to determining the volume of range vegetation. Warren and

Olsen (1964) introduced the first forestry

application of the LIS technique in estimation of

logging residue in New Zealand. Before LIS was

introduced, down woody debris was sampled by a

census of all down woody pieces within a defined

area or the use of strip samples. Also known as

line-intercept and planar intercept sampling with

quibbling differences aside (Gregoire and Valentine

2003, Van Wagner 1982a,b), the fundamental

concept of LIS is that sampling of down woody

debris occurs along transect lines (fig. 2.2). One of

the advantages of the LIS technique is that the total

volume in a sampled area can be estimated

9

2. PLOT-BASED SAMPLING PROTOCOL

Figure 2.1.—DWM indicator sampling

design on an FIA plot (inventory years

2002-present).
10

by measuring only the diameter or crosssectional area of a down woody piece at the

point of interception by the transect line

(Warren and Olsen 1964). Based on this early

work, there have been a number of refinements

and field applicability alterations for LIS that

are reflected in the methods used by the FIA

program. Van Wagner (1964) introduced a

method for requiring only field measurements

of the diameter of each woody piece at transect

intersection for determining volume. De Vries

(1973, 1974) proposed numerous extensions to

the mathematical basis of LIS, and Pickford and

Hazard (1978) carried out a series of simulation

studies. Brown and Roussopolous (1974) introduced a method for eliminating bias from nonhorizontal lean angles of individual woody

pieces. Most research of down woody sampling

techniques during the 1960s and 1970s was

compiled and released in the form of a

handbook on inventorying downed woody fuels

(Brown 1974). Although restrictive in its array

of published estimators and elementary in its

methodology, this handbook (Brown 1974) still

serves as a key reference for determining fuel

loadings across the Western U.S. (Van Wagner

1982a,b).

One point of great confusion regarding LIS is

that the properties of the estimators can be

derived using both design- and model-based

inference. Examples where LIS is derived under

the design-based paradigm include Gregoire

(1998), Gregoire and Monkevich (1994),

Gregoire and Valentine (2003), Kaiser (1983),

and Williams and Gove (2003). Examples

where model-based inference is used include

Bell et al. (1996), Brown (1974), De Vries

(1973), Van Wagner (1982a,b), and Warren and

Olsen (1964).

With the increased emphasis on the nontimber

attributes of coarse woody debris during the past

years, there has been an increase in the

development of down woody sampling methods.

Recent attention has been given to three possible

sources of bias of an LIS estimator. The first is

orientation bias. This bias applies only to the

model-based approach to LIS and is the bias that

occurs when the pieces of DWM tend to be

oriented in a particular direction (see Bell et al.

1996), which violates the assumption of a

completely random orientation of the pieces of

CWD. The second is bias associated with

sampling along the boundary of the population of

interest (see Ducey et al. 2004, Gregoire and

Monkevich 1994, Kaiser 1983 for solutions in the

context of design-based inference). The final

source of bias is in the potential for applying

incorrect estimators in situations where L, Y, X,

and triangular transect arrangements are used

(Gregoire and Valentine 2003, Marshal et al. 2000,

and Ringvall and Stahl 1999). There have also

been several studies of the spatial arrangement of

woody debris transects within a sampled forest

area (Bell et al. 1996, Nemec and Davis 2002).

Additionally, new methodology has been proposed

for rapid, design-unbiased assessment of coarse

woody debris volumes (Bebber and Thomas 2003,

Gove et al. 1999, Stahl 1998, Williams and Gove

2003). Finally, there has been increased emphasis

on using LIS techniques to estimate not only

down woody volumes, but also a range of

ecosystem attributes (De Vries 1973, 1974; Kaiser

1983; Marshall et al. 2000; Waddell 2002).

Figure 2.2—Line-intersect sampling planes.
11

The sampling protocol for the DWM components

of litter, duff, and fuelbed involves sampling their

depth at various points (point sampling) within

FIA subplots (fig. 2.3). Duff, litter, and fuelbed are

assumed to be strata of the forest floor such that

multiple measurements of their depth may

adequately estimate their tonnage and volume (fig.

2.3). Brown (1974) includes simple methodology

for recording duff and fuelbed depths that occurs

simultaneously as down woody material sampling

occurs.

The DWM indicator samples five components on

each FIA microplot: dead herbs, live herbs, dead

shrubs, live shrubs, and litter coverage (fig. 2.1).

Within each microplot, the cover and maximum

height of each shrub/herb component is recorded.

To produce estimates of tonnage and/or volume of

live/dead shrub/herbs, detailed information on

species and form must be collected. Because the

vegetation structure and diversity indicator collects

this information, the DWM indicator does not

collect the data necessary for input to shrub/herb

prediction equations. However, data from phase 2

and the vegetation indicator (i.e., shrub species

and forest type) may be combined with DWM

height/coverage data to estimate shrub/herb

volumes/tonnage (Brown and Marsden 1976). Fire

scientists may use the height and coverage of

shrubs/herbs on the microplot in general assessments of understory fuel ladders (fuel complex).

Litter coverage is sampled to estimate the dispersal

of litter per unit area of the forest floor. Litter

coverage information may augment variance

information from the point estimates of litter depth

taken on the plot.

The sampling protocol for estimating slash pile

volume/tonnage is based on Hardy (1996). We can

assume that slash piles are merely conglomerations

of woody debris where using transect sampling

would be impractical and hazardous. If woody

debris is packed into a shape sufficient for ocular

delineation, then the dimensions of the shape (fig.

2.4) may be recorded along with an estimate of the

packing ratio. The packing ratio, otherwise termed

density, is an estimate of the ratio of wood volume

to total volume within any defined shape. Bulk

density estimates based on the species composition

from transect sampled CWD and estimates of

packing ratio may be used in slash pile volume

equations (Hardy 1996) to provide estimates of

tonnage for sampled slash piles.

FIA subplots

Sample locations on subplot

Estimate of depth of

fuelbed, litter, and

duff at each sample

location

Figure 2.3.—Depth sampling of duff,

litter, and fuelbed on an FIA subplot.

Figure 2.4.—Shape codes for slash piles.
12

2.2 Fine Woody Debris

The DWM indicator defines fine woody debris

(FWD) as down woody pieces with a diameter

less than 3 inches at the point of transect intersection. FWD does not include dead branches

attached to standing trees, dead foliage, bark

fragments, or cubicle rot. FWD is sampled

using 6- and 10-foot slope-distance transects

co-located on the 150 degree CWD transect on

each of the four FIA subplots (fig. 2.1). Three

size classes for FWD are related to the hour

classes often referenced by fire scientists (table

1.1). Because it would be impractical for field

crews to measure the diameter of hundreds of

small FWD pieces, field crews estimate tally

counts by FWD size class. A 6-foot slopedistance transect is used to tally the smallest

class of FWD, while a 10-foot slope-distance

transect is used to tally medium and large

FWD. The slope (%) of each transect is

recorded for use in FWD estimators.

2.3 Coarse Woody Debris

The target population for the DWM indicator is

not the population of all fallen dead trees in a

forest. Rather, the indicator samples a CWD

population of broad interest to numerous FIA

customers. Consequently, the DWM indicator

defines coarse woody debris (CWD) as downed

pieces of wood with a minimum small-end

diameter of at least 3 inches and a length of at

least 3 feet (for most decay classes). CWD pieces

must be detached from a bole and/or not be selfsupported by a root system. Additionally, CWD

pieces must have a lean angle more than 45

degrees from vertical. The decay class of each

CWD piece is rated according to a five-class decay

scale (Maser et al. 1979, Sollins 1982) (table 2.1).

Decay class information allows determination of

tonnage and habitat condition for CWD pieces.

CWD pieces classified as most decayed (class 5)

must have a length of at least 5 feet. The transect

Table 2.1.—Decay class information used for rating CWD pieces

Decay Structural Texture of Color of Invading Branches and

class integrity rotten portions wood roots twigs

1 Sound, freshly Intact, no rot; Original Absent If branches are

fallen, intact logs conks of stem color present, fine

decay absent twigs are still

attached and

have tight bark

2 Sound Mostly intact; Original Absent If branches are

soft (starting to color present, many

decay) but can’t be fine twigs are

pulled apart by hand gone; those

remaining have

peeling bark

3 Heartwood sound; Hard, large Reddish Sapwood Branch stubs will

piece supports its pieces; sapwood brown or only not pull out

own weight can be pulled apart original color

by hand or sapwood

absent

4 Heartwood rotten; Soft, small Reddish or Through- Branch stubs pull

piece does not blocky pieces; a light brown out out

support its own metal pin can be

weight, but pushed into

maintains its shape heartwood

5 None, piece no Soft; powdery Red-brown Through- Branch stubs and

longer maintains its when dry to dark out pitch pockets have

shape, it spread out brown usually rotted

on the ground down
13

diameter, large-end diameter, small-end diameter,

species, decay class, and length are recorded for

each CWD piece (except decay class 5).

CWD pieces are selected for sampling based on

intersection with any of three 24-foot horizontal

distance transects emanating from the center of

each FIA subplot (azimuths of 30, 150, and 270

degrees) (fig. 2.1). CWD pieces are tallied with

each transect intersection, regardless of the

number of intersections. The motivation for using

a multi-segmented plot arrangement is to avoid the

design bias associated with the failure of the

assumption of a nonrandom orientation of pieces

of CWD (Bell et al. 1996, Van Wagner 1964).

Many authors have voiced concerns about using

LIS estimators with transects placed in “Y”

arrangements (Gregoire and Valentine 2003,

Nemec and Davis 2002, Waddell 2002). In an

attempt to address this issue, each arm of the “Y” is

treated as an individual transect. There has been

some concern about whether the sampling strategy

of the “Y” shaped plot and current estimators used

by the DWM indicator lead to either a design or

model bias (Gregoire and Valentine 2003, Nemec

and Davis 2002, Waddell 2002). Simulation

studies by Bell et al. (1996) indicate that “Y”

arranged CWD transects and their associated

estimators have a design bias that is often smaller

than would occur if the orientation of the pieces of

CWD is not completely random, and a single

transect with a fixed orientation is used.

2.4 Duff/Litter/Fuelbed

The DWM indicator defines duff as an organic

forest floor layer consisting of decomposing leaves

and other organic material. Individual plant parts

should not be recognizable in the duff layer. Litter

is defined as a forest floor layer of freshly fallen

leaves, needles, twigs, cones, bark chunks, dead

moss, dead lichens, dead herbaceous stems, and

flower parts. The fuelbed is the accumulated mass

of all DWM components above the top of the duff

layer (excluding live shrubs/herbs). The DWM

indicator measures the depth of duff, litter, and

fuelbed at 12 locations (24-foot slope-distance on

each CWD transect). Measurement errors occur

when crews are not properly trained for identifying

the duff layer from mineral soils and the litter

layer. Additionally, field crews must be properly

trained on how to measure the depth of the

fuelbed. Fuelbed measurements are not used to

estimate tonnage, but rather to describe the spatial

dispersion of fuels from the forest floor up toward

the canopy (the fuel complex).

