United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-190

April 2007

The Photoload Sampling Technique:

Estimating Surface Fuel Loadings

From Downward-Looking 

Photographs of Synthetic Fuelbeds 

Robert E. Keane and Laura J. Dickinson
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Keane, Robert E.; Dickinson, Laura J. 2007. The photoload sampling technique: estimating 

surface fuel loadings from downward-looking photographs of synthetic fuelbeds. General 

Technical Report RMRS-GTR-190. Fort Collins, CO: U.S. Department of Agriculture, ForestService, 

Rocky Mountain Research Station. 44 p.

Abstract __________________________________________

Fire managers need better estimates of fuel loading so they can more accurately predict the potential fire behavior and effects of alternative fuel and ecosystem restoration treatments. This report 

presents a new fuel sampling method, called the photoload sampling technique, to quickly and accurately estimate loadings for six common surface fuel components (1 hr, 10 hr, 100 hr, and 1000 hr 

downed dead woody, shrub, and herbaceous fuels). This technique involves visually comparing fuel 

conditions in the field with photoload sequences to estimate fuel loadings. Photoload sequences are 

a series of downward-looking and close-up oblique photographs depicting a sequence of graduated 

fuel loadings of synthetic fuelbeds for each of the six fuel components. This report contains a set of 

photoload sequences that describe the range of fuel component loadings for common forest conditions 

in the northern Rocky Mountains of Montana, USA to estimate fuel loading in the field. A companion 

publication (RMRS-RP-61CD) details the methods used to create the photoload sequences and 

presents a comprehensive evaluation of the technique.

Keywords: Fuel load loading, fuel sampling, fuelbeds, photographs, wildland fire, and fuels

The Authors _____________________________________________

Robert E. Keane is a Research Ecologist with the USDA Forest Service, Rocky Mountain Research 

Station at the Missoula Fire Sciences Laboratory, Missoula, MT. Since 1985, Keane has developed 

various ecological computer models for the Fire Ecology and Fuels Research Project for research 

and management applications. His most recent research includes the synthesis of a First Order Fire 

Effects Model; construction of mechanistic ecosystem process models that integrate fire behavior and 

fire effects into succession simulation; restoration of whitebark pine in the Northern Rocky Mountains; 

spatial simulation of successional communities on the landscape using GIS and satellite imagery; and 

the mapping of fuels and fire regimes for fire behavior prediction and hazard analysis. He received 

his B.S. degree in forest engineering in 1978 from the University of Maine, Orono; his M.S. degree 

in forest ecology from the University of Montana, Missoula, in 1985; and his Ph.D. degree in forest 

ecology from the University of Idaho, Moscow, in 1994. 

Laura J. Dickinson has been a Biological Science Technician since 2002 with the USDA Forest 

Service Rocky Mountain Research Station at the Missoula Fire Sciences Laboratory, Missoula, MT. 

She has contributed to several projects within the Fire Ecology and Fuels Research Project including data collection and analysis for studies on whitebark pine, relict ponderosa pine mortality in the 

Bob Marshall Wilderness Area, fuels, and fuel sampling techniques. She received her B.S. degree in 

Aquatic Wildlife Biology in 2003 from the University of Montana, Missoula.
We thank Wayne Lyngholm, Myron Holland, Curtis 

Johnson, and Daniel Covington of the USDA Forest 

Service Rocky Mountain Research Station Missoula Fire 

Sciences Laboratory for all their help in the field and in the 

photography studio. We thank Ian Grob and Jim Kautz of 

the Missoula Technology Development Center, Montana, 

for the use of their photo studio and equipment, and their 

expertise in taking still photographs. We also thank the 

people who participated in the evaluation and review 

of the method: Eva Karau, Alisa Keyser, Kathy Gray, 

Elizabeth Reinhardt, Ed Matthews, Sharon Hood, Mick 

Harrington, Melanie Miller, Dennis Simmerman, Richard 

Hannah, Mitch Dougherty, Matt Reeves, Kori Buford, 

Colin Hardy, Russ Parsons, Helen Smith of the Rocky 

Contents___________________________________

Page

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

Photoload Sampling Background ...................................................2

General Description ..................................................................2

The Photoload Sequences ............................................................3

Photoload Sampling Protocol.........................................................4

Determining if Photoload Sampling is Right for You ........................4

Preparing for Photoload Sampling................................................5

Determining the Scale of Sampling for Your Assessment ..................5

 Using the microplot approach ................................................6

 Using the macroplot approach ................................................7

 Using the stand approach ......................................................8

Making the Photoload Fuel Loading Estimates ..............................8

 Adjusting visual estimates ......................................................9

 Calibrating your eye for estimating loadings ........................... 10

 Estimating fine woody fuel loading ....................................... 10

 Estimating log fuel (1000 hr) loading .................................... 11

 Estimating shrub and herbaceous loading .............................. 12

 Estimating litter and duff loadings.......................................... 13

References ........................................................................... 13

Appendix A—Photoload Sequences for Northern Rocky 

Mountain Fuelbeds ............................................................... 15

Appendix B—Tables of Log and Branch Loading by Diameter

 and Length ..................................................................... 31

Appendix C—Photoload Plot Form and Cheat Sheet ..................... 41

Mountain Research Station Missoula Fire Sciences 

Laboratory; Laura Ward, Steve Slaughter, Matt Galyardt, 

Brandon Sheehan, and Vick Applegate of the Lolo National Forest; Vicki Edwards of the Clearwater National 

Forest; and John Caratti and Duncan Lutes, Systems for 

Environmental Management. We also thank those who 

provided excellent reviews of the technique: Jim Brown 

and Bill Fischer, retired USDA Forest Service scientists; 

Duncan Lutes, Systems for Environmental Management; 

Roger Ottmar and Clint Wright, USDA Forest Service, 

Pacific Northwest Research Station, FERA Group; and 

Curtis Johnson and Rudy King, USDA Forest Service 

Rocky Mountain Research Station.

Acknowledgments

The use of trade or firm names in this publication is for reader information and does 

not imply endorsement by the U.S. Department of Agriculture of any product or service

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 

Introduction

	 Comprehensive estimates of fuel loadings in forest

and rangeland ecosystems of the United States are critical to accurately predict the fire behavior and effects of

alternative fuel and ecosystem restoration treatments

to save lives, property, and ecosystems (Laverty and

Williams 2000; GAO 2003, 2004). Fuel loadings, along

with fuel moisture, are the most important factors that

firemanagers can controlfor planning and implementing

prescribed burn treatments. Sophisticated fire models

such as FOFEM (Reinhardt and Keane 1998; www.

frames.gov) and CONSUME (Ottmar and others 1993;

www.fs.fed.us/nw/fera/consume.html) require loading

estimates so that they can be used to plan, prioritize,

design, and implement important fuel treatments for

restoring historical fire regimes and reducing hazardous

fuels to save lives and property (Mutch 1994; Laverty

and Williams 2000).

	 Measuring surface fuel loadings in the field is difficult because it requires a complex integration of several

sampling methods designed for implementation at disparate scales. Downed dead woody fuels are typically

sampled using planar intersect techniques (van Wagner

1968; Brown 1970, 1971, 1974) as implemented into

many surface fuel inventory sampling systems such as

FIREMON (www.fire.org/firemon) (Lutes and others

2006). Planar intersect techniques were only designed

for estimating downed woody fuel loadings at the stand

level using linear transects that define sampling planes.

Dead and live shrub and herbaceous fuels must be either

measuredusingtime-consumingdestructivemethodsthat

involve clipping these fuels within small microplots or

using indirect techniques such as allometric regression

equationsfromcanopy cover and height estimates.Loadings of duff and litter are often estimated as the product

of duff depths and bulk densities measured at various

points along the fuel transects or from collecting and

The Photoload Sampling Technique:

Estimating Surface Fuel Loadings From Downward 

Looking Photographs of Synthetic Fuelbeds 

Robert E. Keane and Laura J. Dickinson

weighing a subsample (Brown and others 1982). Many

times, the scale and error of surface fuel measurements

are incompatible and inconsistent across the fuel components; log loading, for example, often varies at greater

spatial scales than fine fuel loading because of log size.

These methods are often time-consuming and require

extensive training and field expertise. What is needed is

an inexpensive, easy, and quick fuel sampling technique

that can provide consistent estimates of fuel loadings at

the level of accuracy required by the fire behavior and

effects models for fuel treatment planning. These fuel

loading estimates must be accurate enough to be used

as inputs to fire behavior and effects models, and they

must also accurately quantify the amount oflive and dead

carbon on the ground for managing carbon budgets.

	 This report presents a comprehensive fuel sampling

protocol for quickly and accurately estimating surface

fuel component loadings using a system called the

photoload sampling technique that involves making

visual estimates ofloading froma sequence of downward

looking photographs depicting graduated fuel loadings

by six fuel components. A detailed sampling protocol is

presented so that loadings can be estimated at various

levels of effort and scale. The photoload sequences in

this report were specifically developed to describe the

range of fuel loadings for common fuel components in

the northern Rocky Mountains of Montana, USA. Also

included is a plot form for use in the field.

