The Photoload Sampling Technique · The photoload sampling technique: estimating surface fuel loadings from downward-looking photographs of synthetic fuelbeds. ... Harrington, Melanie

United StatesDepartmentof Agriculture

Forest Service

Rocky MountainResearch Station

General TechnicalReport 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, Forest Service, Rocky Mountain Research Station. 44 p.

Abstract __________________________________________

Fire managers need better estimates of fuel loading so they can more accurately predict the po-tential 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 ac-curately 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 includ-ing 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 Na-tional 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-�90. 2007

Introduction Comprehensive estimates of fuel loadings in forestandrangelandecosystemsoftheUnitedStatesarecriti-caltoaccuratelypredictthefirebehaviorandeffectsofalternative fuel and ecosystem restoration treatmentsto save lives, property, and ecosystems (Laverty andWilliams2000;GAO2003,2004).Fuelloadings,alongwithfuelmoisture,arethemostimportantfactorsthatfiremanagerscancontrolforplanningandimplementingprescribed burn treatments. Sophisticated fire modelssuch as FOFEM (Reinhardt and Keane 1998; www.frames.gov)andCONSUME(Ottmarandothers1993;www.fs.fed.us/nw/fera/consume.html) require loadingestimates so that they can be used to plan, prioritize,design, and implement important fuel treatments forrestoringhistoricalfireregimesandreducinghazardousfuelstosavelivesandproperty(Mutch1994;LavertyandWilliams2000). Measuringsurfacefuelloadingsinthefieldisdiffi-cultbecauseitrequiresacomplexintegrationofseveralsamplingmethodsdesignedforimplementationatdis-paratescales.Downeddeadwoodyfuelsaretypicallysampledusingplanarintersecttechniques(vanWagner1968; Brown 1970, 1971, 1974) as implemented intomanysurfacefuelinventorysamplingsystemssuchasFIREMON (www.fire.org/firemon) (Lutes and others2006).Planarintersecttechniqueswereonlydesignedforestimatingdownedwoodyfuelloadingsatthestandlevelusinglineartransectsthatdefinesamplingplanes.Deadandliveshrubandherbaceousfuelsmustbeeithermeasuredusingtime-consumingdestructivemethodsthatinvolveclippingthesefuelswithinsmallmicroplotsorusingindirecttechniquessuchasallometricregressionequationsfromcanopycoverandheightestimates.Load-ingsofduffandlitterareoftenestimatedastheproductofduffdepthsandbulkdensitiesmeasuredatvariouspoints along the fuel transects or fromcollecting and

The Photoload Sampling Technique:

Estimating Surface Fuel Loadings From Downward Looking Photographs of Synthetic Fuelbeds

Robert E. Keane and Laura J. Dickinson

weighingasubsample(Brownandothers1982).Manytimes,thescaleanderrorofsurfacefuelmeasurementsareincompatibleandinconsistentacrossthefuelcom-ponents;logloading,forexample,oftenvariesatgreaterspatialscalesthanfinefuelloadingbecauseoflogsize.These methods are often time-consuming and requireextensivetrainingandfieldexpertise.Whatisneededisaninexpensive,easy,andquickfuelsamplingtechniquethatcanprovideconsistentestimatesoffuelloadingsatthelevelofaccuracyrequiredbythefirebehaviorandeffectsmodelsforfuel treatmentplanning.Thesefuelloadingestimatesmustbeaccurateenoughtobeusedasinputstofirebehaviorandeffectsmodels,andtheymustalsoaccuratelyquantifytheamountofliveanddeadcarbononthegroundformanagingcarbonbudgets. This reportpresentsacomprehensivefuelsamplingprotocolforquicklyandaccuratelyestimatingsurfacefuel component loadings using a system called thephotoload sampling technique that involves makingvisualestimatesofloadingfromasequenceofdownwardlookingphotographsdepictinggraduatedfuelloadingsbysixfuelcomponents.Adetailedsamplingprotocolispresentedsothatloadingscanbeestimatedatvariouslevelsofeffortandscale.ThephotoloadsequencesinthisreportwerespecificallydevelopedtodescribetherangeoffuelloadingsforcommonfuelcomponentsinthenorthernRockyMountainsofMontana,USA.Alsoincludedisaplotformforuseinthefield. AcompanionreportbyKeaneandDickinson(2007;RMRS-RP-61CD) details the set of methods used toconstructthephotoloadsequencespresentedheresothatphotographscanbetakenforlocalfueltypesorspecializedconditions.Thecompanionreportalsopresentsanevalu-ationofthephotoloadsamplingtechniquebycomparingthephotoloadestimatesmadebymanyparticipantsinafieldstudywiththefuelloadingsactuallymeasuredon1m2and100m2microplots.

2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

Photoload Sampling Background

General Description

Thephotoloadsamplingprotocol isafuelsamplingtechniqueusedtoestimatetheloadingofsurfacefuelsforanumberoffiremanagementobjectivesbutprimarilyforthepredictionoffireeffects.Thistechniqueusesaseriesofdownward-orsideward-lookingphotographsofsyntheticfuelbedsofgraduallyincreasingfuelloadingsasreferenceforvisuallyestimatingfuelloadingsinthefield.Yousimplymatchthefuelloadingconditionsob-servedonthegroundwithoneofthephotoloadpicturesinthesetforthatfuelcomponent.Youcanalsoadjustfor thespatialdistribution,diameter,degreeofdecay,anddepthofloadingacrossthesamplespace.Thepho-toloadtechniquecanbeusedtoestimatefuelcomponentloadingsatamicroplot,macroplot,stand,orlandscapescaleatvariouslevelsofeffortdependingonyourneeds,objectives,andavailableresources(samplingtimeandfunds).Thistechniquecanonlybeusedtoestimatetheloadingofsurfacefuelsanddoesnotprovideestimatesofcanopycharacteristics.Italsoisn’tdesignedtoestimateloadingsofduffandlitterlayers. Wedesigned this sampling technique tobeusedbyfiremanagers,fuelspecialists,andresearcherstoquicklyestimate fuel loadings by fuel component. However,it is just oneof themany sampling tools available tosampleormonitorfuelloadings,suchasphotoseries(forexample,seeFischer1981)orplanarintersectmethods(seeFIREMON,Lutesandothers2006).Thephotoloadtechniqueisnotintendedtoreplacethepreviouslydevel-opedprotocolsandmethods.Rather,itisintendedtobeaviablealternativewhentheobjectivesofthesamplingeffortandtheresourcesavailabletoperformthesam-plingmatchthedesigncharacteristicsofthephotoloadtechnique.Forexample,afiremanagementagencymightrequire theaccurate estimationof fuel loadsbut theirfieldcrewshavelimitedexperienceinplanarintersectfuelsamplingandtheremaybelittlefundingavailablefortraining,thereforethephotoloadtechniquemaybeaviableoption. Photoloadtechniquesarebestusedwhen: • Fuelsamplingexperienceislow—Thephotoload

techniquecanbequicklylearnedandunderstood.Ittakeslessthanadaytobecomeeffectivewiththephotoloadtechnique.

• Availablesamplingtimeislimited—Thephotoloadtechnique is a relatively quick and inexpensivemethod that provides moderately precise andreasonablyaccuratefuelloadings.

This protocol will eventually be included in theFIREMON sampling system (Lutes and others 2006;www.frames.nbii.gov/firemon) as a separate method.FIREMON(aFIREMONitoringandinventorysystem)consistsofasetofsamplingmethods,databases,andplotsheetsforsamplingfuels,firebehavior,vegetation,andbiophysicalsettings.BybecomingapartoftheFIRE-MONsystem,thephotoloadsamplingprotocolcanbenestedwithinanynumberofothersamplingmethodstoobtainafullyintegratedsamplingschemedesignedtofit any application fromdocumenting changes in fuelloadingsaftertreatmenttoassessingfuelconsumptionusingmodeling.Forexample,theusercanquantifyfuelloadingswiththephotoloadtechniqueanddescribetreedensitywiththeFIREMONTreeDatatechniqueatthesamesamplesiteandwithinthesamesamplingspace(aplotforexample). Thetypicalfuelbediscomposedofmanyfuelcompo-nentswiththetypesanddefinitionsofthesecomponentsoften dictated by the objective of the fuel samplingproject.Sincemostfuelsamplingeffortsareinitiatedtoquantifyfuelsforfirebehaviorandeffectsprediction,weusedthesamecomponentsinthephotoloadsamplingprotocol.Sixfuelcomponentsareexplicitlyrecognizedinthephotoloadtechnique: • 1hour:<1cm(0.25inch)diameterdowned,dead,

woodyfuels • 10hour:1-2.5cm(0.25-1.0inch)diameterdowned,

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

dead,woodyfuels • 1000hour(logs):7+cm(3+inch)diameterdowned,

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

2inchesdiameter) • Herb:Deadandlivegrassandforbfuels

Theloadingisvisuallyestimatedforeachfuelcom-ponent.Wedidnotincludeduffandlitterlayerfuelsinthismethodbecausetheirloadingismostlydependentonlayerdepthwhichisdifficulttoestimatefrompho-tographs.But,wedescribehowestimatesoflitterandduffloadingscanbemadeusingtheFIREMONmethodslinkedtothisphotoloadsamplingprotocol. Thisreportconsistsof fourparts thatare integratedtogethertoformthephotoloadsamplingtechnique.Thebodyofthisreportpresentsthebackgroundandsamplingprotocolusedforthephotoloadsamplingtechnique.Ap-pendixAcontainsthesetofphotoloadsequencesforthesixsurfacefuelcomponentsintegratedintothephotoloadsamplingtechnique.Thephotoloadsequencesconsistofa

�USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

seriesofphotographsoffuelbedswithgraduallyincreas-ing fuel loadings. They were developed for commonfuelbedsthatoccurintheNorthernRockyMountains,especially thosearoundwesternMontana,USA.Pho-toloadsequencesforother locally importantfuelbeds,suchas shrubandherbaceousspeciesnot included inAppendixA,mustbedevelopedusingmethodsdetailedin thecompanion report (KeaneandDickinson2007;RMRS-RP-61CD).AppendixBcontainsasetoftablesforcomputinglargebranch(100hour)andlogloadingsfromestimatedbranchorloglengthsasanalternativeor companionmethod to estimating largewoody fuelloads within the photoload sampling technique. Last,AppendixCcontainspossibleplotformsandcheat-sheetsthat can be used for estimating and recording photo-load-derivedfuelloadingswithinasamplingarea.Oneplotformallowsyoutorecordthedetailsofphotoloadestimationswhilethesecondisusedonlytorecordthefinal loadings.These appendicesweredesigned toberemovedfromthisreportusingarazororscissorsand

thenplacedinaclipboardforreferenceinthefield.Werecommendthesepagesbelaminatedsotheylastlongerandareprotectedagainstwaterdamage.