2.5 Shrubs and Herbs

The DWM indicator defines shrubs as herbaceous

plants with woody stems. Herbs are defined as

nonwoody herbaceous plants, but also include

ferns, moss, lichens, sedges, and grasses. Five fuel

categories are estimated on each microplot: live

shrubs, dead shrubs, live herbs, dead herbs, and

litter. The cover from 0 to 100 percent in 10

percent classes is estimated for each of the five fuel

categories. The tallest height of all fuel categories

(excluding litter) is estimated within the microplot.

If available by region, fuel-loading models may use

coverage, height, and forest type/understory

vegetation information to predict fuel-loading

tonnage for microplot fuel categories. Additionally,

height and coverage information may be used to

estimate the spatial dispersion of microplot fuels

similar to the fuelbed measurement.

2.6 Slash Piles

Slash or residue piles are defined as CWD in piles

created directly from human activity or from

natural events that prohibit safe measurement by

CWD transect. If the center of any slash pile

coincides with the area sampled by any FIA subplot (24-foot radius) the slash pile is determined as

“in.” The shape of each tallied slash pile is

classified according to a shape code (fig. 2.4)

(Hardy 1996). According to the shape code

classification, certain dimensions of the pile are

measured to the nearest foot. The packing ratio of

each pile is estimated. Field crews must be

properly trained to ocularly estimate the packing

ratio (density) of slash piles. Typically, the packing

ratio should not exceed 40 percent.
14

2.7 Transect Segmenting

To properly attribute estimates of DWM

components to appropriate condition classes

occurring on any single FIA plot, the condition

class along each of the three 24-foot CWD

transects is recorded. The condition classes

mapped by the DWM indicator match those

recorded during phase 2 sampling. This process

of recording condition classes along CWD

transects is called condition class segmenting.

Because CWD, FWD, duff, litter, and fuelbed

are measured along CWD transects, the

condition class segmenting on these transects is

used to facilitate DWM condition class

estimation. Additionally, because the microplot

is not mapped according to condition class, the

condition of the microplot center is thought

sufficient for subsequent estimation. The

likelihood of the microplot falling on a

condition boundary is very small. Thus, any

bias associated with the lack of condition class

mapping on the microplot is thought to be

minimal.
15

oriented, the sampling transects are oriented in

multiple directions to reduce an orientation bias

(Bell et al. 1999, van Wagner 1968). The sample

locations are randomly placed across the

populations to account for the concern that pieces

of FWD and CWD are not randomly located.

Fewer attributes are estimated for FWD than for

CWD. For FWD, only the volume and weight are

estimated. Estimating other attributes would

require measuring the length of each piece of

FWD, which would be impractical and costprohibitive for a national inventory program,

especially where a large number of pieces of FWD

are encountered. For CWD, the volume, weight,

number of pieces, suitable habitat, and other

attributes can be estimated. Detailed measurements

are taken on all pieces of CWD that intersect a

transect. The reason for the additional measurements is that habitat assessments often require

information that describes the size, shape, and

condition of logs to determine if they meet

minimum habitat requirements. These habitat

assessments are essential for some customer

groups. For example, national forests in the Pacific

Northwest have adopted guidelines for a minimum

retention of CWD. An example given by Marshall

et al. (2002) is: “Mixed-conifer stands east of the

Cascade Range and the Eastside Forest Plan

Amendment stipulate that 15 to 20 pieces per acre,

6 or more feet long, with a total linear length of

100 to 140 feet of pieces 12 inches and greater in a

small end diameter.”

3.1.1.1 Fine Woody Debris Estimators

De Vries (1986, p. 242-276) provides derivations

and discussions of estimators for fine and coarse

woody debris sampled by LIS. Many different

estimators could be used for estimating FWD, but

due to the time-intensive labor required to

measure it, only a single estimator is used to

estimate FWD volume and weight. The advantage

of this estimator is that the length of FWD is not

required for estimation. The estimator of volume

or weight of FWD for a single transect of length L

Producing estimates of DWM components requires

coupling appropriate estimators with database

programs. Because the users and possible applications of DWM indicator data are so varied, we

cannot exhaustively document all possible

estimators and database routines here. Instead, a

broader view of estimating DWM component

values will be presented allowing individual

analysts to modify the estimators to suit their

needs. Hence, the selection of appropriate

estimators depends on the type of analysis being

performed. Estimation occurs at three levels: (1)

estimating DWM for an individual plot or portion

of a plot for classifying the plot (e.g., suitable

habitat or fire risk); (2) estimating DWM means for

mapping and monitoring; (3) estimating population totals. An important complicating factor is

that some plots will cover multiple condition

classes. For example, a plot may fall on the

boundary between a recent clearcut and a mixed

hardwood-softwood stand. In situations such as

this, an analyst may want to determine DWM

attributes on each of the different condition classes

to avoid possible anomalies in the analysis due to

the mixing of data from different conditions.

In this section, we begin with the basic methodology for estimation and then provide the

estimators used to address the three general types

of estimation (i.e., classification, mapping/

monitoring, population totals).

3.1 Estimation Methods

3.1.1 The Approach to Inference for Fine and

Coarse Woody Debris

Inference for the FWD and CWD is model-based.

The assumed model structure for line-intersect

sampling is that the location and orientation of the

logs within the population is both completely

random and independent. These assumptions

might not hold in real populations, so the sample

design is such that the estimators will be robust to

violations of the assumed model. To account for

the possibility that the logs are not randomly

3. ESTIMATION
∑=

n

i

i d

1

2

for FWD pieces intersected by a transect is as

follows:

 (3.1)

where y is volume per unit area, k is a constant

that accounts for unit conversions (see volume

constants, table 3.1), f is a constant for converting the estimates to per acre or per hectare

values (see table 3.2), a is the nonhorizontal

(lean) angle correction factor for the piece of

FWD, c is the slope correction factor, L is the

total length of the transect, di

 is the diameter of

the piece at the point of intersection, and n is

the number of pieces intersected by the transect.

The nonhorizontal correction factor, a, may

range from 1.00 to 1.40 in value depending on

the angle of lean from vertical (0-45 degrees) of

each FWD piece (Brown 1974) and is determined by:

 (3.1a)

where a is the nonhorizontal lean angle correction factor and h is the angle of tilt for each

FWD piece from horizontal (after Van Wagner

1982a,b).

Because the DWM indicator does not collect the

lean angle of individual FWD pieces, analysts

must either assume no lean angle or use

published lean-angle correction factors for their

particular region and/or forest condition.

,

∑=

=

n

i

i y kfac L d

1

2 2 ( )(π /8 )

cos( )

1

h

a =

2 c = 1+ (%slope /100)

Additionally, because FIA samples FWD using 6-

and 10-foot slope-distance transect lengths, these

lengths are corrected for slope using the following

equation:

(3.1b)

where c is the slope correction factor and %slope is

the slope percent (Brown 1974).

The weight of FWD is estimated by multiplying the

volume by a species- and condition-specific

correction factor that accounts for specific gravity.

Thus,

 (3.2)

where y(w) is down wood dry weight per unit area.

This estimate is derived from the volume estimator,

y, multiplied by G, which is the specific gravity

(Brown 1974, Van Wagner 1982a,b) conversion

factor (see appendix 8.5).

Both estimators, y and y(w), are further modified to

simplify data collection. Instead of actually

measuring the diameter of each piece of FWD,

field crews tally the number of pieces of FWD in

each of the three FWD size classes (table 1.1). For

both equations (3.1 and 3.2), the summation

is replaced with ns

ds

2

, where ns is the number

of pieces of FWD in size-class s and ds

2

 is the

squared mean diameter for pieces within an FWD

diameter class. These values might be empirically

derived by species because of the differences in

∑=

= =

n

i

i

w y Gy Gacfk L d

1

( ) 2 2 ( )(π /8 )

Table 3.1.—Equation (3.1 and 3.2) constants (k) for some length, volume, and weight

units (Van Wagner 1982)

 Unit combinations k

dL V W

cm m m3

/m2 - 0.0001234

cm m m3

/ha - 1.234

cm m - kg/m2 0.1234

cm m - tons/ha 1.234

in. ft ft3

/ft2 - 0.008567

in. ft ft3

/ac - 373.3

in. ft - lb/ft2 0.5348

in. ft - tons/ac 11.65

16

,

,
branching patterns/sizes and because the distribution of individual FWD sizes within any size class

(especially for 1-3 inches) is usually left-skewed.

For example, ds

 of ponderosa pine in the 1- to 3-

inch class is 3.12 with a mean diameter of around

1.77 (left-skewed) (Brown 1974). For more

common forest types in the Western U.S., there are

ds available in the literature. For less common forest

types, default values for either conifer or hardwood

forest types are used. A partial list is given in

appendix 8.4. Many unit combinations and

constants may be used in equations 3.1 and 3.2

depending on desired estimates (table 3.1).

3.1.1.2 Coarse Woody Debris Estimators

Numerous model-unbiased estimators exist for

assessing CWD when sampling with a single

straight-line transect (cf., Brown 1974, De Vries

(1986, p. 58, eq. 29, p. 60, eq. 33), Stahl et al.

(2001, eqs. 6, 7, 8)). The estimators used with the

FIA DWM indicator were chosen for two reasons:

(1) to be compatible with existing data, and (2) to

have the smallest possible variance. Minimizing the

variance of the estimator is an important consideration because line-intersect sampling often requires

long transects to achieve high levels of precision

(Pickford and Hazard 1978).

In the available literature, De Vries (1986) provides

the most complete treatment of line-intersect

sampling from the model-based perspective and

Brown (1974) provides additional material that is

necessary to complete the assessment of CWD.

Thus, the results of these two authors will form the

basis of the estimation process for CWD.

The formula used for computing an estimate per

unit area value when sampling with a single

transect of length L is

 (3.3)

where y is a model-unbiased estimator of the

attribute of interest per unit area, f is used to

convert the estimate into a per acre or per hectare

value, L is total transect length, yi

 is the attribute of

interest for CWD piece i, and l

i

 is the length of the

piece (see table 3.2).