	 A companion report by Keane and Dickinson (2007;

RMRS-RP-61CD) details the set of methods used to

construct the photoload sequences presented here so that

photographscanbetakenforlocalfueltypesorspecialized

conditions.The companion report also presents an evaluation of the photoload sampling technique by comparing

the photoload estimates made by many participants in a

field study with the fuel loadings actually measured on

1 m

2 and 100 m2 microplots.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

Photoload Sampling Background

General Description

	 The photoload sampling protocol is a fuel sampling

technique used to estimate the loading of surface fuels

for a number offiremanagement objectives but primarily

for the prediction of fire effects. This technique uses a

series of downward- orsideward-looking photographs of

synthetic fuelbeds of gradually increasing fuel loadings

as reference for visually estimating fuel loadings in the

field. You simply match the fuel loading conditions observed on the ground with one of the photoload pictures

in the set for that fuel component. You can also adjust

for the spatial distribution, diameter, degree of decay,

and depth of loading across the sample space. The photoload technique can be used to estimate fuel component

loadings at a microplot, macroplot, stand, or landscape

scale at variouslevels of effort depending on your needs,

objectives, and available resources (sampling time and

funds). This technique can only be used to estimate the

loading ofsurface fuels and does not provide estimates of

canopy characteristics. It also isn’t designed to estimate

loadings of duff and litter layers.

	 We designed this sampling technique to be used by

firemanagers,fuelspecialists, and researchersto quickly

estimate fuel loadings by fuel component. However,

it is just one of the many sampling tools available to

sample ormonitorfuel loadings,such as photo series(for

example, see Fischer 1981) or planar intersect methods

(see FIREMON, Lutes and others 2006). The photoload

technique is not intended to replace the previously developed protocols and methods. Rather, it is intended to be

a viable alternative when the objectives of the sampling

effort and the resources available to perform the sampling match the design characteristics of the photoload

technique.For example, a firemanagement agencymight

require the accurate estimation of fuel loads but their

field crews have limited experience in planar intersect

fuel sampling and there may be little funding available

for training, therefore the photoload technique may be a

viable option.

	 Photoload techniques are best used when:

	 •	 Fuel sampling experience is low —The photoload

technique can be quickly learned and understood.

It takes less than a day to become effective with the

photoload technique.

	 •	 Available sampling time islimited—The photoload

technique is a relatively quick and inexpensive

method that provides moderately precise and

reasonably accurate fuel loadings.

	 This protocol will eventually be included in the

FIREMON sampling system (Lutes and others 2006;

www.frames.nbii.gov/firemon) as a separate method.

FIREMON (a FIRE MONitoring and inventory system)

consists of a set ofsamplingmethods, databases, and plot

sheets for sampling fuels, fire behavior, vegetation, and

biophysical settings. By becoming a part of the FIREMON system, the photoload sampling protocol can be

nested within any number of other sampling methods to

obtain a fully integrated sampling scheme designed to

fit any application from documenting changes in fuel

loadings after treatment to assessing fuel consumption

using modeling. For example, the user can quantify fuel

loadings with the photoload technique and describe tree

density with the FIREMON Tree Data technique at the

same sample site and within the same sampling space (a

plot for example).

	 The typical fuelbed is composed of many fuel components with the types and definitions of these components

often dictated by the objective of the fuel sampling

project. Since most fuel sampling efforts are initiated to

quantify fuels for fire behavior and effects prediction,

we used the same componentsin the photoload sampling

protocol. Six fuel components are explicitly recognized

in the photoload technique:

	 •	 1 hour: <1 cm (0.25 inch) diameter downed, dead,

woody fuels

	 •	 10 hour: 1-2.5 cm (0.25-1.0 inch) diameter downed,

dead, woody fuels

	 •	 100 hour: 2.5-7 cm (1-3 inch) diameter downed,

dead, woody fuels

	 •	 1000 hour(logs): 7+ cm(3+ inch) diameter downed,

dead woody fuels

	 •	 Shrub: Dead and live shrubby fuels (< 5 cm or

2 inches diameter)

	 •	 Herb: Dead and live grass and forb fuels

	 The loading is visually estimated for each fuel component. We did not include duff and litter layer fuels in

this method because their loading is mostly dependent

on layer depth which is difficult to estimate from photographs. But, we describe how estimates of litter and

duffloadings can be made using the FIREMON methods

linked to this photoload sampling protocol.

	 This report consists of four parts that are integrated

together to form the photoload sampling technique. The

body ofthisreport presentsthe background and sampling

protocol used for the photoload sampling technique.Appendix A contains the set of photoload sequences for the

sixsurface fuel componentsintegratedintothe photoload

samplingtechnique.Thephotoloadsequences consistof a
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 

series of photographs offuelbeds with gradually increasing fuel loadings. They were developed for common

fuelbeds that occur in the Northern Rocky Mountains,

especially those around western Montana, USA. Photoload sequences for other locally important fuelbeds,

such as shrub and herbaceous species not included in

Appendix A, must be developed using methods detailed

in the companion report (Keane and Dickinson 2007;

RMRS-RP-61CD). Appendix B contains a set of tables

for computing large branch (100 hour) and log loadings

from estimated branch or log lengths as an alternative

or companion method to estimating large woody fuel

loads within the photoload sampling technique. Last,

Appendix Ccontainspossibleplotforms andcheat-sheets

that can be used for estimating and recording photoload-derived fuel loadings within a sampling area. One

plot form allows you to record the details of photoload

estimations while the second is used only to record the

final loadings. These appendices were designed to be

removed from this report using a razor or scissors and

then placed in a clipboard for reference in the field. We

recommend these pages be laminated so they last longer

and are protected against water damage.

The Photoload Sequences

As mentioned, the photoload sequences that we developed for northern Rocky Mountain forests are found

in Appendix A. Each fuel component was photographed

independentlysothat anaccurate estimate canbeobtained

without the confusion of including other components in

the photos. The photoload sequences are organized in a

series of pictures with each picture showing increasing

fuel loadings. All pictures for the fine woody fuels (1,

10, 100 hour fuels) were taken from directly overhead

looking downward at a 1m x 1m fuel bed (fig. 1a). Shrub

and herb fuel components are taken from overhead and

from the side at eye level (oblique) (fig. 1b and 1c).

Log fuel loadings (1,000 hour woody) are only taken

at eye level from the side (oblique) but on 100 m

2 plots

(fig. 1d).The loading forthe fuel in each picture isshown

Figure 1—Examples of pictures in a photoload 

sequence: a) 1 hour fuels (0.5 kg m–2), b) shrub fuels 

(ninebark, 0.15 kg m–2), c) herbaeous fuels (pinegrass, 

0.8 kg m–2), and d) logs (5.0 kg m–2).

a b

c

d
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

in both English and metric units at the top of the photo

(Appendix A). For fine woody fuels, there is a scale in

the bottom right corner to help calibrate the user’s eye

for size. We placed a familiar object next to the side of

the oblique picture in the shrub and herb photos to also

help calibrate your eye and to provide reference for the

average height of the plants in the photos.

	 The photoload sequences were designed using the

metric measuring units of kg m

–2 for many reasons.

First, these units more accurately describe the fuels at

the scale of development of the photoload sequences.

Second, these units are more appropriate for describing

the spatial distribution of the fuels components used in

photoloads, especiallyfinewoody,shrub, andherbaceous

fuels. Third, it is easier to visualize the weight and area

of these units than the conventional units of tons per

acre. You can simply multiply metric loading estimates

by 4.46 to convert kg m

–2 to tons acre–1 (conversion from

kg to lb is 0.454). And last, we felt that a square meter

represents the smallest practical scale of evaluation for

fine woody, shrub, and herbaceous fuels.

	 The format we chose for the photographs in Appendix

A represents a compromise between convenience and

sampling scale. The pictures are large enough to allow

sufficient resolution between two similar loadings for

most purposes, but small enough to obtain a comprehensive set of loadings for each plant on just one page.

Depending on the needs and accuracy of your own

study objectives, you may require additional resolution

between photos(more photos), orless pagesto bring into

the field (less photos). You can always create your own

photoload sequences using the extensive set of pictures

contained on the companion CD (Keane and Dickinson

2007; RMRS-RP-61CD).

Photoload Sampling Protocol

Determining if Photoload Sampling is 

Right for You

	 Many sampling methods can be used to estimate fuel

loadings and each has advantages and disadvantages.

Complex sampling strategies,such asfixed area plot and

planar intersect techniques, are accurate and somewhat

repeatable, but they can require extensive expertise,

time, and funding to implement depending on the fuel

component sampled and the objective of the sampling

effort(seeKeaneandDickinson2007;RMRS-RP-61CD).

These extensive procedures are used when sampling objectivesrequire high quality and accurate data. However,

sometimes fuel sampling must be done with minimal

funding, limited time, and lack of sampling expertise.

In these cases, visual estimates of fuel loadings may be

the only alternative.

	 There are threeocularmethodsfor estimatingfuelloadings. The photo series method uses oblique photographs

of stand conditions across a variety of habitat types and

cover types that occur within the management area (see

Fischer 1981 or Ottmar and others 2004 for examples).

