ARTICLE

Mapping fuels in Yosemite National ParkSeth H. Peterson, Janet Franklin, Dar A. Roberts, and Jan W. van Wagtendonk

Abstract: Decades of fire suppression have led to unnaturally large accumulations of fuel in some forest communities in thewestern United States, including those found in lower and midelevation forests in Yosemite National Park in California. Weemployed the Random Forests decision tree algorithm to predict fuel models as well as 1-h live and 1-, 10-, and 100-h dead fuelloads using a suite of climatic, topographic, remotely sensed, and burn history predictor variables. Climate variables andelevation consistently were most useful for predicting all types of fuels, but remotely sensed variables increased the kappaaccuracymetric by 5%–12% age points in each case, demonstrating the utility of using disparate data sources in a topographicallydiverse region dominated by closed-canopy vegetation. Fire history information (time-since-fire) generally only increased kappaby 1% age point, and only for the largest fuel classes. The Random Forests models were applied to the spatial predictor layers toproduce maps of fuel models and fuel loads, and these showed that fuel loads are highest in the low-elevation forests that havebeen most affected by fire suppression impacting the natural fire regime.

Résumé : La suppression du feu pendant plusieurs décennies a entraîné d'importantes accumulations de combustibles danscertaines communautés forestières de l'Ouest des États-Unis, incluant celles qu'on retrouve dans les forêts situées a basse etmoyenne altitude dans le parc national de Yosemite en Californie. Nous avons utilisé l'algorithme arborescent de décision selonla méthode des forêts aléatoires pour élaborer des modèles de prédiction des combustibles aussi bien que des charges decombustibles vivants de 1 h etmorts de 1, 10 et 100 h a l'aide d'une suite de variables indépendantes climatiques, topographiques,obtenues par télédétection et portant sur l'historique des feux. Les variables climatiques et l'altitude étaient invariablement lesplus utiles pour prédire tous les types de combustibles mais les variables obtenues par télédétection augmentaient la métriquede précision kappa de 5 a 12 points de pourcentage dans chaque cas, démontrant l'utilité d'utiliser des sources disparates dedonnées dans une région où la topographie varie et qui est dominée par un couvert forestier fermé. L'information concernantl'historique des feux (l'intervalle entre les feux) augmentait généralement kappa de seulement 1 point de pourcentage et celaseulement pour les classes de combustibles les plus importantes. Les modèles obtenus par la méthode des forêts aléatoires ontété appliqués aux couches de prédiction spatiale pour produire des cartes des modèles de combustibles et des charges decombustibles qui montraient que les charges de combustibles sont les plus importantes dans les forêts a basse altitude danslesquelles la suppression du feu a eu le plus d'effet en modifiant le régime naturel des feux. [Traduit par la Rédaction]

IntroductionFire is an integral part of ecosystems in the western United

States. Decades of fire suppression have led to unnaturally largeaccumulations of fuel in some forest communities, includingthose found in lower and midelevation forests in Yosemite Na-tional Park (YNP) (Skinner and Chang 1996). This increasedamount of available fuel has likely contributed to a marked in-crease in burned area in the United States: 28 million ha burnedbetween 2000 and 2009 compared to 13 million ha burned in eachof the previous three decades (http://www.nifc.gov/fire_info/fires_acres.htm). Westerling et al. (2006) demonstrated that awarmer, drier climate cycle is also a likely factor in recent in-creases in area burned, especially for higher elevation forests. Inan effort to return fire to the YNP ecosystem, the park has per-formed prescribed fires since 1970 and allowed wildland fires toburn under prescribed conditions since 1972 (van Wagtendonkand Root 2003). Fuels have been sampled throughout the park,but spatial maps of fuels would aid in prioritizing areas in need offuel management activities, such as prescribed burning.

Fuel models describe the amount and condition of the surfacefuels (e.g., leaf and needle litter, fallen branch wood, and smalllive trees and shrubs) throughwhichmost fires burn (Albini 1976).They are used, in concert with information on weather and topog-raphy, to predict the growth of prescribed or wildland fires by fire

spread models such as FARSITE (Finney 2004) or HFire (Petersonet al. 2009), allowing for fire risk assessment of active, prescribed,and modeled fires. Remote sensing provides the opportunity toefficiently develop maps of fuel models for large areas; however,surface fuels are not directly visible to remote sensing systems formost fuel models due to overstory vegetation (Keane et al. 2001).Hence, ancillary data describing site potential for growing vege-tation (i.e., surface fuels) can be incorporated to map fuels.

A majority of the fuel studies using remote sensing have involvedpredicting and mapping fuel models via direct or indirect mapping(Keane et al. 2001). In indirect mapping, vegetation mapping is per-formed first, and then each vegetation class is assigned to a fuelmodel using a look-up table (e.g., Keane et al. 2000). However, avegetation class may be composed of multiple types of fuel depend-ing on vegetation condition and density and other factors, such asstand history, so this approach is not commonly used (Keane et al.2001). In direct mapping, the classification algorithm directly pre-dicts fuel models for each pixel (e.g., Riaño et al. 2002).

Accuracies (percent correct classification) when predicting fuelmodels using remotely sensed data have ranged from 50% to 85%,with kappa coefficients of agreement (Congalton 1991) rangingfrom 0.03 to 0.54 for studies in coniferous ecosystems (e.g., Keaneet al. 2000, 2002; van Wagtendonk and Root 2003; Rollins et al.2004; Falkowski et al. 2005) and from 0.54 to 0.79 for studies in

Received 11 May 2012. Accepted 12 November 2012.

S.H. Peterson and D.A. Roberts. Department of Geography, University of California at Santa Barbara, CA 93106, USA.J. Franklin. School of Geographical Sciences & Urban Planning, Arizona State University, Tempe, AZ 85287, USA.J.W. van Wagtendonk. US Geological Survey, Western Ecological Research Center, Yosemite Field Station, El Portal, CA 95318-0700, USA.

Corresponding author: Seth H. Peterson (e-mail: seth@geog.ucsb.edu).

