NOTE / NOTE

Estimating consumption and remaining carbon inburned slash piles

Alex J. Finkral, Alexander M. Evans, Christopher D. Sorensen, andDavid L.R. Affleck

Abstract: Fuel reduction treatments to reduce fire risk have become commonplace in the fire adapted forests of westernNorth America. These treatments generate significant woody debris, or slash, and burning this material in piles is a commonand inexpensive approach to reducing fuel loads. Although slash pile burning is a common practice, there is little informa-tion on consumption or even a common methodology for estimating consumption. As considerations of carbon storage andemissions from forests increase, better means of quantifying burn piles are necessary. This study uses two methods, sectorsampling and a form of line intersect sampling, for estimating both the percent consumption and conversion to charcoal inslash piles of ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) in northern Arizona, USA. On aver-age, burning released between 92% and 94% of the carbon in each slash pile to the atmosphere and converted between 0.05and 0.34 Mg C·ha–1 to charcoal across the treatment area. These results demonstrate that burning slash piles converts signif-icant quantities of carbon stored in wood to atmospheric carbon and charcoal, both of which should be considered as forestcarbon accounting is further refined. Sector sampling and line intersect strategies produced similar estimates of consump-tion; however, the line intersect protocol was more easily and rapidly implemented.

Résumé : Les traitements de réduction des combustibles pour diminuer les risques d’incendie sont devenus courants dansles forêts adaptées au feu de l’ouest de l’Amérique du Nord. Ces traitements génèrent une quantité importante de débris li-gneux, ou déchets de coupe, et le brûlage d’empilements de ce matériel est une pratique commune et peu coûteuse pour ré-duire la charge de combustibles. Bien que le brûlage des tas de déchets de coupe soit pratique courante, on possède peud’information sur la consommation, ou même sur une méthodologie commune pour estimer la consommation. Étant donnéqu’on se préoccupe davantage du stockage et des émissions de carbone forestier, il faut avoir de meilleurs moyens de quanti-fier les piles brûlées. Cette étude utilise deux méthodes d’échantillonnage, la méthode des secteurs et une forme de transectsd’intersection linéaire, pour estimer le pourcentage de consommation et de conversion en charbon de bois dans les piles dedéchets de coupe de pin ponderosa (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) dans le nord de l’Arizona, auxÉtats-Unis. En moyenne, le brûlage a dégagé vers l’atmosphère entre 92% et 94% du carbone dans chaque pile de déchetsde coupe et converti entre 0,05 et 0,34 Mg C·ha–1 en charbon de bois dans l’ensemble de la zone traitée. Ces résultats dé-montrent que le brûlage des tas de déchets de coupe transforme d’importantes quantités de carbone emmagasiné dans lebois en carbone atmosphérique et en charbon de bois, dont on devrait tenir compte à mesure qu’on raffine davantage lacomptabilisation du carbone forestier. Les stratégies d’échantillonnage par la méthode des secteurs ou celle des transectsd’intersection linéaire produisent des estimations de consommation similaires mais le protocole des transects d’intersectionlinéaire a été plus facile et plus rapide à implanter.

[Traduit par la Rédaction]

Introduction

Vast areas of conifer-dominated forests across the westernUnited States are increasingly at risk of stand-replacing wild-fires (Fulé et al. 2004a; Noss et al. 2006). A century of fire

suppression, intensive grazing, and logging in the southwest-ern United States has created ponderosa pine (Pinus ponder-osa Douglas ex P. Lawson and C. Lawson) forests that areheavily stocked with small-diameter trees (Covington andMoore 1994a; Allen et al. 2002). Past forest management

Received 26 March 2012. Accepted 11 July 2012. Published at www.nrcresearchpress.com/cjfr on 22 August 2012.

A.J. Finkral* and C.D. Sorensen.** School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA.A.M. Evans. The Forest Guild, Santa Fe, NM 87504, USA.D.L.R. Affleck. College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA.

Corresponding author: Alex J. Finkral (e-mail: alex@forestlandgroup.com).*Present address: The Forestland Group, 1512 E Franklin Street, Chapel Hill, NC 27514, USA.**Present address: USDA Forest Service, Stanislaus National Forest, Mi Wuk Village, CA 95346, USA.

