United States 

Department of 

Agriculture 

Forest Service 

Pacific Northwest 

Research Station 

General Technical 

Report 

PNW-GTR-372 

September 1996 

c3 3” 

Estimating .Live Fuels 

for Shrubs and Herbs 

With BIOPAK 

Joseph E. Means, Olga N. Krankina, Hao Jiang, 

and Hongyan Li 
Disclaimer

Authors

The BIOPAK software is in the public domain and the recipient may neither assert

any propriety rights thereto nor represent them to anyone as other than Governmentproduced programs.

The use of trade, firm, or corporation names in this publication is for the information

and convenience of the reader. Such use does not constitute an official endorsement

or approval by the U.S. Department of Agriculture of any product or service to the

exclusion of others that may be suitable.

JOSEPH E. MEANS was a research forester, U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200

S.W. Jefferson Way, Corvallis, OR 97331; OLGA N. KRANKINA is research

associate, Department of Forest Science, and HAO JANG is a graduate student,

Department of Mathematics, Oregon State University, Corvallis, OR 97331; and

HONGYAN LI is with Rogue Wave, 260 S.W. Madison Avenue, Corvallis, OR 97330.
Abstract

Contents

Means, Joseph E.; Krankina, Olga N; Jiang, Hao; Li, Hongyan. 1996. Estimating

live fuels for shrubs and herbs with BIOPAK. Gen. Tech. Rep. PNW-GTR-372.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 21 p.

This paper describes use of BIOPAK to calculate size classes of live fuels for shrubs

and herbs. A library of equations to estimate such fuels in the Pacific Northwest and

northern Rocky Mountains is presented and used in an example. These methods can

be used in other regions if the user first enters fuel size-class equations for a given

region into a new library by using the library editor supplied with BIOPAK. Fuel size

classes can be estimated in three ways: (1) When appropriate plant measurements

are available, fuel classes can be estimated directly for species that have equations

in the library or species with similar growth forms. (2) When appropriate plant measurements are not available, fuel classes can be estimated in two steps, first by estimating total aboveground biomass for individual plants and then by estimating biomass in fuel classes from total aboveground biomass. (3) The equations provided can

be used to develop new equations that estimate fuels from plot-level estimates of species cover (and possibly other measures).

Keywords: Live fuels, fuel size classes, software, plant biomass, Pacific Northwest.

northern Rocky Mountains.

1

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2

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5

7

8

10

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16

19

Introduction

Methods Used To Build the Fuel Equation Library

Search for Equations and Data

Developing Equations

Automating Calculation of Sums of Fuel Size Classes

Construction of a Library of Fuel Size-Class Equations

Results: Techniques for Calculating Fuel Biomass

Installing the Fuel Equation Library FUELLIB3

Method 1, Direct Calculation of Fuel Biomass From Plant Measurement

Method 2, Two-Phase Calculation of Fuel Biomass

Method 3, Build Equations Based on Cover by Using New Field Data

Common and Scientific Names

Acknowledgments

Literature Cited

Appendix A-Species in the Fuel Equation Library, FUELLIB3

Appendix B-Example of Method 1

Appendix C-Forms
Obtaining the

Software and

The software system described herein requires BIOPAK (Means and others 1994) to

run. The BIOPAK users guide and additional copies of this publication can be

obtained from:

Pacific Northwest Research Station

PNW Publications

P.O. Box 3890

Portland, OR 97230-3438

(503) 326-7128, Fax: (503) 326-2455

PNW Publication Request Address: FSWA/S=RIS/OU=RO6A@MHS.AlTMAlL.COM

The fuels equation library described herein and BIOPAK software can be obtained

(1) with technical support for a fee, or (2) without technical support for free (see

below).

Obtaining Software and All BIOPAK software and manuals are distributed by the Forest Resources Systems

Manuals With Technical Institute (FORS), a nonprofit organization that searches for and distributes software

support with forestry-related applications. Users who purchase from FORS will have the

benefit of their technical support. The BIOPAK users guide (Means and others 1994)

and software cost US$50, except for users in countries with currency exchange problems (such as developing and third world countries), the fee is reduced to US$25.

The comparable prices for this publication and the accompanying fuels equation

library are US$20 and US$lO. FORS can be contacted at:

The Forest Resource Systems Institute

122 Helton Court

Florence, AL 35630

(205) 767-0250 Fax: (205) 767-3768 Internet: DaveFORS@AOL.COM

Obtaining the Software

Without Technical

support

The full BIOPAK software (to be used with the BIOPAK users guide, Means and

others 1994) and the fuel size-class equation library (to be used with this publication)

can be obtained for free. You may obtain them over the Internet by using a World

Wide Web browser such as Netscape or Mosaic at URL:

“http://www.fsl.orst.edu/rogues/meansj/biopak.htm”. Please fill out the short

registration form you will find when you download BIOPAK.
Introduction Reliable predictions of fire behavior and effects require accurate estimates of fuels

characteristics. Fuels characteristics are often broadly estimated as one of the 13

standard fuel models (Anderson 1982). When the best of these fuel models results

in poor predictions of fire behavior, new fuel models can be constructed (Burgan and

Rothermel 1984). This requires estimates of the mass per unit area of the different

fuel components; for example, trees, slash, and natural residue.

Systems are available for estimating fuels for trees (for example, DEBMOD,

Hilbruner and Wordell 1992) logging slash (Maxwell and Ward 1976a, 1976b; Van

Wagner 1968), and natural residues (Blonski and Schramel 1981, Maxwell and Ward

1980) and predicting slash from stand treatments (Brown and others 1977, Hilbruner

and Wordell 1992) given appropriate field measurements. In many forest plant communities, shrubs and herbs make up the majority of fuels available to carry surface

fire, based on plant cover data for Cascade forests (Halvorson and others 1986,

Hemstrom and others 1987) and Rocky Mountain forests (Cooper and others 1991,

Steele and others 1983). No system has been available, however, for estimating live

fuels of shrubs and herbs from field measurements until that described here.