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Can. J. For. Res. 43: 7–17 (2013) dx.doi.org/10.1139/cjfr-2012-0213 Published at www.nrcresearchpress.com/cjfr on 13 November 2012.

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shrublands and woodlands (e.g., Riaño et al. 2002; Poulos et al.2007; Poulos 2009) (Table 1). Kappa is an accuracy measure, rang-ing from0 to 1, that accounts for pixels that are classified correctlysimply by chance, making it more robust than percent correctlyclassified. The higher accuracies for the shrubland/woodlandstudiesmay have been achieved because the vegetation/fuel is notbeing obscured by tree canopies for these ecosystems. To ourknowledge, Rollins et al. (2004) provided the only previous predic-tions of surface fuel loads in forested ecosystems; surface fuel loadwas measured as a continuous variable but discretized into threeordinal categories (low, medium, and high) for model trainingand validation. Their accuracy was 52%, with a kappa of 0.20.

We used the Random Forests (RF) algorithm (Breiman 2001)implemented in R (R Development Core Team 2010) for our fuelclassification analysis. A majority of fuel classification studies(e.g., Keane et al. 2002; Rollins et al. 2004; Falkowski et al. 2005;Poulos et al. 2007; Poulos 2009) have used classification trees (CT)(Breiman et al. 1984). RF have not yet been used in the fuels liter-ature, although they are in common use in other disciplines (e.g.,Nicodemus et al. 2010; Bi and Chung 2011). CT recursively dividethe data into more homogeneous groups of the dependent vari-able through binary splits of the explanatory variable that bestreduces deviance at each particular node. A weakness in this ap-proach is that each division only optimizes the classification ofthe two groups generated by the split — it does not necessarilyoptimize overall classification accuracy. The series of splits makesup a single tree. The RF algorithm generates an ensemble of treeswhose predictions are averaged to determine the value for eachdata record, resulting inmore robust predictions. A large numberof different trees are generated by (1) evaluating a random subset ofthe explanatory variables to make any given split (the number ofpredictor variables evaluated at each split is equal to the squareroot of the number of predictor variables used in the model) and(2) using bootstrapped subsets of data to generate the trees andthe remaining data to evaluate them. The first step means thattrees are less likely to suffer from an optimal initial split thatmight have a detrimental effect on accuracy further down the treeand the second that accuracy reported by RF is based on indepen-dent subsamples, a further improvement over CT. Variable impor-tance is determined by permuting the explanatory variables andmeasuring the resulting effect on classification accuracy (Breiman2001). RF is a nonparametric model so the accuracy of the trees isnot affected bymulticollinearity in the predictor variables (Bi andChung 2011), which is important for this research, as certain vari-ables (e.g., maximum temperature and growing degree-days) arecorrelated to some extent. However, multicollinearity may have asmall effect on the absolute magnitudes of variable importance,although this is not found for all data sets (Nicodemus et al. 2010),and the relative order of variable importance is not affected (Biand Chung 2011).

In this study, we estimated fuel models and fuel loads in 1-h liveand 1-, 10-, and 100-h dead size classes using remotely sensed and

other environmental data as predictors and RF as the classifica-tion tool. Remote sensing provides information about the currentcondition of the vegetation, while other environmental variablesprovide information about the potential productivity of a givenarea. Remotely sensed explanatory variables included LandsatThematic Mapper (TM) band reflectances, fractional cover of basicscene constituents, called endmember (EM) fractions, and severalvegetation indices (VIs) that have been used to estimate fuel prop-erties, such as the Normalized Difference Vegetation Index (NDVI)(Rouse et al. 1973) and Visible Atmospherically Resistant Index(VARI) (Gitelson et al. 2002). In addition, a suite of topographicvariables, climatic variables, and burn history information wereused as predictors. Several aspects of this study are innovative.Although CT have been used previously for mapping fuels, to ourknowledge, this study is the first to use the RF algorithm to do so.In the remote sensing literature, although it is common to includetopographic variables to predict fuel characteristics (e.g., Riañoet al. 2002), other variable types, such as climatic variables, areless commonly used. To our knowledge, only one prior studyhas incorporated fire history into a model predicting fuels(Sánchez-Flores and Yool 2004), although many studies have sug-gested incorporating stand history information (e.g., Keane et al.2001). Additionally, some predictors are more time-intensive tocalculate than others (e.g., elevation versus potential solar insola-tion, raw band values versus EM fractions), so we examined therelative merits of using simple versus more complex variables forpredicting fuels. Finally, a large number of plots were sampled.Only Keane et al. (2000) sampled more plots, but within a studyarea that was an order of magnitude larger, resulting in a lowersampling density than ours.

Material and methods

Study siteYNP extends over 300 000 ha in the Sierra Nevada mountain

range of California, USA (Fig. 1). Elevation ranges from approxi-mately 600 to 4000 m. Precipitation ranges from 80 mm at lowerelevations on thewest side to 120mmmidslope on thewest (wind-ward) side to 70 mm east (leeward) of the Sierra Nevada. Climatesof the park range from Mediterranean at lower elevations on thewestern side to alpine at high elevations to continental on theeastern side. This topoclimatic variability supports a large num-ber of vegetation communities. Chaparral shrublands cover thewestern foothills, with manzanita (Arctostaphylos viscida Parry) andbuckbrush (Ceanothus cuneatus (Hook.) Nutt.) shrubs and an over-story of short oaks (canyon live oak (Quercus chrysolepis Liebm.) andinterior live oak (Quercus wislizeni A. DC.)) and sparse foothill pine(Pinus sabiniana Douglas ex Douglas). This shrubland grades intopure ponderosa pine (Pinus ponderosa Douglas ex P. Lawson &C. Lawson) forest, which then becomes mixed with incense cedar(Calocedrus decurrens (Torr.) Florin), sugar pine (Pinus lambertianaDouglas), California black oak (Quercus kelloggii Newberry), and

Table 1. Fuel model prediction papers in the literature that report classification accuracies.

