cajander larch (larix cajanderi) biomass distribution, ﬁre ......l. t. berner et al.: cajander...

Biogeosciences 9 3943ndash3959 2012wwwbiogeosciencesnet939432012doi105194bg-9-3943-2012copy Author(s) 2012 CC Attribution 30 License

Biogeosciences

Cajander larch (Larix cajanderi) biomass distribution fire regimeand post-fire recovery in northeastern Siberia

L T Berner1 P S A Beck1 M M Loranty 1 H D Alexander2 M C Mack2 and S J Goetz1

1The Woods Hole Research Center 149 Woods Hole Road Falmouth MA 02540-1644 USA2University of Florida Department of Biology PO Box 118525 Gainesville FL 32611 USA currently at Colgate University Department of Geography 13 Oak Drive Hamilton NY 13346 USA currently at University of Texas ndash Brownsville Department of Biological Sciences 80 Fort Brown BrownsvilleTX 78520 USA

Correspondence toL T Berner (lbernerwhrcorg)

Received 23 May 2012 ndash Published in Biogeosciences Discuss 25 June 2012Revised 10 September 2012 ndash Accepted 16 September 2012 ndash Published 15 October 2012

Abstract Climate change and land-use activities are in-creasing fire activity across much of the Siberian bo-real forest yet the climate feedbacks from forest distur-bances remain difficult to quantify due to limited informa-tion on forest biomass distribution disturbance regimes andpost-disturbance ecosystem recovery Our primary objec-tive here was to analyse post-fire accumulation of Cajan-der larch (Larix cajanderiMayr) aboveground biomass for a100 000 km2 area of open forest in far northeastern SiberiaIn addition to examining effects of fire size and topogra-phy on post-fire larch aboveground biomass we assessed re-gional fire rotation and density as well as performance ofburned area maps generated from MODIS satellite imageryUsing Landsat imagery we mapped 116 fire scar perimetersthat dated c 1966ndash2007 We then mapped larch abovegroundbiomass by linking field biomass measurements to tree shad-ows mapped synergistically from WorldView-1 and Land-sat 5 satellite imagery Larch aboveground biomass tendedto be low during early succession (le 25 yr 271plusmn 26 g mminus2n = 66 [meanplusmn SE]) and decreased with increasing eleva-tion and northwardly aspect Larch aboveground biomasstended to be higher during mid-succession (33ndash38 yr 746plusmn

100 g mminus2 n = 32) though was highly variable The highvariability was not associated with topography and poten-tially reflected differences in post-fire density of tree re-growth Neither fire size nor latitude were significant pre-dictors of post-fire larch aboveground biomass Fire activitywas considerably higher in the Kolyma Mountains (fire ro-tation = 110 yr fire density = 10plusmn10 fires yrminus1

times104 kmminus2)

than along the forest-tundra border (fire rotation = 792 yrfire density = 03plusmn 03 fires yrminus1

times 104 kmminus2) The MODISburned area maps underestimated the total area burned inthis region from 2000ndash2007 by 40 Tree shadows mappedjointly using high and medium resolution satellite imagerywere strongly associated (r2

asymp 09) with field measurementsof forest structure which permitted spatial extrapolation ofaboveground biomass to a regional extent Better understand-ing of forest biomass distribution disturbances and post-disturbance recovery is needed to improve predictions of thenet climatic feedbacks associated with landscape-scale forestdisturbances in northern Eurasia

1 Introduction

Forests in the Russian Federation cover approximately 800million ha and contain the largest vegetation carbon pooloutside of the tropics (Goodale et al 2002 Houghton etal 2007) which along with their low surface albedo makethem an important component of Earthrsquos climate system (Bo-nan 2008) Since instrumentation began in the 1880s av-erage winter and summer air temperatures across northernEurasia have risen 2C and 135C respectively (Groismanand Soja 2009) and climate models predict a 3ndash7C in-crease in mean annual temperature across much of the regionby the end of the 21st century (IPCC 2007) Higher temper-atures with little or no change in precipitation have resultedin drier conditions and increased risk of fire across much of