Our goal was to provide a system for estimating dry mass of live fuels given measurements for shrubs and herbs in the Pacific Northwest and northern Rocky Mountains of the United States, and relying on existing sources of equations and data. A

few species of grass also are included. We believe this system could be used in other

areas of the world if equations predicting fuel size classes are developed for local

species.

This tool can be used only with BIOPAK (Means and others 1994)) a software package that links a library of equations that predicts plant components with vegetation

data sets and estimates plant components by area. The library that comes with

BIOPAK contains almost 1,200 equations from rain forests through deserts of the

Pacific Northwest. Its library editor allows the building of new libraries for different

regions and continents.’

The terms “plant component biomass” and “plant component” refer to plant biomass

(dry weight basis) separated by form and function into parts, such as leaves, stem,

branch, wood, and bark. “Fuels” and “fuel size classes” refer to plant biomass (dry

weight basis) separated into standard fuel size classes defined below.

Methods Used To

Build the Fuel

Equation Library

Our approach included surveying the literature, obtaining existing data, fitting new

equations, building a new fuel equation library, testing this library, modifying BIOPAK

software, and testing the combined library and software. We did no new field

sampling to develop these equations. Our main concern was to create equations for

above-ground biomass of shrub fuels. Equations giving total aboveground biomass

for herbs (and grasses) can be used directly to estimate l-hour time-lag fuels (term

defined below).

Search for Equations

and Data

We first surveyed the literature and contacted people who we believed might have

fuel size-class equations or data from which equations could be built, or could lead

us to others who had such data. Often, literature or contacts with people led us to

more literature and additional contacts. We found four studies with useful data.

1

 Ordering information is at the front of this paper, “Obtaining

the Software and BIOPAK.”
Though some of these studies provided fuel tables, none provided suitable fuel size

class equations. Each, however, had collected but did not publish data on biomass

by size classes from which we could build equations. Brown (1976) collected data for

25 shrub species from the northern Rocky Mountains. Kauffman and Martin (1990)

collected data for California black oak and tanoak in southwestern Oregon.2

 Sapsis

and Kauffman (1991) obtained data for big sagebrush. Martin and others (1981)

collected data for big sagebrush, bitterbrush, snowbrush ceanothus, and greenleaf

manzanita in eastern Oregon.

Developing Equations Equations were fit to estimate total aboveground biomass (BAT) and to estimate

biomass of fuels in the following size classes when data were available:

Diameter range Time lag

Plant component code

in fuel equation library

Inches Hours

0 - I/4

(including foliage) 1 FS1

l/4 - 1 10 FS2

1-3 100 FS3

3-8 1,000 FS4

Often several unique equations were fit to predict the same component from different

predictor variables, thereby recognizing that different users may have different sets of

plant measurements available as predictors. Most equations predict fuels or BAT for

an individual plant from measurements such as diameter of the stem at the base,

crown width, crown length, and crown height. Some equations use plant cover (in percent) on a plot as a predictor variable and predict fuels or BAT for a l-square-meter

plot. BIOPAK adjusts the output values to the plot size given by the user. Data sets

for most species had no 100-hour or l,000-hour fuels. Rarely, the available data had

been separated into diameter classes slightly different from those above, but we

judged these differences to be insignificant.

Equations to predict fuel size classes for individual plants were developed from the

unpublished data, first by graphic examination of relations among variables and then

by using standard regression techniques (Draper and Smith 1981, Neter and others

1983). We avoided poorly fitting model forms by examining residuals for bias. We

transformed the dependent variable when needed to avoid nonuniform variance of

the residuals.

2

 Scientific names for all species are given in appendix A
Automating Calculation We modified BIOPAK (Means and others 1994) to allow automatic calculation of the

of Sums of Fuel Size following sums:

Classes

Plant component code Fuel size classes summed automatically by

in fuel equation library BIOPAK to calculate this plant component

FS1 for tree, shrub, and

coppice life forms

BBS + BFT

FSl for herb, grass, and

Carex life forms

BST + BFT + BIT

F02 FS1 + FS2

F03 FS1 + FS2 + FS3

F04 FS1 + FS2 + FS3 + FS4

In BIOPAK, BBS = small branch biomass, BFT = total foliage biomass, BST = total

stem biomass, BIT = inflorescence biomass. Use of automatic summation is described briefly below and in the BIOPAK users guide (Means and others 1994).

Construction of a

Library of Fuel

Size-Class Equations

We extracted all the equations for BAT for shrubs, and for BAT, total foliage, and

inflorescence biomass of herbs, grasses, and sedges from the BIOPAK (Means and

others 1994) equation library. For the herb, grass, and sedge equations, we provided

for the BAT equations to estimate l-hour fuels by changing the code for the estimated plant component from BAT to FS1 they retain their original equation numbers.

These equations came from the literature and unpublished sources, and some were

fit from unpublished data.

Then we combined these with the equations for fuel size classes and BAT fit in this --

study, and placed them in an equation library for use by BIOPAK. This fuel equation

library, FUELLIB (version FUELLIB3 at the time of this writing), contains documentation for each equation: equation form and coefficients as in BIOPAK and as in the

original source, location of raw data (if known), source of the equation (for example,

literature citation or person[s] responsible), and sources of the data (for example,

geographic location, plant association). This library, as all BIOPAK equation libraries,

can be viewed and edited with the library editor in BIOPAK.

3
At the time of this writing, FUELLIB contains 272 equations in the following

categories:

Life form(s) Plant component

Number of Number of

species equations

Grass and Carex FS1 8 19

Herb BFT 14 20

BST 3 3

FS1 47 96

Shrub BFT 25 25

FS1 25 52

FS2 22 44

FS3 8 15

The final library is projected to contain equations for the species in appendix A.