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coupled with the effects of climate variability have led to ahigher frequency of large wildfires, longer wildfire durations,and longer wildfire seasons in the western United States(Grissino Mayer and Swetnam 2000; Westerling et al. 2006).These trends have prompted broad agreement for the need toreestablish less dense stands and restore historic frequent fire(Covington and Moore 1994b; Covington et al. 1997; Fulé etal. 1997). To meet this need, forest thinning treatments,known alternatively as restoration, fire risk reduction, andfuels reduction treatments, have become widely implemented(Covington et al. 1997; Finkral and Evans 2008). These silvi-cultural practices generally extract small-diameter trees andproduce large amounts of woody debris (Fulé et al. 2002;Hjerpe and Kim 2008).At the same time, forests are increasingly recognized as

systems with the potential to mitigate the rising concentra-tions of greenhouse gases in the atmosphere by offsettinganthropogenic carbon dioxide emissions. While forest man-agement practices promise opportunities for emissions man-agement, the processes of measuring, verifying, andexchanging the carbon benefits of forest biomass are ex-tremely complex (Brown 2002; Birdsey 2006; Ruddell et al.2007).Woody debris produced during thinning treatments sub-

stantially increases fuel loadings (Fulé et al. 2002, 2004b).Currently, there are few commercial markets for this woodydebris, so it is commonly piled and burned (Farnsworth etal. 2003; Fulé et al. 2004b; Seymour and Tecle 2005). Forexample, approximately 25 000 ha of piles were burned onNational Forest Service lands in Washington and Oregon in2005 alone (Wright et al. 2009). Slash piles are typicallymade either by hand or by machine and are not burned untilsnow cover exists or an extended wet weather episode occurs(Farnsworth et al. 2003). While there are several differentmethods available for estimating mass and volume in slashpiles (Johnson 1984; Hardy 1996; Wright et al. 2009), esti-mates of the percentage of wood mass consumed when pilesare burned are vague, ranging between 75% and 95% (Hardy1996). Other work has focused on consumption and impactsof broadcast rather than pile burns (Brown et al. 1991; Good-rick et al. 2010) or wildfires (Donato et al. 2009). Moreover,consumption can change dramatically depending on fuel con-ditions and fire behavior. For example, in experimental burnsin a Sierra Nevada mixed-conifer forest consumption rangedfrom 15% to 92% (Kauffman and Martin 1989).The portion of slash piles converted to charcoal may be

small, but because it is essentially an inert form of carbonand an addition to carbon stored in forest soils, it is signifi-cant to greenhouse gas emissions estimates, and hence cli-mate change (Laird 2008). Charcoal also increases the water-holding capacity of soil (Karhu et al. 2011), reduces soil bulkdensity, provides a source of cation exchange sites (Gundaleand DeLuca 2006), and has long residence times in soil (ap-proximately 2000–9000 years) (Gavin et al. 2003).This study uses two different methods to refine the estima-

tion of the fate of carbon stocks and fluxes in forest standsfor the purpose of improving our understanding of carbondynamics in managed landscapes. The objective of the studyis to quantify the amount of carbon consumed in pile burningas part of a fire hazard reduction thinning project as well as

separating the residual carbon into wood and charcoal catego-ries.

Methods

Study site and silvicultural treatmentThe study site was located on the Northern Arizona Uni-

versity Centennial Forest (35°09′N, 111°43′W) and was do-minated by ponderosa pine with scattered Gambel’s oak(Quercus gambelii Nutt). The elevation was 2155 m abovesea level with 0%–15% slopes. Soils were derived from ben-morite parent material and were moderately deep, fine toclayey-skeletal, very cobbly loam (Miller et al. 1995). Priorto harvest, Finkral and Evans (2008) conducted a census ofall the trees (n = 51 240) that showed there were 579 tree-s·ha–1 and the basal area was 22 m2·ha–1, 99% of which wasponderosa pine.The treatment prescription was a thinning that included re-

tention of all live pre-settlement age (at least 135 years ofage) ponderosa pine trees, oak trees, and snags (Covingtonet al. 1997) on the study site. Post-settlement trees greaterthan 41 cm diameter at breast height were left unless thetrees were a safety hazard. Post-treatment, there were 163trees·ha–1 and the stand basal area was reduced by 36% to14 m2·ha–1. Standing dead trees were reduced from 52 tree-s·ha–1 with a basal area of 0.71 m2·ha–1 to 5.4 trees·ha–1 witha basal area of 0.46 m2·ha–1. See Finkral and Evans (2008)for further details.Cut trees with a diameter at breast height smaller than