Published by Copernicus Publications on behalf of the European Geosciences Union

3944 L T Berner et al Cajander larch biomass distribution

the larch (Larix spp) dominated boreal forests of Siberia(Groisman et al 2007) While the boreal forest biome ofnorthern Eurasia acts as a net sink for atmospheric CO2(Goodale et al 2002) the strength of this terrestrial car-bon sink has weakened over the last decade due to increasedfire emissions and warming-induced increases in decomposi-tion of soil organic matter (Hayes et al 2011) The strengthof the sink will likely continue to diminish because risingair temperatures are expected to further increase fire activ-ity (Stocks et al 1998) Climate-induced intensification ofthe fire regime will affect forest carbon pools surface energybudgets and hydrologic processes yet the net effect of thesefeedbacks on the climate system remains poorly understood(Goetz et al 2007 Bonan 2008)

Fire is a dominant control on stand structure and compo-sition in Siberian forests with stand-replacing fires punc-tuating the start and end of successional cycles (Furyaevet al 2001 Schulze et al 2012) The accumulation ofcarbon after fire disturbance in Siberiarsquos larch forests de-pends on characteristics of the fire regime tree biology andsite-level conditions but details of these interactions arenot fully understood particularly in the northeastern per-mafrost zone (Kajimoto et al 2010) Larch reestablishmentand biomass accumulation depends on nonlinear interactionsamong fire periodicity and severity (Furyaev et al 2001Schulze et al 2012) climate conditions (James 2011 Lloydet al 2011) site micro-topography and permafrost (Koike etal 2010 Zyryanova et al 2010) and availability of seeds(Abaimov et al 2000 Sofronov and Volokitina 2010) Firefrequency (eg fire rotation or fire return interval) whichis the interval of time between successive disturbances av-erages around 80 yr in northern larch forests though exhibitsconsiderable topographic and regional variability (Furyaev etal 2001 Kharuk et al 2011) If successive larch cohorts aredestroyed by fire prior to reaching maturity and outside seedsources are not available then forests can convert to non-arboreal vegetation (Sofronov and Volokitina 2010) Alter-natively very infrequent fires in northern Siberia can leadto the long-term accumulation of moss and duff which canblock seedling establishment and cause larch forests to con-vert to tundra (Sofronov and Volokitina 2010) Periodic firesthus help maintain larch forests and associated floristic di-versity (Zyryanova et al 2010 Schulze et al 2012) thoughthe affects of individual fires depends largely on fire severity

Given Russiarsquos size low population density and logisti-cal challenges associated with accessing fires there is lim-ited information on fire location frequency size and severity(Conard and Ivanova 1997 Sukhinin et al 2004) The fed-eral government historically monitored and selectively sup-pressed fires across approximately 60 of forested landshowever economic instability in the 1990s and early 2000sled to a reduction fire suppression and monitoring activi-ties (Sukhinin et al 2004) The northern open woodlandsof Siberia have always fallen outside of the protected zoneand thus little is known about fire regime in this expansive

region (Sofronov and Volokitina 2010) Analysis of satelliteimagery has helped shed light on spatial and temporal firedynamics across the country however regional fire mappinghas primarily focused on central Siberia (Kovacs et al 2004George et al 2006 Sofronov and Volokitina 2010) Firemapping at national (Soja et al 2004 Sukhinin et al 2004Soja et al 2006) to supranational (Roy et al 2005 2008)scales has also been carried out though these efforts reliedon medium to coarse resolution imagery (eg MODIS andAVHRR) Fire maps created from coarse resolution imageryacross broad spatial scales require external validation usinghigher resolution datasets (eg Landsat) though these in-dependent fire records are often quite limited in availability(Roy et al 2008) In spite of their limitations Earth observ-ing satellites are powerful tools that can help improve our un-derstanding of fire dynamics in remote regions such as Rus-siarsquos boreal forest

For similar reasons as Russiarsquos fire regimes are not wellunderstood there is considerable uncertainty in the magni-tude and distribution of Russiarsquos forest carbon stocks Pub-lished estimates of biomass in Russiarsquos forests range from46ndash148 Pg (Goodale et al 2002 Houghton et al 2007)Satellite analysis has helped improve our understanding ofthe distribution of forest biomass however since no satel-lites directly measure biomass it is necessary to derivesurrogate variables from satellite data that can be linkedwith field measurements (Baccini et al 2012) Efforts tomodel forest biomass distribution by linking field and satel-lite measurements have met with mixed success in the bo-real biome (Houghton et al 2007 Fuchs et al 2009Leboeuf et al 2012) Quantifying climate impacts associ-ated with forest disturbances in Siberia necessitates under-standing forest biomass distribution disturbance regimes andpost-disturbance ecosystem recovery however there is con-siderable uncertainty in each of these areas