Many fire managers are using these methods, but recent

research has shown resultant ocular estimates may be

inaccurate and inconsistent for some fuel components

because these same components are not visible in the

photoseriesphotographs(Lutes1999).Anothertechnique

is using a fuel model that is identified in the field using

various attributessuchasvegetationcomposition,fuelbed

characteristics, and expected fire behavior (Anderson

and others 1982; Sandberg and others 2001; Lutes and

others [in prep]). A fuel model has loadings assigned

to each fuel component for use in various mapping and

modeling activities. This may be the easiest method but

the small number of fuel models used to represent the

wide variety of fuel conditions may preclude an accurate

estimation of loading. Moreover, there are very few fuel

classificationsthatprovide comprehensivekeyingcriteria

for consistently identifying the fuel loading conditions.

Thephotoloadtechnique isthe thirdalternative forocular

estimation of fuel loads. It may be desirable because the

photos portray graduated fuelbed loadings, the fuels are

completely visible, and the estimates are made at the

appropriate spatial scale that best matches spatial fuel

distribution.

	 With this in mind, we recommend the following. If

there issufficient time, expertise, and fundsto obtain fuel

loading estimates, we suggest that the manager use the

planarintersectmethodoffuelsamplingtomeasure these

loadings(useprotocolsdetailedinFIREMON).The fixed

areatechniqueisalsousefulandsometimesmoreaccurate,

but it can require prohibitively long sampling times and

it may be difficult to rectify the sampling intensity with

the variability of fuels within in a heterogeneous area. It

ismore appropriate forresearch applications.Ifsampling

crews have not been trained for planar intercept, photo

series,orfixedareamethods,thenthephotoloadtechnique

is a viable alternative and may be better than the photo

seriesmethod.Ifsamplingcrewshave experience inphoto

series fuels estimation, then photo series may be more

desirable but not as accurate as photoload techniques.

If loadings for locally important fuel components are

not present in the photoload sequences or photo series

photographs, the fuel model method is perhaps the only

alternative but there needsto be a fuel model key for your
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

area.We also recommend that you use several methodsin

a nested strategy to ensure the most accurate estimation

of fuels. For example, planar intercept techniques can

be used every 10th plot to provide consistent calibration

for the photo series and photoload estimations or the

photoload technique can be used first, and estimates can

then be checked using photo series estimates.

Preparing for Photoload Sampling

	 We recommendthe followingequipmentbepurchased,

obtained, or fabricated for use in photoload sampling.

	 •	 Plot frame—A square meter plot frame with the

squaremeter areameasuredonthe insidedimensions

of the frame. You can construct this of wood, PVC

pipe, or metal rods.

	 •	 Go-no-go gauge—A device that has the widths of

each woody size class upper diameter range.

	 •	 Clear plastic ruler—Arulerthat canbe conveniently

stored in a cruiser’s vest. This ruler will be used to

estimate log diameters and branch lengths. It can

also be used to measure duff and litter depths if

needed.

	 •	 Cloth tape—The lengthofthe tapewouldbedictated

by the sampling design. This tape would be used to

locate sampling points.

	 •	 Clipboard—This clipboardshouldhave the abilityto

allow quick reference to the photoload sequences.

	 •	 Calculator—Thisishandyforsumminglogdiameters

and lengths.

	 •	 Nails –Long nails(>20 cm) are handy for anchoring

long microplot transect lines either permanently or

semi-permanently.

	 •	 Photoload procedures and sequences—We suggest

that the photoload sequences be cut from this document and laminated to waterproof the pages.

	 •	 Digital camera—We recommend that pictures are

taken of the sample site and some microplot conditions for future reference.

Determining the Scale of Sampling for 

Your Assessment

	 The first task in photoload sampling is deciding the

scale at which to make the loading assessment. The

scale of sampling is mostly determined by the sampling

objective and the sample unit. The sampling objective

defines the accuracy required for the loading estimates

and the sampling unit defines the spatial resolution that

the loading estimates are intended to describe.

	 The sampling objective is the purpose of the sampling

effort and it dictatesthe details of any sampling effort. If

the fuelssampling project was designed to quantify fuels

for input into fire effects models, the accuracy of the fuel

loading estimates may not need to be high because of the

coarse resolution of fire effects simulation. Conversely,

high accuracy is needed when a sampling project is concerned with monitoring fuel loadings after management

treatments. If the sampling objective requires accurate

estimates of fuel loadings, then, as mentioned above,

planar intersect or fixed area techniques are warranted

as long as field crews are properly trained.

	 The sample unit is the finest area where fuel loading

values are needed forsummary asspecified by the objective. For example, the monitoring of the effect of a fuel

treatment would probably be done at the stand level(area

inside the treatment boundaries), whereas the inventory

of fuel loadings on a plot would require the fuel loadings

be estimated for the area within plot boundaries. The

size of the sampling unit dictates the scale of photoload

implementation.Since the photoloadtechniqueperforms

best when the sampling scale is small (about one square

meter), it is important that the estimates of fuel loading

forlarger areas(coarserscales) accountforthepatchiness

of loading across that sampling area.

	 The first factor to address when designing a photoload

sampling project isthe desired accuracy ofthe photoload

estimates as specified by the objectives. Since ocular

estimates are more accurate if estimated within small

plot frames, the most accurate sampling approaches use

a random or systematically stratified network of one

squaremetermicroplotswithinthe sampleunit.However,

if the objective implies that only a general description

of fuels and their loadings are desired, such as input to

computer models for alternative treatment evaluation,

thentheuser canestimate loadingsforthe entire sampling

unit instead of using microplots. The sampling objective

will always be tempered by the resources available for

sampling. For example, if time and funding are limited,

then the time-intensive microplot option is probably not

possible and loading estimates might have to be done at

the larger sample unit level. Since most fuel sampling

projects collectloadingmeasurementsto be used asinput

to fire behavior and effects models, it is important that

the sampling intensity reflect the resolution and accuracy

required by the models. One must always remember

that the quality of model predictions increases with the

accuracy of the input parameters. Last, if the sampling

objective specifiesthe need for an estimate of fuel variation, then the microplotsampling at the desired sampling

scale is the best alternative.

	 We feel that the photoload method can be best implementedatoneofthreescales:themicroplot,themacroplot,
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

and the stand scales. These scales can be integrated into

a nested sampling design to improve loading estimates.

The best way to illustrate thisisto describe four common

sampling situations. First,say a fire manager must quantify fuels for a 100 acre (40 ha) stand but does not have

the expertise for conventional sampling techniques and

does not have the time for nested microplot methods. In

this case, perhapsthe bestmethod for estimating loadings

involves traversing the stand and mentally determining

an estimate of loading for each fuel component using the

photoload technique (stand level photoload estimate).

Second, say the fire manager realizes that there may be

more time available to get a more accurate answer. The

managermighttheninstallfourmacroplots(large,0.04 ha

or 0.1 ac circular plots) that represent the four common

fuel loading conditions observed within the stand and, at

each macroplot, the manager estimates fuels within the

macroplotboundariesusingone loadingestimate for each

ofthe sixfuel components(macroplotphotoloadestimate

summarized to the stand level). The manager would then

need to estimate the proportion ofthe stand that each plot

representsto get a weighted average by area forthe entire

stand for the loading of each fuel component. Third, the

same managerrealizesthat more accurate and defensible

estimates are needed because the project objective includes monitoring and more time is made available. The

manager might then install a grid of 25 microplots (1 m

2

square plots on a 5 x 5 grid size) within each of the four

macroplots to obtain a better estimate of loading and its

variance (microplot photoload estimates summarized to

the macroplot level that is then summarized to the stand

level).Or,themanagermightplaceoneormoremicroplots

in an area or multiple areas of the macroplot that would

be representative of the loading for that macroplot subarea (much like the method described above for placing

macroplots in stands). Fourth and last, say the manager

has sufficient time and wants the best possible estimate

using the photoload technique. Here, the manager might

install a series of systematic transects within the stand

and establish a microplot at fixed intervals along each

transect (for example, one microplot every 50 meters)

(microplot photoload estimates summarized directly to

stand level). The ability of the photoload technique to

adapt to various scale and accuracy issues makes it a

flexible and robust sampling method. We feel landscape

level estimates of fuel loadings (one estimate of loading

for each of the six components for the entire landscape)

may be inappropriate but possible albeit expensive. The

bestway to quantify fuelloadingsforthe entire landscape

is to sample loadings for all stands that comprise that

landscape.

	 With all this in mind, we recommend the microplot

approach always be used in photoload sampling unless

time, funding, and field experience are limited, in which

case we recommend that loadings be assessed at the scale

that best matches the spatial resolution required by the

predictive fire models and the sampling objective. The

amount of time and funding available to perform the

photoload loading must be determined to try to match

the resources available for sampling and the sampling

objective to the sampling scale. If accurate answers are

required but time and money prevent microplot sampling across large stands, then use microplot sampling

on macroplots or macroplot sampling across the stand

area. Procedures for sampling at each of the scales are

detailed next.

	 You should first decide on a convention for macroplot

and microplot shape and establishment. We suggest that

the macroplot be square and the sides oriented in the four

cardinal directions, but otherstrategies may work just as

well, such as orienting the sides so they are upslope and

cross slope. A grid of microplots can also be established

in circular plots to allow more efficient integration into

othersampling efforts,such astree population sampling.

We also suggest that the corner of the microplot frame

always be established in the southwest corner along a

transect so that the microplots are always on the top and

along the right hand side of the transect. Be sure to record all methods and the sampling design specifications

to ensure that the project can be repeated and analyzed

correctly. There is a metadata database in FIREMON

to record these sampling specifications, or the user can

simply enter these specifications in a notebook for later

reference.