Paper

Variables used

Fuel modelaccuracy (%)

Fuel modelkappaRemotely sensed Topographic Climatic Other

Keane et al. 2000 Raw bands × × × 36–38 0.26–0.28Keane et al. 2002 Raw bands, image products × × × 65–84 0.03–0.44Riaño et al. 2002 Raw bands, image products × 82.8 0.79van Wagtendonk and Root 2003 Multidate NDVI 54.3 0.39Rollins et al. 2004 Raw bands, image products × × × 53.9 0.27Falkowski et al. 2005 Raw bands, image products × × 62.3 0.54Poulos et al. 2007 Raw bands, image products × 70.2 0.54Poulos 2009 Raw bands, image products × 84.1 0.76

Note: NDVI is the Normalized Difference Vegetation Index. Image products are transformations of raw bands other than NDVI and include othervegetation indices, endmember fractions, tasseled cap transformations, and principal components analysis transformations.

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Fig. 1. False color Landsat Thematic Mapper image of central California from August 1985. Shrubland and broadleaf woodland appears brightred, conifer forest dark red, senesced grasslands grey, and exposed granite white. The boundary for Yosemite National Park is delineated inwhite. Note the strong topographic gradient within the park, reflected in the vegetation patterns.

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white fir (Abies concolor (Gordon & Glendl.) Lindl. Ex Hildebr.) withincreasing elevation. Red fir (Abies magnifica A. Murray) becomesprevalent at approximately 2200 m, with western white pine(Pinus monticola Douglas ex D. Don), western juniper (Juniperus oc-cidentalis Hook.), and Jeffrey pine (Pinus jeffreyi Balf.) becomingcommon at higher elevations. Subalpine forest, beginning at ap-proximately 2800 m, is dominated by lodgepole pine (Pinus con-torta Douglas ex Loudon), mixed with mountain hemlock (Tsugamertensiana (Bong.) Carrière), and then finally whitebark pine(Pinus albicaulis Engelm.) at treeline. Meadows comprising herbs,grasses, sedges, and shrubs occur at all elevations (Parker 1989).

Fuel dataField sampling was performed in YNP between 1987 and 1998.

Fuel model and vegetation biomass were recorded at 1076 plotsthat were 0.1 ha in size, and each plot was centered within a 1 haarea of homogeneous fuel conditions. Four fuel models (Albini1976) are common in the park: 1, 5, 8, and 9. Fuel model 1 (n = 180plots) refers to herbaceous meadows, model 5 (n = 172 plots) tochaparral shrublands, model 8 (n = 416 plots) to areas with a short-needled conifer overstory and light surface fuels, andmodel 9 (n =237 plots) to areas with a long-needled conifer overstory andheavier surface fuels. In addition, portions of YNP, particularlyhigh-elevation areas, consist of exposed granite domes or talusslopes, which are not burnable in a fire (defined as fuel model 99).Potential unburnable areas were identified on Landsat TM imag-ery based on sharp differences in image brightness between greenvegetation and soil or rock. Two hundred and twenty-four bandAirborne Visible/InfraRed Imaging Spectrometer (AVIRIS) datafrom these areas were then inspected for lignocellulose absorp-tion features that would indicate the presence of highly flamma-ble nonphotosynthetic vegetation (NPV), which can be confusedwith bare rock when using only the six Landsat bands; 207 NPV-free unburnable areas were assigned to fuel model 99.

Fuel load was measured in four categories: 1-h live fuels and 1-,10-, and 100-h dead fuels. One-hour fuels are less than 0.64 cm indiameter, 10-h fuels are between 0.64 and 2.54 cm in diameter,and 100-h fuels are between 2.54 and 7.62 cm in diameter. Activefire spread is primarily a function of 1- and 10-h fuel loads, while100-h fuels may allow a fire to smolder and reignite under moredangerous burning conditions. Dead and down woody fuels datawere collected using the planar intercept method (Brown 1974).Standing fuels inventories followed the guidelines and photo-graphs in Burgan and Rothermel (1984). Biomass values werehighly skewed, so the continuous distributions were discretizedinto three classes representing no, low, and high biomass valuesfor this study; the thresholds (Table 2) were based on naturalbreaks in the fuel loadings used to define fuel models (Albini1976). The dependent and independent variables used in thisstudy are presented in Tables 2 and 3, respectively.

Topographic variablesTopographic variables add discriminating power to models pre-

dicting vegetation parameters because they are related to localvariability in coarse-scale environmental controls on solar insola-tion, soil moisture balance, and temperature regime (Franklin1995). This could aid in predicting site productivity, which wouldinfluence the amount of surface fuels.

A 10 m resolution digital elevation model (DEM) was acquiredfrom the United States Geological Survey (USGS). Elevation, slope,and aspect were used as predictors. Slope and aspect werederived using standard tools in ENVI (http://www.exelisvis.com/ProductsServices/ENVI.aspx) image processing software, and as-pect was converted to “southwestness” using cos(aspect − 225°),transforming it from a circular to linear scale with more ecologi-cal meaning (Beers et al. 1966). Three complex topographic vari-ables (Wilson and Gallant 2000) were also computed: landscapeposition (Fels 1994), potential solar insolation (Hetrick et al. 1993),

and the topographic moisture index (TMI) (Moore et al. 1991).Landscape position is related to soil water availability and soildepth and texture (Franklin 1995) and was calculated using ENVI/IDL. TMI is a functionof theupslope catchment area (UCA) and slope;it is ameasureof relative sitewater availability (weassumedconstantsoil transmissivity). The TAPES-G program (Gallant andWilson 1996)was used to calculate UCA; themaximum input grid size allowed byTAPES-G is 5000 by 5000 cells, so a 30 m DEM, acquired from theUSGS (http://seamless.usgs.gov),was used to calculateUCAand slope.Potential solar insolation controls energy balance and evapotranspi-ration and was calculated using the Solarflux arc macro language(AML) for ARC/INFO's GRID module (Hetrick et al. 1993) for the twoequinoxes and two solstices (Julian days 80, 173, 267, and 356).