Our primary objective here was to improve our under-standing of landscape-level carbon cycling dynamics follow-ing fire in the Cajander larch forests of northeastern SiberiaWe focused on quantifying aboveground tree biomass (AGB)because trees drive much of the landscape-level variabilityin AGB in this ecosystem For instance trees in this regioncan account for over 95 of total stand AGB (Alexander etal 2012) We examined trajectories of larch AGB accumu-lation following fire and how accumulation was affected byfire size and topography To accomplish this we conducteda geospatial analysis using satellite imagery from multiplesensors to map larch AGB and fire scars (c 1966ndash2007)across approximately 100 000 km2 of the Kolyma River wa-tershed Secondary objectives included quantifying fire rota-tion fire density and fire-size distribution as well as evaluat-ing regional performance of the global MODIS burned areaproduct (MCD45A1) (Roy et al 2005 2008) based on firesmapped from 30 m resolution Landsat imagery We also de-vised a technique to map regional AGB by linking field mea-surements of AGB to tree shadows that were synergistically

Biogeosciences 9 3943ndash3959 2012 wwwbiogeosciencesnet939432012

L T Berner et al Cajander larch biomass distribution 3945

mapped using high (50 cm) and medium (30 m) spatial res-olution satellite imagery In addition to AGB we assessedthe relationships among tree shadows and canopy cover treeheight and tree density

2 Materials and methods

21 Study area

The study area covered approximately 100 000 km2 of theKolyma River watershed in far northeastern Russian (Fig 1)The region is underlain by continuous permafrost and hasa cold dry and continental climate Annual air tempera-ture averagesminus125C though average daily temperaturesrange fromminus40C to +13C Precipitation averages 200ndash215 mm yrminus1 and total summer evaporation is twice as highas summer precipitation (Corradi et al 2005) Global cli-mate models predict a 15ndash20 increase in precipitationacross the region by the end of the 21st century (IPCC 2007)

Cajander larch is the only tree species in our study areaand dominates the permafrost zone from the Lena Riverin Central Yakutia to the Sea of Okhost in the Far East(Krestov 2003 Abaimov 2010) Cajander larch are a shade-intolerant deciduous needleleaf conifer capable of grow-ing on permafrost and withstanding very short cool grow-ing seasons (Abaimov et al 2000) In northeastern Siberiathese trees are generallylt 10 m tall and AGB rarely ex-ceeds 10 kg mminus2 (Kajimoto et al 2010) Trees can an-nually produce up to 65 kg haminus1 of wind-dispersed seeds(Abaimov 2010) Forest succession after stand replacingfires generally involves a multi-decadal shrub stage fol-lowed by larch re-achieving dominance 20ndash50 yr after fire(Zyryanova et al 2007 Alexander et al 2012) Deciduousshrubs in the region include though are not limited to wil-low (Salix pulchra S alaxensis andS glauca) birch (Be-tula divaricata and B exilis) and alder (Alnus fruticosa)while evergreen shrubs include Siberian dwarf pine (Pinuspumila) cowberry (Vaccinium vitis-idaea) dwarf Labradortea (Ledum decumbens) and large-flowered wintergreen (Py-rola grandiflora) (Petrovsky and Koroleva 1979)

22 Satellite and geospatial data

Our analysis made use of reflectance data from theWorldView-1 and Landsat series of satellites (Table 1) Aspart of the biomass mapping process we used panchro-matic WorldView-1 images with a nadir spatial resolutionof 50 cm that were provided by the University of MinnesotaPolar Geospatial Center The seven images which coveredapproximately 2150 km2 were acquired at local noon on21 April 2009 with an average off nadir view angle of 2

(Fig 2b c) On the day that the WorldView-1 images wereacquired snow depth near Cherskii was approximately 56 cm(Davydov unpublished data) We mapped AGB and firescars across approximately 100 000 km2 using 30 m resolu-