The need to classify pieces of CWD into specific

habitat classes requires additional information on

each piece of CWD (e.g., length and small- and

large-end diameters). Given these additional

measurements, the volume of an individual piece

( / ) 2 1

i

n

i

i y l

L

f y ∑=

= π ,

17

Table 3.2.—Equations to estimate per unit area values for attributes of individual CWD pieces (De Vries 1986, Waddell 2002)

Attribute Equationa Units for each equation variablea

L V l D f

Cubic feet per ac (π/2L)(Vft/li

)f ft ft3 ft in. 43,560 ft2/ac

Cubic meters per ha (π/2L)(Vm/li

)f m m3 m cm 10,000 m2/ha

Logs per ac (π/2L)(1/li

)f ft - ft - 43,560 ft2/ac

Logs per ha (π/2L)(1/li

)f m - m - 10,000 m2/ha

Tons per ac [(π/2L)(Vft/li

)f][0.0312(G)] ft ft3 ft in. 43,560 ft2/ac

Kg per ha [(π/2L)(Vm/li

)f][1000(G)] m m3 m cm 10,000 m2/ha

Mg of carbon per ha

 for softwoods [(π/2L)(Vm/li

)f][0.521(G)] m m3 m cm 10,000 m2/ha

Mg of carbon per ha

 for hardwoods [(π/2L)(Vm/li

)f][0.491(G)] m m3 m cm 10,000 m2/ha

a

L: total length of the transect line, Vft: volume in cubic feet of individual piece, Vm: volume in cubic meters of an individual piece, l

i

: length of

individual CWD piece, f: conversion factor for per acre and per hectare values, G: specific gravity, which will need to be reduced by the

necessary decay reduction factor.
can be determined using Smalian’s formula

(Husch et al. 1972, p. 101)

 (3.4)

where Vft is volume in cubic feet; 

ds, and dL are

the small- and large-end diameters (in.) of the

CWD piece, respectively; and l is the CWD

piece length in feet.

Because of the advanced decay of logs in decay

class 5, only the transect diameter is collected. A

different estimator is used for this class because

pieces of CWD in decay class 5 are often so

inconsistently formed that the volume cannot be reliably determined using Smalian’s or other

simple formulae. The estimator used for CWD

in decay class 5 is

 (3.5)

which is derived from a FWD estimator

(equation 3.2). Volume information used in conjunction with

equation 3.2 will reduce the variance of the

decay class 5 CWD estimator. Unpublished

simulation studies suggest that reductions in the

variance rnaging from 3 to 30 percent are not

uncommon when comparing equation 3.2 to

equation 3.1. The attributes and the appropriate

formulae for both English and metric units are

listed in table 3.2.

As given by De Vries (1986, p. 252), variance

and a sample-based variance estimator for an

individual transect is given by:

 (3.6)

and

 (3.7)

However, as noted by Lucas and Seber (1977),

these estimators are likely to be unreliable, so

estimating the variance through replication is

preferable. Because CWD piece locations are

assumed to be random and independent, which

might not accurately reflect actual field conditions in all situations; care should be given when

applying these variance estimators. 18

144

(/8)()

22

ddl

V

SL

ft

+

=

π

,

∑=

=

n

i

i

d

L

f

y

1

2

2

8π

,

.

For a rather expeditious estimation of certain

limited attributes of CWD, analysts may prefer

Brown’s (1974) estimators. Equations 3.1 and 3.2

may be used to estimate the weight and volume of

CWD. However, application of Brown’s (1974)

estimators does not utilize numerous variables

collected by the DWM indicator on individual

CWD pieces (i.e., CWD piece length, large-end

diameter, and small-end diameter) that may be

used for habitat classification or more detailed

CWD analyses beyond that of volume or weight

determinations. Because CWD is sampled using a

horizontal transect, the slope correction factor may

be set to 1 or removed from the equation. Additionally, because the DWM indicator collects decay

class information, G may be reduced to account for

decay reductions (DCR) in specific gravity

(Waddell 2002) (table 3.3). Table 3.3.—Decay class reduction factors for CWD by

decay class and species group (from Waddell 2002)

Decay class Species group

Softwoods Hardwoods

1 1.00 1.00 2 0.84 0.78 3 0.71 0.45

4 and 5 0.45 0.42

3.1.2 Duff, Litter, and Fuelbed

We assume that duff, litter, and fuelbed depths are

sampled using simple random sampling (SRS),

even though the 12 sampling locations are

systematically arranged. The estimate of the mean

depth, and its associated variance estimator, are

given by:

 (3.8)

 (3.9)

f

ni

i

n

y

y

f ∑=

=

1

,

(1)

()

1

1

2

2

2

−

−

=

∑

∑

=

=

ff

ni

f

ni

i

i

y

nn

ny

y

s

f

f

,

∑=

=

n

i

ii

lyL

Vary

1

2

2

()

π

2

1

2

)()

21

()(

∑=

=

n

i

ii

lyl

Vary
19

where y is mean depth, sy

2

 is the varience, yi is the

depth at the ith point, and nf

is total number of

points falling in the forested condition.

To determine litter and duff weights per area, the

estimators of mean depth are multiplied by fixed

conversion factors to estimate the number of tons

per unit areas from equation 3.8,

 (3.10)

where y is the mean depth of duff or litter, BD is

bulk density (i.e., weight per unit volume, lbs/ft3

,

see appendix 8.5 for example of BD values), and k

is unit-area conversion values (21.78 for tons/acre

with depth in feet) (10,000 for kg/ha with depth in

meters).

3.1.3 Shrubs and Herbs

Because shrub and herb attributes are estimated

using SRS, mean and variance estimators (equation

3.9 and 3.10) may be applied to determine mean

values (i.e., mean maximum live shrub height) per

sample unit (condition class or plot level). As an

alternative to reporting mean shrub/herb height

and coverage, all these measurements may be

incorporated into a single measure of the height of

this fuel complex known as the integrated fuel

depth. Integrated fuel depth scales the maximum

height of all shrub/herb components based on its

associated coverage and then determines a mean

value:

Integrated Fuel Depth =

 (3.11)

where n is the number of shrub/herb components,

hi

 is the height of the ith component, and ci is the

coverage of the ith component.

3.1.4 Slash

Slash or residue pile volumes and weights are

determined through estimators provided by Hardy

(1996). The first step in estimation is to determine

the net volume of the slash pile based on the pile’s

shape and associated sampled dimensions (fig. 2.4)

using equations in table 3.4. Estimates of a pile’s net

Y y(BD)(k) DLF = ,

∑ ∑ = =

n

i

i

n

i

i i h c c

1 1

{( ( /100)}/{( ( ))/100}

volume may be converted to an estimator of pile

weight (ypile) using:

 (3.12)

where Vol is the net volume of the pile, BD is bulk

density (mass per unit volume, i.e., lbs/ft3

), P is the

packing ratio or density of the slash pile, and k is a

unit conversion constant.

y Vol(BD)(P)(k) pile =

Table 3.4.—Equations for determining net volume of

slash piles based on shape code (Hardy 1996)

Shape code Net volume equationa

1 (πhw2/8)(PR)

2 (πwlh/4)(PR)

3b (πl

1

[h1

2

+h2

2

+(h1h2)]/6)(PR)

3c (πl

1

[w1

2

+w2

2

+(w1w2)]/24)(PR)

4 [(l1

+l2)(w1+w2)(h1+h2)/8](PR)

a hi

, l

i

, and wi refer to pile dimensions according to

shape code and PR is packing ratio,

b equation if using heights,

c

 equation if using widths

A field crew visually determines the location of the

center of a slash pile. Subsequently, the pile is

considered “in” if the center point of the pile falls

within the boundary of one of the four 1/24-acre

subplots. An estimator of the pile weight per unit

area for the slash piles found on one FIA subplot is

 (3.13)

where af

 is the area of the subplot covering forested

land in the appropriate units, and yi

pile is the pile

weight of the ith slash pile on the subplot, and n is

the number of slash piles on the subplot.

3.2 Estimation for Plot

Classification

Most classification schemes apply a label to a plot

based on the estimated per acre value found on the

plot. One complicating factor is that FIA plots (and

f

n

i

pile

i

pile

a

y

y

∑= = 1 , ,
∑ ∑=

( )

1

( ) j

k c

j

j L c

20

forest inventory plots in general) cover neither

an acre nor a hectare. An accepted practice for

the purpose of classification is to assume that

the plot subsamples either the hectare or acre

surrounding the subplot center. This assumption

will also be extended to plots that straddle

multiple condition classes.

3.2.1 FWD and CWD Classification Methods

for a Single Plot

When the entire plot falls in a single condition

class, there are kF

 = 4 FWD transects and kc = 12

CWD transects. In this situation, the per acre

(hectare) estimators for FWD and CWD are

simply the mean of the estimators derived from

the individual transect (equations 3.1 and 3.3).

For example,

 (3.14)

where yj

 is the estimator of any CWD attribute

derived from the jth transect using equation 3.3.

In the remainder of the discussion, we will focus

on classification issues for CWD because this

attribute is generally much more important

when classifying the plot for potential habitat.

Estimation for CWD becomes more complicated

whenever a plot straddles more than one

condition because there are multiple approaches

to combining the estimators from the individual

transects and because the number of transects

and length of each transect in the condition class

are not necessarily constant. To address this

situation requires some additional notation, so

let Lj

(c) be the length of the j

th transect in

condition class c and k(c) be the number of

transects that at least partially cover condition

class c. Using the result that the variance

increases proportionally with the length of the

transect (equation 3.5) and the results of

De Vries (1986, p. 254), the best linear unbiased

estimator is given by:

 (3.15)

∑=

= C k

j

j

C

CWD y k

Y

1

1 ,

∑∑ ∑ = =

=

=

( )

1 1

( )

1

2 ( )

( )

k c

j

n

i ij

ij

k c

j

j

j

l

y

L c

f Y c

π ,

where yij and l

ij are the attribute of interest and

length of the piece of CWD, respectively for the i

th

piece of CWD tallied on the j

th transect (for each

condition on each individual subplot), and Y(c) is

the per acre (hectare) estimator for condition class c.

Two points are worth noting. First, a piece of debris

may fall across two or more transects. Additionally, a

severely bowed CWD piece may intersect the same

transect twice. When these situations occur, the

piece is tallied once for each transect that it crosses

(i.e., (yij = yi

, j,)). Second, caution must be used when

classifying plots that straddle multiple condition

classes because Y(c) for a condition class can be

highly variable and produce nonsensical estimates.

These situations can occur whenever a large attribute

(yij) is tallied on a very short total transect length

(i.e., is “small”). The other concern is that

no pieces of CWD will be tallied on a short section

of transect, which will lead to a zero estimate of

CWD per acre in areas that may actually have much

higher levels. Although situations such as this are

generally rare in FIA, these partial plots should be

dropped from the analysis and the classification for

the plot that represents the majority of the plot

should be used. This result is illustrated in the

example in appendix 8.3.