The Photoload Sequences

Asmentioned, thephotoloadsequences thatwede-velopedfornorthernRockyMountainforestsarefoundinAppendixA.Eachfuelcomponentwasphotographedindependentlysothatanaccurateestimatecanbeobtainedwithouttheconfusionofincludingothercomponentsinthephotos.Thephotoloadsequencesareorganizedinaseriesofpictureswitheachpictureshowingincreasingfuelloadings.Allpicturesforthefinewoodyfuels(1,10,100hourfuels)weretakenfromdirectlyoverheadlookingdownwardata1mx1mfuelbed(fig.1a).Shrubandherbfuelcomponentsaretakenfromoverheadandfrom the side at eye level (oblique) (fig. 1b and 1c).Log fuel loadings (1,000hourwoody)areonly takenateyelevelfromtheside(oblique)buton100m2plots(fig.1d).Theloadingforthefuelineachpictureisshown

Figure 1—Examples of pictures in a photoload sequence: a) � hour fuels (0.5 kg m–2), b) shrub fuels (ninebark, 0.�5 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-�90. 2007

inbothEnglishandmetricunitsatthetopofthephoto(AppendixA).Forfinewoodyfuels,thereisascaleinthebottomrightcornertohelpcalibratetheuser’seyeforsize.Weplacedafamiliarobjectnexttothesideoftheobliquepictureintheshrubandherbphotostoalsohelpcalibrateyoureyeandtoprovidereferencefortheaverageheightoftheplantsinthephotos. The photoload sequences were designed using themetric measuring units of kg m–2 for many reasons.First,theseunitsmoreaccuratelydescribethefuelsatthe scaleofdevelopmentof thephotoloadsequences.Second,theseunitsaremoreappropriatefordescribingthespatialdistributionofthefuelscomponentsusedinphotoloads,especiallyfinewoody,shrub,andherbaceousfuels.Third,itiseasiertovisualizetheweightandareaof these units than the conventional units of tons peracre.Youcansimplymultiplymetricloadingestimatesby4.46toconvertkgm–2totonsacre–1(conversionfromkgtolbis0.454).Andlast,wefeltthatasquaremeterrepresentsthesmallestpracticalscaleofevaluationforfinewoody,shrub,andherbaceousfuels. TheformatwechoseforthephotographsinAppendixA represents a compromisebetweenconvenienceandsamplingscale.Thepicturesarelargeenoughtoallowsufficient resolutionbetween twosimilar loadings formostpurposes,but small enough toobtainacompre-hensivesetofloadingsforeachplantonjustonepage.Depending on the needs and accuracy of your ownstudyobjectives,youmayrequireadditionalresolutionbetweenphotos(morephotos),orlesspagestobringintothefield(lessphotos).YoucanalwayscreateyourownphotoloadsequencesusingtheextensivesetofpicturescontainedonthecompanionCD(KeaneandDickinson2007;RMRS-RP-61CD).

Photoload Sampling Protocol

Determining if Photoload Sampling is Right for You

Manysamplingmethodscanbeusedtoestimatefuelloadings and each has advantages and disadvantages.Complexsamplingstrategies,suchasfixedareaplotandplanarintersecttechniques,areaccurateandsomewhatrepeatable, but they can require extensive expertise,time,andfundingtoimplementdependingonthefuelcomponentsampledandtheobjectiveofthesamplingeffort(seeKeaneandDickinson2007;RMRS-RP-61CD).Theseextensiveproceduresareusedwhensamplingob-jectivesrequirehighqualityandaccuratedata.However,sometimes fuel sampling must be done with minimal

funding, limitedtime,andlackofsamplingexpertise.Inthesecases,visualestimatesoffuelloadingsmaybetheonlyalternative. Therearethreeocularmethodsforestimatingfuelload-ings.Thephotoseriesmethodusesobliquephotographsofstandconditionsacrossavarietyofhabitattypesandcovertypesthatoccurwithinthemanagementarea(seeFischer1981orOttmarandothers2004forexamples).Manyfiremanagersareusingthesemethods,butrecentresearchhasshownresultantocularestimatesmaybeinaccurateand inconsistent forsomefuelcomponentsbecause thesesamecomponentsarenotvisible in thephotoseriesphotographs(Lutes1999).Anothertechniqueisusingafuelmodelthatisidentifiedinthefieldusingvariousattributessuchasvegetationcomposition,fuelbedcharacteristics, and expected fire behavior (Andersonandothers1982;Sandbergandothers2001;Lutesandothers [in prep]).A fuel model has loadings assignedtoeachfuelcomponentforuseinvariousmappingandmodelingactivities.Thismaybetheeasiestmethodbutthesmallnumberoffuelmodelsusedtorepresentthewidevarietyoffuelconditionsmayprecludeanaccurateestimationofloading.Moreover,thereareveryfewfuelclassificationsthatprovidecomprehensivekeyingcriteriaforconsistentlyidentifyingthefuelloadingconditions.Thephotoloadtechniqueisthethirdalternativeforocularestimationoffuelloads.Itmaybedesirablebecausethephotosportraygraduatedfuelbedloadings,thefuelsarecompletely visible, and the estimates are made at theappropriatespatialscalethatbestmatchesspatialfueldistribution. With this inmind,we recommend the following. Ifthereissufficienttime,expertise,andfundstoobtainfuelloadingestimates,wesuggestthatthemanagerusetheplanarintersectmethodoffuelsamplingtomeasuretheseloadings(useprotocolsdetailedinFIREMON).Thefixedareatechniqueisalsousefulandsometimesmoreaccurate,butitcanrequireprohibitivelylongsamplingtimesanditmaybedifficulttorectifythesamplingintensitywiththevariabilityoffuelswithininaheterogeneousarea.Itismoreappropriateforresearchapplications.Ifsamplingcrewshavenotbeentrainedforplanarintercept,photoseries,orfixedareamethods,thenthephotoloadtechniqueisaviablealternativeandmaybebetterthanthephotoseriesmethod.Ifsamplingcrewshaveexperienceinphotoseriesfuelsestimation,thenphotoseriesmaybemoredesirablebutnotasaccurateasphotoloadtechniques.If loadings for locally important fuel components arenotpresentinthephotoloadsequencesorphotoseriesphotographs,thefuelmodelmethodisperhapstheonlyalternativebutthereneedstobeafuelmodelkeyforyour

5USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

area.Wealsorecommendthatyouuseseveralmethodsinanestedstrategytoensurethemostaccurateestimationoffuels.Forexample,planarintercept techniquescanbeusedevery10thplottoprovideconsistentcalibrationfor the photo series and photoload estimations or thephotoloadtechniquecanbeusedfirst,andestimatescanthenbecheckedusingphotoseriesestimates.

Preparing for Photoload Sampling

Werecommendthefollowingequipmentbepurchased,obtained,orfabricatedforuseinphotoloadsampling. • Plot frame—A square meter plot frame with the

squaremeterareameasuredontheinsidedimensionsoftheframe.Youcanconstructthisofwood,PVCpipe,ormetalrods.

• Go-no-go gauge—Adevicethathasthewidthsofeachwoodysizeclassupperdiameterrange.

• Clear plastic ruler—Arulerthatcanbeconvenientlystoredinacruiser’svest.Thisrulerwillbeusedtoestimate logdiametersandbranch lengths. Itcanalso be used to measure duff and litter depths ifneeded.

• Cloth tape—Thelengthofthetapewouldbedictatedbythesamplingdesign.Thistapewouldbeusedtolocatesamplingpoints.

• Clipboard—Thisclipboardshouldhavetheabilitytoallowquickreferencetothephotoloadsequences.

• Calculator—Thisishandyforsumminglogdiametersandlengths.

• Nails –Longnails(>20cm)arehandyforanchoringlongmicroplottransectlineseitherpermanentlyorsemi-permanently.

• Photoload procedures and sequences—Wesuggestthatthephotoloadsequencesbecutfromthisdocu-mentandlaminatedtowaterproofthepages.

• Digital camera—Werecommendthatpicturesaretakenofthesamplesiteandsomemicroplotcondi-tionsforfuturereference.