Fig 1Study area map of the lower Kolyma River watershed whichfeeds into the East Siberian Sea in the Russian Far East The studyarea spanned approximately 100 000 km2 of open larch forest thattransitioned into tundra along the northern edge

tion Landsat images (WRS-2 path 104 rows 11ndash14 Table 1Fig 2a d) provided by the United Stated Geological SurveyEarth Resources Observation and Science Center Biomassmapping was based on four along-track Landsat 5 scenes ac-quired on 26 July 2007 These were the most recent cloud-and smoke-free images available for the region and definedthe spatial extent of the study area Though mapping AGBusing Landsat imagery acquired during periods with snowcover has yielded better results than using snow-free images(Wolter et al 2012) we were unable to find winter imageryof our study area through either the USGS (httpglovisusgsgov) or NASA (httpreverbechonasagov) and thereforeused images acquired during summer

Fire scar mapping was based on 59 Landsat scenes thatwere acquired either 1972ndash1974 (n = 12) or 1995ndash2007 (n =

47) Landsat data are not available from 1975ndash1995 (here-after referred to as ldquoLandsat blackout periodrdquo) across easternSiberia because there were no operational ground receivingstations Furthermore there were no Landsat images avail-able for the southernmost quarter of the study area (path 104row 14) until 1999 The Landsat images that we used to mapfires were acquired between June and September preferen-tially under cloud-free conditions late in the growing seasonWhile WorldView-1 and Landsat reflectance data formed thebasis of our analysis we augmented the analysis with ancil-lary geospatial datasets

The six ancillary geospatial datasets included models oftopography burned area climate vegetation productivityand tree canopy cover (Table 1) The topography dataset wasthe 1 arc-second (sim 30 m) digital elevation model (DEM)generated by the Japanese Ministry of Economy Trade andIndustry and NASA from Advanced Spaceborne ThermalEmission and Reflection Radiometer data (ASTER GDEM

wwwbiogeosciencesnet939432012 Biogeosciences 9 3943ndash3959 2012


Fig 2Maps of the northeastern portion of the Kolyma River water-shed showing the field sampling locations as well as the(a) Landsat5 and(b) WorldView-1 satellite imagery used in the analysis To il-lustrate the large difference in spatial resolution between sensorsFig 2c d show close-up views for WorldView-1 and Landsat 5 re-spectively The Landsat 5 imagery is displayed using bands 4 3 1(RGB)

v2) (Meyer et al 2011) The burned area dataset whichspanned 2000ndash2007 was the 500 m monthly burned areaproduct (MCD43A1 v5) generated from Moderate Resolu-tion Imaging Spectroradiometer (MODIS) sensors carriedaboard NASArsquos Aqua and Terra satellites The gridded cli-mate data included monthly averages of temperature and pre-cipitation that were produced by the University of East An-glia Climate Research Unit (CRU v31) at 05 resolutionfrom climate station data As a proxy for vegetation pro-ductivity we used a normalised difference vegetation index(NDVI) dataset generated from Advanced Very High Reso-lution Radiometer (AVHRR) data by NASArsquos Global Inven-tory Modelling and Mapping Studies (GIMMS v3G) project(Tucker et al 2005) This 8 km resolution biweekly datasetruns from 1981 through to the present The final two datasetsincluded the MODIS vegetation continuous fields product(MODIS VCF v5 MOD44B) (Hansen et al 2003) whichmapped global tree canopy cover (TCC) at 250 m resolutionand the related TCC map generated by Ranson et al (2011)from MODIS VCF (v4) in an effort to improve TCC esti-mates along the circumpolar taiga-tundra ecotone

23 Field measurements of aboveground biomass

Fieldwork was conducted near Cherskii in the Sakha Repub-lic (Yakutia) though the study area also included portionsof the more mountainous Chukotka Autonomous Okrug andthe Magadan Oblast (Fig 2a) Field data used in this studywere collected during July 2010 and 2011 at 25 sites In2010 Alexander et al (2012) inventoried 17 Cajander larchsites and in 2011 Bunn and Frey (unpublished) inventoriedeight additional sites Field estimates of larch aboveground

biomass (AGBfield) were derived from allometric equationsthat related diameter at breast height or basal diameter toAGBfield See Alexander et al (2012) for more details