3.2.2 Duff, Litter, and Fuelbed Classification

Methods for a Single Plot

Estimation of duff, litter, and fuelbed (DLF) for a

single plot is given by equation 3.11. The estimator

must be modified slightly whenever a plot straddles

more than one condition to properly estimate the

DLF for each condition. The estimator for a specific

condition class c is

 (3.16)

where nc

 is the number of points falling in condition

class c, and δi

 = 1 if the points fall in condition class

c, and δi

 = 0, otherwise. If multiple conditions are

encountered on a single plot, then one estimate is

generated for each condition (i.e., YDLF(c) and YDLF(c’)

are calculated for conditions c and c’, respectively).

c

nj

i

i i

DLF

n

y

Y c BD k y c BD k

∑= = = 1 ( ) ( )( )( )( ) ( )( )

δ

,
21

3.2.3 Slash Pile Classification Methods for a

Single Plot

It will often be useful to classify individual plots

based on certain components of the DWM indicator

(i.e., CWD, duff, and litter) in an effort to look for

linkages between fuel characteristics and other

forest conditions. For example, an analyst may

want to determine if 1- and 10-hour fuel levels are

higher on private or public lands. However, it

seems unlikely that any meaningful information can

be gleaned from the location and size of slash piles

because these piles are infrequently sampled.

Therefore, only an estimator of pile weight per unit

area is given in this section.

A ratio of means estimator is used to combine the

information from each of the four FIA subplots.

Thus, the estimator for the weight of slash piles per

unit area is

 (3.17)

where yif

pile is the weight of the i

th pile on the jth

subplot, nj and aj(f) are the number of slash piles

and the area of forest on subplot j, respectively.

3.3 Estimation of Population Totals

by Combining Information from

Multiple Plot Estimates

As we discussed in section 3.2.1, the per acre

estimates for individual condition classes on a

single plot can sometimes be very erratic or

unrealistic. Although this would appear to pose

problems when estimating population totals, it does

not because the estimation of population totals uses

a fundamentally different approach to combining

the data across plots.

A difficult decision for inventories such as FIA is

the description of the target population and the

selection of an appropriate sample unit (Williams

and Eriksson 2002). The target population for the

DWM estimators is all downed woody material

covering areas that meet the FIA definition of

forested land. Due to the low sampling intensity of

approximately one plot every 96,000 acres, the

analyses for DWM are usually carried out over areas

as large or larger than aggregated counties or States.

FIA defines forest land area as land that is at least

10 percent stocked by trees of any size, or land

∑

∑∑

=

= = = k

j

j

k

j

n

i

pile

ij

Pile

a f

y

Y

j

1

1 1

( )

,

formerly having such tree cover, and not currently

developed for a nonforest use. The minimum area

and width requirements for classification as forest

land are one acre in size and 120 feet wide,

respectively. Grazed woodlands, reverting fields and

pastures that are not actively maintained, are

included if the above criteria are met. For estimating population totals, the sample unit will be

treated as a point. One advantage of using this

target population and sample unit is that the area of

the target population, denoted by AF

, is already

estimated by the FIA program using a combination

of the phase 1 and 2 data. Another advantage is that

the sample sizes in phase 2 are so large that the

variance of the estimator, ÂF

, is essentially zero for

most DWM analyses.

The population-level estimator requires some

additional notation. Let M be the number of plots

that fall in the target population, let m = 1,...M

index the FIA cluster plots, and let k*

 index the

number of transects, microplots, or duff/litter

points, with * indicating the type of attribute being

estimated (i.e., FWD, CWD, duff, litter, fuelbed,

slash pile). For example, kc is the number of CWD

transects that at least partially cover land that meets

the definition of forest. Thus, if all M plots completely covered forested land, the estimator would

be:

 (3.18)

where yijm is the attribute of interest for the i

th piece

of CWD tallied on the j

th transect of the mth cluster

plot. However, it is not reasonable to assume that

all plots will fall within the boundary of the forest;

some plots will likely straddle the forest-nonforest

boundary. In estimating population totals, the

length of each transect covering the forest, which is

denoted L(f), is used and the estimator is

 (3.19)

∑ ∑ ∑ = = =

= i j n

i ijm

ijm M

m

k

j f l

y

M k L

Y

1 1 2 1

1 1 π ,

∑ ∑ ∑∑ ∑ = = = =

=

= =

( )

1 1 1 1

( )

1

2 ( )

1 1 ( )

k f

j

n

i ijm

ijm

M

m

M

m

k f

j

j

m

j

l

y

L f M

Y

M

Y f π

At the individual plot level, averaging the k(f)

individual estimators generates the estimator for the

point-based sample unit. This estimator is modelunbiased under the assumption that the level of

.
22

CWD tallied is linearly related to the length of

the transect that falls within the forested

condition. The population-level estimator is

generated by averaging the M cluster plot

estimators.

As in the case of classifying individual plots,

estimation is more complicated when the goal is

to generate estimates for subpopulations. These

estimates might be for a single condition class

(e.g., old growth, private lands) or for pieces

with specific characteristics. For example, an

analyst interested in the abundance of CWD

pieces that could serve as dens for Canadian

lynx might be interested in knowing the total

number of hollow CWD pieces in decay class 1

or 2 that are longer than 30 feet and have a

large-end diameter greater than 45 inches. Both

estimation problems can be addressed by

treating the population as a domain of interest

(Cochran 1977, Chapter 2.13) within the forest

population. Some additional notation is required

because a subpopulation can be related to both

the condition class where the CWD piece is

located (i.e., condition class c) and the characteristics of each individual piece, which will be

denoted by ∂. Thus, let Y(c,∂) denote the per

acre estimator for all CWD in condition, c, that

have the characteristic ∂. The estimator for the

domain of interest requires the definition of the

indicator variable, which takes on the value of 1

when the piece of CWD is in the subpopulation

of interest and 0 otherwise.

The population-level estimator for the domain of

interest is:

 (3.20)

where δijm is 1 if CWD piece i on transect j of

plot m falls on condition class c and domain ∂.

Note that all transects within the population of

interest (i.e., forested land) are used in the

estimation. An example is given in appendix

8.2. Comparing the examples in appendices 8.1

and 8.2 illustrates the difference between

estimating for classification and estimating

population characteristics.

3.4 Estimation for Monitoring and

in Conjunction with other FIA

Inventories

DWM data can be used to monitor trends in DWM

using a variety of techniques (e.g., Urquhart et al.

1998) and in conjunction with other inventories.

Other components of the FIA inventory program

may be used in estimation/analytical procedures

with the DWM indicator. Phase 1 provides forest/

nonforest stratification information necessary to

produce population estimates of DWM components. Additionally, phase 1 may provide imagery

required for mapping DWM estimates (e.g., fuel

maps) (Woodall 2003, Woodall et al. 2004). Phase

2 inventories may be used to both provide strata

for population estimates (e.g., forest type, county,

and ownership class) and data for development of

phase 2/phase 3 modeling efforts. If models can be

developed to predict DWM components for all

phase 2 plots (based on the subset of sampled

phase 3 plots), then additional estimation procedures can be developed for alternative population

estimates and mapping methodologies.

Because DWM sampling occurs directly on FIA

inventory plots in conjunction with other phase 3

indicators, cross-indicator estimation and analysis

procedures are possible and warrant future

exploration (O’Neill et al. 2004, 2005). Two forest

health indicators that are most likely candidates for

estimation and analysis in conjunction with the

DWM indicator are soils and vegetative structure

and diversity. Carbon pools may be estimated

conjointly between the soils and DWM inventories.

The DWM indicator provides the data for CWD

and FWD carbon estimation, and the soils

indicator provides litter and duff carbon estimates

(O’Neill et al. 2004). The vegetative structure and

diversity indicator may help refine shrub/herb

processing algorithms. Currently, the DWM

indicator does not collect species information for

shrub/herb components, whereas the vegetation

indicator may provide valuable constants for

processing data from the DWM microplot. Analysts

who undertake cross-indicator analyses and

estimation procedures should be aware of any

potential overlaps among indicators (e.g., soils and

DWM both sample duff depths) and discrepancies

in the spatial location of indicator sampling (e.g.,

soils indicator samples off-plot for carbon while

the DWM indicator collects samples only on the

FIA subplot).

∑ ∑∑ ∑ ∑ = = =

=

=

∂

∂ = =

( )

1 1 1

( )

1

1

( , )

2 ( )

1 1 ( , )

k f

j

n

i ijm

ijm

M

m

k f

j

j

M

m

m

j

l

y c

L f M

Y

M

Y c

π δ ,
23

3.5 DWM Data Processing

Applying any estimator to DWM data sets requires

database programming; an overview and outline of

the necessary database program will be provided in

this section. Data processing algorithms, within the

framework of the National Information Management System (NIMS), facilitate the production and

management of DWM field data and subsequent

core tables. A processing algorithm manipulates

DWM field data such that DWM estimators may be

applied and concise output tables produced at the

plot, condition class, and population levels. The

first step for data processing involves applying

DWM estimators at the plot level. Because estimation requires individual DWM component-,

transect-, and plot-level measurements for proper

application of estimation protocol, processing codes

are predominated by database management

routines. Hence, construction of a DWM processing

program entails a rather complicated architecture of

DWM data management with DWM estimators

(with associated equation constants see appendix

8.5) inserted at critical junctures in the code.

However, the processing code (appendix 8.4) can

be loosely organized according to DWM component

and the data requirements of each estimator (fig.

3.1).

Figure 3.1.—Data processing flowchart

for fire science-oriented core table.
24

Change in DWM would be most efficiently

estimated by re-measuring the same set of plots

at two points in time. This cannot be accomplished on all FIA plots because a small portion

of the plots will either leave or enter the

population that meets FIA’s definition of forest

land when the plot is measured at the second

visit. FIA field crews have raised an additional

concern, which is that a substantial amount of

damage/disturbance to both FWD and CWD can

occur in the process of data collection on the

plot, regardless of how careful the field crews

are. This damage to the resource could cause

inflated estimates of the rates of decay. One

solution would be to move at least some of the

data collection for DWM off of the plot and use

these data to test or adjust for damage due to

trampling.