12.7 cm were piled with other logging residues includingbranches, tops, and stumps. Both hand-piling and mecha-nized methods (Figs. 1a and 1b) were used to concentratethe material, and shortly after harvesting operations con-cluded, pile dimensions were measured to estimate their bio-mass volumetrically (as per methods in Hardy 1996).Machine piles, typically located on log landings or other cen-tral processing sites, are larger than hand piles and often in-clude larger diameter pieces of woody material.Piles were burned in January 2009 at a time when there

was snow cover on the ground. As required by local regula-tions and regional standards (Farnsworth et al. 2003), thepiles were consolidated two or three times during burning toensure that the majority of each pile was consumed. After theslash piles were burned, we randomly selected 19 piles — 13hand piles and 6 machine piles — to measure the residualcarbon not combusted during burning. We used two methods,sector sampling and a form of line intersect sampling, to esti-mate consumption and charcoal production.

Sector samplingThe “footprints” of the burned slash piles were irregular-

shaped polygons (Figs. 1c and 1d), so we used a sector sam-pling approach that is well suited for measuring small non-uniform areas (Iles and Smith 2006). At each slash pile, weused a randomly generated azimuth originating at the approx-imate center of the footprint to establish the right side of awedge-shaped sector. The remaining side of the sector wasestablished by using a 36° sector angle, which gave the sec-tor a sampling weight of 10%. All wood and charcoal wasremoved from the sector and separated into the followingsize classes: <2.5, <7.6, <15.2, <30.5, and ≥30.5 cm. Any

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charcoal attached to unburned wood was carefully removedand added to the rest of the charcoal collected from the sec-tor. All charcoal and all wood in each size class wereweighed. We randomly collected and weighed three subsam-ples of charcoal, three subsamples of wood in each of thetwo smallest size classes, and one subsample of wood ineach of the remaining larger size classes. Each subsample ofcharcoal was about 0.25 L in volume and each subsample ofwood was about 2.5 cm in length.Dry masses of charcoal and wood in each size class were

obtained by oven-drying subsamples at 105 °C until a con-stant mass was achieved. We averaged the dry to wet massratio of all subsamples within each size class and within thecharcoal samples. We calculated the total dry biomass of thecharcoal and of each wood size class within each sector bymultiplying by the averaged dry to wet mass ratio of the ap-propriate size class. We converted the sector total of woodbiomass into carbon, assuming a carbon concentration of48% (Birdsey 1992; Kaye et al. 2005). Similarly, we con-verted the sector total of charcoal biomass into carbon afterdetermining the average carbon content of the subsamples(N = 57) using a calibrated Flash Elemental Analyzer 1112Series (Thermo Finnigan). Finally, we summed the sector es-timates of residual carbon in the charcoal with the estimatesin each wood size class and multiplied by 10 (the sampling

mass of the sectors) to determine the total residual carbon ineach burned slash pile.

Line intersect samplingLine intersect sampling (LIS) was originally adapted to

downed woody material inventory by Warren and Olsen(1964) and Brown (1974). In most cases, LIS involves estab-lishing a randomly oriented line or cluster of lines within atract and then tallying all of the pieces of (wood or charcoalin this case) that are crossed by the sample lines (Evans andDucey 2010). The only measurement then needed on individ-ual pieces is their cross-sectional area at the intersection withthe sample line if volume per hectare is the only parameter ofinterest. The probability of a piece being included in the sam-ple is proportional to its length (which is a key contributor topiece volume), which makes LIS more efficient for estimat-ing downed wood volume than fixed-area plots (Sikkink andKeane 2008; Affleck 2010). LIS also tends to minimize non-detection errors because the search is focused on a single line(Jordan et al. 2004).In our LIS campaign, we established four transects from

the approximate center of the burn pile footprint to the edgealong the long and short axes of the footprint. We measuredthe diameter of each piece of wood (perpendicular to thepith) or charcoal at the point at which the transect crossed

Fig. 1. (a) Hand-piled slash, (b) machine-piled slash, and (c) hand-piled and (d) machine-piled burn footprints with representations of lineintersect transects and sector sampling.