Climatic variablesClimatic variables have been shown to be strongly correlated

with vegetation patterns (Woodward 1987). Climate data were ac-quired from DAYMET (http://www.daymet.org/; Thornton et al.1997), which provides average climate variables over the period1980–1997 at a 1 km spatial resolution for the United States.DAYMET applied a Gaussian function, which uses an inverse dis-tance weighting algorithm and a smoothing filter, to dailyweather station data to regress temperature and precipitationagainst elevation to generate daily gridded weather output. Thesedata were then temporally aggregated to provide monthly andannual summaries for each grid cell. Four variables were used inthis study: average July maximum temperature, average Decem-ber minimum temperature, average annual mean precipitation,and annual growing degree-days. These have been shown to beimportant predictors of broad-scale vegetation patterns in mon-tane California (Franklin 1998).

Fire historyThe fire history of an area could affect fuels in a number of

ways. For instance, a recently burned area should have a lowamount of surface fuels, especially in the readily burnable 1- and10-h size classes. As time-since-fire increases, surface fuels in allsize classes should accumulate. Repeated fires in a given area cantype convert shrublands to grasslands or could act to removesurface fuel below a forest canopy.

Two spatially explicit data sets were combined to generate afire history for YNP and the surrounding areas. The State ofCalifornia has an extensive fire history database, recordingmost fires greater than 1 acre (0.4 ha) in size since 1910. Addi-tionally, YNP has a fire history database covering the years 1930to present that records all fires including management ignitedprescribed fires. Two metrics were calculated: the number ofyears since the last fire at the time a plot was sampled and thenumber of times in the record that a plot burned prior tosampling. Of the 1076 vegetated plots, 839 did not burn in therecord prior to sampling, 126 burned once, 53 burned twice,and 58 burned more than two times; 163 plots burned at leastonce subsequent to sampling.

Remotely sensed dataGeoreferenced Landsat TM data were acquired from the USGS

(http://glovis.usgs.gov/). Late summer − early fall imagery was cho-sen to assure the scenes were free of both clouds and snow. Theimages were processed to surface reflectance using a relative ra-diometric calibration technique (Furby and Campbell 2001) with a1999 Landsat Enhanced Thematic Mapper (ETM+) image that hadbeen processed to surface reflectance using ACORN (http://www.aigllc.com/) as the reference image.

Three different types of Landsat data products were used aspredictors: raw reflectance values (bands 1−5 and 7), VIs, and EMfractions (Table 3). Riaño et al. (2002) found that raw reflectance

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values were good predictors of fuel models. The VIs tested in thisstudy were NDVI, Vegetation Index Green (VIG) and VARI (Gitelsonet al. 2002), and Normalized Difference Infrared Index usingbands 5 and 7 (NDII5 and NDII7) (Hunt and Rock 1989). VIs havebeen shown to be related to site productivity by many authors(e.g., Gamon et al. 1995), which would in turn be related to fuelloads. NDVI, VIG, and VARI are sensitive to chlorophyll content ofvegetation whereas NDII5 and NDII7 are sensitive to leaf watercontent. Spectral mixture analysis (SMA) (Roberts et al. 1993) is atechnique that determines the percentages of basic landscapeconstituents, EMs, that are required to reproduce the reflectanceof each pixel. The output from SMA are EM fraction images, whichare spatial maps of these percentages. EMs included were greenvegetation (GV), NPV, soil/rock, and shade; a forested area wouldhave EM fractions on the order of 40% GV, 15% NPV, 5% soil, and40% shade, as an example. EM fractions, especially NPV, couldpredict fuel amount in grasslands (Numata et al. 2007), and the GVand shade fractions are related to site productivity and aboveground biomass in forested areas (Peddle et al. 2001).

In this study, EM fractions were calculated using Multiple EMSMA (MESMA), a technique that allows the number and types ofEMs to vary on a per-pixel basis, thus accounting for within-classheterogeneity (i.e., two or more suitable rock EMs in a scene) andvariation in surface heterogeneity (i.e., pixels best described bytwo EMs, such as a tree composed of leaves and shadows com-pared with more complex pixels composed of bare soil, leaves,and litter: Roberts et al. 1998). MESMAwas performed for two (e.g.,GV and shade), three (e.g., GV, NPV, and shade), and four EMmodels, and these were combined on a per-pixel basis under

the principle of parsimony to obtain a four EM fraction image(Roberts et al. 1998).

To derive meaningful measures of cover fractions, it is impor-tant to identify a suitable set of EMs. Potential EMs can be derivedfrom satellite, airborne, or field observations. In this study, EMs

Table 2. Dependent variables used in this study.

Variable Categories

Fuel models Albini (1976) fuel models 1, 5, 8, and 91-h (<0.64 cm) live None (0–0.224 Mg·ha−1), low (0.224–2.24 Mg·ha−1), high (>2.24 Mg·ha−1)1-h (<0.64 cm) dead None (0–0.224 Mg·ha−1), low (0.224–3.36 Mg·ha−1), high (>3.36 Mg·ha−1)10-h (0.64–2.54 cm) dead None (0–0.224 Mg·ha−1), low (0.224–1.68 Mg·ha−1), high (>1.68 Mg·ha−1)100-h (2.54–7.62 cm) dead None (0–0.224 Mg·ha−1), low (0.224–2.24 Mg·ha−1), high (>2.24 Mg·ha−1)

Table 3. The four types of predictor variables used in this study.

Abbreviation Variable Range of values

Topographic (T)elev Elevation 320–4007 mslp Slope 0–90°swness Southwestness −1–1lpos4 Landscape position −1032–1035psiXXX Potential solar insolation on four dates 0–20525894 W·m−2

tmi Topographic moisture index −2.07–23.97

Climatic (C)maxt July maximum temperature 14–37 °Cmint December minimum temperature −16–4 °Cppt Annual mean precipitation 11–203 mmgdd Annual growing degree-days 604–6498 degree-days

Remote sensingRaw 1985 Landsat TM raw bands, 1985 scene 0–1Raw avg3 Landsat TM raw bands, average of three highest values

in time series0–1

VI 1985 Landsat TM NDVI, VARI, VIG, NDII5, NDII7, 1985 scene −1–1VI avg3 Landsat TM NDVI, VARI, VIG, NDII5, NDII7, average of

three highest values in time series−1–1

SMA 1985 Landsat TM GV, NPV, soil, shade, 1985 scene 0%−100%SMA avg3 Landsat TM GV, NPV, soil, shade, average of three

highest values in time series0%−100%

Fire historybhist Number of burns between 1910 and sampling year 0–7ysf Years between most recent burn and sampling year 0–90 years, unburned

Fig. 2. Reflectance spectra for the six Landsat Thematic Mapperbands for the endmembers (EMs) used to model the Landsatscenes of Yosemite National Park with spectral mixture analysis.GV-4m82_mf2, green vegetation EM from a mixed fir stand imagedby the July 2003 AVIRIS image; NPV-u9potr11av, nonphotosyntheticvegetation EM from an Analytical Spectral Devices (ASD) handheldspectrometer measurement of poplar bark; soil-granite image EM,rock from a granite dome imaged by a 1999 Landsat ETM+ scene;soil-rockb.000, rock imaged by an ASD.