Because most field crews do not permanently mark

CWD or FWD pieces, it is assumed that each

individual woody piece will not be relocated and

measured during remeasurement activities. Unlike

phase 2 standing tree protocols that track individual trees over time to assess ecosystem change,

the DWM sample protocols are designed to

estimate plot-level down woody attributes. Over

large scales, DWM protocols and estimators are

designed to indicate whether fuels, wildlife habitat,

or carbon pools are significantly different. Therefore, plot-level DWM estimates may be compared

over time; however, results must be couched in the

underlying statistical theory and general goals of

the DWM indicator. Change is most likely best

estimated among large strata such as forest or stand

types.

4. CHANGE ESTIMATION
25

To facilitate efficient and accurate analysis of DWM

data, forest inventory analysts should be well

versed in the sampling protocol, estimators, and

processing of DWM field data. Beyond this

knowledge of the DWM indicator, being aware of

all analyses possible with DWM data can aid

analysts with their task of DWM data dissemination

and interpretation. Analysis of DWM data follows a

hierarchy based on level of sophistication and data

processing: field data, plot-summary core tables,

population core tables, tabular/graphical summaries, and maps. Analysis of DWM data will also

depend on access to actual data, with FIA analysts

having access to actual plot locations. Data users

outside of FIA may have reduced access to plot

locations and less ability to link DWM data to all

phases of FIA’s inventory program. However, the

analysis presented in this section should be widely

available to all.

5.1 Field Data

Although field data provide the base of any

analytical exploration of DWM, analysts will only

infrequently deal with raw, field data. Field data are

organized into one of six tables (tables 1.2-1.7).

There is no processing of field data present in

DWM data tables. Researchers engaged in specific

DWM studies may use these data sets where their

investigations require unique processing and/or

summarization of field data. Otherwise, analysts

may want to focus their resources on provided plot

and population core tables and mapping efforts.

Examples of processing code are provided in the

appendices merely as a starting point for analysts

who wish to conduct their own field data processing for specific/regional research objectives.

5.2 Plot Summary Core Tables

Plot summary core tables provide application and

summarization of DWM estimators at the plot level.

Core tables are currently produced for, but not

5. DWM ANALYTICAL GUIDELINES

limited to, three science disciplines: fire, carbon,

and wildlife. These tables may serve as the basis of

DWM investigations for most analysts. If analysts

prefer using alternative DWM estimators or wish to

add their own refinements to the DWM processing

algorithms, they will need to process field data

independently. There is no single way to process

DWM data that allows analysts the freedom to

pursue their own scientific explorations. Provided

DWM core tables are simply one set of processed

DWM tables that will be publicly available.

A fire science-oriented core table (table 1.8)

contains estimates of fuel loadings per unit area for

each DWM phase 3 plot using one set of DWM

estimators. State/regional forest analysts may use a

fire core table to summarize fuel loadings for

delineated areas of at least a “super county” size

(super county may be a conservation district that

encompasses numerous counties). Holistic assessments of fire hazard may be gained by combining

the fire core table with ancillary data sets such as

topography, phase 2 stand information, meteorological data, and wildland-urban interface information. Carbon processing algorithms that contain

estimators, constants, and data set fields desired by

the carbon modeling scientific community produce

carbon-oriented core tables (table 5.1). Because the

soils indicator collects information on duff and

litter, the DWM carbon core table is constructed to

avoid data set overlap of carbon pools. Analysts

may use the carbon core tables, in combination

with other phases of the FIA inventory, to assess

and monitor carbon pools of forest ecosystems for

delineated areas. Wildlife core tables (table 5.2) are

outputs from advanced data processing of CWD

field data (table 1.2). Wildlife biologists want

information about the quantity and quality of

habitat for fauna with CWD niches. Hence, wildlife

core tables contain not only volume and tonnage

estimates of CWD per plot, but also size- and

decay-class distributions along with species

composition (table 5.2).
26

Table 5.1.—Processed DWM data oriented toward carbon estimation, plot level

State County Plot FWD* CWD* Duff* Slash*

xxx 17 385 0.7 7.2 16.7 0

xxx 31 434 2.7 3.5 22.3 0

xxx 31 436 0.9 3.7 12.1 0

xxx 31 439 1.3 5.1 17.8 0

xxx 31 442 0.2 2.2 21.0 0

xxx 31 446 3.7 0.4 7.4 4.9

xxx 31 109 3.1 1.3 2.9 0

xxx 31 120 2.5 5.5 34.2 0

xxx 31 900 1.2 2.3 15.0 0

*mg/ha

Table 5.2.—Processed DWM data oriented toward wildlife emphases, plot level

Plot CWD Size class* (pieces/acre) Decay class (pieces/acre)

(ft3

/ac) 3.0- 8.0- 13.0- 18.0-

7.9 12.9 17.9 22.9 1 2 3 4 5

xxx 205.53 68 0 0 0 0 0 0 0 68

xxx 974.18 252 0 0 0 0 0 0 51 201

xxx 582.52 139 0 0 0 0 0 37 102 0

xxx 835.57 159 0 0 0 0 81 0 38 40

xxx 978.84 409 0 0 0 0 0 0 0 409

xxx 479.56 166 108 0 0 0 180 0 94 0

xxx 585.30 0 0 71 0 0 0 0 71 0

xxx 968.23 355 69 0 0 0 0 0 400 24

xxx 819.73 0 0 14 0 159 0 0 0 14

*Transect diameter (in.)
27

5.3 Population-Level Core Tables

Expansion of DWM plot estimates to larger area

estimates (i.e., super county, State, or regional

scales) produces population core tables. This type

of core table is of interest to analysts seeking single

estimates of DWM components for delineated areas

as opposed to the distribution of plot-level values

as provided by plot core tables. Estimates of carbon

among CWD and forest floor components are most

likely to be included in a population core table.

Although not a population estimate, per acre means

of DWM components among forest type strata are

recommended as core tables to be presented in

required reports (tables 5.3 and 5.4). These

summaries by forest type may provide fuel (table

Table 5.3.—Proposed State-level DWM core table containing means of fuel loading variables by forest type

Forest 1-hr 10-hr 100-hr 1,000-hr Duff Litter Fuelbed Shrub/

type depth (ft) herbs (ft)

Spruce/Fir 0.1 1.1 2.0 3.9 25.0 7.6 1.1 2.2

Pine 0.2 2.0 0.2 10.1 18.6 4.4 0.8 2.9

Elm/Ash 0.8 0.4 1.9 8.7 12.9 7.3 1.7 1.4

Beech/Birch 1.1 0.9 2.2 7.8 32.0 10.1 1.2 4.0

Oak 0.0 1.2 0.9 11.1 18.0 3.4 0.9 3.3

Oak/Pine 0.7 1.3 2.9 14.0 17.5 2.9 3.0 2.1

* All estimates tons/acre except where indicated

Table 5.4.—Proposed State-level DWM core table containing means of both carbon estimates and coarse woody debris attributes by forest type

Forest type FWD CWD CWD Size class* (pieces/acre) Decay class (pieces/acre)

Carbon Carbon (ft3

/ac) 3.0- 8.0- 13.0- 18.0- 1 2 3 4 5

(Mg/ha) (Mg/ha) 7.9 12.9 17.9 22.9

Spruce/Fir 8.9 20.2 205.5 68 5 0 0 0 2 0 3 68

Pine 11.1 58.2 974.1 252 15 1 3 6 0 13 51 201

Elm/Ash 8.7 13.8 582.5 139 12 0 0 0 6 37 102 6

Beech/Birch 5.5 19.8 585.3 10 22 71 0 2 30 0 71 0

Oak 18.9 25.6 968.2 355 45 0 4 2 0 0 400 2

Oak/Pine 15.4 30.1 819.7 22 8 14 0 30 0 7 0 7

*Transect diameter (in.)

5.3) and habitat (table 5.4) summaries by an

ecologically significant stratification (i.e., forest

types). For certain areas, other ecological delineations such as Bailey’s (1995) ecological provinces or

FIA units may be considered as summarization

strata. Analysts may need to combine forest types

into broader forest type groups to achieve a level of

statistical significance. Finally, although not

included in tables 3.4 and 3.5, standard errors of

the means may be included to aid users in data

evaluation.

5.4 Tabular and Graphical

Summaries

DWM data summary tables may be presented by

numerous methods (figs. 5.1 and 5.2). The scale
28

A

B

C

Figure 5.1.—Examples of DWM plot

summaries. (A) Mean tons/acre of DWM

attributes by State, (B) Distribution of sample

plots according to DWM attrbutes in userdefined area, (C) Mean of DWM attribute by

ancillary data such as forest type.

Figure 5.2.—Example of DWM population

summaries, estimates of DWM attributes for

a State (total tons, 1,000).
29

and available DWM sample size determine which

DWM summaries are available to analysts. Summarizing county-scale DWM plots may not be

possible, where counties at best have only one

DWM sample plot. Analysts must determine the

variances associated with their summaries to

determine which scale is appropriate for summary.

Hence, often the most logical scale for summary of

DWM data is the State or super-county level at

normal sample intensity (fig. 5.1a). DWM data

summaries at scales smaller than a State may be

permissible in States with extensive forest areas or

when sample intensification occurs. The difference

between plot and population summary charts is the

units of summarization (figs. 5.1 and 5.2). Plotlevel summarizations will often be mean per unit

area values (fig. 5.1a) (i.e., mean tons/acre of CWD

for DWM plots) or distribution of plot-level

estimates (fig. 5.1b). Additionally summaries may

occur by linking phase 2 stand information to

DWM core tables providing additional data such as

forest type and landowner information (fig. 5.1c).

DWM plot core tables may be produced using this

ancillary information to fill user requests (fig. 5.1c).

Population core table summaries will often provide

estimates of an entire population of DWM components within a defined area (fig. 5.2). Defined areas

will once again depend on the sample size of DWM

Figure 5.3.—Data contributions from all

phases of the FIA inventory program

provide numerous methodologies for

creating national fuels maps.

phase 3 plots contained within. If variances allow,

analysts may produce a summary chart for supercounty scales. Obvious from examples (tables 5.3,

5.4; figs. 5.1, 5.2), analysts may use DWM core

tables in conjunction with the entire FIA inventory

to produce summary tables and figures to satisfy

numerous needs of user groups.

5.5 Maps

Maps of DWM components may be created by a

number of methods requiring various data inputs

and levels of sophistication (fig. 5.3). At the very

least, perturbed locations of DWM phase 3 plots

and associated estimates of DWM components are

necessary for creating maps. Ancillary data sets that

aid the DWM map creation process are ecological

provinces, phase 2 data, and phase 1 forest/nonforest maps. Maps let analysts use their own

creative freedom to provide new and desired

outputs for users.