Determining the Scale of Sampling for Your Assessment

Thefirst taskinphotoloadsamplingisdecidingthescale at which to make the loading assessment. Thescaleofsamplingismostlydeterminedbythesamplingobjectiveandthesampleunit.Thesamplingobjectivedefinestheaccuracyrequiredfortheloadingestimatesandthesamplingunitdefinesthespatialresolutionthattheloadingestimatesareintendedtodescribe. Thesamplingobjectiveisthepurposeofthesamplingeffortanditdictatesthedetailsofanysamplingeffort.If

thefuelssamplingprojectwasdesignedtoquantifyfuelsforinputintofireeffectsmodels,theaccuracyofthefuelloadingestimatesmaynotneedtobehighbecauseofthecoarseresolutionoffireeffectssimulation.Conversely,highaccuracyisneededwhenasamplingprojectiscon-cernedwithmonitoringfuelloadingsaftermanagementtreatments.Ifthesamplingobjectiverequiresaccurateestimates of fuel loadings, then, as mentioned above,planarintersectorfixedareatechniquesarewarrantedaslongasfieldcrewsareproperlytrained. Thesampleunitisthefinestareawherefuelloadingvaluesareneededforsummaryasspecifiedbytheobjec-tive.Forexample,themonitoringoftheeffectofafueltreatmentwouldprobablybedoneatthestandlevel(areainsidethetreatmentboundaries),whereastheinventoryoffuelloadingsonaplotwouldrequirethefuelloadingsbe estimated for the areawithinplot boundaries.Thesizeofthesamplingunitdictatesthescaleofphotoloadimplementation.Sincethephotoloadtechniqueperformsbestwhenthesamplingscaleissmall(aboutonesquaremeter),itisimportantthattheestimatesoffuelloadingforlargerareas(coarserscales)accountforthepatchinessofloadingacrossthatsamplingarea. Thefirstfactortoaddresswhendesigningaphotoloadsamplingprojectisthedesiredaccuracyofthephotoloadestimates as specified by the objectives. Since ocularestimates aremore accurate if estimatedwithin smallplotframes,themostaccuratesamplingapproachesusea random or systematically stratified network of onesquaremetermicroplotswithinthesampleunit.However,iftheobjectiveimpliesthatonlyageneraldescriptionoffuelsandtheirloadingsaredesired,suchasinputtocomputermodels for alternative treatment evaluation,thentheusercanestimateloadingsfortheentiresamplingunitinsteadofusingmicroplots.Thesamplingobjectivewillalwaysbetemperedbytheresourcesavailableforsampling.Forexample,iftimeandfundingarelimited,thenthetime-intensivemicroplotoptionisprobablynotpossibleandloadingestimatesmighthavetobedoneatthelargersampleunitlevel.Sincemostfuelsamplingprojectscollectloadingmeasurementstobeusedasinputtofirebehaviorandeffectsmodels,itisimportantthatthesamplingintensityreflecttheresolutionandaccuracyrequired by the models. One must always rememberthatthequalityofmodelpredictionsincreaseswiththeaccuracyoftheinputparameters.Last,ifthesamplingobjectivespecifiestheneedforanestimateoffuelvaria-tion,thenthemicroplotsamplingatthedesiredsamplingscaleisthebestalternative. Wefeelthatthephotoloadmethodcanbebestimple-mentedatoneofthreescales:themicroplot,themacroplot,

� USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

andthestandscales.Thesescalescanbeintegratedintoanestedsamplingdesigntoimproveloadingestimates.Thebestwaytoillustratethisistodescribefourcommonsamplingsituations.First,sayafiremanagermustquan-tifyfuelsfora100acre(40ha)standbutdoesnothavetheexpertiseforconventionalsamplingtechniquesanddoesnothavethetimefornestedmicroplotmethods.Inthiscase,perhapsthebestmethodforestimatingloadingsinvolvestraversingthestandandmentallydetermininganestimateofloadingforeachfuelcomponentusingthephotoload technique (stand level photoload estimate).Second,saythefiremanagerrealizesthattheremaybemoretimeavailabletogetamoreaccurateanswer.Themanagermighttheninstallfourmacroplots(large,0.04haor0.1accircularplots)thatrepresentthefourcommonfuelloadingconditionsobservedwithinthestandand,ateachmacroplot,themanagerestimatesfuelswithinthemacroplotboundariesusingoneloadingestimateforeachofthesixfuelcomponents(macroplotphotoloadestimatesummarizedtothestandlevel).Themanagerwouldthenneedtoestimatetheproportionofthestandthateachplotrepresentstogetaweightedaveragebyareafortheentirestandfortheloadingofeachfuelcomponent.Third,thesamemanagerrealizesthatmoreaccurateanddefensibleestimatesareneededbecausetheprojectobjectivein-cludesmonitoringandmoretimeismadeavailable.Themanagermighttheninstallagridof25microplots(1m2squareplotsona5x5gridsize)withineachofthefourmacroplotstoobtainabetterestimateofloadinganditsvariance(microplotphotoloadestimatessummarizedtothemacroplotlevelthatisthensummarizedtothestandlevel).Or,themanagermightplaceoneormoremicroplotsinanareaormultipleareasofthemacroplotthatwouldberepresentativeoftheloadingforthatmacroplotsub-area(muchlikethemethoddescribedaboveforplacingmacroplotsinstands).Fourthandlast,saythemanagerhassufficienttimeandwantsthebestpossibleestimateusingthephotoloadtechnique.Here,themanagermightinstallaseriesofsystematictransectswithinthestandandestablishamicroplotatfixedintervalsalongeachtransect(forexample,onemicroplotevery50meters)(microplotphotoloadestimatessummarizeddirectlytostandlevel).Theabilityofthephotoloadtechniquetoadapt tovarious scaleandaccuracy issuesmakes it aflexibleandrobustsamplingmethod.Wefeellandscapelevelestimatesoffuelloadings(oneestimateofloadingforeachofthesixcomponentsfortheentirelandscape)maybeinappropriatebutpossiblealbeitexpensive.Thebestwaytoquantifyfuelloadingsfortheentirelandscapeis tosample loadingsforallstands thatcomprise thatlandscape.

Withall this inmind,werecommendthemicroplotapproachalwaysbeusedinphotoloadsamplingunlesstime,funding,andfieldexperiencearelimited,inwhichcasewerecommendthatloadingsbeassessedatthescalethatbestmatchesthespatialresolutionrequiredbythepredictivefiremodelsandthesamplingobjective.Theamount of time and funding available to perform thephotoloadloadingmustbedeterminedtotrytomatchtheresourcesavailableforsamplingandthesamplingobjectivetothesamplingscale.Ifaccurateanswersarerequired but time and money prevent microplot sam-plingacrosslargestands,thenusemicroplotsamplingonmacroplotsormacroplotsamplingacrossthestandarea.Proceduresforsamplingateachofthescalesaredetailednext. Youshouldfirstdecideonaconventionformacroplotandmicroplotshapeandestablishment.Wesuggestthatthemacroplotbesquareandthesidesorientedinthefourcardinaldirections,butotherstrategiesmayworkjustaswell,suchasorientingthesidessotheyareupslopeandcrossslope.Agridofmicroplotscanalsobeestablishedincircularplotstoallowmoreefficientintegrationintoothersamplingefforts,suchastreepopulationsampling.Wealsosuggestthatthecornerofthemicroplotframealwaysbeestablishedinthesouthwestcorneralongatransectsothatthemicroplotsarealwaysonthetopandalongtherighthandsideofthetransect.Besuretore-cordallmethodsandthesamplingdesignspecificationstoensurethattheprojectcanberepeatedandanalyzedcorrectly.There is ametadatadatabase inFIREMONtorecordthesesamplingspecifications,ortheusercansimplyenterthesespecificationsinanotebookforlaterreference.

Using the microplot approach—Themicroplotap-proachinvolvesusingplotframes(microplot)todelineateasmallsamplingareatovisuallyestimatefuelcomponentloadings.Inthephotoloadsamplingtechnique,thesizeorareaofthemicroplotshouldbethesameasthefuelbedsphotographed in the photoload photo sequences; wesuggestthatthemicroplotframesbeexactlyonemetersquare(1mby1m)tomatchthedimensionsinthepho-toloadpictures.Althoughothersizedplotframescouldbeused,youmustadjusttheestimatedloadingstoaccountfordifferencesinplotframearea.Themicroplotframesidesshouldbeonemeterlongmeasuredontheinsidedimensions,nottheoutsidedimensions.Werecommendthatonecorneroftheframeremainunattachedsothattheframecanbeopenedtoincludelargetreesoranyotherobstruction.Wealsosuggestthattheuserbuildseveralplotframesastheywillcomeinhandyifthereismore


thanonepersononafieldcreworifaframebreaks.WemadeourplotframesoutofplasticPVCpipeandused90degreecornerpiecestobindthelengthstogetherwithglue;however,anymaterialfromwoodtometalbarswilldo. Inthemicroplotapproach,themicroplotsareinstalledonagridwithinthesampleunitinadesignthatfullydescribesthespatialdistributionoffuelloadingacrossthesampleunitwithoutpreconceivedorstatisticalbias.Werecommendrandomlyestablishingastartingpointforthefirsttransect,andthenestablishingasystematicgridthatevenlyplacesthemicroplotsacrosstheentiresampleunit.Thebeginningandendofeachtransectcanbemarkedwithanironpipe,rebar,orlargenailthatisperma-nentlyortemporarilydrivenintotheground(monitoringapplicationswouldrequirepermanentestablishmentoftransects)(seeFIREMONforpermanentestablishmentofplotsortransects).Ultimately,theusershouldstrivefora10percentsampleofthesamplingareabuttimeandfundingwillnearlyalwaysdictatethata1percentsampleismorefeasible.Forexample,ifthemacroplotis400m2(20mby20m)thena10percentsamplewouldbe40m2or40microplots.Thesemicroplotscouldbeinstalledon4transectsthatare5metersapartandtheplotframewouldbeplacedevery2metersoneachtransect.Inmonitoringapplications,itisimportantthatanailbedriveninateachmicroplotlocationtomakesurethatfutureestimatesaredoneonthesamepieceofground.Wesuggestatleasttwocornersbemarkedwiththenailsforeachmicroplot.Wefoundthatplasticropeinabrightcolorseemstoworkwellfortransectsbutclothmeasuringtapesandstringalsoworkequallywell.Theropescanbemarkedatfixed-lengthintervalstodefinetheplace-mentofthemicroplotplotframealongthetransect.Besuretoassigneachmicroplotanumberandrecordthisnumber,alongwiththefuelcomponentloadingsintheplotform(s)(usethesubplotfieldinAppendixC). Standlevelmicroplotgridsaremoredifficulttodesignbecausestandboundariesarerarelysquareorrectangular.Moreover,a10percentsampleina largestandmightresultinaprohibitivelylargenumberofmicroplots.Forexample,a100acre(40ha)standwouldrequirearound40,486microplotsof1 squaremeter for a10percentsampleresultinginanimpracticalsampletarget(eventhe4,048microplotsrequiredfora1percentsampleseemsex-cessive).Therefore,thedesignandimplementationofthemicroplotsamplinggridwouldneedtobeacompromisebetweenfeasibilityandstatisticalvalidity.Wesuggestthatusersmatchthetimeavailabletospendsamplingonestandwiththetimeittakestorecordloadingsforthesixcomponentsatonemicroplot(1-5minutes)and