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were derived from three sources: a July 2003 airborne AVIRISimage of Shaver Lake in the Sierra Nevada of California, field-measured reflectance spectra collected using an ASD handheldspectrometer (Analytical Spectral Devices, Boulder, Colorado),and the 1999 Landsat ETM+ image. The three data sources wererequired to account for limitations of the Landsat sensor, inwhicha 30 m pixel often includes a mixture of more than one material.This is especially true for NPV and GV, in which bark, wood, andindividual tree crowns rarely cover a large enough area to fill apixel. By contrast, specific rock types found in Yosemite coverlarge areas and fill entire pixels. Both AVIRIS-derived and field-measured spectra were resampled from their original wave-lengths to the six wavelengths measured by Landsat and thencombined with the Landsat spectra into a single library of poten-tial EMs. To identify the most representative EMs, the library ofpotential EMs was applied to the 1999 Landsat ETM+ scene andEMs were selected based on their spectral fit, assessed by a lowroot mean squared error (RMSE), and the number of pixels in theimage theymodeled successfully (i.e., produced physically reason-able estimates of fractional cover). The GV EM that was selectedcame from a mixed fir stand imaged in the July 2003 AVIRIS im-age. An ASD spectrum of poplar bark (Roberts et al. 2004) wasselected for NPV. Rock was more spectrally variable than GV andNPV, so we used two EMs: an ASD spectrum of rock (rockb.000:Roberts et al. 2004) and a Landsat image EM of granite from theHalf Dome area of Yosemite Valley (Fig. 2).

Landsat TM data for each year from 1984 to 1998, with theexception of 1995 (no cloud-free image available), were acquired.Values were summarized in two different ways: (1) values from the1985 Landsat TM image (before any of the plots were sampled)

were used and (2) the average of the three highest values for eachpixel in the time series was calculated to provide a more robustmeasure of actual site conditions. Thus, neither set of remotelysensed variables would be affected by post-sampling fire remov-ing the vegetation. Using values from the closest Landsat imageprior to field samplingwas explored but results are not presented,as accuracies were lower because phenological variability con-founded the results. These two summary statistics of the remotelysensed data were used as explanatory variables in the model.

AnalysisThe RF package in R was used to generate and aggregate large

numbers of decision trees, predicting fuel model and biomassclasses using topographic, climatic, fire history, and remotelysensed explanatory variables. By default, 500 trees are grown eachtime the RFmodel is run, and themodel was run 20 times for eachof the 29 variable combinations in Table 4, so results are an aver-age of 10 000 trees for each variable combination.

Results

Fuel model predictionsThe kappa classification accuracy statistic for fuel models based

on single types of variables (e.g., just topographical variables)ranged from 0.352 to 0.468 (Table 4). Combining variable types ledto notable increases in kappa; topographic and climatic combinedhad a kappa of 0.487, and adding VIs from 1985 (model 12) in-creased kappa to 0.618. Adding burn history increased kappaslightly to 0.621 (model 18) (Table 4), as four additional plots of fuelmodel 1 became correctly classified (Table 5).

Table 4. Prediction accuracy and kappa coefficients for fuel predictions using 29 different sets of predictor variables in Random Forest (RF)models.