Using the microplot approach—The microplot approachinvolvesusingplotframes(microplot)todelineate

a smallsamplingarea tovisuallyestimate fuel component

loadings.Inthephotoloadsamplingtechnique,the sizeor

area of the microplot should be the same as the fuelbeds

photographed in the photoload photo sequences; we

suggest that the microplot frames be exactly one meter

square (1 m by 1 m) to match the dimensions in the photoloadpictures.Althoughothersizedplotframes couldbe

used, you must adjust the estimated loadings to account

for differences in plot frame area. The microplot frame

sides should be one meter long measured on the inside

dimensions, not the outside dimensions. We recommend

that one corner ofthe frame remain unattached so that the

frame can be opened to include large trees or any other

obstruction. We also suggest that the user build several

plot frames as they will come in handy if there is more
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

than one person on a field crew or if a frame breaks. We

made our plot frames out of plastic PVC pipe and used

90 degree corner piecesto bind the lengthstogether with

glue; however, any material from wood to metal bars

will do.

	 In the microplot approach, the microplots are installed

on a grid within the sample unit in a design that fully

describes the spatial distribution of fuel loading across

the sample unit without preconceived or statistical bias.

We recommend randomly establishing a starting point

for the first transect, and then establishing a systematic

grid that evenly places the microplots across the entire

sampleunit.Thebeginningandendof eachtransect canbe

markedwithanironpipe,rebar,orlargenailthatispermanently ortemporarily driven into the ground (monitoring

applications would require permanent establishment of

transects) (see FIREMON for permanent establishment

of plots or transects). Ultimately, the user should strive

for a 10 percent sample of the sampling area but time

and funding will nearly always dictate that a 1 percent

sample is more feasible. For example, if the macroplot is

400 m

2 (20 m by 20 m) then a 10 percent sample would

be 40 m

2

or 40 microplots. These microplots could be

installed on 4 transectsthat are 5meters apart and the plot

frame would be placed every 2 meters on each transect.

In monitoring applications, it is important that a nail be

driven in at each microplot location to make sure that

future estimates are done on the same piece of ground.

We suggest at least two corners be marked with the nails

for each microplot. We found that plastic rope in a bright

colorseemstoworkwellfortransectsbut clothmeasuring

tapes and string also work equally well. The ropes can

be marked at fixed-length intervals to define the placement of the microplot plot frame along the transect. Be

sure to assign each microplot a number and record this

number, along with the fuel component loadings in the

plot form(s) (use the subplot field in Appendix C).

	 Stand level microplot grids are more difficult to design

because standboundaries are rarelysquareorrectangular.

Moreover, a 10 percent sample in a large stand might

result in a prohibitively large number of microplots. For

example, a 100 acre (40 ha) stand would require around

40,486 microplots of 1 square meter for a 10 percent

sample resulting in an impracticalsample target(even the

4,048microplotsrequiredfora1percentsampleseemsexcessive).Therefore,thedesignandimplementation ofthe

microplotsampling grid would need to be a compromise

between feasibility and statistical validity. We suggest

that users match the time available to spend sampling

one stand with the time it takes to record loadings for

the six components at one microplot (1-5 minutes) and

calculate grid sampling density. So, if four stands need

to be sampled in one day (eight hour working day), that

means that there is roughly 120 minutes (two hours)

per stand or 24 microplots per stand assuming a five

minute microplot sampling time. This could be put on

a grid across the stand that matches the stand shape and

size. We found that our photoload evaluators averaged

approximately6.3minutespermicroplottoestimate loadings of all fuel components including the time it took to

estimate log loadings at the subplot level. We also found

thatmicroplotsamplingtimesrangedfrom2.7minutesfor

the most experienced evaluatorsto over 10.1 minutesfor

novice fuelsamplers.These timestended to increasewith

increasing loadings with the longest times for the slash

sites (7.2 minutes) and heavy fuel units (6.3 minutes).

Times for most people decreased as more microplots

were evaluated, especially for the subplot estimates of

log loadings, as people learned how to efficiently use the

log loading table (Keane and Dickinson 2007; RMRSRP-61CD).

	 Another option for the microplot approach is using

double sampling as a general framework for the applicationofthephotoloadtechnique.Indoublesampling,visual

estimates of fuel loadings are obtained on all microplots

in the sampling unit, but a subset of these microplots

is also destructively sampled (fuel is collected, sorted,

dried, and weighted) just after the visual estimates are

made.Regression techniques can then be used to develop

calibration relationshipsto adjust the visual estimates using the destructively sampled data.Alarge subsetshould

be obtained that spans the entire range of loading values

for each fuel component.

Using the macroplot approach—This approach will

probably be the most common one used in fire management.Here the usertraversesthemacroplot and estimates

a loading that best represents the macroplot as a whole.

Again, a macroplot is usually about 0.1 acres in size

and is often circular or square. The user must account

for the spatial distribution of fuel in the plot and adjust

the estimate accordingly. We recommend the following

procedure:

	 1.	Visually divide the macroplot into areas where there

are obvious differences in fuels.

	 2.	Estimate the proportion of those divisions to the

entire macroplot area.

	 3.	Estimate the loading of each fuel component for

each of the divisions.

	 4.	Calculate a weighted average by area of the

loadings.

	 5.	Record the loading on the plot sheet.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

	 Novices of the photoload method will probably need

to write down the proportional areas and related loadings

to accurately calculate the weighted average loading, but

more experienced field people will find that they can

actually perform many of the calculations in their head.

Remember, the resolution of the photoload estimates is

quite low so the proportional areas and weighted average

calculations need not have three or four decimal places.

For example, we recommend the proportional areas be

in classes of 10 percent and loading estimate never have

more than two decimal points (0.02 for example).

	 Sampling times for the macroplot approach will vary

by evaluator and site conditions, but we found that it

took about 5.1 to over 10 minutes to estimate loadings

of all six surface fuel components for a macroplot. This

estimate will decrease with increasing sampling experience and decreasing fuel loadings.

Using the stand approach—Although fire managers

mightthinkthatthisisthemostdesirable scale atwhich to

estimate fuel loadings, we suggest thatstands be divided

into homogenous areas of fuel loadings to more accurately estimate a loading forthe entire stand. Moststands

are quite large and it may take time to properly traverse

the entire area, which makes it difficult to remember or

visualize the distribution of fuel conditions across the

sub-areas. Therefore, we suggest the user install a grid

of either microplots or macroplots to systematically

sample the stand. The number of plots in the grid would

be dictated by a number of factors, most notably the

time available to sample the stand. Follow the guidance

presented in the previous two approaches for the proper

methods for micro- or macroplot sampling.

	 If a gridded sampling strategy is impossible and the

user hastime for only one estimate,we recommend using

the same procedures for the stand as for the macroplot.

The following procedures should be followed:

	 1.	Visually divide the stand into areasthat reflect obvious differences in fuel loadings.

	 2.	Record these divisions on a stand map.

	 3.	Estimate the proportion of those divisions to the

entire stand area.

	 4.	Estimate the loading of a fuel component for each

of the divisions.

	 5.	Calculate a weighted average by area of the

loadings.

	 6.	Record the loading on the plot sheet.

	 The detail and resolution of the stand divisions will

probably be dictated by the sampling objective. If a

quick estimate of fuel loadings is needed to compute a

fire effect, then the divisions should only reflect major

loadingdifferences(lowandhighloadings,for example).

However, if the fuels are needed to develop a fire prescription, then all fuel complexes should be described

and accounted for in the final estimate.

	 Last, we strongly recommend that the area proportions

and assigned loadingsfor each sub-stand be recorded for

later use. This is important for the accurate calculation

of fire effects. Models such as FOFEM and CONSUME

are point models that predict fire effects for a point on

the landscape. The user of these models must take the

point predictions and summarize themto the spatialscale

of application. To do this for a stand, we recommend

that the fuel loadings for each of the stand divisions be

used to simulate fire effects with the predicted effects

then summarized to the stand level by the area weighted

average.

Making the Photoload Fuel 

Loading Estimates

	 Estimating fuel loading with the photoload technique

involvesmatching the conditions observed on the ground

within the sampling unit with the conditions in the set

of photographs of loadings provided in the photoload

sequences (Appendix A). The conditions are matched

only on visual assessment of loading characteristics; no

otherfactorssuchasfuelbedappearance,color,orwetness

should be considered. The usershould try to match loadings between the photosin the photoload sequences with

the loadingsobservedontheground.However, estimating

fuel loadings using only ocular guesses is not as simple

as it appears. Many factors must be accounted for in the

ocular estimate to obtain the most accurate fuel loadings.

The four most important factors are spatial distribution,

degree of decay, branch diameter, and fuel depth.

	 The photoload sampling technique was designed to

estimate loadingsfor the fuel componentsthat are above

the litter layer and plainly visible and identifiable. Some

partsoftwigs andbranches areburiedinthe litter andduff.

Donotinclude theburiedmaterialinthe loadingestimate.