calculategridsamplingdensity.So,iffourstandsneedtobesampledinoneday(eighthourworkingday),thatmeans that there is roughly 120 minutes (two hours)per stand or 24 microplots per stand assuming a fiveminutemicroplotsamplingtime.Thiscouldbeputonagridacrossthestandthatmatchesthestandshapeandsize.Wefoundthatourphotoloadevaluatorsaveragedapproximately6.3minutespermicroplottoestimateload-ingsofallfuelcomponentsincludingthetimeittooktoestimatelogloadingsatthesubplotlevel.Wealsofoundthatmicroplotsamplingtimesrangedfrom2.7minutesforthemostexperiencedevaluatorstoover10.1minutesfornovicefuelsamplers.Thesetimestendedtoincreasewithincreasingloadingswiththelongesttimesfortheslashsites(7.2minutes)andheavyfuelunits(6.3minutes).Times for most people decreased as more microplotswereevaluated,especiallyforthesubplotestimatesoflogloadings,aspeoplelearnedhowtoefficientlyusethelogloadingtable(KeaneandDickinson2007;RMRS-RP-61CD). Another option for the microplot approach is usingdoublesamplingasageneralframeworkfortheapplica-tionofthephotoloadtechnique.Indoublesampling,visualestimatesoffuelloadingsareobtainedonallmicroplotsin the samplingunit,buta subsetof thesemicroplotsisalsodestructivelysampled(fueliscollected,sorted,dried,andweighted)justafterthevisualestimatesaremade.Regressiontechniquescanthenbeusedtodevelopcalibrationrelationshipstoadjustthevisualestimatesus-ingthedestructivelysampleddata.Alargesubsetshouldbeobtainedthatspanstheentirerangeofloadingvaluesforeachfuelcomponent. Using the macroplot approach—Thisapproachwillprobablybethemostcommononeusedinfiremanage-ment.Heretheusertraversesthemacroplotandestimatesaloadingthatbestrepresentsthemacroplotasawhole.Again, a macroplot is usually about 0.1 acres in sizeandisoftencircularorsquare.Theusermustaccountforthespatialdistributionoffuelintheplotandadjusttheestimateaccordingly.Werecommendthefollowingprocedure: 1.Visuallydividethemacroplotintoareaswherethere

areobviousdifferencesinfuels. 2.Estimate the proportion of those divisions to the

entiremacroplotarea. 3.Estimate the loading of each fuel component for

eachofthedivisions. 4.Calculate a weighted average by area of the

loadings. 5.Recordtheloadingontheplotsheet.


Novicesofthephotoloadmethodwillprobablyneedtowritedowntheproportionalareasandrelatedloadingstoaccuratelycalculatetheweightedaverageloading,butmoreexperienced fieldpeoplewill find that theycanactuallyperformmanyofthecalculationsintheirhead.Remember,theresolutionofthephotoloadestimatesisquitelowsotheproportionalareasandweightedaveragecalculationsneednothavethreeorfourdecimalplaces.Forexample,werecommendtheproportionalareasbeinclassesof10percentandloadingestimateneverhavemorethantwodecimalpoints(0.02forexample). Samplingtimesforthemacroplotapproachwillvarybyevaluator and site conditions,butwe found that ittookabout5.1toover10minutestoestimateloadingsofallsixsurfacefuelcomponentsforamacroplot.Thisestimatewilldecreasewithincreasingsamplingexperi-enceanddecreasingfuelloadings. Using the stand approach—Althoughfiremanagersmightthinkthatthisisthemostdesirablescaleatwhichtoestimatefuelloadings,wesuggestthatstandsbedividedintohomogenousareasoffuelloadingstomoreaccu-ratelyestimatealoadingfortheentirestand.Moststandsarequitelargeanditmaytaketimetoproperlytraversetheentirearea,whichmakesitdifficulttorememberorvisualize thedistributionoffuelconditionsacross thesub-areas.Therefore,wesuggesttheuserinstallagridof either microplots or macroplots to systematicallysamplethestand.Thenumberofplotsinthegridwouldbe dictated by a number of factors, most notably thetimeavailabletosamplethestand.Followtheguidancepresentedintheprevioustwoapproachesforthepropermethodsformicro-ormacroplotsampling. Ifagriddedsamplingstrategyisimpossibleandtheuserhastimeforonlyoneestimate,werecommendusingthesameproceduresforthestandasforthemacroplot.Thefollowingproceduresshouldbefollowed:

1.Visuallydividethestandintoareasthatreflectobvi-ousdifferencesinfuelloadings.

2.Recordthesedivisionsonastandmap. 3.Estimate the proportion of those divisions to the

entirestandarea. 4.Estimatetheloadingofafuelcomponentforeach

ofthedivisions. 5.Calculate a weighted average by area of the

loadings. 6.Recordtheloadingontheplotsheet.

Thedetailandresolutionof thestanddivisionswillprobably be dictated by the sampling objective. If aquickestimateoffuelloadingsisneededtocomputeafireeffect,thenthedivisionsshouldonlyreflectmajor

loadingdifferences(lowandhighloadings,forexample).However,ifthefuelsareneededtodevelopafirepre-scription,thenallfuelcomplexesshouldbedescribedandaccountedforinthefinalestimate. Last,westronglyrecommendthattheareaproportionsandassignedloadingsforeachsub-standberecordedforlateruse.Thisisimportantfortheaccuratecalculationoffireeffects.ModelssuchasFOFEMandCONSUMEarepointmodelsthatpredictfireeffectsforapointonthelandscape.Theuserofthesemodelsmusttakethepointpredictionsandsummarizethemtothespatialscaleofapplication.Todo this fora stand,we recommendthatthefuelloadingsforeachofthestanddivisionsbeusedtosimulatefireeffectswiththepredictedeffectsthensummarizedtothestandlevelbytheareaweightedaverage.

Making the Photoload Fuel Loading Estimates

Estimatingfuelloadingwiththephotoloadtechniqueinvolvesmatchingtheconditionsobservedonthegroundwithinthesamplingunitwiththeconditionsinthesetofphotographsof loadingsprovided in thephotoloadsequences (AppendixA).Theconditionsarematchedonlyonvisualassessmentofloadingcharacteristics;nootherfactorssuchasfuelbedappearance,color,orwetnessshouldbeconsidered.Theusershouldtrytomatchload-ingsbetweenthephotosinthephotoloadsequenceswiththeloadingsobservedontheground.However,estimatingfuelloadingsusingonlyocularguessesisnotassimpleasitappears.Manyfactorsmustbeaccountedforintheocularestimatetoobtainthemostaccuratefuelloadings.Thefourmostimportantfactorsarespatialdistribution,degreeofdecay,branchdiameter,andfueldepth. The photoload sampling technique was designed toestimateloadingsforthefuelcomponentsthatareabovethelitterlayerandplainlyvisibleandidentifiable.Somepartsoftwigsandbranchesareburiedinthelitterandduff.Donotincludetheburiedmaterialintheloadingestimate.Anythingburiedbelowthelitterlayerisconsidereddufforlitterandshouldbesampledusinganothertechnique(wesuggesttheFIREMONFuelLoadingmethods).Thisisalsotrueforlogs.Thecentralaxisalongthelongitu-dinallengthofthelogneedstobeabovethelitterlayertobe considered1000hourwoody fuels.Rotten logsarethemostdifficulttoidentifyforsamplingbecausetheyarebrokenanditisdifficulttoidentifythecentralaxis.Theplanarintersectmethod(Brown1971)detailedinFIREMONrecommendsvisuallyreconstructingtheoriginallogsizeforrottenlogsthathavefallenapart.


We found throughextensive testing that theorder thatloadingsareestimatedbyfuelcomponentsisimportantformanypeople.Manyfoundthatconfusionwasminimizedifthefuelcomponentswiththelowestloadingswereestimatedfirstandthosewiththehighestloadingsestimatedlast.Wesuggestthattheuserfirstenterzeroforeachfuelcomponentnotevidentwithinthesampleunit.Then,entertheloadingsforthosecomponentswithminimalloadings,suchasshrubsandherbs.Thisusuallyleavesonlyoneortwocomponentsleftandtheloadingsfortheseareeasilyestimatedbecauseallotherfuelshavebeeneliminated.Wesuggestthatlogloadingsalwaysbeestimatedlastbecausetheyareusuallydoneata100m2scale.

Adjusting visual estimates—Any estimate of fuelloadingmustbeadjustedtoaccountfor thevariabilityandpropertiesofthefuelwithinthesamplingunit.Theloading of any fuel component is rarely evenly anduniformly distributed across a sampling unit, and thisis especially true for woody fuels. Fuels are normallyclusteredinpilescalled“jackpots”becausetheoriginsofmostfuelsareusuallyfromtrees,andtreesareusuallyclusteredandclumpedwithinastand.Therefore,theuserofthephotoloadtechniqueshouldalwaysaccountforthespatialdistributioninthevisualloadingestimates.Thisisdonebymatchingphotoloadpictureswithalllevelsoffuelloadingwithinthesampleunitandthenperformingaweighedaverageoftheseloadingswiththeestimatedaerialproportionsof thefuel loading levelswithin thesamplingunit.Asanexample,saywehavea1,000m2plotand,bymatchingphotoloadsequencepictures,foundthattheocularestimatesforfuelloadingswere0.1kgm–2for10percentoftheplot,and1.1kgm–2for50percentoftheplot,and2.0kgm–2for40percentoftheplot;then,thefinalloadingwouldbe1.36kgm–2(0.1x10+1.1x50+2x40dividedby100).Thisconceptcanbeusedtoadjustloadingsatanyscale,mostoftenwithinamicroplot,macroplotorstand.Manypeoplemayfindthatitiseasierandquickertoperform thisweighted average in their headswhileothers,especiallynovices,needtowritetheinformationontheplotsheet(seethefirstplotforminAppendixC).Someoftheevaluatorsofthephotoloadmethodfounditusefultovisualizewhatthefuelinthesamplingunitwouldlooklikeifeachcomponentwasevenlydistributedonthegroundandthisvisualizationwascomparedwithphotoloadpictures.Inthephotoloadsamplingtechniqueevaluation,wefoundthatmanyevaluatorshadtroublemaking the mathematical calculations and writing thesubsequentanswercorrectlyontotheplotform.Werec-ommendthatnovicephotoloadusersrecordallproportionandscaleestimatesontotheplotformandperformthecalculationsafterthevisualestimatesarecompleted.