The first type of map is the most basic for analysts

to create—a map of fuzzed plot locations and

associated estimates of DWM components. An

analyst may use GIS software to display fuzzed plot

locations colored according to associated DWM

component. Based on regional needs, these basic

maps may be tailored to suit specific, regional
30

issues. As an example, increased DWM sampling

intensity of DWM plots in the Boundary Waters

of Minnesota may be “zoomed” in for users to

gain a better idea of the distribution of fuel

loadings in the scale of interest, with the rest of

the DWM sampling grid providing context for

these fuel loadings (fig. 5.4). Maps such as these

can answer the critical question: are my local

fuel loadings different from those in the rest of

my region?

Ancillary data sets that are spatial in nature may

also be used to create maps of DWM components (fig. 5.5). Mean values of DWM components may be estimated for any spatial data

layer. Maps may be created in unison with

ecological province, forest type, or fire condition

class maps (fig. 5.5).

Maps may be created using interpolation techniques (fig. 5.6). Interpolation simply involves

predicting the values of DWM components

between all sample points to create a continuous

map of predicted DWM values (Woodall et al.

2004). DWM maps may be created one of two

ways by using interpolation methodologies. First,

an analyst may constrain interpolation to forested

areas as defined by FIA phase 1 stratification

imagery. Second, an analyst may simply overlay an

interpolated map of DWM values on that of a

forest nonforest map, thus masking out all

nonforest areas (fig. 5.6).

Figure 5.4.—Estimates of total tons of DWM

for Boundary Waters Canoe Area, northern

Minnesota, 2001 DWM inventory.

Figure 5.5.—Mean estimates of total tonnage

fuel loading for North Central ecological

provinces based on 2001-2002 DWM

inventory.
31

Maps may be created by intensive modeling

efforts based on FIA phase 2 data (fig. 5.7).

This process uses both phase 2 and 3 data to

create models whereby DWM components are

estimated for every phase 2 plot. Once

satisfactory models are created, DWM

components may be estimated for every phase

2 plot and mapped (fig. 5.7). Although the

sampling intensity is much greater for phase 2

plots, the efficacy of this methodology

depends on the explanatory power of the

models established between phase 2 and 3

plots.

The mapping of DWM indicator data is a

relatively recent analytical undertaking that

allows maximum freedom for analysts with a

few stringent guidelines. Every method for

producing maps of DWM estimates has

advantages and disadvantages, and it is

suggested that analysts explore all their

options.

Figure 5.7.—Interpolation of predicted down fuels for FIA Phase 2 plots based on Phase 2/Phase 3 fuel models,

North Central States 2001-2002 (McRoberts et al. 2004).

Figure 5.6.—Interpolation techniques for

creating regional maps of DWM

component estimates.

+

Inverse distance

weighting of coarse

woody debris plotlevel estimates, FIA

Phase 3 Down Woody

Material plots, 2001-

2003

Forest/nonforest

NLCD coverage for

North Central

States
32

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2004.

A walkthrough solution to the boundary overlap problem. Forest

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M.J. 1999.

Point relascope sampling of downed coarse woody debris.

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The reflection method of line-intersect sampling to eliminate

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34
Coarse Woody Debris (CWD): Down pieces of

wood with a minimum small-end diameter of

at least 3 inches and a length of at least 3 feet

(excluding decay class 5). CWD pieces must

be detached from a bole and/or not be selfsupported by a root system with a lean angle

more than 45 degrees from vertical.

Decay class: Rating of individual coarse woody

debris according to a 5-class decay scale

defined by the texture, structural integrity,

and appearance of pieces. Scale ranges from

freshly fallen trees to completely decomposed

cubicle rot heaps.

Down Woody Materials (DWM): A term used to

collectively describe attributes estimated by

the Down Woody Materials indicator. A

majority of the indicator’s components are

down and dead forest materials: fine woody

debris, coarse woody debris, duff, litter, slash,

live and dead herb and shrubs, and fuelbed

depths.

Duff: Organic forest floor layer consisting of

decomposing leaves and other organic

material in which individual plant parts are

not recognizable.

Fine Woody Debris (FWD): Down woody pieces

with a diameter less than 3 inches at the point

of transect intersection excluding dead

branches attached to standing trees, dead

foliage, bark fragments, or cubicle rot.

Fuelbed: Accumulated mass of all DWM

components above the top of the duff layer

(excluding live shrubs/herbs).

Fuel Hour Classes: Fuel classes defined by the

amount of time it roughly takes for moisture

conditions to fluctuate. Larger coarse woody

debris will inherently take longer to dry out

than smaller fine woody pieces (Small

FWD=1-hour, Medium FWD=10-hour, Large

FWD=100-hour, CWD=1,000-hour).

Herbs: Nonwoody herbaceous plants, but also ferns,

moss, lichens, sedges, and grasses.

Integrated Fuel Depth: Estimator that scales the

maximum height of all shrub/herb components

based on their associated coverage. Useful for

incorporating all four shrub/herb measurements

into single estimate of fuel ladder heights.

Line-Intersect Sampling (LIS): Sampling technique

by which sampling planes are installed in

defined areas of interest whereby intersection of

down woody debris with sampling planes are

used to estimate coarse and fine woody

populations.

Litter: Forest floor layer of freshly fallen leaves,

needles, twigs, cones bark chunks, dead moss,

dead lichens, dead herbaceous stems, and

flower parts.

Pile Density: The density of coarse woody debris in

slash piles within the volume defined by the

shape code and dimensions estimated by field

crews, also known as packing ratio.

Shrubs: Herbaceous plants with woody stems.

Slash: Otherwise known as residue piles, coarse

woody debris in piles created directly from

human activity or from natural events that

prohibit safe measurement by transects.

Transect Diameter: Diameter of coarse woody

pieces at the point of intersection with sampling

planes.

Transect Segment: Sections of transects that lie

entirely within one condition class whereby one

24-foto transect that lies across two condition

classes will have two transect segments.

“Y” Transect: The spatial arrangement of sampling

transects on FIA subplots whereby they radiate

out from subplot center at obtuse angles of 120

degrees from each other.

35

7. GLOSSARY
8.1 Example: Classifying Plots

Based on the Level of CWD

This example will illustrate the estimation

process when the goal is to classify the forested

conditions found on the plot based on the level

of CWD. For this discussion, assume the goal is

to classify each plot into categories that correspond to arbitrarily selected low, medium, and

high levels of CWD, where the low category may

be defined as lands having CWD levels of < 350

ft3

/acre, the medium category may be 351-600

ft3

/acre, and the high category may be lands

having more than 600 ft3

/acre.

Figure 8.1 depicts an FIA plot that covers three

different forest conditions. Subplots 1 and 2 fall

8. APPENDICES

completely within condition class c = 1, which is

land that meets the definition of low levels of CWD

volume. Subplot 3 straddles condition classes c = 1

and c = 2, with condition class c = 2 meeting the

definition of high levels of CWD volume. Subplot

4 falls predominantly in condition class c = 1, but a

small portion falls in condition class c = 3, which

also has a high level of CWD, but for the purpose

of discussion it will be a distinctly different

condition class. For example, condition class 3

could be privately owned land and condition class

2 could be National Forest land.

The data for calculating the level of CWD on this

plot are given in tables 1.2 and 1.5. These data

must be further manipulated and combined to

Condition 3

High Level of CWD

(> 600 ft3/acre)

Condition 1

Low Level of CWD

(< 200 ft3/acre)

Condition 2

High Level of CWD

(> 600 ft3/acre)

4 3

2

1

Figure 8.1.—Hypothetical plot map and

corresponding condition classes for CWD

estimation procedure example one, appendix

8.1.

36
perform the estimation. The first step is to determine the volume of each piece of CWD using

equation 3.4 in conjunction with columns 7,8, and

9 in table 1.2.

Vft

0.56

10.14

4.61

1.34

17.15

4.66

1.64

85.34

8.48

The next task is to calculate the total transect length

within each condition (i.e., L(c),c = 1,2,3) using the

information in table 1.5. This yields

and

All that remains is to estimate the volume per acre

of CWD on each condition using equation 3.10,

where the per unit area conversion factor is f =

43,560, and the length of each piece, (l

ij), is given

in table 1.2. This yields cubic foot volume per acre

estimates of Y(1) = 303.7, Y(2) = 931.4, and Y(3) =

30,130.

Note that the estimate for condition class c = 3

greatly exceeds any reasonable level of CWD

volume in the Eastern U.S. This outrageous overestimate occurs because a single piece of CWD is

tallied on a very short section of transect ( Lj

(3)

= 3.4). This result should not be surprising given

that the literature clearly states (e.g., Marshall et al.

2000, Nemac and Davis 2002, Pickford and Hazard

1978) that total transect lengths of close to 1,000

meters are required to achieve a high level of

precision with LIS estimators. Thus, whenever the

total transect length in condition class c is small

(i.e., Lj

(c) ≤ 24 ft or one transect length at a

minimum), the resulting estimate is likely to

incorrectly classify the condition. Such results

should not be used in any analysis because the

sampling effort (i.e., the length of transect) is far

too short to produce reliable information.

37

8.2 Example: Estimation of Population Totals for CWD

This example will illustrate the estimation process

when the goal is to estimate the population total for

CWD as well as the total for one of the condition

classes. As illustrated in appendix 8.1, it is possible

to generate unrealistic estimates for individual

condition class when classifying the level of CWD

in each condition class.

The data used in this example include the FIA plot

data used in the appendix 8.1 and one additional

plot; these plots are illustrated in figure 8.2 and will

be referred to as plot 1 and plot 2, respectively.

The first task is calculating the volume for each

piece of CWD, which gives

 Vft Vft

 plot 1 plot 2

0.56 53.78

10.14 31.02

4.61 2.13

1.34 0.99

17.15 8.96

4.66 10.83

1.64 8.2

85.34 8.2

8.48 10.12

The next task is to calculate the total transect length

that falls within the population, which is the total

length of transects that fall on land that meets FIA’s

definition of forest. Using the data in table 1.5

yields

for plots 1 and 2, respectively.

All that remains is to estimate the volume per acre

of CWD for the population using equation 3.11,

where the per unit area conversion factor is f =

43,560, and the length of each piece, (l

if), is given

in table 1.2. This yields cubic foot volume per acre

estimates of plot 1 and 2 of Y1

 = 785.8, Y2 =

1,218.0, and the overall population estimate of Y =

1/2(785.8 + 1,218.0) = 1,001.9 ft3

/acre.