Anything buried below the litter layer is considered duff

or litter and should be sampled using another technique

(we suggest the FIREMON FuelLoadingmethods).This

is also true for logs. The central axis along the longitudinal length of the log needs to be above the litter layer

to be considered 1000 hour woody fuels. Rotten logs

are the most difficult to identify for sampling because

they are broken and it is difficult to identify the central

axis. The planar intersect method (Brown 1971) detailed

in FIREMON recommends visually reconstructing the

original log size for rotten logs that have fallen apart.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

	 We found through extensive testing that the order that

loadings are estimated by fuel components is important for

many people. Many found that confusion was minimized if

thefuelcomponentswiththelowestloadingswereestimated

first and those with the highest loadings estimated last. We

suggest that the userfirst enter zero for each fuel component

not evident within the sample unit. Then, enter the loadings

forthose componentswithminimalloadings,suchasshrubs

and herbs. This usually leaves only one or two components

left and the loadings for these are easily estimated because

all other fuels have been eliminated. We suggest that log

loadings always be estimated last because they are usually

done at a 100 m

2 scale.

Adjusting visual estimates—Any estimate of fuel

loading must be adjusted to account for the variability

and properties of the fuel within the sampling unit. The

loading of any fuel component is rarely evenly and

uniformly distributed across a sampling unit, and this

is especially true for woody fuels. Fuels are normally

clustered in piles called “jackpots” because the origins

of most fuels are usually from trees, and trees are usually

clustered and clumped within a stand. Therefore, the user

of the photoload technique should always account for the

spatial distribution in the visual loading estimates. This

is done by matching photoload pictures with all levels of

fuel loading within the sample unit and then performing

a weighed average of these loadings with the estimated

aerial proportions of the fuel loading levels within the

samplingunit.As anexample,saywehave a1,000 m

2

plot

and, bymatching photoload sequence pictures,found that

the ocular estimatesfor fuel loadings were 0.1 kg m

–2 for

10 percent ofthe plot, and 1.1 kg m

–2 for 50 percent ofthe

plot, and 2.0 kg m

–2 for 40 percent of the plot; then, the

finalloadingwouldbe1.36kgm

–2 (0.1x10+1.1x50+2x40

dividedby100).Thisconceptcanbeusedtoadjustloadings

at any scale, most often within a microplot, macroplot or

stand. Many people may find that it is easier and quicker

to perform this weighted average in their heads while

others, especially novices, need to write the information

on the plot sheet (see the first plot form in Appendix C).

Some of the evaluators of the photoload method found

it useful to visualize what the fuel in the sampling unit

would look like if each componentwas evenly distributed

on the ground and this visualization was compared with

photoload pictures. In the photoload sampling technique

evaluation, we found that many evaluators had trouble

making the mathematical calculations and writing the

subsequent answer correctly onto the plot form. We recommendthatnovicephotoloadusersrecordallproportion

and scale estimates onto the plot form and perform the

calculations after the visual estimates are completed.

	 The depth of the fuelbed must also be accounted for in

the estimate ofloading using the photoload technique, especially shrub and herb components. Fortunately, woody

fuels on most fuelbeds have virtually no depth under

natural conditions. However, shrub and herb fuelbeds

have depth (measured as average plant height) and this

dimension must be included in the photoload process to

adjust for the ocular estimate. The photoload technique

assumesthat changesin fuel depth are proportional to the

loadings. This assumption may be an oversimplification

of reality, but there is little research to support any other

approach.Eachofthepicturesforshrubandherbfuelbeds

in Appendix A contains a height of the plant material.

Thisisthe height that we measured as we constructed the

fuelbeds to be photographed. We suggest that once the

photoload picture is matched to the fuel conditionsin the

field and the loading has been determined, the loading

estimate should be adjusted for differences in observed

and pictured plant height.Thisis done bymultiplying the

estimated loading by the proportional change in height

from the picture to the observed fuelbed. For example,

if the photoload shrub height is 1 meter and the matched

loading is 2.0 kgm

–2 butthe observed height ofthe shrubs

in the field is 2 meters, then the actual loading would

be twice the estimated loading computed as 4.0 kg m

–2

(2.0 kg m

–2

x 2 meters / 1 meter) because the height in

the field is twice the height in the photoload picture.

	 Ifthe littersurface isnotvisible fordowneddeadwoody

material, as in slash and activity fuelbeds, then the same

procedure should be done to compute that loading except

the depth of the photoload picture fuelbed is assumed to

be the highest diameter of the woody size class. Use the

picture for the fine woody fuel load that best portraysthe

top of the fuelbed and then adjust that loading by fuel

depth.Soaslashbedcomposedofa10hourwoodyfuelbed

that is 10 cm deep might be matched with the photoload

picture of 5 kg m

–2 but the actual loading would be the

product of the photoload estimated loading (5.0 kg m

–2)

and the depth of the fuelbed (0.1 meters) divided by the

largest diameter of the 10 hour class (this fuel class goes

from0.6 cm(0.25 inches)to 2.5 cm(1 inch)so the largest

diameter is 0.025 meters) so the final loading estimate

would be 20 kg m

–2 (5.0 kg m–2

x 0.1 m depth / 0.025 m

diameter).

	 The degree of decay for downed woody fuels can also

influence the accuracy of fuel load estimates and adjustments should be made to correct for the amount of rot.

The photoload sampling technique assumes all downed

dead woody fuel is sound. So, any observed decay will

reduce the loading estimates. We suggest that the following factors be used to adjust sound fuel loadings to
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

account for degree of decay using the decay classes as

implemented in FIREMON.

	 • Decay class 1—No need to adjust for decay

	 • Decay class 2—No need to adjust for decay

	 • Decay class 3—Multiply loadings by a factor of 0.90

	 • Decay class 4—Multiply loadings by a factor of 0.75

	 • Decay class 5—Multiply loadings by a factor of 0.50

	 These values were computed from values taken from

Brown (1970) and Busse (1994).

	 Another adjustment is for woody particle diameter in

the large woody fuels (100 hr and 1000 hr logs). There

is a pronounced diameter bias when estimating loading

for woody size classes greater than 1 inch (2.5 cm) in

diameter. The range of diameters in large woody fuels

is so large that photographs depicting a loading for the

fuels with diameters at the small end of the size class

may underestimate loadings by a factor of nine for 100 hr

fuels because loading increases by the square of the

diameter. It is important that the user make sure that the

diameters observed in the photoload sequences are the

same as those observed in the sampling unit. If not, then

the user should use the tables provided inAppendix B to

adjust the photoload loadings or approximate loadings

directly. Follow the instructions presented for each table

in Appendix B and estimate loadings accordingly.

	 The photoload technique allowsthe user the flexibility

to pick a loading that may be between two consecutive

pictures in a photoload sequence. For example, say the

userfound that the conditions observed on the ground for

1 hr woody fuels did not exactly match any one picture

but the loading was definitely greater than the 1.0 kg m

–2

picture but lessthan the 1.2 kg m

–2 picture (seeAppendix

A).Therefore, the user hasthe ability to visually extrapolate between pictures and decide on a more appropriate

loading. For example, the loading would be estimated at

1.1 kg m

–2 if the observed loadings appear to be exactly

halfway between the two photos. If the conditions were

just a bit more than the 1.0 kg m

–2 photo, then the user

might record 1.01 kg m

–2

as the estimate. Users actually

have the ability to assign any fuel loading and they are

not confined to using only the loadings printed on the

top of the photoload sequence photographs.

	 In summary, we suggest that each visual estimate of

fuel loadings follow these guidelines:

	 1.	Selecttwophotoloadpicturesthatboundtheobserved

loading on the sample unit.

	 2.	Computealoadingestimatebyextrapolatingbetween

pictures.

	 3.	Adjust that loading for fuelbed depth for that

component.

	 4.	Adjust that loading to account for degree of decay.

	 5.	Adjust that loading for the spatial distribution of

fuels within the sample frame.

	 6.	Adjust loading for differences in diameter for large

woody fuels.

	 It may be that the target accuracy defined by the sampling objective does not require thissix-tiered process of

ocular estimation, but we believe that every photoload

estimate should be done according to this procedure and

users will find that it will become second nature after a

while.The loadinginthephotoloadsequencesisprovided

in both English and metric units. It is important that the

user select the appropriate units for assessment and be

consistent when completing the plot sheet.

Calibrating your eye for estimating loadings—We

foundthattheabilityofuserstoconsistentlyandaccurately

estimate woody fuels is mostly dependent on their level

of expertise. Because of this, it is important that users of

the photoload technique calibrate their eye so that they

can consistently and accurately estimate loadings. This

calibration can be done by repeating the methodsthat we

used for measuring the reference fuel loading conditions

in the evaluation of the photoload technique (see Keane

andDickinson 2007;RMRS-RP-61).We suggest that the

user build 1x1 meter square plot frames and go to the

field and estimate loadings within the plot frame using

the photoload sequences and protocol. Then, the user

should collect, dry, and weigh by fuel component, and

compare themeasuredloadingswiththeocular estimates.

We also suggest that the users take photos of the 1x1

meter frames before sampling so that they can compare

their measured loadings with the photoload pictures to

calibrate their eye in future field seasons or to teach the

photoload technique to others.

	 Another method to calibrate photoload woody fuel

estimates is to define a plot of known area and install

a number of transects to measure woody fuel using the

planar intersect technique (FIREMON, Lutes and others

2006). We suggest that at least 20-30 transects be established and measured within the defined area to get the

most accuratewoodyfuelloadings.The computedwoody

fuel loadings by size class can be compared to photoload

estimates for the defined area. Again, pictures should

be taken of the plot and fuel conditions to document the

fuelbed conditions for use in training future crews.