RFmodel

Predictor variablesused

Fuel modelpredictions

1-h livepredictions

1-h deadpredictions

10-h deadpredictions

100-h deadpredictions

Accuracy Kappa Accuracy Kappa Accuracy Kappa Accuracy Kappa Accuracy Kappa

1 T 0.512 0.352 0.448 0.135 0.503 0.182 0.483 0.221 0.484 0.2002 C 0.559 0.425 0.538 0.284 0.528 0.238 0.520 0.279 0.574 0.3483 T + C 0.610 0.487 0.562 0.315 0.579 0.308 0.553 0.327 0.568 0.3344 T + C + bhist 0.610 0.487 0.563 0.316 0.579 0.307 0.559 0.336 0.571 0.3395 T + C + ysf 0.612 0.489 0.564 0.317 0.582 0.311 0.562 0.341 0.576 0.3466 VI 1985 0.530 0.378 0.450 0.147 0.487 0.153 0.492 0.236 0.491 0.2147 VI avg3 0.508 0.347 0.452 0.146 0.499 0.172 0.466 0.197 0.477 0.1878 SM 1985 0.462 0.294 0.438 0.129 0.493 0.176 0.477 0.215 0.476 0.1939 SM avg3 0.536 0.391 0.482 0.203 0.505 0.187 0.491 0.235 0.499 0.22410 Raw 1985 0.579 0.448 0.484 0.201 0.521 0.212 0.540 0.309 0.548 0.30011 Raw avg3 0.592 0.468 0.494 0.218 0.523 0.213 0.493 0.238 0.536 0.28012 T + C + VI 1985 0.708 0.618 0.599 0.372 0.597 0.334 0.586 0.377 0.566 0.33013 T + C + VI avg3 0.706 0.616 0.599 0.373 0.601 0.339 0.566 0.347 0.571 0.33814 T + C + SM 1985 0.700 0.609 0.600 0.376 0.618 0.371 0.593 0.388 0.583 0.35515 T + C + SM avg3 0.699 0.608 0.594 0.364 0.611 0.359 0.588 0.380 0.587 0.36216 T + C + Raw 1985 0.696 0.603 0.601 0.376 0.618 0.372 0.595 0.390 0.584 0.35817 T + C + Raw avg3 0.696 0.603 0.577 0.340 0.600 0.341 0.589 0.382 0.601 0.38318 T + C + VI 1985 + bhist 0.711 0.621 0.598 0.371 0.596 0.333 0.586 0.377 0.573 0.34019 T + C + VI avg3 + bhist 0.709 0.620 0.602 0.376 0.600 0.338 0.571 0.354 0.577 0.34620 T + C + VI 1985 + ysf 0.707 0.617 0.597 0.369 0.599 0.338 0.587 0.379 0.573 0.34021 T + C + VI avg3 + ysf 0.705 0.615 0.601 0.375 0.605 0.346 0.571 0.355 0.576 0.34622 T + C + SM 1985 + bhist 0.701 0.611 0.598 0.373 0.618 0.370 0.596 0.392 0.584 0.35823 T + C + SM avg3 + bhist 0.701 0.610 0.592 0.361 0.611 0.358 0.590 0.383 0.591 0.36824 T + C + SM 1985 + ysf 0.702 0.612 0.600 0.375 0.619 0.372 0.598 0.395 0.588 0.36325 T + C + SM avg3 + ysf 0.700 0.608 0.591 0.359 0.614 0.363 0.592 0.386 0.592 0.37026 T + C + Raw 1985 + bhist 0.696 0.603 0.601 0.377 0.618 0.371 0.598 0.396 0.590 0.36627 T + C + Raw avg3 + bhist 0.697 0.605 0.576 0.337 0.601 0.344 0.592 0.386 0.605 0.39028 T + C + Raw 1985 + ysf 0.697 0.604 0.599 0.373 0.617 0.369 0.599 0.397 0.590 0.36629 T + C + Raw avg3 + ysf 0.695 0.602 0.578 0.340 0.601 0.343 0.594 0.389 0.606 0.390

Note: The best model for each dependent variable is in bold and was chosen based on having the highest kappa. Abbreviations for predictor variables are definedin Table 3.

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Inspection of the contingency table reveals that misclassifica-tions were ecologically interpretable given the canopy structureof the different fuel models (Table 5). The shrub fuel model class(fuel model 5) had the most confusion with the other fuel models;sparse shrubs can be spectrally similar to grasslands and denseshrubs to closed-canopy forest. Fuel models 8 and 9, with treeoverstories and varying amounts of surface fuels, showed themost confusion with each other, but accuracies were generallyhigh. Fuel model 1 (grass) was confused with fuel model 8 (treeoverstory with light surface fuels); fuel models 5 and 9 havemuchhigher surface fuel loads. Accuracy was highest for the unburn-able class owing to high spectral separability between vegetatedand unvegetated areas.

Vegetation indices were most important for separating fuelmodels, specifically NDVI and NDII7 (Table 6). Elevation was alsoimportant, as might be expected for this study area spanning alarge elevation range with correlated vegetation distributions.The next most important predictors were the climate variables,the remaining VIs, and slope. The remaining topographic vari-ables showed low predictive importance.

Fuel load predictionsClassification accuracies for fuel loads (1-h live and 1-, 10-, and

100-h dead) were lower than for fuel models (Table 4). The fourtypes of fuel loads showed similar trends in accuracy, with thecombination of climatic variables, topographic variables, and rawLandsat bands providing the highest classification accuracies.Raw reflectance bands effectively discriminated all of the types offuel loads slightly better than EM fractions. The vegetation indicesshowed lower accuracies, likely because they are primarily sensi-tive to living, not dead, vegetation. A fire history variable in-creased accuracy slightly for three of the four fuel classes.

The averaged contingency tables for the best models in Table 4show, not unexpectedly, that confusion is greater for fuel loadclasses that are adjacent on the ordinal scale (Table 5). This gen-erally results in the lowest user's/producer's accuracy for the classin the middle (low fuel load). This points to the difficulty in dis-cretizing a continuously distributed variable; however, regres-sions using fuel load as a continuous variable performed poorly atpredicting high fuel load values because of the sparseness of highvalues to train the model, resulting in very low R2 values.

Precipitation, elevation, and December average minimum tem-perature were very important for each of the fuel loads predic-tions (Table 6). Annual precipitation was the most influentialpredictor of 1-h live fuel load, likely because the growth of finefuels, such as shallow-rooted annual grasses, is highly dependenton water availability. Additionally, the maps of live and dead fuelpredictions show that fuel accumulation is highest in shrublandsand lower elevation forests (Fig. 3c−3f). The number of growingdegree-days ranked higher for predicting dead as opposed to livefuels.

Reflectance in the shortwave infrared (SWIR) (Landsat TMbands5 and 7) provides the best separation between living and deadvegetation (Fig. 2), and these bands helped discriminate three ofthe four fuel size classes. The visible bands (bands 1, 2, and 3)differentiate GV (grass for 1-h live and tree canopies for 10- and100-h dead) from surface background (Franklin et al. 1991).

Spatial predictionsThe best variable combinations from Table 4 were used to gen-

erate a RF model for each of the five fuel variables, and the Rpackage yaImpute (Crookston and Finley 2007) was used to applythose models to maps of the independent variables, producingclassificationmaps for fuel models and fuel loads (Fig. 3). Themapof predicted fuel models (Fig. 3b) shows a number of interestingspatial patterns. The shrub fuel model (red) is most common atlower elevations outside YNP and south-facing slopes of the tworiver valleys on the west side of YNP. Fuel model 8 (green) isT

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common at lower elevations and north-facing slopes of the rivervalleys. Fuel model 9 (dark green) is widespread throughout YNP.The grass fuel model (yellow) is most common in sparsely vege-tated high-elevation areas on the east side of YNP and intermixedin the chaparral on the southwest side of YNP.