Results: Techniques

for Calculating Fuel

We provide methods for calculating live fuel size classes for shrubs and herbs for

which equations are available. Construction of modified or new fuel models will reBiomass quire additional information, including dead fuels and fuelbed depth (Burgan and

Rothermel 1984). The live fuel estimates that BIOPAK can provide must be integrated

with this additional information following a technique such as that of Burgan and

Rothermel (1984; also Burgan 1987).

We provide three methods for estimating live fuels:

Method 1. Calculate fuels directly for species with fuel equations in FUELLIB and for

species having similar growth forms, if the plant measurements required by the

equations are available.

Method 2. Alternatively, first calculate BAT by using equations for a wide array of species and geographic areas and measurements on individual plants, then calculate

fuels from BAT.

Installing the Fuel

Equation Library,

FUELLIB3

Method 3. For field plots, take plot cover by species and individual plant measurements, calculate fuels from the measurements, and build equations to predict fuels

from estimates of-plot cover by species. This technique could allow estimation of fuels

for the large number of existing inventory and ecology plots.

The following descriptions assume the user has knowledge and experience with

BIOPAK and its users guide (Means and others 1994). Tutorials in the users guide

can provide that background. In the following material, italic text indicates sections in

the BIOPAK users guide.

Copy FUELS.EXE into the BIOPAK directory. Type “FUELS” at the DOS prompt to

unpack the fuel library. This will allow you to run the example and use the library for

your own work.

4
Method 1, Direct

Calculation of Fuel

Biomass From Plant

Measurements

Requirements: The same plant measurements must be taken as the original

investigator took. In FUELLIB, this usually means basal stem diameter and may

additionally mean stem length, crown width, crown length, or crown height.

Advantage: Only one run through BIOPAK is required. Predictions are most likely to

be reliable for those species and areas for which equations were developed.

Disadvantage: Particular plant measurements are required. Relative to method 2,

predictions are more likely to be poor if equations are used for different species and

outside the general areas from which plants were sampled to build the equations.

PlanningStep 1: Determine the vegetation types, and their important species, for which you

need modified or new fuel models. Usually these will be indicated by inaccurate fire

behavior predictions when those fuel models are used in BEHAVE.

Step 2: Map the ranges of these vegetation types where they are important to-you.

Step 3: Ideally, before field sampling, examine and follow the Suggestions for Using

BlOPAK (Means and others 1994).

Step 4: Field sampling should allow for making the desired estimates with known

confidence by following a valid statistical design, using randomized, systematic, and

stratified designs as appropriate (see, for example, Cochran 1977, Thompson 1992).

Suggestions for doing this are outlined here. Other valid statistical designs also are

possible.

Step 4a: Identify locales (four are probably sufficient) for sampling fuels

throughout the range of each type.

Step 4b: On a map or aerial photo of each locale, identify locations for plots that

span the range of variability in the type at that locale. Consider transects of plots

that cross environmental gradients, such as from top to bottom of a hillslope or

ridge to ridge across a watershed (fig. 1). The total number of plots for one fuel

type should be at least 20, when the type is fairly uniform, and more than 40 plots

would give very good coverage. Because the process of building a fuel model

involves adjusting measured fuel characteristics until simulated fire behavior

matches observed, many plots probably are not worthwhile.

Step 4c: Set the plot size so that every plot will include at least three of the large

plants dominating the fuel type (fig. 2). Some plots may contain many such plants.

This can be done on the first field day. If plot size is not corrected for slope, then

slopes over 10 percent must be recorded for each plot.

Step 4d: For the species important in these types, determine the types of plant

measurements you must take. First go into the Library Editor, bring in FUELLIB,

find the equations you want to use for each species, and then note the

parameters needed to drive these equations. Many shrubs take diameter at the

base of the stem, and others take crown average diameter and height.

5
Figure l-Transects of plots designed to sample the range of variability in fuels on a south slope and

across the headwaters of a watershed.

Figure 2-Example plot in vegetation dominated by shrubs.

6
Field and office workItems in italics refer to sections in Means et al. (1994).

Step 5: Collect data by following your plan. A field sheet such as that in appendix C

can be used. Measure every plant in a plot or include in a cover estimate. You will

also need to collect other data needed to build a complete fuel model as described

by Burgan and Rothermel (1984; Burgan 1987). Such data include, for example, average shrub height, and dead and down fuels (methods are described by Hilbruner and

Wordell 1992).

Step 6: Format the lnput Data File in the manner required for BIOPAK (Means and

others 1994). Data must be in fixed columns with one observation per record.

Step 7: Design a Run. Specify the input data format, request that BIOPAK calculate

FS1, FS2, and FS3, and make any Equation Reassignments needed (for example,

to obtain calculations for species not in FUELLIB).

Step 8: Calculate Plant Components. Title the run and specify the input data file, run

design file, fuels equation library, and intermediate binary file. Request a Summarized

Equation Use Report and examine it to see that BIOPAK used the equations you specified in Equation Reassignments and you are satisfied with the equations BIOPAK

chose.

Step 9: Generate Reports for viewing or for use by other programs. Look at the

individual Plant Report(s) and Plot Summary Report(s) carefully to ascertain that

estimates are reasonable.

Step 10: Use the fuel estimates and other data you collect to make a new fuel model

following procedures such as those in Burgan and Rothermel (1984; Burgan 1987).

An example of method 1 is given in appendix B.

Method 2, Two-Phase

Calculation of Fuel

Biomass

The user can first calculate BAT and then calculate fuels, using equations based on

BAT. BAT is calculated using equations most suited for the species and locale. Fuels

are calculated from BAT by using the fuel size-class equations that have more limited

coverage of species and locales. This requires two runs through BIOPAK.

Requirements: Particular plant measurements for calculating BAT must be taken in

the same way as those used to build the equations.