Estimating fine woody fuel loading—The most important guidance for estimating loadings of fine woody

components is to first correctly identify the right fuel

components. Three questions must be answered for the

observed fuels to be sampled—are the fuels: 1) down,
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 11

2) dead, and 3) woody. The fine fuel particles must be

unattached from their parent stems and be below the

6 foot (2 meter) surface fuel height to be considered

down; down fuels are those fuel components that are

not attached to live or upright dead plants and are entirely on the ground or below 6 feet (2 meters). Larger

fuels, such as logs and large 100 hr branches or boles,

may look like snags and may not seem down but this is

a gray area and we suggest you follow the rule that all

woody fuel originating from a tree bole is considered

down woody if it leans at an angle greater than 45 degrees from the zenith angle (less than 45 degrees from

the horizontal ground). If it is at an angle greater than

45 degrees above horizontal, it can only be considered

down if it is a broken bole or branch from a tree where

at least one end of the bole is touching the ground (not

supported by its own vegetation or other branches). The

most confusing situation in the field is dead branches

that are attached to a live or dead upright tree but are

below the 6 foot (2 meter) sampling height. They might

even touch the ground. These are not considered down

fuels because they are not detached as yet.

	 “Dead” fuels are fuels that have no live foliage or

branchwood material. Fresh slash and newly broken

branches with green foliage are still considered dead

even though they are technically alive because we assume they will eventually be dead. Dormant does not

mean dead. Dormant plants with no live foliage do not

count as dead fuel. Examples include shrubs that have

lost their leaves in the autumn and winter. Many people

are confused by woody fuel identification and tend to

put stalks of annual plants, for example, into the woody

category when in fact the stalks are dead herbaceous.

Remember needles, detached grass blades, pine cones,

and pieces of bark on the ground are considered litter.

Since litter loading is not assessed in photoload, be sure

not to confuse litter fuels with fine woody fuel.

	 When sampling finewoody loadingwith the photoload

technique,theusershouldassessthe conditionswithinthe

samplingunit(microplot,macroplot,stand) concentrating

on loading characteristics and select a photo from the

photoload sequences presented in Appendix A that best

matches the fuel loading conditions that correlates with

the fuel on the ground. If loading seems to fall between

twoofthephotos, choose the appropriate loadingbetween

the two loading values in Appendix A, and record it on

your data sheet. Be sure to use a “go-no-go” gauge (see

FIREMON methods)to measure the diameters of woody

fuels to more accurately estimate loadings.

	 It issometimes helpful to visually line up the fuel in the

photoloadphotograph,andestimatethedistancealongone

side of the square that the fuel occupies. Then, compare

this length with that observed in the sample unit (microplot). Our evaluators found it helpful to concentrate on

fuel length to visually compare photoload pictures (they

visually added up the length of fuel in the photo with the

length offuel observed on the ground). Pay special attention to the range of diametersin both the photo series and

in your sampling unit. Some of the images in Appendix

A may not have the range of diameters observed within

the sampling unit area.Therefore, the estimate ofloading

may need to be increased or decreased depending on the

differences between diameter distributions.

	 The most difficult task to perform in the photoload

visual approximations is to distinguish between the fine

woody fuel size classes with only your eye. Many twigs

have tapered diameters that start as 10 hour fuels (diameters greater than 0.25 inches) and then become 1 hour

fuels(diameterslessthan 0.25 inches)somewhere up the

stem. This is another major source of error in photoload

estimates.Manypeople findit confusing tovisuallyseparate 1 hr from 10 hr fuels and 10 hr from 100 hr fuels. It

takes practice but eventually most people become quite

accomplished at visually identifying the three fine fuel

size classes.We suggest users ofthe photoload technique

take a “go-no-go” gauge or a clear plastic ruler into the

field to quickly identify portions of wood into the appropriate size classes. Tips on the use of these two items

are detailed in FIREMON (Lutes and others 2006).

Estimating log fuel (1000 hr) loading—Log loadings are estimated at a different scale than the other fuel

components in photoload. Logs, because of their large

size, are estimated at a subplot level and we highly

recommend these subplots be 100 m

2 (10 meters by

10 meters) to match the photoload pictures and tables.

If the subplots are not 100 m

2 then you can’t use the

tables in Appendix B.

	 Log loadings are especially difficult to estimate using

the photoload technique because the pictures do not adequately capture the diameter distribution of the logs in

the sample plot.Since log loadingsincrease by the square

of the diameter, small changes in diameter can result in

large changes in log loading. Moreover, log rot can also

influence loading. As a result, the user must pay special

attention to the distribution of log diameters and log

lengths on the sample unit. The loading estimated with

the photoload picture must be adjusted to account for the

difference in diameters between logs in the pictures and

logs observed in the field.

	 The easiest method isto estimate the average diameter

on the sample unit and adjust the loadings accordingly.

However, the calculation of the average diameter of the
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

logs to estimate loading is also problematic. Since loading is calculated by volume, and volume is calculated

from log cross-sectional area and length, and the cross

sectional area is calculated by the log diameter squared,

then loading is related to the square of the diameter. So,

the average diameter should be a quadratic mean rather

than an arithmetic mean to accurately calculate volume.

This means that larger diameter logs should be given

more weight than small diameter logs. So, the calculation of the most accurate average diameter must be done

using the square root of the average of the sum of the

diameters squared (called the quadratic mean diameter

or QMD). There are tables in Appendix B to help with

this calculation.

	 We recommend that the photoload user use the log

loading tables in Appendix B to calibrate, adjust, and

refine the ocular measurements obtained by the photoload sequences in Appendix A. This alternative method

can be used in two ways. First it can be used as a tool to

check the loading estimate youmade using the photoload

sequences described above. Second, it can be used alone

without consulting the photo series. To use these tables,

the usersimply estimatesthe average diameter ofthe logs

within a 100 m

2 fixed area (we suggest 10 by 10 meters

so that it corresponds to the area in the photoload log

pictures) and the length oflog in the area.These estimates

are then referenced in the tables to get the loading. The

user can measure log diameters and length with a ruler

or tape to get more accurate loading estimates. A more

accurate but slower method is to group logs into diameter classes and find lengths by diameter class and use

the mid-point of the diameter class and the length in the

class to find the loading, and then sum up all loadings

by class for a final loading estimate. The most accurate

but time intensive method is to measure the log length

and diameters of each end of the log to find the loading

for each log, then sum up loadings across all logs. The

integration of this tabular technique with the photoload

techniqueshouldprovideconsistentestimatesofloadings,

especially when the loadings are high. We also suggest

that this same process be used to adjust 100 hour woody

fuel loading since loadings can vary greatly across that

diameter class width (1 to 3 inches or 2.5 to 7.5 cm).

	 Here are some guidelinesthat will help with the photoload estimates oflog loadings. First, be sure that logsthat

have their central axislyingabove theduff andlitterlayer;

logsbelowdufflayer are consideredduff.Second,be sure

that only log fuels are measured; eliminate the parts of

the log that are less than 3 inches (7.5 cm) in diameter.

Be sure to record the average rot class of the logs on the

sample area.Thismay be importantfor adjusting the final

loading values. And last, adjust all estimates to account

for differences in observed and photoload diameters.

	 In summary, we suggest you follow these steps to accurately and consistently estimate log loadings using the

photoload technique. To find a loading using the 6 inch

and 10 inch photo sequences follow these steps:

	 1.	Estimate the quadratic mean diameter (QMD) for

logs within the sample area (square root of the average of the diameters squared). Record the QMD in

the Observed QMD of the photoload plot form (first

plot form in Appendix C).

	 2.	Choose the photoload sequence of imitation logs (6

inch or 10 inch) that most closely matches the average QMD of the logs in your sample area. Record

which series you are using, by writing a “6” or a

“10” in photoload QMD field.

	 3.	Determine the photo fromthe selected log seriesthat

mostresemblesthe loading conditionsin the sample

unit you are evaluating. Remember to evaluate logs

on a 100 m

2

area. Record this loading on the plot

form.

	 4.	Find the diameter conversion factor in Table 1 of

AppendixBusing the observedQMDand photoload

QMD and write the conversion factor in the plot

form. Record this on the plot form.

	 5.	Calculate the final loading by adjusting the ocular

photoload estimate (step 2 and Column A on plot

form) for diameter differences by multiplying by

the conversion factor. Record this final estimate in

the appropriate box on the plot form.

	 6.	Refine the estimate of loading using the second

method that uses the tables in Appendix B. This is

done only if there is time.

	 7.	Adjust the loading using the rot class multipliers

mentioned previously.

	 The user should record all estimates on the plot form

and all intermediate calculations should also be written

directly on the plot form. There is plenty of room for

calculations and summary statistics.