Thedepthofthefuelbedmustalsobeaccountedforintheestimateofloadingusingthephotoloadtechnique,es-peciallyshrubandherbcomponents.Fortunately,woodyfuels on most fuelbeds have virtually no depth undernatural conditions.However, shrubandherb fuelbedshavedepth(measuredasaverageplantheight)andthisdimensionmustbeincludedinthephotoloadprocesstoadjustfortheocularestimate.Thephotoloadtechniqueassumesthatchangesinfueldepthareproportionaltotheloadings.Thisassumptionmaybeanoversimplificationofreality,butthereislittleresearchtosupportanyotherapproach.EachofthepicturesforshrubandherbfuelbedsinAppendixAcontainsaheightoftheplantmaterial.Thisistheheightthatwemeasuredasweconstructedthefuelbedstobephotographed.Wesuggestthatoncethephotoloadpictureismatchedtothefuelconditionsinthefieldandtheloadinghasbeendetermined,theloadingestimateshouldbeadjustedfordifferencesinobservedandpicturedplantheight.Thisisdonebymultiplyingtheestimatedloadingbytheproportionalchangeinheightfromthepicturetotheobservedfuelbed.Forexample,ifthephotoloadshrubheightis1meterandthematchedloadingis2.0kgm–2buttheobservedheightoftheshrubsinthefieldis2meters,thentheactualloadingwouldbetwicetheestimatedloadingcomputedas4.0kgm–2(2.0kgm–2x2meters/1meter)becausetheheightinthefieldistwicetheheightinthephotoloadpicture. Ifthelittersurfaceisnotvisiblefordowneddeadwoodymaterial,asinslashandactivityfuelbeds,thenthesameprocedureshouldbedonetocomputethatloadingexceptthedepthofthephotoloadpicturefuelbedisassumedtobethehighestdiameterofthewoodysizeclass.Usethepictureforthefinewoodyfuelloadthatbestportraysthetopofthefuelbedandthenadjustthatloadingbyfueldepth.Soaslashbedcomposedofa10hourwoodyfuelbedthatis10cmdeepmightbematchedwiththephotoloadpictureof5kgm–2buttheactualloadingwouldbetheproductofthephotoloadestimatedloading(5.0kgm–2)andthedepthofthefuelbed(0.1meters)dividedbythelargestdiameterofthe10hourclass(thisfuelclassgoesfrom0.6cm(0.25inches)to2.5cm(1inch)sothelargestdiameteris0.025meters)sothefinalloadingestimatewouldbe20kgm–2(5.0kgm–2x0.1mdepth/0.025mdiameter). Thedegreeofdecayfordownedwoodyfuelscanalsoinfluencetheaccuracyoffuelloadestimatesandadjust-mentsshouldbemadetocorrectfortheamountofrot.Thephotoloadsamplingtechniqueassumesalldowneddeadwoodyfuelissound.So,anyobserveddecaywillreduce the loadingestimates.Wesuggest that thefol-lowingfactorsbeusedtoadjustsoundfuelloadingsto

�0 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

accountfordegreeofdecayusingthedecayclassesasimplementedinFIREMON.

•Decayclass1—Noneedtoadjustfordecay •Decayclass2—Noneedtoadjustfordecay •Decayclass3—Multiplyloadingsbyafactorof0.90 •Decayclass4—Multiplyloadingsbyafactorof0.75 •Decayclass5—Multiplyloadingsbyafactorof0.50

ThesevalueswerecomputedfromvaluestakenfromBrown(1970)andBusse(1994). Anotheradjustmentisforwoodyparticlediameterinthelargewoodyfuels(100hrand1000hrlogs).Thereisapronounceddiameterbiaswhenestimatingloadingforwoodysizeclassesgreaterthan1inch(2.5cm)indiameter.Therangeofdiametersinlargewoodyfuelsissolargethatphotographsdepictingaloadingforthefuelswithdiametersat thesmallendofthesizeclassmayunderestimateloadingsbyafactorofninefor100hrfuels because loading increases by the square of thediameter.Itisimportantthattheusermakesurethatthediametersobservedinthephotoloadsequencesarethesameasthoseobservedinthesamplingunit.Ifnot,thentheusershouldusethetablesprovidedinAppendixBtoadjustthephotoloadloadingsorapproximateloadingsdirectly.FollowtheinstructionspresentedforeachtableinAppendixBandestimateloadingsaccordingly. Thephotoloadtechniqueallowstheusertheflexibilitytopickaloadingthatmaybebetweentwoconsecutivepicturesinaphotoloadsequence.Forexample,saytheuserfoundthattheconditionsobservedonthegroundfor1hrwoodyfuelsdidnotexactlymatchanyonepicturebuttheloadingwasdefinitelygreaterthanthe1.0kgm–2picturebutlessthanthe1.2kgm–2picture(seeAppendixA).Therefore,theuserhastheabilitytovisuallyextrapo-latebetweenpicturesanddecideonamoreappropriateloading.Forexample,theloadingwouldbeestimatedat1.1kgm–2iftheobservedloadingsappeartobeexactlyhalfwaybetweenthetwophotos.Iftheconditionswerejustabitmorethanthe1.0kgm–2photo,thentheusermightrecord1.01kgm–2astheestimate.Usersactuallyhavetheabilitytoassignanyfuelloadingandtheyarenotconfinedtousingonlytheloadingsprintedonthetopofthephotoloadsequencephotographs. Insummary,wesuggestthateachvisualestimateoffuelloadingsfollowtheseguidelines: 1.Selecttwophotoloadpicturesthatboundtheobserved

loadingonthesampleunit. 2.Computealoadingestimatebyextrapolatingbetween

pictures. 3.Adjust that loading for fuelbed depth for that

component.

4.Adjustthatloadingtoaccountfordegreeofdecay. 5.Adjust that loading for the spatial distribution of

fuelswithinthesampleframe. 6.Adjustloadingfordifferencesindiameterforlarge

woodyfuels.

Itmaybethatthetargetaccuracydefinedbythesam-plingobjectivedoesnotrequirethissix-tieredprocessofocularestimation,butwebelievethateveryphotoloadestimateshouldbedoneaccordingtothisprocedureanduserswillfindthatitwillbecomesecondnatureafterawhile.TheloadinginthephotoloadsequencesisprovidedinbothEnglishandmetricunits.Itisimportantthattheuserselecttheappropriateunitsforassessmentandbeconsistentwhencompletingtheplotsheet. Calibrating your eye for estimating loadings—Wefoundthattheabilityofuserstoconsistentlyandaccuratelyestimatewoodyfuelsismostlydependentontheirlevelofexpertise.Becauseofthis,itisimportantthatusersofthephotoloadtechniquecalibratetheireyesothattheycanconsistentlyandaccuratelyestimateloadings.Thiscalibrationcanbedonebyrepeatingthemethodsthatweusedformeasuringthereferencefuelloadingconditionsintheevaluationofthephotoloadtechnique(seeKeaneandDickinson2007;RMRS-RP-61).Wesuggestthattheuserbuild1x1metersquareplotframesandgotothefieldandestimateloadingswithintheplotframeusingthephotoload sequences andprotocol.Then, theusershouldcollect,dry,andweighbyfuelcomponent,andcomparethemeasuredloadingswiththeocularestimates.Wealsosuggest that theusers takephotosof the1x1meterframesbeforesamplingsothattheycancomparetheirmeasuredloadingswiththephotoloadpicturestocalibratetheireyeinfuturefieldseasonsortoteachthephotoloadtechniquetoothers. Another method to calibrate photoload woody fuelestimatesistodefineaplotofknownareaandinstallanumberoftransectstomeasurewoodyfuelusingtheplanarintersecttechnique(FIREMON,Lutesandothers2006).Wesuggestthatatleast20-30transectsbeestab-lishedandmeasuredwithinthedefinedareatogetthemostaccuratewoodyfuelloadings.Thecomputedwoodyfuelloadingsbysizeclasscanbecomparedtophotoloadestimates for thedefined area.Again, pictures shouldbetakenoftheplotandfuelconditionstodocumentthefuelbedconditionsforuseintrainingfuturecrews. Estimating fine woody fuel loading—Themostim-portantguidanceforestimatingloadingsoffinewoodycomponentsistofirstcorrectlyidentifytherightfuelcomponents.Threequestionsmustbeansweredfortheobservedfuelstobesampled—arethefuels:1)down,

��USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

2)dead,and3)woody.Thefinefuelparticlesmustbeunattached from theirparent stemsandbebelow the6 foot (2meter) surface fuelheight tobe considereddown;downfuelsare thosefuelcomponents thatarenotattachedtoliveoruprightdeadplantsandareen-tirelyonthegroundorbelow6feet(2meters).Largerfuels,suchaslogsandlarge100hrbranchesorboles,maylooklikesnagsandmaynotseemdownbutthisisagrayareaandwesuggestyoufollowtherulethatallwoodyfueloriginatingfromatreeboleisconsidereddownwoodyifitleansatananglegreaterthan45de-greesfromthezenithangle(lessthan45degreesfromthehorizontalground).Ifitisatananglegreaterthan45degreesabovehorizontal,itcanonlybeconsidereddownifitisabrokenboleorbranchfromatreewhereatleastoneendoftheboleistouchingtheground(notsupportedbyitsownvegetationorotherbranches).Themostconfusingsituationinthefieldisdeadbranchesthatareattachedtoaliveordeaduprighttreebutarebelowthe6foot(2meter)samplingheight.Theymighteventouchtheground.Thesearenotconsidereddownfuelsbecausetheyarenotdetachedasyet. “Dead” fuels are fuels that have no live foliage orbranchwood material. Fresh slash and newly brokenbranches with green foliage are still considered deadeventhoughtheyaretechnicallyalivebecauseweas-sumetheywilleventuallybedead.Dormantdoesnotmeandead.Dormantplantswithnolivefoliagedonotcountasdeadfuel.Examplesincludeshrubsthathavelosttheirleavesintheautumnandwinter.Manypeopleareconfusedbywoodyfuel identificationandtendtoputstalksofannualplants,forexample,intothewoodycategorywhen in fact thestalksaredeadherbaceous.Rememberneedles,detachedgrassblades,pinecones,andpiecesofbarkonthegroundareconsideredlitter.Sincelitterloadingisnotassessedinphotoload,besurenottoconfuselitterfuelswithfinewoodyfuel. Whensamplingfinewoodyloadingwiththephotoloadtechnique,theusershouldassesstheconditionswithinthesamplingunit(microplot,macroplot,stand)concentratingon loadingcharacteristicsandselectaphotofromthephotoloadsequencespresentedinAppendixAthatbestmatchesthefuelloadingconditionsthatcorrelateswiththefuelontheground.Ifloadingseemstofallbetweentwoofthephotos,choosetheappropriateloadingbetweenthetwoloadingvaluesinAppendixA,andrecorditonyourdatasheet.Besuretousea“go-no-go”gauge(seeFIREMONmethods)tomeasurethediametersofwoodyfuelstomoreaccuratelyestimateloadings. Itissometimeshelpfultovisuallylineupthefuelinthephotoloadphotograph,andestimatethedistancealongone