Estimating the attributes for either condition classes

or domains requires the defining the indicator

(1) 224.9, (2) 59.5,

(2)

1

(1)

1

∑ = ∑ = = =

k

j

j

k

j

Lj L

(3) 3.4

(3)

1

∑ = =

k

j

Lj

(3)

1

∑=

k

j

L

k c

j

( )

1

∑=

( ) 288, ( ) 237.9

( )

1

( )

1

∑ = ∑ = = =

L f L f

k f

j

j

k f

j

j ,

.
38

variable δ(c,∂) that takes on the value of 1 when

a piece of CWD is tallied on condition class c

and meets the definition of domain ∂. To

illustrate, assume that the goal is to generate an

estimate of the volume of CWD in condition

class c = 2 for all logs in decay classes 1, 2, or 3.

From table 1.2, it can be discerned that δ(c =

2,∂) = (0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0).

Using equation 3.13 yields Y(c,∂) = 76.5 ft3

/acre.

8.3 Example: Estimation of Mean

Fuel Loadings by Forest Type

for Large Area

The citizens of the state of “C” would like

estimates of their fuels for common forest types

of their forests. They are not interested in who

owns these forests or where the fuels are exactly

located. Rather, they would like a broad estimate

of where they stand when it comes to risks of

wildfire. State “C” has only 28 DWM plots

established over a full cycle of inventory. The

estimates of fuel loadings for these plots are

provided either by the national DWM indicator

Condition 1

High Level of CWD

(> 600 ft3/acre)

4 3

2

1

Condition 9

Non-Forest

Figure 8.2.—Hypothetical plot map and

corresponding condition classes for CWD

estimation procedure example two, appendix

8.2.

advisor, regional FIA staff, or a national data

distribution web site. Next, the analyst must link

the unique identifier of each DWM plot with

corresponding phase 2 inventory data to summarize fuel estimates by forest type or any other forest

condition attribute. An important note is that one

DWM plot may contain more than one forested

condition, thus there may be numerous forest

types for one DWM plot. If fuel estimates were

determined at the condition class level, then they

may be easily averaged. However, plot estimates

may be available only at the plot level. In this

instance the condition proportion of each forest

type may be used to weight the mean of the fuel

loadings determined for each forest type. An

example of such PL-SQL code is provided in

appendix 8.4. Once mean and standard errors

estimates of fuels by forest types are determined for

state “C” (table 8.3.1), analysts must determine

how to display the results. With only 28 DWM

plots sampled in state “C,” certain forest types have

only 1 plot while others have only 2 or 3 (table

8.3.1). We suggest that means for strata, such as

forest type, be presented only if more than a few
39

plots have been sampled in said strata. Because

forest conditions are so varied across the United

States, analysts will have to rely on interpretation of

standard errors to determine the appropriate

sample size for including means in any inventory

reports. For state “C”, the pine forest types may be

combined into the conifer forest type group with 3

plots, the oak/hickory forest type group may

remain the same with 17 plots, while the remaining

forest types may be combined into an “other

hardwoods” forest type group with 8 plots (table

8.3.1). Analysts should couch decisions on how to

present fuel estimates, which often have a low

sample size, in the ecological significance of a forest

type group and the variance associated with mean

estimates.

Table 8.3.1.—Initial fuel loading estimates by forest type for hypothetical state “C” presented in appendix section 8.3

Forest type group #Plots 1-hr 10-hr 100-hr 1,000-hr Duff Litter Shrubs/Herbs*

White/Red/Jack pine 2 1.04 2.88 1.69 1.58 7.47 5.05 0.38

Oak/Pine 1 0.97 3.04 1.20 0.32 4.27 1.76 1.17

Oak/Hickory 17 0.38 1.05 1.49 3.74 5.56 2.19 1.23

Elm/Ash/Cottonwood 3 0.13 0.34 1.92 24.31 2.35 0.51 3.10

Maple/Beech/Birch 4 0.36 0.56 1.37 44.25 2.03 10.26 0.37

Aspen/Birch 1 0.33 2.43 1.81 4.48 2.63 3.16 0.10

*feet.
40

Species Genus Species Specific Slash bulk Fine woody debris (dia2) Litter Duff bulk

number gravity density Small Medium Large bulk density density

 (lbs/ft3

) (in.) (in.) (in.) (lbs/ft3) (lbs/ft3)

10 Abies spp. 0.34 21.20

11 Abies amabilis 0.40 25.00

12 Abies balsamea 0.34 21.20 0.005 0.194 2.402 1.1 7.2

14 Abies bracteata 0.36 22.50

15 Abies concolor 0.37 23.10 0.013 0.205 1.792 4.9 11.4

16 Abies fraseri 0.34 21.20

17 Abies grandis 0.35 21.80 0.0122 0.304 2.87 5.07 11.07

19 Abies lasiocarpa 0.31 19.30 0.0122 0.304 3.12 5.07 11.07

20 Abies magnifica 0.36 22.50 0.016 0.205 2.517 7.5 11.4

21 Abies magnifica var.

shastensis 0.36 22.50

22 Abies procera 0.37 23.10

41 Chamaecyparis lawsoniana 0.39 24.30

42 Chamaecyparis nootkatensis 0.42 26.20

43 Chamaecyparis thyoides 0.31 19.30

50 Cupressus spp. 0.44 27.50

60 Juniperus spp. 0.44 27.50

64 Juniperus occidentalis 0.44 27.50 0.012 0.25 2.157 4.4 11.1

69 Juniperus monosperma 0.45 28.10

70 Larix spp. 0.44 27.50

71 Larix laricina 0.49 30.60 0.041 0.169 2.82 1 2

72 Larix lyallii 0.48 30.00

73 Larix occidentalis 0.48 30.00 0.0151 0.238 2.17 5.07 11.07

81 Calocedrus decurrens 0.37 23.10

90 Picea spp. 0.38 23.70

91 Picea abies 0.38 23.70

92 Picea breweriana 0.33 20.60

93 Picea engelmannii 0.33 20.60 0.0122 0.304 2.87 5.07 11.07

94 Picea glauca 0.37 23.10 0.006 0.24 2.91 1.1 7.2

95 Picea mariana 0.38 23.70 0.006 0.24 2.91 1.1 7.2

96 Picea pungens 0.38 23.70

97 Picea rubens 0.38 23.70

98 Picea sitchensis 0.37 23.10

101 Pinus albicaulis 0.37 23.10 0.02 0.188 2.286 3.7 11

102 Pinus aristata 0.37 23.10

103 Pinus attenuata 0.37 23.10 0.016 0.194 1.499 2.4 13.7

104 Pinus balfouriana 0.37 23.10 0.018 0.143 1.987 5.6 13.1

105 Pinus banksiana 0.40 25.00 0.021 0.266 3.207 1.1 7.2

106 Pinus edulis 0.50 31.20

107 Pinus clausa 0.46 28.70

108 Pinus contorta 0.38 23.70 0.0201 0.344 2.87 5.89 10.15

109 Pinus coulteri 0.37 23.10

110 Pinus echinata 0.47 29.30

111 Pinus elliotti 0.54 33.70

112 Pinus engelmannii 0.37 23.10

8.4 DWM PROCESSING CONSTANTS
41

Species Genus Species Specific Slash bulk Fine woody debris (dia2) Litter Duff bulk

number gravity density Small Medium Large bulk density density

 (lbs/ft3

) (in.) (in.) (in.) (lbs/ft3) (lbs/ft3)