Estimating shrub and herbaceous loading—The

first step in estimating shrub and herb loadings with

the photoload technique is to identify the plant species

within the sample unit. The user must estimate loading

by matching the group ofspecies occurring in the sample

area with one or more of the photoload sequences in

Appendix A. However, the photoload sequences in Appendix A do not contain all undergrowth plant species

in the northern Rocky Mountains, only those that were

common in the western Montana study area (Keane and

Dickinson 2007; RMRS-RP-61CD). Only seven shrub
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 13

species,two grassspecies, and two forb species are found

in Appendix A. Therefore, it is necessary to match the

morphology of the species observed in the field with the

species presented in the photoload sequences(Appendix

A). If there are several species of vegetation on your

plot, you may choose to use several photo series, one

for each species, and then sum the individual loadings

to make a final loading estimate of shrub or herbaceous

components. Or, you can rate the loading as a collection

of species using the most similar photoload sequence.

	 Finding the appropriate picture in the photoload

sequences is a bit more difficult for shrub and herbs

because these fuels have depth (plant height), species

differences, and heterogeneous distributions. Therefore,

we have provided a side view along with a top view to

help in estimating loadings. For shrubs and herbs, the

user should try to match the pictures with field conditions based on three characteristics: species (previously

discussed), cover and density. Once a picture is chosen,

then the corresponding loading must be adjusted for

differences between height in the picture and height in

the sample area. The average height of each plant photo

seriesisindicated at the top of each page in the photoload

sequences of Appendix A. Adjust shrub and herbaceous

loading only when the average plant height in the photos

is different from the average height of plants on your

plot.

	 We recommend that the user follow these steps to

determine an adjusted loading:

	 1.	Choose a shrub or herb photoload sequence that best

representsthevegetationonyoursample area.Match

to the closest species, genus, or morphology.

	 2.	Visually estimate the loading using standard photoload procedures.Write thisloading on the photoload

plot form in Appendix C.

	 3.	Estimate the height of the shrub or herb present

within the sample unit. This is estimated as an integrated average across the entire sampling area. A

hint isto visually drape a sheet overthe shrub orfuel

component and estimate the average height across

the entire sheet. Write this height in the top of the

division on the plot form.

	 4.	Write thephotoloadheightforthe sequence thatisbeingused onthe plotform.Thisheightshould be atthe

top of each page in the shrub or herb sequence.

	 5.	Calculate the height ratio (divide observed height

by photoload height) and multiply the ratio by the

loading estimate to calculate the final height. Write

in the appropriate field of the plot form.

	 6.	Estimate final loading by multiplying the visual

estimate of loading by the height ratio.

This process can be repeated several times and the sum

of the final loadings can be entered into the final field if

more than one photo series of plants are used.

	 We recommend that shrub and herbaceous loadings be

estimated when the plants are still green and at the peak

of their growth (end of the growing season). However,

this may be impossible for many sampling efforts. Users

should avoid sampling too early in the year before new

growth and too late when some plants have been eaten or

deteriorated.If early orlate sampling isthe only option, be

suretoonlyratethefuelsthatareobservedatthattime—do

not try to recreate optimum growth—­unlessthe sampling

objective requires that you adjust for phenology.

Estimating litter and duff loadings—We do not

present any methods for estimating duff and litter loads

inthephotoloadtechnique.However,we stronglyrecommend that loadings for these fuel components be estimated using themethods presented in FIREMON (Lutes

and others 2006) and linked to the methods presented

in this study. We recommend that duff plus litter depth

and percent of that depth that is litter be measured in

two opposing corners of each microplot used to estimate

loadings. Duff and littermeasurements can be written on

the FIREMON plot form or the Photoload plot form.

References

Anderson, H. E. 1982.Aidsto determining fuel modelsfor estimating

fire behavior. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station, General Technical

Report INT-122. 44 p.

Brown, J. K. 1970. A method for inventorying downed woody fuel.

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

Intermountain Research Station. General Technical Report INT16. 44 p.

Brown,J.K.1971.Aplanarintersectmethodforsamplingfuelvolume

and surface area. Forest Science 17:96-102.

Brown,J. K. 1974. Handbook for inventorying downed woody material. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station. General

Technical Report INT-16. 68 p.

Brown, J. K., R. D. Oberheu, and C. M. Johnston. 1982. Handbook

for inventorying surface fuels and biomass in the Interior West.

Ogden, UT: U.S. Department ofAgriculture, Forest Service, IntermountainForest andRangeExperimentStation.GeneralTechnical

Report INT-129. 88 p.

Busse, M. D. 1994. Downed bole-wood decomposition in lodgepole

pine forests of central Oregon. Soil Science Society of America

Journal. 58:221-227.

Fischer, W. C. 1981. Photo guide for appraising downed woody fuels

in Montana forests:Interior ponderosa pine, ponderosa pine-larchDouglas-fir,larch-Douglas-fir, andInteriorDouglas-fir covertypes.

Ogden, UT: U.S. Department ofAgriculture, Forest Service, IntermountainForest andRangeExperimentStation.GeneralTechnical

Report INT-97. 67 p.

GAO.2003.Additional actionsrequiredtobetteridentifyandprioritize

lands needing fuelsreduction.Report toCongressionalRequesters

GAO-03-805. Washington, DC; General Accounting Office.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

GAO. 2004. Forest Service and BLM need better information and a

systematic approach for assessing risks of environmental effects.

GAO-04-705. Washington, DC. General Accounting Office.

Keane, R. E. and L. J. Dickinson. 2007. Development and evaluatin

of the photoload sampling technique. Fort Collins, CO: U.S. Department ofAgriculture,ForestService,RockyMountainResearch

Station. Res. Pap. RMRS-RP-61CD.

Laverty, L. and J. Williams. 2000. Protecting people and sustaining

resources in fire-adapted ecosystems—a cohesive strategy. Forest

Service response to GAO Report GAO/RCED 99-65.Washington,

DC: U.S. Department of Agriculture, Forest Service.

Lutes, D. C. 1999.Acomparison of methodsfor the quantification of

coarse woody debris and identification of its spatial scale: a study

fromtheTenderfootExperimentalForest,Montana.Missoula,MT:

The University of Montana. Thesis. 120 p.

Lutes, D. C., R. E. Keane, and J. F. Caratti. [In prep]. Fuel Loading

Models: A national classification of wildland fuelbeds for fire effects modeling. Canadian Journal of Forest Research.

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

Sutherland, and L. J. Gangi. 2006. FIREMON: Fire effects monitoring and inventory system. Fort Collins, CO: U.S. Department

of Agriculture Forest Service. Rocky Mountain Research Station,

General Technical Report RMRS-GTR-164CD.

Mutch, R. W. 1994.Areturn to ecosystem health.Journal of Forestry.

92:31-33.

Ottmar, R. D., M. F. Burns, J. N. Hall, and A. D. Hanson. 1993.

CONSUME users guide. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station.

General Technical Report PNW-GTR-304. 44 p.

Ottmar,R.D.,R.E.Vihnanek,C.S.Wright, andD.Olsen. 2004.Stereo

photo series for quantifying natural fuels. Volume VII: Oregon

white oak, California deciduous oak, and mixed conifer with shrub

typesin thewesternUnited States.U.S.Department ofAgriculture,

Forest Service, National Wildfire Coordinating Group, National

Interagency Fire Center. 76 p.

Reinhardt, E. and R. E. Keane. 1998. FOFEM—a First Order Fire

Effects Model. Fire Management Notes. 58:25-28.

Sandberg, D. V., R. D. Ottmar, and G. H. Cushon. 2001. Characterizing fuels in the 21st century. International Journal of Wildland

Fire. 10:381-387.

van Wagner, C. E. 1968. The line intersect method in forest fuel

sampling. Forest Science. 14:20-26.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 15

Appendix A—Photoload Sequences for Northern 

Rocky Mountain Fuelbeds
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30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 31

Appendix B—Tables of Log and Branch Loading by Diameter and Length

Photoload log diameter conversion table—This table is used to adjust photoload estimated log loadings for the difference in log diameters between the photoload 

photo sets and the log diameters observed in the field.

Observed average log diameter Photoload log picture diameter set

(in) (cm) 6 inch 10 inch

 3 7.62 0.25 0.09

 4 10.16 0.44 0.16

5 12.7 0.69 0.25

 6 15.24 1.00 0.36

7 17.78 1.36 0.49

8 20.32 1.78 0.64

9 22.86 2.25 0.81

 10 25.40 2.78 1.00

 11 27.94 3.36 1.21

 12 30.48 4.00 1.44

 13 33.02 4.69 1.69

 14 35.56 5.44 1.96

 15 38.10 6.25 2.25

 16 40.64 7.11 2.56

 17 43.18 8.03 2.89

 18 45.72 9.00 3.24

	 To adjust photoload loading for log diameters in the

field, use the following steps:

	 1.	Select either the 6 inch or 10 inch photoload photo

set based on the similarity of log diameters found

in the sample area.

	 2.	Determine the photo in that set that most resembles

loadings in the area you are evaluating.

	 3.	Determine the average diameter of 1000 hour fuel

in the area you are evaluating and find that diameter

in column one ortwo ofthe table above.Remember,

the quadratic mean diameter is a better estimate of

average log diameter. The formula for quadratic

mean diameter (QMD) is:

QMD

d

n = ∑ 2

where d is log diameter and n is the number of logs.

	 4.	Find the conversion factor in column three or four,

depending on which photo series you used in step

2, and determine the conversion factor.

	 5.	Multiply the conversion factor by the loading you

estimated from step 2. The product is the final loading of 1000 hour fuel of your sample area.