sideofthesquarethatthefueloccupies.Then,comparethislengthwiththatobservedinthesampleunit(micro-plot).Ourevaluatorsfoundithelpfultoconcentrateonfuellengthtovisuallycomparephotoloadpictures(theyvisuallyaddedupthelengthoffuelinthephotowiththelengthoffuelobservedontheground).Payspecialatten-tiontotherangeofdiametersinboththephotoseriesandinyoursamplingunit.SomeoftheimagesinAppendixAmaynothavetherangeofdiametersobservedwithinthesamplingunitarea.Therefore,theestimateofloadingmayneedtobeincreasedordecreaseddependingonthedifferencesbetweendiameterdistributions. Themostdifficult task toperform in thephotoloadvisualapproximationsistodistinguishbetweenthefinewoodyfuelsizeclasseswithonlyyoureye.Manytwigshavetapereddiametersthatstartas10hourfuels(diam-etersgreaterthan0.25inches)andthenbecome1hourfuels(diameterslessthan0.25inches)somewhereupthestem.Thisisanothermajorsourceoferrorinphotoloadestimates.Manypeoplefinditconfusingtovisuallysepa-rate1hrfrom10hrfuelsand10hrfrom100hrfuels.Ittakespracticebuteventuallymostpeoplebecomequiteaccomplishedatvisuallyidentifyingthethreefinefuelsizeclasses.Wesuggestusersofthephotoloadtechniquetakea“go-no-go”gaugeoraclearplasticrulerintothefieldtoquicklyidentifyportionsofwoodintotheap-propriatesizeclasses.TipsontheuseofthesetwoitemsaredetailedinFIREMON(Lutesandothers2006). Estimating log fuel (1000 hr) loading—Logload-ingsareestimatedatadifferentscalethantheotherfuelcomponentsinphotoload.Logs,becauseoftheirlargesize, are estimated at a subplot level and we highlyrecommend these subplots be 100 m2 (10 meters by10meters)tomatchthephotoloadpicturesandtables.If thesubplotsarenot100m2thenyoucan’tuse thetablesinAppendixB. Logloadingsareespeciallydifficulttoestimateusingthephotoloadtechniquebecausethepicturesdonotad-equatelycapturethediameterdistributionofthelogsinthesampleplot.Sincelogloadingsincreasebythesquareofthediameter,smallchangesindiametercanresultinlargechangesinlogloading.Moreover,logrotcanalsoinfluenceloading.Asaresult,theusermustpayspecialattention to the distribution of log diameters and loglengthsonthesampleunit.Theloadingestimatedwiththephotoloadpicturemustbeadjustedtoaccountforthedifferenceindiametersbetweenlogsinthepicturesandlogsobservedinthefield. Theeasiestmethodistoestimatetheaveragediameteronthesampleunitandadjusttheloadingsaccordingly.However,thecalculationoftheaveragediameterofthe


logstoestimateloadingisalsoproblematic.Sinceload-ingiscalculatedbyvolume,andvolumeiscalculatedfromlogcross-sectionalareaandlength,andthecrosssectionalareaiscalculatedbythelogdiametersquared,thenloadingisrelatedtothesquareofthediameter.So,theaveragediametershouldbeaquadraticmeanratherthananarithmeticmeantoaccuratelycalculatevolume.Thismeans that largerdiameter logs shouldbegivenmoreweightthansmalldiameterlogs.So,thecalcula-tionofthemostaccurateaveragediametermustbedoneusingthesquarerootoftheaverageofthesumofthediameterssquared(calledthequadraticmeandiameterorQMD).TherearetablesinAppendixBtohelpwiththiscalculation. We recommend that the photoload user use the logloading tables inAppendixB tocalibrate,adjust, andrefinetheocularmeasurementsobtainedbythephoto-loadsequencesinAppendixA.Thisalternativemethodcanbeusedintwoways.Firstitcanbeusedasatooltochecktheloadingestimateyoumadeusingthephotoloadsequencesdescribedabove.Second,itcanbeusedalonewithoutconsultingthephotoseries.Tousethesetables,theusersimplyestimatestheaveragediameterofthelogswithina100m2fixedarea(wesuggest10by10meterssothat itcorrespondstotheareainthephotoloadlogpictures)andthelengthofloginthearea.Theseestimatesarethenreferencedinthetablestogettheloading.Theusercanmeasurelogdiametersandlengthwitharulerortapetogetmoreaccurateloadingestimates.Amoreaccuratebutslowermethodistogrouplogsintodiam-eterclassesandfindlengthsbydiameterclassandusethemid-pointofthediameterclassandthelengthintheclasstofindtheloading,andthensumupallloadingsbyclassforafinalloadingestimate.Themostaccuratebuttimeintensivemethodistomeasuretheloglengthanddiametersofeachendofthelogtofindtheloadingforeachlog,thensumuploadingsacrossalllogs.Theintegrationofthistabulartechniquewiththephotoloadtechniqueshouldprovideconsistentestimatesofloadings,especiallywhentheloadingsarehigh.Wealsosuggestthatthissameprocessbeusedtoadjust100hourwoodyfuelloadingsinceloadingscanvarygreatlyacrossthatdiameterclasswidth(1to3inchesor2.5to7.5cm). Herearesomeguidelinesthatwillhelpwiththephoto-loadestimatesoflogloadings.First,besurethatlogsthathavetheircentralaxislyingabovetheduffandlitterlayer;logsbelowdufflayerareconsideredduff.Second,besurethatonlylogfuelsaremeasured;eliminatethepartsofthelogthatarelessthan3inches(7.5cm)indiameter.Besuretorecordtheaveragerotclassofthelogsonthesamplearea.Thismaybeimportantforadjustingthefinal

loadingvalues.Andlast,adjustallestimatestoaccountfordifferencesinobservedandphotoloaddiameters. Insummary,wesuggestyoufollowthesestepstoac-curatelyandconsistentlyestimatelogloadingsusingthephotoloadtechnique.Tofindaloadingusingthe6inchand10inchphotosequencesfollowthesesteps:

1.Estimatethequadraticmeandiameter(QMD)forlogswithinthesamplearea(squarerootoftheaver-ageofthediameterssquared).RecordtheQMDintheObservedQMDofthephotoloadplotform(firstplotforminAppendixC).

2.Choosethephotoloadsequenceofimitationlogs(6inchor10inch)thatmostcloselymatchestheaver-ageQMDofthelogsinyoursamplearea.Recordwhichseriesyouareusing,bywritinga“6”ora“10”inphotoloadQMDfield.

3.Determinethephotofromtheselectedlogseriesthatmostresemblestheloadingconditionsinthesampleunityouareevaluating.Remembertoevaluatelogsona100m2area.Recordthisloadingontheplotform.

4.FindthediameterconversionfactorinTable1ofAppendixBusingtheobservedQMDandphotoloadQMDandwrite the conversion factor in theplotform.Recordthisontheplotform.

5.Calculatethefinalloadingbyadjustingtheocularphotoloadestimate(step2andColumnAonplotform) for diameter differencesbymultiplyingbytheconversionfactor.Recordthisfinalestimateintheappropriateboxontheplotform.

6.Refine the estimate of loading using the secondmethodthatusesthetablesinAppendixB.Thisisdoneonlyifthereistime.

7.Adjust the loadingusing the rot classmultipliersmentionedpreviously.

Theusershouldrecordallestimatesontheplotformandallintermediatecalculationsshouldalsobewrittendirectlyon theplot form.There isplentyof roomforcalculationsandsummarystatistics. Estimating shrub and herbaceous loading—Thefirst step in estimating shrub and herb loadings withthephotoloadtechniqueistoidentifytheplantspecieswithinthesampleunit.Theusermustestimateloadingbymatchingthegroupofspeciesoccurringinthesamplearea with one or more of the photoload sequences inAppendixA.However,thephotoloadsequencesinAp-pendixAdonotcontainallundergrowthplantspeciesinthenorthernRockyMountains,onlythosethatwerecommoninthewesternMontanastudyarea(KeaneandDickinson2007;RMRS-RP-61CD).Onlysevenshrub


species,twograssspecies,andtwoforbspeciesarefoundinAppendixA.Therefore,itisnecessarytomatchthemorphologyofthespeciesobservedinthefieldwiththespeciespresentedinthephotoloadsequences(AppendixA). If there are several speciesof vegetationonyourplot,youmaychoose touseseveralphotoseries,oneforeachspecies,andthensumtheindividualloadingstomakeafinalloadingestimateofshruborherbaceouscomponents.Or,youcanratetheloadingasacollectionofspeciesusingthemostsimilarphotoloadsequence. Finding the appropriate picture in the photoloadsequences is a bit more difficult for shrub and herbsbecause these fuelshavedepth (plantheight), speciesdifferences,andheterogeneousdistributions.Therefore,wehaveprovidedasideviewalongwithatopviewtohelpinestimatingloadings.Forshrubsandherbs, theusershouldtrytomatchthepictureswithfieldcondi-tionsbasedonthreecharacteristics:species(previouslydiscussed),coveranddensity.Onceapictureischosen,then the corresponding loading must be adjusted fordifferencesbetweenheightinthepictureandheightinthesamplearea.TheaverageheightofeachplantphotoseriesisindicatedatthetopofeachpageinthephotoloadsequencesofAppendixA.Adjustshrubandherbaceousloadingonlywhentheaverageplantheightinthephotosisdifferent fromtheaverageheightofplantsonyourplot. We recommend that the user follow these steps todetermineanadjustedloading: 1.Chooseashruborherbphotoloadsequencethatbest

representsthevegetationonyoursamplearea.Matchtotheclosestspecies,genus,ormorphology.