113 Pinus flexilis 0.37 23.10 0.024 0.194 2.682 6.2 14

114 Pinus strobiformis 0.35 21.80

115 Pinus glabra 0.41 25.60

116 Pinus jeffreyi 0.37 23.10 0.033 0.198 2.747 2 10.5

117 Pinus lambertiana 0.34 21.20 0.019 0.227 2.11 2.5 10

118 Pinus leiophylla 0.37 23.10

119 Pinus monticola 0.35 21.80 0.014 0.219 1.792 3.1 8.7

120 Pinus muricata 0.37 23.10

121 Pinus palustris 0.54 33.70

122 Pinus ponderosa 0.38 23.70 0.0342 0.238 3.12 2.25 9.67

123 Pinus pungens 0.49 30.60

124 Pinus radiata 0.37 23.10

125 Pinus resinosa 0.41 25.60 0.031 0.242 2.518 2.5 4.3

126 Pinus rigida 0.47 29.30

127 Pinus sabiniana 0.37 23.10 0.021 0.146 2.009 2.1 8.1

128 Pinus serotina 0.51 31.80

129 Pinus strobus 0.34 21.20 0.012 0.176 2.56 1.1 7.2

130 Pinus sylvestris 0.41 25.60

131 Pinus taeda 0.47 29.30

132 Pinus virginiana 0.45 28.10

133 Pinus monophylla 0.50 31.20

133 Pinus nigra 0.41 25.60

134 Pinus discolor 0.50 31.20

135 Pinus arizonica 0.37 23.10

202 Pseudotsuga menziesii 0.45 28.10 0.0122 0.304 2.87 6.33 9.52

211 Sequoia sempervirens 0.34 21.20

212 Sequoiadendron giganteum 0.34 21.20 0.021 0.198 2.644 8.8 10.1

221 Taxodium distichum var.

nutans 0.42 26.20

231 Taxus brevifolia 0.60 37.40

241 Thuja occidentalis 0.29 18.10 0.066 0.221 2.19 5.07 11.07

242 Thuja plicata 0.31 19.30 0.0122 0.304 2.87 5.07 11.07

251 Torreya californica 0.34 21.20

260 Tsuga spp. 0.38 23.70

263 Tsuga heterophylla 0.42 26.20 0.007 0.226 2.11 7.2 11.5

264 Tsuga mertensiana 0.42 26.20 0.007 0.226 2.11 7.2 11.5

299 softwood 0.38 23.70

300 Acacia spp. 0.60 37.40

310 Acer spp. 0.49 30.60

311 Acer barbatum 0.54 33.70

312 Acer macrophyllum 0.44 27.50

313 Acer negundo 0.44 27.50

314 Acer nigrum 0.52 32.40

315 Acer pensylvanicum 0.44 27.50

316 Acer rubrum 0.49 30.60 0.028 0.159 2.517 0.9 6

317 Acer saccharinum 0.44 27.50

318 Acer saccharum 0.56 34.90
42

319 Acer spicatum 0.44 27.50

321 Acer glabrum 0.44 27.50

322 Acer grandidentatum 0.44 27.50

330 Aesculus californica 0.38 23.70

330 Aesculus spp. 0.33 20.60

341 Ailanthus altissima 0.37 23.10

350 Alnus spp. 0.37 23.10

355 Amelanchier spp. 0.66 41.20

361 Arbutus menziesii 0.58 36.20

367 Asimina triloba 0.47 29.30

370 Betula spp. 0.48 30.00

371 Betula alleghaniensis 0.55 34.30

372 Betula lenta 0.60 37.40

373 Betula nigra 0.56 34.90

374 Betula occidentalis 0.53 33.10

375 Betula papyrifera 0.48 30.00 0.016 0.167 2.89 0.9 6

376 Betula papyrifera var.

commutata 0.48 30.00

379 Betula populifolia 0.45 28.10

381 Bumelia lanuginosa 0.47 29.30

391 Carpinus caroliniana 0.58 36.20

400 Carya spp. 0.62 38.70

401 Carya aquatica 0.61 38.10

402 Carya cordiformis 0.60 37.40

403 Carya glabra 0.66 41.20

404 Carya illinoensis 0.60 37.40

405 Carya laciniosa 0.62 38.70

406 Carya myristicaeformis 0.56 34.90

407 Carya ovata 0.64 39.90

408 Carya texana 0.54 33.70

409 Carya tomentosa 0.64 39.90

421 Castanea dentata 0.40 25.00

422 Castanea pumila 0.40 25.00

423 Castanea ozarkensis 0.40 25.00

430 Castanopsis spp. 0.42 26.20

450 Catalpa spp. 0.38 23.70

460 Celtis spp. 0.49 30.60

461 Celtis laevigata 0.47 29.30

462 Celtis occidentalis 0.49 30.60

471 Cercis canadensis 0.58 36.20

475 Cercocarpus ledifolius 1.00 62.40

476 Cercocarpus montanus 1.00 62.40

479 Cercocarpus intricatus 1.00 62.40

481 Cladrastis lutea 0.52 32.40

490 Cornus spp. 0.64 39.90

491 Cornus florida 0.64 39.90

492 Cornus nuttallii 0.58 36.20

Species Genus Species Specific Slash bulk Fine woody debris (dia2

) Litter Duff bulk

number gravity density Small Medium Large bulk density density

 (lbs/ft3

) (in.) (in.) (in.) (lbs/ft3) (lbs/ft3)
43

Species Genus Species Specific Slash bulk Fine woody debris (dia2) Litter Duff bulk

number gravity density Small Medium Large bulk density density

 (lbs/ft3

) (in.) (in.) (in.) (lbs/ft3) (lbs/ft3)

500 Crataegus spp. 0.62 38.70

510 Eucalyptus spp. 0.67 41.80

521 Diospyros virginiana 0.64 39.90

531 Fagus grandifolia 0.56 34.90

540 Fraxinus spp. 0.54 33.70

541 Fraxinus americana 0.55 34.30

542 Fraxinus latifolia 0.50 31.20

543 Fraxinus nigra 0.45 28.10

544 Fraxinus pennsylvanica 0.53 33.10

545 Fraxinus profunda 0.54 33.70

546 Fraxinus quadrangulata 0.53 33.10

551 Gleditsia aquatica 0.60 37.40

552 Gleditsia triacanthos 0.60 37.40

555 Gordonia lasianthus 0.37 23.10

571 Gymnocladus dioicus 0.50 31.20

580 Halesia spp. 0.32 20.00

591 Ilex opaca 0.50 31.20

600 Juglans spp. 0.51 31.80

601 Juglans cinerea 0.36 22.50

602 Julglans nigra 0.51 31.80

611 Liquidambar styraciflua 0.46 28.70

621 Liriodendron tulipifera 0.40 25.00

631 Lithocarpus densiflorus 0.58 36.20

641 Maclura pomifera 0.76 47.40

650 Magnolia spp. 0.45 28.10

660 Malus spp. 0.61 38.10

680 Morus spp. 0.59 36.80

681 Morus alba 0.59 36.80

682 Morus rubra 0.59 36.80

691 Nyssa aquatica 0.46 28.70

692 Nyssa ogeche 0.46 28.70

693 Nyssa sylvatica 0.46 28.70

701 Ostrya virginiana 0.63 39.30

711 Oxydendrum arboreum 0.50 31.20

712 Paulownia tomentosa 0.38 23.70

721 Persea borbonia 0.51 31.80

731 Platanus occidentalis 0.46 28.70

740 Populus spp. 0.37 23.10

741 Populus balsamifera 0.31 19.30 0.022 0.258 2.89 0.9 6

742 Populus deltoides 0.37 23.10

743 Populus grandidentata 0.36 22.50 0.022 0.258 2.89 0.9 6

744 Populus heterophylla 0.37 23.10

745 Populus deltoides 0.37 23.10

745 Populus sargentii 0.37 23.10

746 Populus tremuloides 0.35 21.80 0.022 0.258 2.89 0.9 6

747 Populus balsamifera 0.31 19.30

748 Populus fremontii 0.34 21.20
44

749 Populus angustifolia 0.34 21.20

752 Populus alba 0.37 23.10

755 Prosopis spp. 0.58 36.20

760 Prunus spp. 0.47 29.30

761 Prunus pensylvanica 0.36 22.50

762 Prunus serotina 0.47 29.30

763 Prunus virginiana 0.36 22.50

765 Prunus nigra 0.47 29.30

766 Prunus americana 0.47 29.30

800 Quercus spp. 0.58 36.20

801 Quercus agrifolia 0.70 43.70

802 Quercus alba 0.60 37.40

803 Quercus arizonica, grisea 0.70 43.70

804 Quercus bicolor 0.64 39.90

805 Quercus chrysolepis 0.70 43.70

806 Quercus coccinea 0.60 37.40

807 Quercus douglasii 0.51 31.80

808 Quercus durandii 0.60 37.40

809 Quercus ellipsoidalis 0.56 34.90

810 Quercus emoryi 0.70 43.70

811 Quercus engelmannii 0.70 43.70

812 Quercus falcata var.

falcata 0.52 32.40

813 Quercus falcata var.

pagodaefolia 0.61 38.10

814 Quercus gambelii 0.64 39.90

815 Quercus garryana 0.64 39.90

816 Quercus ilicifolia 0.56 34.90

817 Quercus imbricaria 0.56 34.90

818 Quercus kelloggii 0.51 31.80

819 Quercus laevis 0.52 32.40

820 Quercus laurifolia 0.56 34.90

821 Quercus lobata 0.64 39.90

822 Quercus lyrata 0.57 35.60

823 Quercus macrocarpa 0.58 36.20 0.028 0.1 2.82 0.9 6

824 Quercus marilandica 0.56 34.90

825 Quercus michauxii 0.60 37.40

826 Quercus muehlenbergii 0.60 37.40

827 Quercus nigra 0.56 34.90

828 Quercus nuttalli 0.56 34.90

829 Quercus oblongifolia 0.70 43.70

830 Quercus palustris 0.58 36.20

831 Quercus phellos 0.56 34.90

832 Quercus prinus 0.57 35.60

833 Quercus rubra 0.56 34.90 0.028 0.1 2.82 0.9 6

834 Quercus shumardii 0.56 34.90

835 Quercus stellata 0.60 37.40

Species Genus Species Specific Slash bulk Fine woody debris (dia2

) Litter Duff bulk

number gravity density Small Medium Large bulk density density

 (lbs/ft3

) (in.) (in.) (in.) (lbs/ft3) (lbs/ft3)
45

Species Genus Species Specific Slash bulk Fine woody debris (dia2) Litter Duff bulk

number gravity density Small Medium Large bulk density density

 (lbs/ft3

) (in.) (in.) (in.) (lbs/ft3) (lbs/ft3)

837 Quercus velutina 0.56 34.90

838 Quercus virginiana 0.80 49.90

839 Quercus wislizeni 0.70 43.70

840 Quercus incana 0.56 34.90

843 Quercus hypoleucoides 0.70 43.70

901 Robinia pseudoacacia 0.66 41.20

902 Robinia neomexicana 0.66 41.20

920 Salix spp. 0.36 22.50

925 Sapium sebiferum 0.47 29.30

931 Sassafras albidum 0.42 26.20

935 Sorbus americana 0.42 26.20

945 Tamarix spp. 0.40 25.00

950 Tilia spp. 0.32 20.00

951 Tilia americana 0.32 20.00 0.028 0.1 2.82 5.07 11.07

952 Tilia heterophylla 0.32 20.00

970 Ulmus spp. 0.50 31.20

971 Ulmus alata 0.57 35.60

972 Ulmus americana 0.46 28.70

973 Ulmus crassifolia 0.57 35.60

974 Ulmus pumila 0.46 28.70

975 Ulmus rubra 0.48 30.00

976 Ulmus serotina 0.57 35.60

977 Ulmus thomasii 0.57 35.60

980 Aleurites fordii 0.47 29.30

981 Umbellularia californica 0.51 31.80

981 Vaccinium arboreum 0.47 29.30

983 Melia azedarach 0.47 29.30

984 Planera aquatica 0.53 33.10

985 Cotinus obovaus 0.47 29.30

990 Olneya tesota 1.00 62.40

998 hardwood 0.51 31.80

999 unknown 0.46 28.70

Constants may be used only as a guide for initial data analysis/processing. Citations: Brown et al. 1982; Loomis 1977; Nalder et al. 1997,

1999; Roussopoulos and Johnson 1973; USDA 1999.

Because of the lack of required constant information for application of numerous DWM estimators, users may want to use the species

compositions of each DWM plot to derive their own plot-specific constants. This may be done by averaging the slash bulk density, FWD

diameters, litter bulk density, and duff bulk density for each species of each CWD piece identified on the CWD transects. The result would be

a list of DWM estimation constants based on the unique species composition of every plot.
46

A

Figure 8.3.—Historic sample designs for

the Forest Health Monitoring and FIA

DWM programs: 1999 (A), 2000 (B), and

2001 (C).

8.5 Historical DWM Sample Designs, 1999-2001

B
47

C
Woodall, Christopher, Williams, Michael.

2005. Sampling protocol, estimation, and analysis procedures for the down woody

materials indicator of the FIA program. Gen. Tech. Rep. NC-256. St. Paul, MN: U.S.

Department of Agriculture, Forest Service, North Central Research Station. 47 p.

Provides the rationale and context for a national inventory of down woody material.

Documents the various woody material components sampled by the DWM indicator, the

sampling protocol used to measure the DWM components, and estimation procedures.

Provides guidance on managing and processing DWM data and incorporating that data

into pertinent inventory analyses and research projects.

KEY WORDS: forest inventory, down woody materials, estimation, analysis, sampling.
MISSION STATEMENT

We believe the good life has its roots in clean air, sparkling water, rich soil, healthy economies and

a diverse living landscape. Maintaining the good life for generations to come begins with everyday

choices about natural resources. The North Central Research Station provides the knowledge and

the tools to help people make informed choices. That’s how the science we do enhances the

quality of people’s lives.

For further information contact:

Or visit our web site:

www.ncrs.fs.fed.us

North Central

Research Station

USDA Forest Service

1992 Folwell Ave., St. Paul, MN 55108