	 The following set of tables represents an alternative

method of determining woody fuel loading of branches

and logs using an average diameter and length. The

tables were constructed so that once users determine

the average diameter of the woody fuel component and

the total length of that component, they can reference

the table to determine loading. The conversion to loading assumed a log density that is 400 kg m

–3, which is

typical for sound northern Rocky Mountain tree species

as an aggregate. However, you can proportionally adjust

the values in the tables to reflect wood density of rotten

logs.The tables are arranged first by downed deadwoody

fuel component—100 hour (branches) and 1000 hour

(logs). Then, the tables are arranged by the units used to

estimatediameter andlengthobservations.There are four

tables for each fuel component. The first table is used if

the diameters and lengths were measured in inches and

feet, respectively, and the loading is desired in tons per

acre. The second table has inches and feet for diameter

and length, but loading is in kg per square meter (a unit

thatismore easily visualized).The third and fourth tables

have diameter and length in centimeters and meters but

the loading is kg m

–2 for one table and tons per acre for

the other.
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

One important reminder on estimating loading using 

this technique.The loading of 100 hrfuels are estimated

for a sampling area of one square meter. The loading

of 1000 hr fuels (logs) are estimated on 100 m

2

area

(10 meters by 10 meters). All the tables are constructed

using these plot dimensions.

	 The following steps are used to estimate loading using

these tables:

	 1.	Measure the length of all woody fuel particlesin the

fuel component(100hror1000hr)withinthe sample

area (1 or 100 m

2

). This can be visually estimated

or actually measured using a cloth tape.

	 2.	Estimate the average diameter across all logs within

the sample area. The best estimates of average

diameter are done using the quadratic mean square

estimate where the sum of the squares of all woody

fuel particles are estimated and then divided by

the number of particles and then the square root is

taken.

	 3.	Find the table that matches the appropriate woody

fuel component and the units desired.

	 4.	Find the loading by crossreferencing the length and

diameter.

	 5.	Use linear extrapolation across rows or columns if

the diameter or lengths do not match the categories

listed in the table.

	 6.	Record the final loading on the plot sheet.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 33

Branch 100 hour wood fuels (1-3 inches dia)—Estimated on a 1 m

2 plot—English

units for diameters (in) and lengths (ft)—English units for loadings (tons acre–1)

Branch 100 hr loadings in tons acre–1
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

Branch 100 hour wood fuels (1-3 inches dia)—Estimated on a 1 m

2 plot—English

units for diameters (in) and lengths (ft)—Metric units for loadings (kg m–2)

Branch 100 hr loadings in kg m–2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

190. 2007

35

Branch 100 hour wood fuels (2-8 cm dia)—Estimated on a 1m2 plot—Metric unitsfor diameters(cm) and lengths(m)—­English units for loadings (tons acre–1)

Branch 100 hr loadings in tons acre–1
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

190. 2007

Branch 100 hour wood fuels (2-8 cm dia)—Estimated on a 1 m2 plot—Metric unitsfor diameters(cm) and lengths(m)—Metric units for loadings (kg m–2)

Branch 100 hr loadings in kg m–2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

190. 2007

37

Log 1000 hour wood fuels (3+ inches dia)—Estimated on a 100 m2 plot—English units

for diameters (in) and lengths (ft)—English units for loadings (tons acre–1)

Log 1000 hr loadings in tons acre–1
3

8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

190. 2007

Log 1000 hour wood fuels (3+ inches dia)—Estimated on a 100 m2 plot—Metric units for diameters (cm)

and lengths (m)—Metric units for loadings (kg m–2)

Log 1000 hr loadings in kg m–2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

190. 2007

39

Log 1000 hour wood fuels (8+ cm dia)—Estimated on a 100 m2 plot—Metric units for diameters (cm)

and lengths (m)—English units for loadings (tons acre–1)

Log 1000 hr loadings in tons acre–1
4

0 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

190. 2007

Log 1000 hour wood fuels (8+ cm dia)—Estimated on a 100 m2 plot—English units for diameters

(in) and lengths (ft)—Metric units for loadings (kg m–2)

Log 1000 hr loadings in kg m–2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 41

Appendix C—Photoload Plot Form and Cheat Sheet

Diameter reduction table—Find diameter observed in field in first column, then go to log 

picture diameter set used to estimate loading and find the reduction factor.

Observed average log diameter Photoload log picture diameter set

(in) (cm) 6 inch 10 inch

 3 7.62 0.25 0.09

 4 10.16 0.44 0.16

5 12.7 0.69 0.25

 6 15.24 1.00 0.36

7 17.78 1.36 0.49

8 20.32 1.78 0.64

9 22.86 2.25 0.81

 10 25.40 2.78 1.00

 11 27.94 3.36 1.21

 12 30.48 4.00 1.44

 13 33.02 4.69 1.69

 14 35.56 5.44 1.96

 15 38.10 6.25 2.25

 16 40.64 7.11 2.56

 17 43.18 8.03 2.89

 18 45.72 9.00 3.24
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007 43

Using the Plot Sheet

You don’t have to complete all fields on the plot sheet. For most applications you might 

only complete the Final Load field.

Header Information:Complete thisinformation for yourrecords. Use the PlotID as an identifier

in a data file. Use the FIREMON plotID if you are linking photoloads with other FIREMON

techniques. Use date and examiner to help document sampling details. Record StandID if this

plot is sampling a stand.

Subplot field is used if there are more than one photoload estimate per plotsuch as on a transect.

Subplots can be used as plots if only one estimate per plot is desired.

Adjustments

Rot Adjustment: Enter an adjustment factor for down wood rot (if unknown, the adjustment

factors for FIREMON decay classes – 1=1.0, 2=1.0, 3=0.9, 4=0.75, 5=0.5)

Height Adjustment: First enter observed height of component (obs ht) then enter the photoload

height (photo Ht). Calculate adjustment factor by dividing the observed height by photoload

height (example: 1.2 feet measured on plot and 0.8 feet on photoload sequence calculates to a

1.5 = 1.2/0.8).

Diameter Adjustment: Record the quadratic mean diameter (QMD) observed on plot in Obs

QMD and record the QMD of log photoload sequence used (either 6 or 10 in). Look up conversion in Log Conversion table and record in Adj factor.

Spatial Distribution: Traverse plot or stand and match a loading with a proportion of plot and

do this for entire plot. Calculate a weighted average by proportion area of loading and enter in

Loading field. For example, say 10% of plot had 1.0 kg m

–2, 50% had 2.0 kg m–2, and 40% had

3.0 kg m

–2, then the weighted average would be (10x1.0+50x2.0+40x3.0)/100=2.3 kg m–2

Calculations: Multiply the height, diameter, and rot adjustment factors by the Loading field in

the Spatial Distribution set of fields to calcluate the Final Load.

Notes: the height adjustment is only needed for down woody fuelsif the litter layer is not visible

through the woody fuels (slash, for example). Diameter adjustments are only needed for 100 hr

and 1000 hr fuels, but can be used for 1 hr and 10 hr fuels if desired. The weighted average for

spatial distribution is only needed if it is important in sampling objective.

Important Sampling Concepts

	 •	 1, 10, 100, and 1000 Hour fuels must be woody, down, and dead, to be counted. Needles,

grass blades, pine cones, and bark pieces are all considered litter, not down, dead, woody

fuel.

o	 “Woody” refers to a plant with stems, branches or twigs that persist from year to

year.

Photoload

Cheat Sheet
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-190. 2007

o	 “Down” includes all fuel in the sampling plane that is 45 degrees or less above

horizontal. If it is at an angle greater than 45 degrees above horizontal it should

only be considered down if it is the broken bole of a dead tree where at least one

end of the bole is touching the ground (not supported by its own branches or other

live vegetation).

o	 “Dead” has no live foliage. Fresh slash and newly broken branches with green foliage are exceptions because they are technically dead even though they may have

green foliage. Dead branches on live trees that enter the sampling plane should not

be counted. Don’t confuse dead with dormant.

	 •	 When sampling logs, do not count logs that have their central axis lying in or below the

duff layer. These logs burn more like duff and should not be sampled as logs.

	 •	 When sampling logs, you will measure a small and large end of the log. Remember, a log

must be greater than 3 inches in diameter. If the log tapers into a 100 hour fuel, only count

the part of the fuel that is 3 inches and greater. Your small end will never be less than

3 inches.

	 •	 The Log photos in your manual are of 6 inch and 10 inch logs. When you estimate your

log loading it is imperative to adjust for the mean log diameter (quadratic mean diameter

is best) in your subplot and to record this on your plot sheet.

	 •	 One stick can consist of 1, 10, and 100 hour fuel. Record a loading for each fuel component

even if it is one branch or stick.

	 •	 Only sample fuels inside the sample frame. If a stick crosses over or under the frame, only

count the part inside the frame. Ignore the portion outside of the frame.

	 •	 Before you start recording loadings on your plot sheet, eliminate fuel components without

loadings first. Put zeros in the final loading box and then begin determining the loadings

for other fuel components. Please do not put a dash through the final loading box, as this

will be interpreted as not being sampled rather than not being present.

	 •	 When assessing herbaceous and shrub loadings, remember to adjust for the mean height of

the foliage in your physical subplot and to record this on your plot sheet.

	 •	 Always remember to adjust estimated fuel loadings for four factors: spatial distribution,

diameter, decay, and depth.
NOTES


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