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the piece. Pieces of wood that were partially charred wereclassified as wood, not charcoal, a conservative approachconsidering wood’s lower carbon concentration. If the trans-ect crossed a piece more than once, each crossing was treatedas a new piece as per the standard implementation of LIS(Ducey 2001). When the transect crossed a bed of many ad-jacent small pieces of charcoal, we recorded the beginningand the ending distance of the “mat” or “bed” on the trans-ect. Depth measurements were made every 15 cm with thefirst measurement taken at the point where the transect metthe char bed.For each of the four transect legs within a pile footprint,

we determined the total area of wood and charcoal thatcrossed the vertical plane that extends upwards through thepile from the transect by summing (p/2) × (p/4) × diameter2for all of the pieces. This is the standard LIS formula for es-timating total cross-sectional area from piece diameter meas-urements taken perpendicular to the pith and for simplicitywas used here despite the fixed orientation of the transects.Total char bed areas were calculated by averaging the depthmeasurements and then summing length of bed × height ofbed over all of the beds above the transect leg. This gave usfour of these areas in square metres for each pile. For each ofthe four transect legs, we then divided the suspended area ofwood and charcoal by the length of the transect to arrive atan estimate of cubic metres of both wood and charcoal perunit area. We then computed a weighted average (weightedby transect length) of the four estimates separately for woodand charcoal and multiplied this average by the pile area togenerate an estimate of total volume of wood and charcoalin the pile. We converted the cubic measure to mass usingdensity values of 0.38 g·m–3 (USDA Forest Service 1999;Woodall and Monleon 2008) and 0.25 g·m–3 (Gundale andDeLuca 2006) for ponderosa pine wood and ponderosa pinecharcoal, respectively. Last, we converted the totals of woodbiomass and charcoal biomass into carbon using the samecarbon concentrations that were used in the sector samplingapproach.

ResultsSlash piles on the site averaged 2.4 m in height, 6.3 m in

length, and 6 m in width for an average volume of 64 m3.Piles contained an average of 2.3 Mg C·pile–1 (SD = 34.6,N = 19), and across the site, there were 4.1 Mg C·ha–1 ofwoody material in piles before burning, or about 10% of thepre-treatment aboveground carbon (Finkral and Evans 2008).

Sector samplingBased on the sector sampling, a mean of 91.8% (SD =

10.3, N = 19) of all carbon in burned slash piles was releasedto the atmosphere leaving 0.04 Mg C·pile–1 (SD = 0.04, N =19) post-burn (Table 1). In two of the slash piles, less than70% of the carbon was consumed. In all other slash piles,carbon consumption ranged between 87% and 100%. Usingthe sector sampling data and data on the amount of slash inpiles per hectare (Finkral and Evans 2008), 3.8 Mg C·ha–1were released during pile burning.The mean carbon content of charcoal was 78.4% (SD =

6.0, N = 57). On average, charcoal stored 14.5% of the car-bon left after the piles were burned and 1.21% (SD = 1.18,N = 19) of the original pile carbon (Table 1). Based on thesector sampling, the thinning treatment and associated pileburning resulted in 0.05 Mg C·ha–1 converted to charcoaland presumably incorporated into forest soils and seques-tered. Most (69%) of the woody pieces that remained un-burned after the piles were burned were between 2.5 and15 cm in diameter.

Line intersect samplingUsing the LIS sampling, we measured an average con-

sumption of 93.7% (SD = 6.1, N = 19) leaving 0.04 MgC·pile–1 (SD = 0.03, N = 19). The LIS sample found that5.06% (SD = 4.89, N = 19) of the original pile carbon wasconverted to charcoal. This translates to a release of 3.9 MgC·ha–1 to the atmosphere and 0.21 Mg C·ha–1 stored onsite ascharcoal.

Discussion

Our measurements of the 78.4% carbon content in woodcharcoal match well with previous estimates of about 80%(Forbes et al. 2006; Gundale and DeLuca 2006) and areonly slightly higher than the 71%–74% carbon content ofponderosa pine charcoal from field samples reported byBriggs et al. (2012). Similarly, our measurements of 92%(sector) or 94% (LIS) consumption for all piles corroboratethe 90% consumption estimate used in the model CON-SUME (Prichard et al. 2009). However, consumption willfluctuate with season of burn and variable moisture content(Kauffman and Martin 1989).We found a difference in average consumption between the

hand piles and machine piles of 90% and 99% consumption,respectively. The size and the shape of slash piles do not sig-

Table 1. Mean ± SD carbon released and stored from slash pile burning (N = 19) using two samplingmethods.