Advantages: (1) Users have greater choice of biomass equations and parameters to

be measured in the field because there are BAT equations for many more species

and locations than there are fuel equations. (2) This technique will likely provide

better fuels estimates for species and locales in which plants have growth forms that

are much different from those used to develop the fuels size-class equations.

Disadvantage: Requires two runs through BIOPAK.

7
First phase: Calculate total aboveground biomass (BAT)-

Step 1: Ideally, before field sampling, examine and follow the Suggestions for Using

BlOPAK (Means and others 1994).

Step 2: Determine the parameters (plant measurements) needed in the input data by

looking at the BAT equations you plan to use in FUELLIB, using the Library Editor.

You can choose to use equations for one or more plant components other than BAT

if you think they include a high enough proportion of BAT, to be an adequate

estimate. Substituting one or more equations for another is described in Equation

Reassignments. Plant components that are possible to substitute for BAT are:

. total foliage (BFT) for some herbs

. wood + bark, live and dead (BAE), when foliage is a small fraction of total biomass

. live wood + bark + foliage (BAL)

. live wood + bark (BAP), when foliage is a small fraction of total biomass

If a suitable equation for BAT is not available, BIOPAK will automatically attempt to

sum components as mentioned above. If a species does not have an equation(s) in

FUELLIB that you think is adequate, equation(s) for another species that you believe

has similar form could be used.

Step 3: Field sampling should follow a valid statistical design that allows for making

the desired estimates with known confidence (see, for example, Cochran 1977,

Thompson 1992).

Step 4: Format the Input Data File in the manner required for BIOPAK. Data must be

in fixed columns with one observation per record.

Step 5: Use Design a Run to specify the input data format, request that BIOPAK

calculate BAT, and make any Equation Reassignments needed. Equation

reassignments may be necessary to obtain estimates for species not in FUELLIB,

and to estimate BAT by using equations for other plant components when

appropriate (see step 2). In this step, you create your Run Design File.

Step 6: Under Calculate Plant Components, give a title to the run and specify your

input data file, run design file, fuel equation library, and intermediate binary file.

Step 7: Use Generate Reports to create a machine-readable individual plant report.

Its format is given at the top of the report.

Second phase: Calculate biomass in fuel size classesStep 8: Use the machine-readable individual plant report as input to a second run

through BIOPAK to calculate fuel biomass; follow method 1.

Method 3, Build This method involves estimating cover and fuels biomass by species on plots and

Equations Based on building regression models for fuels from this data. You can then use these models

Cover by Using New to estimate biomass in fuel size classes. Care must be taken in statistical design of

Field Data the field sampling (see, for example, Cochran 1977, Thompson 1992) and regression

analysis (see, for example, Neter and others 1983).

Requirements: Field work to estimate total plant cover by species and to estimate

fuel size class from individual plant measurements, for 20 to 40 plots spanning the

range of conditions for which fuel estimates are needed.
Advantages: (1) Users can measure plants in ways required by a wider range of

equations than in methods 1 and 2. (2) New equations can be made to estimate

fuels on the large number of ecology and inventory plots that have plant cover data.

Disadvantages: Requires new field work and the fitting of a potentially large number

of regression models and entering them into an Equation Library.

First phase: Field samplingStep 1: Define the target population of stands for which you want to estimate fuels.

Step 2: Define an unbiased (for example, stratified random) sample of these stands

such that all important shrub, herb, and grass species will be represented on at least

20 of the stands sampled.

Step 3: Establish a plot in each stand large enough so that random inclusion or exclusion of large plants (especially stems of large shrubs) will not cause great fluctuations in biomass estimated from measurements on individual plants. You may want to

use separate, smaller plots for smaller plants. Preexisting plots can be used if of the

appropriate size. In general, larger plots will give less “noise” in regressions but take

more field time.

Step 4: For each plot, record whole-plot values for projected canopy cover (in percent) or other measure(s), such as height, by species. Take the same measure(s)

available in your target population.

Step 5: For each plant in each plot, take the measurements needed by the fuel sizeclass equations in FUELLIB, as in method 1, or BAT equations, as in method 2, that

you plan to use. You may plan to use Equation Reassignments to estimate fuels for

some species not having their own equations.

Second phase: New regressions and an equation libraryStep 6: Use method 1 to estimate fuel biomass by size class for each species and

plot. Save the machine-readable individual plot report for use in fitting regressions.

Step 7: For each fuel size class in each species, fit a regression to predict fuel

biomass from plot cover, plant height or other predictor variables you may have

measured. Take appropriate care to avoid poorly fitting models by examining

residuals for bias and nonuniform variance and taking corrective measures (see, for

example, Draper and Smith 1981, Neter and others 1983).

Step 8: Enter these new equations into an equation library following instructions in

Library Editor in BIOPAK users guide (Means and others 1994). You can add them

to FUELLIB or build a new library. If you add them to an existing equation library, be

sure to avoid existing equation numbers and equation keys.

Third phase: Calculate biomass in fuel size classesStep 9: Biomass in fuel size classes can now be calculated by following method 1

or method 2, using data available for the target population as input, and the fuel

equation library you just built.
Common and Plant scientific nomenclature follows Little (1979) for trees and Hitchcock and

Scientific Names Cronquist (1973) for shrubs.

big sagebrush Artemisia fridentata Nutt.

 bitter-brush Purshia tridentata (Pursh) DC.

California black oak Quercus kelloggii Newb.

greenleaf manzanita Arctosaphylos patula Green

snowbrush ceanothus Ceanothus veluntius Dougl. var. velutinus

tanoak Lithocarpus densiflorus (Hook. & Am.) Rehd.

Acknowledgments

Literature Cited

This publication was developed in cooperation with the Aviation and Fire Management Office, Pacific Northwest Region, USDA Forest Service, with additional support

from the Deschutes and Malheur National Forests. The authors thank Howard C.

Doane and Khalidullah Khan for entering and checking equations in the libraries.

Anderson, Hal E. 1982. Aids to determining fuel models for estimating fire behavior.