2.Visuallyestimatetheloadingusingstandardphoto-loadprocedures.WritethisloadingonthephotoloadplotforminAppendixC.

3.Estimate the height of the shrub or herb presentwithinthesampleunit.Thisisestimatedasanin-tegratedaverageacrosstheentiresamplingarea.Ahintistovisuallydrapeasheetovertheshruborfuelcomponentandestimatetheaverageheightacrosstheentiresheet.Writethisheightinthetopofthedivisionontheplotform.

4.Writethephotoloadheightforthesequencethatisbe-ingusedontheplotform.Thisheightshouldbeatthetopofeachpageintheshruborherbsequence.

5.Calculatetheheightratio(divideobservedheightbyphotoloadheight)andmultiplytheratiobytheloadingestimatetocalculatethefinalheight.Writeintheappropriatefieldoftheplotform.

6.Estimate final loading by multiplying the visualestimateofloadingbytheheightratio.

Thisprocesscanberepeatedseveraltimesandthesumofthefinalloadingscanbeenteredintothefinalfieldifmorethanonephotoseriesofplantsareused. Werecommendthatshrubandherbaceousloadingsbeestimatedwhentheplantsarestillgreenandatthepeakoftheirgrowth(endofthegrowingseason).However,thismaybeimpossibleformanysamplingefforts.Usersshouldavoidsamplingtooearlyintheyearbeforenewgrowthandtoolatewhensomeplantshavebeeneatenordeteriorated.Ifearlyorlatesamplingistheonlyoption,besuretoonlyratethefuelsthatareobservedatthattime—donottrytorecreateoptimumgrowth—unlessthesamplingobjectiverequiresthatyouadjustforphenology.

Estimating litter and duff loadings—We do notpresentanymethodsforestimatingduffandlitterloadsinthephotoloadtechnique.However,westronglyrecom-mendthatloadingsforthesefuelcomponentsbeesti-matedusingthemethodspresentedinFIREMON(Lutesandothers2006)andlinkedtothemethodspresentedinthisstudy.Werecommendthatduffpluslitterdepthandpercentofthatdepththatislitterbemeasuredintwoopposingcornersofeachmicroplotusedtoestimateloadings.DuffandlittermeasurementscanbewrittenontheFIREMONplotformorthePhotoloadplotform.

ReferencesAnderson,H.E.1982.Aidstodeterminingfuelmodelsforestimating

firebehavior.Ogden,UT:U.S.DepartmentofAgriculture,For-estService, IntermountainResearchStation,GeneralTechnicalReportINT-122.44p.

Brown,J.K.1970.Amethodforinventoryingdownedwoodyfuel.Ogden, UT: U.S. Department of Agriculture, Forest Service,IntermountainResearchStation.GeneralTechnicalReportINT-16.44p.

Brown,J.K.1971.Aplanarintersectmethodforsamplingfuelvolumeandsurfacearea.ForestScience17:96-102.

Brown,J.K.1974.Handbookforinventoryingdownedwoodymate-rial.Ogden,UT:U.S.DepartmentofAgriculture,ForestService,Intermountain Forest and Range Experiment Station. GeneralTechnicalReportINT-16.68p.

Brown,J.K.,R.D.Oberheu,andC.M.Johnston.1982.HandbookforinventoryingsurfacefuelsandbiomassintheInteriorWest.Ogden,UT:U.S.DepartmentofAgriculture,ForestService,Inter-mountainForestandRangeExperimentStation.GeneralTechnicalReportINT-129.88p.

Busse,M.D.1994.Downedbole-wooddecompositioninlodgepolepineforestsofcentralOregon.SoilScienceSocietyofAmericaJournal.58:221-227.

Fischer,W.C.1981.PhotoguideforappraisingdownedwoodyfuelsinMontanaforests:Interiorponderosapine,ponderosapine-larch-Douglas-fir,larch-Douglas-fir,andInteriorDouglas-fircovertypes.Ogden,UT:U.S.DepartmentofAgriculture,ForestService,Inter-mountainForestandRangeExperimentStation.GeneralTechnicalReportINT-97.67p.

GAO.2003.Additionalactionsrequiredtobetteridentifyandprioritizelandsneedingfuelsreduction.ReporttoCongressionalRequestersGAO-03-805.Washington,DC;GeneralAccountingOffice.

�� USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

GAO.2004.ForestServiceandBLMneedbetterinformationandasystematicapproachforassessingrisksofenvironmentaleffects.GAO-04-705.Washington,DC.GeneralAccountingOffice.

Keane,R.E.andL.J.Dickinson.2007.Developmentandevaluatinofthephotoloadsamplingtechnique.FortCollins,CO:U.S.De-partmentofAgriculture,ForestService,RockyMountainResearchStation.Res.Pap.RMRS-RP-61CD.

Laverty,L.andJ.Williams.2000.Protectingpeopleandsustainingresourcesinfire-adaptedecosystems—acohesivestrategy.ForestServiceresponsetoGAOReportGAO/RCED99-65.Washington,DC:U.S.DepartmentofAgriculture,ForestService.

Lutes,D.C.1999.Acomparisonofmethodsforthequantificationofcoarsewoodydebrisandidentificationofitsspatialscale:astudyfromtheTenderfootExperimentalForest,Montana.Missoula,MT:TheUniversityofMontana.Thesis.120p.

Lutes,D.C.,R.E.Keane,andJ.F.Caratti.[Inprep].FuelLoadingModels:Anationalclassificationofwildlandfuelbedsforfireef-fectsmodeling.CanadianJournalofForestResearch.

Lutes,D.C.,R.E.Keane,J.F.Caratti,C.H.Key,N.C.Benson,S.Sutherland,andL.J.Gangi.2006.FIREMON:Fireeffectsmoni-toringandinventorysystem.FortCollins,CO:U.S.DepartmentofAgricultureForestService.RockyMountainResearchStation,GeneralTechnicalReportRMRS-GTR-164CD.

Mutch,R.W.1994.Areturntoecosystemhealth.JournalofForestry.92:31-33.

Ottmar,R.D.,M.F.Burns, J.N.Hall, andA.D.Hanson. 1993.CONSUME users guide. Portland, OR: U.S. Department ofAgriculture,ForestService,PacificNorthwestResearchStation.GeneralTechnicalReportPNW-GTR-304.44p.

Ottmar,R.D.,R.E.Vihnanek,C.S.Wright,andD.Olsen.2004.Stereophoto series for quantifyingnatural fuels.VolumeVII:Oregonwhiteoak,Californiadeciduousoak,andmixedconiferwithshrubtypesinthewesternUnitedStates.U.S.DepartmentofAgriculture,ForestService,NationalWildfireCoordinatingGroup,NationalInteragencyFireCenter.76p.

Reinhardt,E.andR.E.Keane.1998.FOFEM—aFirstOrderFireEffectsModel.FireManagementNotes.58:25-28.

Sandberg,D.V.,R.D.Ottmar,andG.H.Cushon.2001.Character-izingfuelsinthe21stcentury.InternationalJournalofWildlandFire.10:381-387.

vanWagner, C. E. 1968.The line intersect method in forest fuelsampling.ForestScience.14:20-26.

�5USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

Appendix A—Photoload Sequences for Northern Rocky Mountain Fuelbeds






2�USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007


2�USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007

2� USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007


2� USDA Forest Service Gen. Tech. Rep. RMRS-GTR-�90. 2007






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

Photoload log diameter conversion table—This table is used to adjust photoload es-timated 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

� 7.�2 0.25 0.09 � �0.�� 0.�� 0.�� 5 �2.7 0.�9 0.25 � �5.2� �.00 0.�� 7 �7.78 �.�� 0.�9 8 20.�2 �.78 0.�� 9 22.8� 2.25 0.8� �0 25.�0 2.78 �.00 �� 27.9� �.�� .2� �2 �0.�8 �.00 �.�� .02 �.�9 �.�9 �� 5.5� 5.�� .9� �5 �8.�0 �.25 2.25 �� 0.�� 7.�� 2.5� �7 ��.�8 8.0� 2.89 �8 �5.72 9.00 �.2�

Toadjustphotoloadloadingforlogdiametersinthefield,usethefollowingsteps: 1.Selecteitherthe6inchor10inchphotoloadphoto

setbasedonthesimilarityoflogdiametersfoundinthesamplearea.

2.Determinethephotointhatsetthatmostresemblesloadingsintheareayouareevaluating.

3.Determinetheaveragediameterof1000hourfuelintheareayouareevaluatingandfindthatdiameterincolumnoneortwoofthetableabove.Remember,thequadraticmeandiameterisabetterestimateofaverage log diameter. The formula for quadraticmeandiameter(QMD)is:

QMD

dn

= ∑ 2

wheredislogdiameterandnisthenumberoflogs.

4.Findtheconversionfactorincolumnthreeorfour,dependingonwhichphotoseriesyouusedinstep2,anddeterminetheconversionfactor.

5.Multiplytheconversionfactorbytheloadingyouestimatedfromstep2.Theproductisthefinalload-ingof1000hourfuelofyoursamplearea.

Thefollowingsetoftablesrepresentsanalternativemethodofdeterminingwoodyfuelloadingofbranchesand logs using an average diameter and length. Thetables were constructed so that once users determinetheaveragediameterofthewoodyfuelcomponentandthe total lengthof that component, they can referencethetabletodetermineloading.Theconversiontoload-ingassumedalogdensitythatis400kgm–3,whichistypicalforsoundnorthernRockyMountaintreespeciesasanaggregate.However,youcanproportionallyadjustthevaluesinthetablestoreflectwooddensityofrottenlogs.Thetablesarearrangedfirstbydowneddeadwoodyfuel component—100 hour (branches) and 1000 hour(logs).Then,thetablesarearrangedbytheunitsusedtoestimatediameterandlengthobservations.Therearefourtablesforeachfuelcomponent.Thefirsttableisusedifthediametersandlengthsweremeasuredininchesandfeet,respectively,andtheloadingisdesiredintonsperacre.Thesecondtablehasinchesandfeetfordiameterandlength,butloadingisinkgpersquaremeter(aunitthatismoreeasilyvisualized).Thethirdandfourthtableshavediameterandlengthincentimetersandmetersbuttheloadingiskgm–2foronetableandtonsperacrefortheother.