The predicted fuel loads maps also show coherent spatial pat-terns. The live fuel load map (Fig. 3c) is distinctive from the threedead fuel load maps (Figs. 3d−3f). Live fuel loads were high (red) intwo areas: chaparral shrublands on the west side of YNP and sage-brush shrublands to the northeast of the park; live vegetationmakes up a majority of the biomass available to shrubland fires(Countryman and Dean 1979). Live fuel loads are modeled to beabsent (green) throughout most of YNP. The regions of low livefuels (yellow) in linear features on the eastside of YNP correspondto (1) sage brush areas and (2) conifers that do not form closed-canopy stands, so that a grass fuel understory could be present.

The three dead fuel loadmaps show similar spatial trends, withthe south and west portions of the study area having the largestarea of high dead fuel loads. These areas have the lowest eleva-tions in the YNP area (Fig. 3a). The lower and middle elevationzones of YNP historically had the highest burn frequencies (due toanthropogenic ignitions and high flammability of pine needlelitter), so the reduction in fires due to active fire suppression hashad its greatest effect on fuel load in these elevations (Skinner andChang 1996). The 1-h dead fuel map (Fig. 3d) is dominated by areasof high fuel loads, with a medium amount of low fuels (yellow)and a small amount of no-fuel (green) areas. These no-fuel areascorrespond to high-elevation areas with exposed granite. The spa-tial extent of the areas of high dead fuel loads (red) are very similarfor 1- and 10-h fuels; however, the remainder of the 10-h dead fuelmap (Fig. 3e) is dominated by the no-fuel class. The no-fuel areas(green) of the 10- and 100-h dead fuel maps are similar, with the100-h fuels map (Fig. 3f) havingmore of the remaining areas in thelow fuel load class.

Discussion and conclusionsMapping surface fuels from space is difficult. Our fuel model

results (accuracy 71.1%, kappa 0.621) improves upon a previousfuel mapping effort in YNP, in which six fuel models plus bar-ren areas were mapped with an accuracy of 54.3% (kappa 0.391)

(van Wagtendonk and Root 2003). These improved map accura-cies probably resulted from the inclusion of climatic and topo-graphic variables as predictors. Other fuel studies have notincluded an unburnable fuels class. Our fuel model (omittingfuel model 99) and biomass prediction accuracies also comparefavorably with the work of Rollins et al. (2004) in a coniferousecosystem in Montana in the western United States (kappa forfuel models 0.54 in our study versus 0.27 in theirs, kappa forfuel loads from 0.37 to 0.41 in our study versus 0.20 in theirs).Our fuel model (omitting fuel model 99) classification accuracywas the same as that of Falkowski et al. (2005) for anotherconiferous ecosystem in the western United States (kappa of0.54 in both cases). Two research groups have attained higherclassification accuracies when mapping fuels. Riaño et al.(2002) were able to use multiple dates of Landsat within thegrowing season to distinguish between deciduous trees withand without understory fuel for their Mediterranean ecosys-tem. Taking advantage of vegetation phenology to aid in sepa-rating classes was not possible for this study, as there is a smallwindow of time when the entire area is both cloud- and snow-free. Additionally, the shrubs and trees of YNP are, for the mostpart, evergreen. Poulos et al. (2007) and Poulos (2009) also re-ported higher classification accuracies; they created their ownfuel models based on statistical groupings of the sampled data,which avoids the uncertainty associated with assigning a fuelmodel to a plot based on expected fire behavior in that plot(Keane et al. 2001) and may have improved map accuracies.Additionally, the Riaño et al. (2002), Poulos et al. (2007), andPoulos (2009) studies were performed in ecosystems with asignificant amount of surface fuels that were not obscured bydense overstory vegetation.

A number of trends emerged with respect to predictor variableselection. Climate-based variables alone explained the most vari-ability in fuel response variables followed by raw Landsat TMreflectance bands and topographic variables. The importance ofelevation and December minimum temperature to all fuel loadspredictions suggests environmental limits on plant growth. Grow-ing degree-days controls tree growth (Miller and Urban 1999), andhence woody debris/fuel production, and was more important forpredicting dead than live fuels. Elevation was more useful for

Table 6. Importancemeasures for each explanatory variables from the best models from Table 4 for predicting fuel models, live 1-hour fuels, anddead 1-, 10-, and 100-h fuels.

Fuel model predictions 1-h live predictions 1-h dead predictions 10-h dead predictions 100-h dead predictions

Variable Decrease Variable Decrease Variable Decrease Variable Decrease Variable Decrease

NDVI 1985 0.1399 ppt 0.0390 elev 0.029 band5 1985 0.0305 band2 avg3 0.0361elev 0.0882 elev 0.0353 band7 1985 0.027 mint 0.0283 band3 avg3 0.0334NDII7 1985 0.0545 mint 0.0282 band5 1985 0.027 band2 1985 0.0274 mint 0.0280ppt 0.0534 band5 1985 0.0263 mint 0.020 elev 0.0262 band1 avg3 0.0234mint 0.0471 band2 1985 0.0239 gdd 0.019 ppt 0.0247 band5 avg3 0.0232maxt 0.0444 band1 1985 0.0227 maxt 0.019 band7 1985 0.0225 gdd 0.0231NDII5 1985 0.0392 maxt 0.0221 band3 1985 0.018 band3 1985 0.0220 elev 0.0227gdd 0.0380 band3 1985 0.0215 ppt 0.014 gdd 0.0216 ppt 0.0207slp 0.0229 gdd 0.0203 band2 1985 0.014 maxt 0.0192 maxt 0.0188VIG 1985 0.0175 band4 1985 0.0167 band1 1985 0.010 band1 1985 0.0161 band7 avg3 0.0155VARI 1985 0.0165 band7 1985 0.0149 psi80 0.006 slp 0.0099 slp 0.0089psi173 0.0156 slp 0.0076 psi267 0.006 band4 1985 0.0075 psi80 0.0072psi356 0.0100 psi267 0.0066 slp 0.006 psi267 0.0063 band4 avg3 0.0071psi267 0.0094 psi80 0.0060 band4 1985 0.006 psi80 0.0060 psi356 0.0064psi80 0.0084 psi173 0.0057 psi173 0.005 psi356 0.0058 psi267 0.0060tmi 0.0071 psi356 0.0054 psi356 0.005 psi173 0.0057 psi173 0.0052bhist 0.0071 bhist 0.0042 tmi 0.002 swness 0.0040 swness 0.0040swness 0.0040 swness 0.0035 swness 0.002 tmi 0.0035 ysf 0.0038lpos4 0.0012 tmi 0.0021 lpos4 −0.001 ysf 0.0017 tmi 0.0024

lpos4 0.0003 lpos4 −0.0002 lpos4 0.0009

Note: Decrease is mean decrease in accuracy when the values of the explanatory variables are permutated. Abbreviations for predictor variables are defined inTable 3.