Res. Pap. INT-122. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station. 22 p.

Andrews, Patricia L. 1986. BEHAVE: fire behavior prediction and fuel modeling

system-BURN subsystem Part 1. Res. Pap. INT-194. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Forest and Range

Experiment Station. 130 p.

Blonski, Kenneth S.; Schramel, John L. 1981. Photo series for quantifying natural

forest residues: southern Cascades, northern Sierra Nevada. Gen. Tech. Rep.

PSW-56. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific

Southwest Forest and Range Experiment Station. 145 p.

Brown, James K. 1976. Estimating shrub biomass from basal stem diameters.

Canadian Journal of Forest Research. 6(2):153-l153-158.

Brown, James K. 1978. Weight and density of crowns of Rocky Mountain conifers.

Res. Pap. INT-197. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station. 56 p.

Brown, James K.; Snell, J.A. Kendall; Bunnell, David L. 1977. Handbook for

predicting slash weight of western conifers. Gen. Tech. Rep. INT-37. Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range

Experiment Station. 35 p

Burgan, Robert E. 1987. Concepts and interpreted examples in advanced fuel

modeling. Gen. Tech. Rep. INT-238. Ogden, UT: U.S. Department of Agriculture,

Forest Service, Intermountain Forest And Range Experiment Station. 40 p.

Burgan, Robert E.; Rothermel, Richard C. 1984. BEHAVE: fire behavior prediction

and fuel modeling system-FUEL subsystem. Gen. Tech. Rep. INT-167. Ogden,

UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and

Range Experiment Station. 126 p.

Cochran, William G. 1977. Sampling techniques, 3d edition. New York, NY: John

Wiley & Sons. 428 p.

10
Cooper, Stephen V.; Neiman, Kenneth E.; Roberts, David W. 1991. Forest habitat

types of northern Idaho: a second approximation. Gen. Tech. Rep. INT-236.

Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 143 p,

Draper, N.R.; Smith, H. 1981. Applied regression analysis, 2d edition. New York,

NY: John Wiley & Sons. 709 p.

Halverson, Nancy M.; Topik, Christopher; Van Vickle, Robert. 1986. Plant

association and management guide for the western hemlock zone, Mt. Hood

National Forest. R6-Ecol-232A-1986. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Region. 111 p.

Hemstrom, Miles A.; Logan, Sheila E.; Pavlat, Warren. 1987. Plant association

and management guide. RG-Ecol257-B-86. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Region. 312 p.

Hilbruner, Michael W.; Wordell, Thomas A. 1992. User’s guide to fuel appraisal.

Portland, OR: U. S. Department of Agriculture, Forest Service, Pacific Northwest

Region. 119 p. [+ appendices].

Hitchcock, C. Leo; Cronquist, Arthur. 1973. Flora of the Pacific Northwest. Seattle,

WA: University of Washington Press. 730 p.

Kauffman, J.B.; Martin, R.E.. 1990. Sprouting shrub response to different seasons

and fuel consumption levels of prescribed fire in Sierra Nevada mixed conifer

ecosystems. Forest Science. 36(3): 748-764.

Little, Elbert L., Jr. 1979. Checklist of United States trees. Agric. Handb. 541.

Washington, DC: U.S. Department of Agriculture, Forest Service. 375 p.

Maxwell, Wayne G.; Ward, Franklin R. 1976a. Photo series for quantifying forest

residues in the: coastal Douglas-fir-hemlock type, coastal Douglas-fir-hardwood

type. Gen. Tech. Rep. PNW-51. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Forest and Range Experiment Station. 103 p.

Maxwell, Wayne G.; Ward, Franklin R. 1976b. Photo series for quantifying forest

residues in the: ponderosa pine type, ponderosa pine and associated species

type. Gen. Tech. Rep. PNW-52. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Forest and Range Experiment Station. 73 p.

Maxwell, Wayne G.; Ward, Franklin R. 1980. Photo series for quantifying natural

forest residues in common vegetation types of the Pacific Northwest. Gen. Tech.

Rep. PNW-105. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Forest and Range Experiment Station. 230 p.

Martin, R.E.; Frewing, D.W.; McClanahan, J.L. 1981. Average biomass of four

Northwest shrubs by fuel size class and crown cover. Res. Note PNW-374,

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Forest and Range Experiment Station. 6 p.

Means, Joseph E.; Hansen, Heather A.; Koerper, Greg J. [and others]. 1994.

Software for computing plant biomass-BIOPAK users guide. Gen. Tech. Rep.

PNW-GTR340. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 184 p.

11
Neter, John; Wasserman, William; Kutner, Michael H. 1983. Applied linear

regression models. Homewood, IL: Richard D. Irwin, Inc. 547 p.

Ottmar, Roger D.; Hardy, Cohn C. 1989. Stereo photo series for quantifying forest

residues in coastal Oregon forests: second-growth Douglas-fir-western hemlock

type, western hemlock-Sitka spruce type, and red alder type. Gen. Tech. Rep.

PNW-231. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Forest and Range Experiment Station.

Ottmar, Roger D.; Hardy, Colin C. 1990. Stereo photo series for quantifying forest

residues in the Douglas-fir-hemlock type of the Willamette National Forest. Gen.

Tech. Rep. PNW-258. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Forest and Range Experiment Station.

Rittenhouse, L.R.; Sneva, F.A. 1977. A technique for estimating big sagebrush

production. Journal of Range Management. 30: 68-70.

Sapsis, David B.; Kauffman, J. Boone. 1991. Fuel consumption and fire behavior

associated with prescribed fires in sagebrush ecosystems. Northwest Science.

65(4):173-179.

Snell, J.A. Kendall; Max, Timothy A. 1985. Estimating the weight of crown

segments for old-growth Douglas-fir and western hemlock. Res. Pap. PNW-329.

Portland, OR: U.S. Department of Agriculture, Pacific Northwest Forest and Range

Experiment Station. 22 p.