Sector sampling Line intersect samplingPre-burn Mg C·pile–1 a 2.3±34.6 2.3±34.6Post-burn Mg C·pile–1 0.04±0.04 0.04±0.03% pile consumption 91.8±10.3 93.7±6.1Charcoal C as % initial pile C 1.20±1.18 5.06±4.89Stand-level atmospheric release (Mg C·ha–1)b 3.8 3.9Stand-level residual charcoal (Mg C·ha–1)b 0.05c 0.21d

aPile dimensions were measured to estimate their biomass volumetrically as per methods in Hardy (1996).bBased on study site described in Finkral and Evans 2008.cBased on mean 4140 kg C·ha–1 in slash piles (from Finkral and Evans 2008) × 1.21% residual charcoal.dBased on mean 4140 kg C·ha–1 in slash piles (from Finkral and Evans 2008) × 5.06% residual charcoal.

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nificantly influence the burnout time once ignited, but the ra-tio of void volume to fuel volume does influence the rate ofcombustion (Johnson 1984). Pre-burn carbon estimationswere calculated volumetrically based on pile dimensions us-ing a method that recommended a 10% packing ratio forhand piles of small ponderosa pine trees and a 25% packingratio for those that were machine piled (Hardy 1996). Thepacking ratio accounts for the ratio of void volume to fuelvolume. Therefore, it is possible that the pre-burn volume es-timates for the two outlier piles, both hand piled, underesti-mated the void volume, which resulted in overestimation ofthe true pile mass. It is also possible that more void volumecaused these piles to burn out sooner, leaving more unburnedresidual slash. These are fertile grounds for further research.Our estimates of 0.05 and 0.21 Mg C·ha–1 of charcoal pro-

duced through pile burning are lower than both the 0.3 MgC·ha–1 estimate of charcoal additions from a single fire underpre-settlement conditions (DeLuca and Aplet 2008) and anestimate of the charcoal generated on downed wood from asevere wildfire (0.3 Mg C·ha–1) (Donato et al. 2009). Our re-sults are in line with those from boreal forest fires (0.24 MgC·ha–1) (Ohlson and Tryterud 2000) but below those from aslash-and-burn treatment in a temperate deciduous forest(Eckmeier et al. 2007). The pile burning in our study concen-trated fuels more so than in these comparisons, which may beresponsible for lower per hectare estimates of residual char-coal.While both sampling methods produced comparable re-

sults, our LIS method required approximately 25% of thetime needed for sector sampling. On average, a two-personcrew was able to measure four transects at each pile footprintin 25 min, not including travel from pile to pile. This timeinvestment to measure pile consumption is similar to initialfuel measurements and would change little, if at all, if trans-ect orientations were randomized rather than fixed. Sectorsampling, on the other hand, required more time-consumingpractices including sorting and weighing materials and inmany cases cutting woody debris that straddled the sectorboundaries. Owing to these practical efficiencies, we are cur-rently investigating further the statistical properties of line in-tersect sampling when transects are randomly oriented butinitiated from a fixed point within a pile.

ConclusionsThe methodologies demonstrated in this study provide new

ways to document a key element of fuel reduction and resto-ration treatments. This type of measurement will be crucialfor measuring additions to soil carbon and emissions fromburning if fuel reduction treatments are included as carbonoffsets (Mignone et al. 2009). Similarly reliable and repeat-able measure of consumption will aid the development of airquality models (Goodrick et al. 2010).Our results demonstrate that pile burning converts signifi-

cant quantities of carbon stored in wood to atmospheric car-bon and charcoal, both of which should be taken into accountas forest carbon accounting is further refined. The storage of0.05–0.34 Mg C·ha–1 is a significant impact of fuel reductiontreatments and approximately equivalent to the carbon re-leases from the logging equipment used to implement thetreatment (Finkral and Evans 2008).

AcknowledgementsFunding for this study was provided by the USDA Forest

Service Rocky Mountain Research Station (JVA No. 08-JV-11221633-246) and the State of Arizona’s Environmental Re-search, Development, and Education for a New Economy(ERDENE) program. Thanks to Adam Bland, Chris Updike,and Jake Simon for assistance with field data collection andto Sabrina Kleinman for assistance with the analysis of char-coal samples.

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