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predicting fuels than the derived topographic variables, likelybecause it is correlated with climate variables (Thornton et al.1997). In contrast, the derived variables relate to variation in soilmoisture and energy balance at the finer scale of the slope facet.When combining variable types, climatic and topographic vari-ables almost always had a higher accuracy than climatic variablesalone, and the addition of remotely sensed variables to the envi-ronmental variables always increased kappa by 0.05–0.12.

The remotely sensed variables that resulted in the greatest in-creases in classification accuracy were either EM fractions fromSMA or the raw reflectance bands. The raw bands tended to giveslightly higher accuracies, but the results are more difficult tointerpret. The addition of a burn history variable (time since fire)had the largest effect on kappa for the two largest fuel size classes.This finding is consistent with that of vanWagtendonk andMoore(2010) who found that only older trees drop fuel in the larger sizeclasses. Still, burn history variables had only a small effect onaccuracy in this study. Only 29 000 ha of the park (roughly 10%)burned during the time period of the three field sampling cam-

paigns, and 31 000 ha burned between 1930 and 1985, so perhapsfires did not impact enough sampled plots for the importance ofburn history to be more detectable in this data set.

It is interesting that elevation was one of the most importantpredictor variables in each of themodels, despite the fact that it isonly a proxy variable for climate patterns (due to environmentallapse rate effects on temperature and orographic enhancement ofprecipitation). Climate data were available at 1 km resolutionwhereas elevation data were available at 10m resolution. So whileelevation represents an indirect gradient (Austin 2002), the finerspatial resolution is useful for fuel predictions.

Patterns in the predicted fuel maps were consistent with thefindings of an independent field-based study within the park. vanWagtendonk and Moore (2010) also found that fuel depositionrates in YNP were highest for the low-elevation ponderosa pineand white fir forests, medium for midelevation Jeffrey pine andred fir forests, and lowest for whitebark pine occurring at thetreeline. Foliage (classified as 1-h dead fuel) was their largest con-tributor to deposited biomass, so the trend found in van Wagten-

Fig. 3. Digital elevation model and predicted fuel maps for the Yosemite National Park area, with the park boundary superimposed. Theimages are (a) digital elevation model (low to high in dark to light as shown in the legend), (b) predicted map of fuel models with legend andpredicted maps of (c) 1-h live fuels, (d) 1-h dead fuels, (e) 10-h dead fuels, and (f) 100-h dead fuels. (For all fuel biomass maps, the three ordinalclasses used for classification are shown: green, no fuel; yellow, low fuel; red, high fuel.) Water is not shown in these small images forsimplicity.

a b

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donk and Moore (2010) is most evident in the dead 1-h dead fuelmap (Fig. 3d). The sharp boundary between high (red) and no-fuel(green) for 10-h fuels evident in Fig. 3e occurs at the border be-tween forests dominated by red fir (van Wagtendonk and Moore2010) (average deposition rate 54.3 g·m–2·year–1) and subalpineforests composed of mixed lodgepole pine (17.5 g·m–2·year–1),whitebark pine (11.8 g·m–2·year–1), and mountain hemlock(11.2 g·m–2·year–1). The fairly uniform area of high fuel loads at lowelevations for the 10-h fuel map (Fig. 3e) is reflected in the fielddata: ponderosa pine, incense cedar, and white fir all had simi-larly high deposition rates (56.6, 43.5, and 74.6 g·m–2·year–1, re-spectively) (van Wagtendonk and Moore 2010). The boundarybetween high (red) and no fuel (green) for 100-h fuels evident inFig. 3f again occurs at the border between red fir and subalpineforests and is borne out by the vanWagtendonk and Moore (2010)data for 100-h fuels: 32.4 g·m–2·year–1 for red fir versus 6.4, 4.4, and4.0 g·m–2·year–1 for the subalpine forest components. The south-west corner of the 100-h fuels map shows a mixture of high- andlow-fuel areas, and this is again supported by the field data: thedeposition rates for 100-h fuels for ponderosa pine, incense cedar,white fir (30.1, 23.8, and 88.9 g·m–2·year–1, respectively), and red firwere variable, too.

The patterns revealed in the maps of fuel loads enable moreinformed decisionmaking by the YNP fire management program.For example, two types of prescribed fires are used to restoreecological integrity and reduce fuel loads: wildland fires are al-lowed to burn under less-dangerous weather and fuel conditions(termed a prescribed natural fire) and prescribed fires are ignitedto reduce understory fuels in known problem areas. Both of thesetypes of fires could be better managed with the fuels maps devel-oped in this study. Potential prescribed natural fires are only al-lowed to burn if their final fire perimeter does not encompassareas of high fuels because this could cause the fire to becomeuncontrollable. Additionally, our fuels maps could aid in target-ing areas most in need of a management ignited prescribed fire.This would be especially beneficial in natural areas that are notreadily monitored on the ground.

The flexible RF CT algorithm used to map fuel models and fourclasses of surface fuels in YNP was able to incorporate remotelysensed data, climatic and topographic variables, and fire historyinformation. All of these disparate types of data were used in themodeling process, and combining them led to improved classifi-cation accuracies. Additionally, themaps generated exhibited log-ical and coherent spatial patterns. These methods should beportable to other forested environs near YNP and throughout thewestern United States.

AcknowledgmentsWe would like to thank the Nature Conservancy and Yosemite

National Park field crews for gathering the extensive data setsused in this study and PeggyMoore for organizing and supervisingthe field crews. Rob Pleszewski collated the field data.

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