Steele, Robert; Cooper, Stephen V.; Ondov, David M. [and others]. 1983. Forest

habitat types of eastern Idaho-western Wyoming. Gen. Tech. Rep. INT-144.

Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest

and Range Experiment Station. 122 p.

Thompson, S.K. 1992. Sampling. New York: John Wiley and Sons, Inc. 343 p.

Uresk, D.W.; Gilbert, R.O.; Rickard, W.H. 1977. Sampling big sagebrush for

phytomass. Journal of Range Management. 30: 31I-314.

Van Wagner, C.E. 1968. The line intersect method in forest fuel sampling. Forest

Science. 14(l): 20-26.

12
Appendix

A-Species in the

Fuel Equation

Library, FUELLIB3

Code Common name

Shrubs (29 species):

ACGL

ALSI

AMAL

ARTR

BERE

CEVE

COST

HODI

HSHRU

Scientific name

Rocky Mountain maple

Tag alder

Service berry

Big sage brush

Creeping Oregongrape

Snowbrush ceanothus

Red-osier dogwood

Ocean-spray

High shrubs, combined data

Acer glabrum

Alnus sifchensis

Amelanchier alnifolia

Arfemisia fridentata

Berberis repens

Ceanothus velutinus

Cornus sfolonifera

Holodiscus disco/or

ACGL, ALSI, AMAL, COST,

PRVI, SALIX, SOSC

Juniperus communis

Lonicera utahensis

Menziesia ferruginea

Philadelphus lewisii

Physocarpus malvaceus

Prunus virginiana

Ribes spp.

Rosa spp.

Rubus idaeus

Rubus parviflorus

Salix spp.

Shepherdia canadensis

Sorbus scopulina

Spirea betulifolia

Symphoricarpos albus

Vaccinium globulare

Vaccinium scoparium

JUCO

LOUT

MEFE

PHLE2

PHMA

PRVI

RIBES

ROSA

RUID

RUPA

SALIX

SHCA

sosc

SPBE

SYAL

VAGL

VASC

Grasses (8 species):

LUCA2

LUZUL

AGROS

Common juniper

Utah honeysuckle

Menziesia

Mockorange

Mallow ninebark

Chokecherry

Gooseberry

Rose

Red raspberry

Thimbleberry

Willow

Soapberry

Cascade mountain-ash

White spirea

Common snowberry

Globe huckleberry

Whortleberry

Field woodrush

Woodrush

Bentgrass

Luzula campestris

Luzula spp.

Agrostis spp.

13
Code Common name Scientific name

CARU

FEAR

FESTU

GRAMI

MUM0

POFE

SILO2

Herbs (63 species):

ADBI

ADPE

ANMA

ARCA

ATFI

BLSP

CASC2

CIRSI

CIVU

CLUN

COAS

COCA

COCA2

COHE

COLA

CRCA

DRAU2

EPAN

EPPA

EPWA

EQUIS

GATR

GNMI

GOOB

Pinegrass Calamagrostis rubescens

Arizona fescue Festuca arizonica

Fescue Festuca spp.

Grasses Graminae spp.

Mountain muhly Muhlenbergia montana

Muttongrass Poa fendleriana

Squirreltail Sitanion longifolium

Trail-plant

Maidenhair fern

Pearly-everlasting

California aralia

Lady-fern

Deer-fern

Scouler’s bellflower

Thistle

Bull thistle

Queen’s cup

Goldthread

Bunchberry dogwood

Horseweed

Varied-leaf collomia

Cutleaf goldthread

Smooth hawksbeard

Spreading wood-fern

Fireweed

Autumn willow-herb

Watson’s willow-herb

Horsetail

Sweetscented bedstraw

Cudweed

Rattlesnake-plantain

Adenocaulon bicolor

Adantium pedatum

Anaphalis margaritacea

Aralia californica

Athyrium felix-femina

Blechnum spicant

Campanula scouleri

Cirsium spp.

Cirsium vulgare

Clintonia uniflora

Coptis asplenifolia

Cornus canadensis

Conyza canadensis

Collomia heterophylla

Coptis laciniata

Crepis capillaris

Dryopteris austriaca

Epilobium angustifolium

Epilobium paniculatum

Epilobium watsonii

Equisetum spp.

Galium triflorum

Gnaphalium microcephalum

Goodyera oblongifolium

14
Code Common name Scientific name

GYDR

HIAL

LAP0

LIB02

LOCR

LOOB

LULA

MAD12

MADIA

MIGU

MOUN

OXOR

PEFR2

PERA

POMU

PTAQ

PYSE

RUPE

SEJA

SESY

SETR

SMST

STC04

THOC

TITR

TRLA

TRLA2

VISE

WHMO

XETE

Oak-fern

White-flowered hawkweed

Leafy peavine

Twinflower

Big deervetch

Lotus

Broadleaf lupine

May-lily

Tar-weed

Yellow monkey-flower

Woodnymph

Oregon oxalis

Sweet coltsfoot

Leafy lousewort

Sword-fern

Bracken

Sidebells

Five-leaved bramble

Tansey ragwort

Wood groundsel

Arrowleaf groundsel

Starry Solomon’s seal

Cooley’s hedge-nettle

Western meadowrue

Trefoil foamflower

Twin clover

Western starflower

Evergreen violet

Whipplevine

Beargrass

Gymnocarpium dryopteris

Hieracium albiflorum

Lathyrus polyphyllus

Linnaea borealis

Lotus crassifolius

Lotus oblongifolius

Lupinus latifolius

Maianthemum dilatatum

Madia spp.