One important reminder on estimating loading using this technique.Theloadingof100hrfuelsareestimatedfora samplingareaofone squaremeter.The loadingof 1000 hr fuels (logs) are estimated on 100 m2 area(10metersby10meters).Allthetablesareconstructedusingtheseplotdimensions. Thefollowingstepsareusedtoestimateloadingusingthesetables:

1.Measurethelengthofallwoodyfuelparticlesinthefuelcomponent(100hror1000hr)withinthesamplearea(1or100m2).Thiscanbevisuallyestimatedoractuallymeasuredusingaclothtape.

2.Estimatetheaveragediameteracrossalllogswithinthe sample area. The best estimates of average

diameteraredoneusingthequadraticmeansquareestimatewherethesumofthesquaresofallwoodyfuel particles are estimated and then divided bythenumberofparticlesandthenthesquarerootistaken.

3.Findthetablethatmatchestheappropriatewoodyfuelcomponentandtheunitsdesired.

4.Findtheloadingbycrossreferencingthelengthanddiameter.

5.Uselinearextrapolationacrossrowsorcolumnsifthediameterorlengthsdonotmatchthecategorieslistedinthetable.

6.Recordthefinalloadingontheplotsheet.


Branch 100 hour wood fuels (1-3 inches dia)—Estimatedona1m2plot—Englishunitsfordiameters(in)andlengths(ft)—English units for loadings (tons acre–1)

Branch 100 hr loadings in tons acre–1


Branch 100 hour wood fuels (1-3 inches dia)—Estimatedona1m2plot—Englishunitsfordiameters(in)andlengths(ft)—Metric units for loadings (kg m–2)

Branch 100 hr loadings in kg m–2


Bran

ch 10

0 hou

r woo

d fu

els (

2-8 c

m d

ia)—

Estim

ated

ona

1m2 pl

ot—

Met

ricun

itsfo

rdia

met

ers(

cm)a

ndle

ngth

s(m

)—En

glish

un

its fo

r lo

adin

gs (t

ons a

cre–1

)

Bra

nch

100

hr

load

ing

s in

to

ns

acre

–1


Bran

ch 10

0 hou

r woo

d fu

els (

2-8 c

m d

ia)—

Estim

ated

on

a1m

2 plo

t—M

etric

uni

tsfo

rdia

met

ers(

cm)a

ndle

ngth

s(m

)—M

etri

c un

its fo

r lo

adin

gs (k

g m

–2)

Bra

nch

100

hr

load

ing

s in

kg

m–2


Log 1

000 h

our w

ood

fuel

s (3+

inch

es d

ia)—

Estim

ated

on

a100

m2 p

lot—

Engl

ish

units

fo

rdia

met

ers(

in)a

ndle

ngth

s(ft)

—En

glish

uni

ts fo

r lo

adin

gs (t

ons a

cre–1

)

Lo

g 1

000

hr

load

ing

s in

to

ns

acre

–1


Log

1000

hou

r w

ood

fuel

s (3+

inch

es d

ia)—

Estim

ated

on

a10

0m

2 plo

t—M

etric

uni

tsfo

rdia

met

ers(

cm)

and

leng

ths(

m)—

Met

ric

units

for l

oadi

ngs (

kg m

–2)

Lo

g 1

000

hr

load

ing

s in

kg

m–2


Log

1000

hou

r w

ood

fuel

s (8

+ cm

dia

)—Es

timat

edo

na

100

m2 p

lot—

Met

ricu

nits

ford

iam

eter

s(c

m)

and

leng

ths(

m)—

Engl

ish u

nits

for

load

ings

(ton

s acr

e–1)

Lo

g 1

000

hr

load

ing

s in

to

ns

acre

–1


Log

1000

hou

r w

ood

fuel

s (8+

cm

dia

)—Es

timat

edo

na

100

m2 p

lot—

Engl

ish

units

ford

iam

eter

s(in

)and

leng

ths(

ft)—

Met

ric

units

for

load

ings

(kg

m–2

)

Lo

g 1

000

hr

load

ing

s in

kg

m–2


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

� 7.�2 0.25 0.09 � �0.�� 0.�� 0.�� 5 �2.7 0.�9 0.25 � �5.2� �.00 0.�� 7 �7.78 �.�� 0.�9 8 20.�2 �.78 0.�� 9 22.8� 2.25 0.8� �0 25.�0 2.78 �.00 �� 27.9� �.�� .2� �2 �0.�8 �.00 �.�� .02 �.�9 �.�9 �� 5.5� 5.�� .9� �5 �8.�0 �.25 2.25 �� 0.�� 7.�� 2.5� �7 ��.�8 8.0� 2.89 �8 �5.72 9.00 �.2�



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:Completethisinformationforyourrecords.UsethePlotIDasanidentifierinadatafile.UsetheFIREMONplotIDifyouarelinkingphotoloadswithotherFIREMONtechniques.Usedateandexaminertohelpdocumentsamplingdetails.RecordStandIDifthisplotissamplingastand.

Subplotfieldisusediftherearemorethanonephotoloadestimateperplotsuchasonatransect.Subplotscanbeusedasplotsifonlyoneestimateperplotisdesired.

Adjustments

Rot Adjustment:Enteranadjustmentfactorfordownwoodrot(ifunknown,theadjustmentfactorsforFIREMONdecayclasses–1=1.0,2=1.0,3=0.9,4=0.75,5=0.5)

Height Adjustment:Firstenterobservedheightofcomponent(obsht)thenenterthephotoloadheight(photoHt).Calculateadjustmentfactorbydividingtheobservedheightbyphotoloadheight(example:1.2feetmeasuredonplotand0.8feetonphotoloadsequencecalculatestoa1.5=1.2/0.8).

Diameter Adjustment:Recordthequadraticmeandiameter(QMD)observedonplotinObsQMDandrecordtheQMDoflogphotoloadsequenceused(either6or10in).Lookupconver-sioninLogConversiontableandrecordinAdjfactor.

Spatial Distribution:Traverseplotorstandandmatchaloadingwithaproportionofplotanddothisforentireplot.CalculateaweightedaveragebyproportionareaofloadingandenterinLoadingfield.Forexample,say10%ofplothad1.0kgm–2,50%had2.0kgm–2,and40%had3.0kgm–2,thentheweightedaveragewouldbe(10x1.0+50x2.0+40x3.0)/100=2.3kgm–2

Calculations:Multiplytheheight,diameter,androtadjustmentfactorsbytheLoadingfieldintheSpatialDistributionsetoffieldstocalcluatetheFinalLoad.

Notes:theheightadjustmentisonlyneededfordownwoodyfuelsifthelitterlayerisnotvisiblethroughthewoodyfuels(slash,forexample).Diameteradjustmentsareonlyneededfor100hrand1000hrfuels,butcanbeusedfor1hrand10hrfuelsifdesired.Theweightedaverageforspatialdistributionisonlyneededifitisimportantinsamplingobjective.

Important Sampling Concepts

• 1,10,100,and1000Hourfuelsmustbewoody,down,anddead,tobecounted.Needles,grassblades,pinecones,andbarkpiecesareallconsideredlitter,notdown,dead,woodyfuel.

o “Woody”referstoaplantwithstems,branchesortwigsthatpersistfromyeartoyear.

PhotoloadCheat Sheet


o “Down” includesall fuel in thesamplingplane that is45degreesor lessabovehorizontal.If it isatananglegreater than45degreesabovehorizontal itshouldonlybeconsidereddownifitisthebrokenboleofadeadtreewhereatleastoneendoftheboleistouchingtheground(notsupportedbyitsownbranchesorotherlivevegetation).

o “Dead”hasnolivefoliage.Freshslashandnewlybrokenbrancheswithgreenfoli-ageareexceptionsbecausetheyaretechnicallydeadeventhoughtheymayhavegreenfoliage.Deadbranchesonlivetreesthatenterthesamplingplaneshouldnotbecounted.Don’tconfusedeadwithdormant.

• Whensamplinglogs,donotcountlogsthathavetheircentralaxislyinginorbelowthedufflayer.Theselogsburnmorelikeduffandshouldnotbesampledaslogs.

• Whensamplinglogs,youwillmeasureasmallandlargeendofthelog.Remember,alogmustbegreaterthan3inchesindiameter.Ifthelogtapersintoa100hourfuel,onlycountthepartof the fuel that is3 inchesandgreater.Yoursmallendwillneverbe less than3inches.

• TheLogphotosinyourmanualareof6inchand10inchlogs.Whenyouestimateyourlogloadingitisimperativetoadjustforthemeanlogdiameter(quadraticmeandiameterisbest)inyoursubplotandtorecordthisonyourplotsheet.

• Onestickcanconsistof1,10,and100hourfuel.Recordaloadingforeachfuelcomponentevenifitisonebranchorstick.

• Onlysamplefuelsinsidethesampleframe.Ifastickcrossesoverorundertheframe,onlycountthepartinsidetheframe.Ignoretheportionoutsideoftheframe.

• Beforeyoustartrecordingloadingsonyourplotsheet,eliminatefuelcomponentswithoutloadingsfirst.Putzerosinthefinalloadingboxandthenbegindeterminingtheloadingsforotherfuelcomponents.Pleasedonotputadashthroughthefinalloadingbox,asthiswillbeinterpretedasnotbeingsampledratherthannotbeingpresent.

• Whenassessingherbaceousandshrubloadings,remembertoadjustforthemeanheightofthefoliageinyourphysicalsubplotandtorecordthisonyourplotsheet.

• Alwaysremembertoadjustestimatedfuelloadingsforfourfactors:spatialdistribution,diameter,decay,anddepth.

NOTES

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Documents

The Photoload Sampling Technique · The photoload sampling technique: estimating surface fuel loadings from downward-looking photographs of synthetic fuelbeds. ... Harrington, Melanie