Mimulus guttatus

Moneses uniflora

Oxalis oregana

Petasites frigidus

Pedicularis racemosa

Polysticum munitum

Pteridium aquilinum

Pyrola secunda

Rubus pedatus

Senecio jacobaea

Senecio sylvaticus

Senecio triangularis

Smilacina stellata

Stachys cooleyae

Thalictrum occidentale

Tiarella trifoliata var. trifoliata

Trifolium latifolium

Trientalis latifolia

Viola sempervirens

Whipplea modesta

Xerophyllum tenax

15
Appendix BExample of

Method 1

Run this example through BIOPAK to help understand method 1 by using the files

provided on the BIOPAK fuel disk. This example assumes access to the BIOPAK

users guide and software (Means and others 1994) and familiarity with its operations

such as that obtained by running the tutorials in the users guide. Italic text indicates

sections in the BIOPAK users guide.

Steps 1, 2 and 3: Jeff and Grace, ecologists along the west side of the Cascade

Range in Oregon, determined that fire behavior in plant communities with dense

growth of tall shrubs in the recent Sunny Beach Fire was not predicted well by

BEHAVE (Andrews 1986). With lots of help from fire behavior analysts, they concluded that the fuel model used did not adequately include the live fuels contributed

by tall shrubs. (This is an important judgment and you may disagree.) They decided

to build a new fuel model that would include better estimates of live shrub fuels. They

knew this vegetation type occurred throughout their district on low elevation sites.

Their next step was to read Suggestions for Using BIOPAK in the BIOPAK users

guide.

Steps 4a-4c: Based on their extensive experience with this type, they identified five

locales for sampling where this type was common and that spanned their Calapooya

National Forest. They knew the high cover of tall shrubs in this type did not differ

greatly and decided to collect data for six plots at each locale and from a total of 30

plots. They knew plants were densely packed and chose 16-square-meter plots

because they would always contain at least three tall shrubs.

Step 4d: They determined the shrub species important as live fuel and noted that fuel

equations are available for very few of these species. They decided, for many of their

species, to use equations for Rocky Mountain species they judged to be of similar

form and thus assumed would have similar relations between measured plant dimensions and biomass of fuel classes. They then started BIOPAK, selected the library

editor, brought in FUELLIB3, examined the Original and Final Equations Window and

determined (most of) the plant measurements required by the fuel equations for these

species (they missed on one species).

Step 5: Jeff and Grace sampled five stands, each having six plots of 16-squaremeters, collecting data needed by BIOPAK and other data needed to build a complete

fuel model for this type as described by Burgan and Rothermel (1984; Burgan 1987).

This took 1 week.

Step 6: Jeff entered the data in a spreadsheet and produced an ASCII file as described in Input Data File with data in fixed-width fields. As they tried to make estimates they noted the crown length and width measurements they had taken for

COCO1

 were not used by any equations for species (such as ALSI or SALIX) with

form similar to COCO. So they went back to their plots and measured diameter at

the base of all COCO. They wished they had determined the equations they wanted

to use (step 4d) more carefully before field sampling. Part of the data from their final

input data file (JEFFGRAC.DAT), for two plots, is shown:

1

 Codes for plant species are defined in appendix A.

16
Plot

Micro

plot

Species

code

Diameter at

the base of

the stem

Length of the

crown

(horizontal)

Width of the

crown

----- Centimeters - - - - - - -

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

1

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

HODI 1.1

HODI 2.7

coca 5.0 240 230

coca 1.4 70 9 0

VAPA 1.4

VAPA 0.6

coca 6.1 200 220

HODI 2.4

HODI 1.1

HODI 2.7

RHMA 3.7

COCO 1.6 7 0 60

COCO 2.8 80 9 9

VAPA 1.9

VAPA 1.6

COCO 1.9 60 50

COCO 2.3 9 0 80

VAPA 1.1

VAPA 1.8

HODI 2.8

HODI 2.2

HODI 2.5

RHMA 3.9

COCO 4.9 3 0 30

Step 7: Grace used the Design a Run section of BIOPAK to specify the

format of their data, request calculations for l-hour, 10-hour and 100-hour

fuels (FSI, FS2, and FS3, respectively, in BIOPAK), and request use of

equations for other species by Equation Reassignments. Grace produced a

Run Design Report (JEFFGRACRDR) by selecting this on the File Menu in

Design a Run. You can see the results of her work by examining JEFFGRACRD

in the Design a Run module of BIOPAK or the ASCII file JEFFGRAC.RDR.

Step 8: Grace then used the Calculate Plant Components module to link their input

data file (JEFFGRAC.DAT), run design file (JEFFGRAC.RD), and equation list file

from the fuel equation library (FUELLlB3.EQN) to calculate the plant components and

put them in an intermediate binary file (JEFFGRAC.IBF) with estimates of fuel

biomass.

17
Step 9: Grace used the Generate Reports module of BIOPAK to produce a report

formatted for people. The results for one stand are in JEFFGRAC.RPT. The results

for the first plot are:

TITLE: Shrub fuels, W. Cascade Tall Shrub fuel type Page 2

PLOT STAND LOCALE FIRST PLANT ID DATE Slope Area Conversion Factor

_____-_ ------- ____-__-_ _-_--_ - -- - _ _ _ - - _ - - - _-_______-_--_--__--_

1 5 0.0% 625.00

Fixed Plot Area Size = 16.00 (m2)

Prism Basal Area Factor = 0.00 (m2/ha)

________________--__-- INDIVIDUAL PLANT REPORT _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Case PLANT ID L Specie - - - - - - - - - Parameter(s) - - - - - - - G S FUELS FUELS FUELS

F A S 100 HOUR 10 HOUR 1HOUR

(g) (g) (g)

1 S HODI 1.1DBA 0.0 0.0 W M O.OON

2 S HODI 2.7DBA 0.0 0.0 W M O.OON

3 s COCO 5.0DBA 240.0len 230.0wid W M 132.141

4 s COCO 1.4DBA 70.0len 90.0wid W M O.OOhi

5 S VAPA 1.4DBA 0.0 0.0 w M O.OOGL

6 S VAPA O.GOe+ODBA 0.0 0.0 W M O.OOGL

7 s COCO 6.1 DBA 200.0len 220.0wid W M 190.971

8 S HODI 2.4DBA 0.0 0.0 w M O.OON

9 S HODI 1.1DBA 0.0 0.0 W M O.OON

Error messages:

G NO EST: Reass match. Spp/LF/Comp not satisfied in EQN file.

L NO EST: Reass match. Maximum ESP (Equation Selection Penalty) exceeded.

N NO EST: No match for Request to Lib in .EQN since Spp/LF/Comp not found at any ESP

V WARNING: Over extrapolation occurred.

h WARNING: Under extrapolation occurred.

i INFORM: No LF in data, LF came from Reassign: Taxonomic LF of Substit section.

m INFORM: A plant component value <= 0.0 was calculated and set to zero.

26.99 19.60

619.03V 186.99V

359.32i 213.75Vi

54.34i 16.88i

51.77i 24.69i

O.OOim 3.21i

452.50i 317.77Vi

410.44v 139.10V

26.99 19.60

_ _ _ _ _ _ _ _ PLOT SUMMARY REPORT, PLOT =l ______________________________________

L Specie Number - - - - - - - - -Parameter(s) - - Number Tree FUELS FUELS FUELS

F Plants DBA LEN WID of BA 100 HOUR IO HOUR 1 HOUR

i n _______ average - - - - - - - - - - - - - plants (m2) (kg) (kg) (kg)

PLOT ___________ per hectare -- _______________-__-

s COCO 3 4.2 170.0 180.0 1875 0.0 201.95h 541.35i 342.75V

S HODI 4 1.8 0.0 0.0 2500 0.0 O.OON 677.15V 228.30V

S VAPA 2 1.0 0.0 0.0 1250 0.0 O.OOG 32.36i 17.44i

S All 9 2.4 170. 180.0 562 0.0 201.95 1250.86 588.49

Grand Tot 9 2.4 170.0 180.0 5625 0.0 201.95 1250.86 588.49

Error messages:

See individual plant report above

Step 10: Using methods described by Burgan (1987) Jeff combined their estimates

of live fuel loading with other data they collected to produce the new fuel model they

wanted for the tall shrub vegetation type.

18
Appendix C-Forms We provide a field form for plant measurements on the next two pages. Items in bold

italics are required in order for BIOPAK to give valid estimates. Most fields in the

heading are optional and are included for use as appropriate. Locale code and date

code are examples and can be used as identifiers of a record or group of records; in

particular, they do not have to stand for locale and date. Slope is needed for BIOPAK

to provide correct areal estimates. Only the first eight characters of codes (six for

species) will be read by BIOPAK.

The first plant measurement column is given the heading “Cover,” a common plotlevel plant measurement. It can be changed and used for any other measurement.

For many plants and species, one line of data is needed for each plant. When cover

is recorded for a species, however, usually only one line is recorded for each plot.

Be sure to specify the units for plot area, slope, elevation, and all plant measurements. Allowable plant measurements (that is, parameters) are given in BIOPAK

users guide (Means and others 1994), Reference: input Data File: Parameters. Units

can be either English or metric, provided they are from the list allowed by BIOPAK as

given in Appendix: Units: Parameter Units: Input Data File-.

19
Field Sheet for Plant Measurements for BIOPAK [ver. 10/30/95]

Locale: Big Summit Prairie, West | Date: 950815 | Page 1 of 2

Locale code: BigSumW Personnel: M. Lewis and A.C. Clarke

For/Dist: Ochoco, Big Summit PIot area: 200 sqft 1 Aspect: 90 Elev: 2000 ft

Veg type: bitterbrush-sage/fescue Veg code: PutrArtr Topo: sideslop

Notes: From jct of 25 and 63 roads, go 2.4 mi. E. to culvert. Walk 250 yards due north to

white PVC pipe at center of circular plot. Width is mean crown diameter. Ht is mean crown

height. DBA is diam at base of stem.

Identifiers Plant Measurements (Equation Parameters)

Slope Life- Cover H t Width CrnRat DBA

Stand Plot % , Species form % in in % in

This page is an example of a filled-out field sheet with plant measurements. Page 21 contains a blank

field sheet to copy for field use.

20
Field Sheet for Plant Measurements for BIOPAK [ver. 10/30/95]

Locale: | Date: | Page of

Locale code: Personnel:

For/Dist: Plot area: Aspect: Elev:

Veg type: | Veg code: Topo:

| Notes:

Identifiers P/ant Measurements (Equation Parameters)

Slope Life- Cover

Stand Plot %,deg Species form %

21

* U.S. GOVERNMENT PRINTING OFFICE: 1996 - 794-553 / 62002 REGION NO. 10
THIS PAGE LEFT BLANK INTENTIONALLY
Means, Joseph E.; Krankina, Olga N.; Jiang, Hao; Li, Hongyan. 1996. Estimating

live fuels for shrubs and herbs with BIOPAK. Gen. Tech. Rep. PNW-GTR-372.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 21 p.

This paper describes use of BIOPAK to calculate size classes of live fuels for shrubs

and herbs. A library of equations to estimate such fuels in the Pacific Northwest and

northern Rocky Mountains is presented and used in an example. These methods can

be used in other regions if the user first enters fuel size-class equations for a given

region into a new library by using the library editor supplied with BIOPAK. Fuel size

classes can be estimated in three ways: (1) When appropriate plant measurements

are available, fuel classes can be estimated directly for species that have equations in

the library or species with similar growth forms. (2) When appropriate plant measurements are not available, fuel classes can be estimated in two steps, first by estimating total aboveground biomass for individual plants and then by estimating biomass in fuel classes from total aboveground biomass. (3) The equations provided can

be used to develop new equations that estimate fuels from plot-level estimates of species cover (and possibly other measures).

Keywords: Live fuels, fuel size classes, software, plant biomass, Pacific Northwest,

northern Rocky Mountains.

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