24 Satellite image preprocessing

The four Landsat scenes used for biomass mapping wereconverted from surface radiance to top of atmosphere re-flectance using the Landsat calibration tool in ENVI (ITTVisual Information Solutions v48) We then masked wa-ter bodies clouds cloud shadows and steep north-facingslopes where shadows obscured underlying vegetation Wa-ter bodies were identified using a 30-class isodata unsuper-vised clustering algorithm with a minimum class-size of 20pixels and then buffered one pixel outward to ensure com-plete removal Clouds and cloud shadows were manually de-lineated and excluded Shadowed northern slopes were re-moved based on topography by selecting areas with slopegt 15 and aspect eithergt 270 or lt 45 Landsat prepro-cessing resulted in a radiometrically calibrated image mosaicvoid of water bodies clouds and areas shaded by clouds andmountains

The WorldView-1 images came orthorectified and cali-brated to top of atmosphere reflectance We mosaiced the sixcontiguous images and then co-registered the WorldView-1mosaic to the Landsat mosaic to ensure proper geographi-cal overlap Co-registration involved identifying 50 match-ing points and then warping the WorldView-1 mosaic us-ing a first-order polynomial (RMSE = 167 m) It was notnecessary to adjust the georeferencing of the single non-contiguous WorldView-1 scene (Fig 2b) We applied theLandsat water mask and the topography shadow mask andthen digitised and removed two small urban zones from theanalysis

25 Aboveground biomass mapping

We used a combination of multi-sensor satellite imagery andfield measurements to map AGB across the study area Thisinvolved first using WorldView-1 to map tree shadow frac-tion (TSFWV) as a surrogate for AGB and then using theseTSFWV data to train a Random Forest model to predict TSFacross a Landsat mosaic (TSFLS) that covered an area about50 times that of the WorldView-1 scenes We then linearlytransformed the TSFLS map into a map of AGB (AGBLS) us-ing a regression equation derived by comparing TSFLS andAGBfield

Individual trees and tree shadows contrasted sharply withthe bright snow-covered land surface in the WorldView-1 images After testing a series of threshold values weclassified pixels astree shadowif reflectance was between017 and 037 This resulted in a 50 cm resolution binarymap showingtree shadowand non-tree shadow The treeshadow map was then aggregated to 30 m resolution to matchthe footprint of the Landsat mosaic Aggregation involved



Table 1Details of the satellite imagery and ancillary geospatial datasets that we used for mapping and analysing larch biomass distributionforest fires and forest biomass recovery after fire in northeastern Siberia

Use Satelliteor Dataset

DataAcquisition

Scenes(n)

Resolution(m)

BiomassMapping

WorldView-1Landsat 5 TM

21 April 200926 July 2007

74

0530

Fire ScarMapping

Landsat 7 ETM+Landsat 5 TM

1999ndash20041995 20052006 2007

2621

3030

Landsat 1ndash3 MSS 1972ndash1974 12 79

Total 1972ndash2007 59 ndash

AncillaryDatasets

Digital Elevation Model(ASTER)

Composite2000ndash2008+

1 30

Tree Canopy Cover(MODIS)

2010Composite2000ndash2005

11

250500

Burned Area(MODIS)

Monthly2000ndash2007

96 500

NDVI(GIMMS AVHRR)

Biweekly1981ndash2007

636 8times 103

CRU Climate (T and PPT) Monthly1901ndash2007

1284 55times 103

calculating the percent of each 30times30 m pixel that was clas-sified astree shadow We defined this value as TSFWV Theimpact of variation in sun angle on TSFWV was assumed tobe minimal because the WorldView-1 images were all ac-quired on the same day at local noon and since Leboeuf etal (2007) showed that normalising TSF based on sun andsensor geometry had a non-discernible impact on the rela-tionship between TSF and field biomass measurements Vari-ation in snow depth was also assumed to have a negligibleeffect on TSFWV due to the minimal topographic relief inthe area Following the calculation of TSFWV we randomlyselected 2500 points that were spaced at least 30 m apartand which were stratified by three TSFWV classes (0ndash1 1ndash30 and 30ndash100 ) The underlying TSF pixel valueswere then extracted and used to train (n = 2000) and evalu-ate (n = 500) a Random Forest model that predicted TSFLSfrom the Landsat data

We used a combination of Landsat reflectance values andband metrics (eg NDVI) as predictor data for the RandomForest model (See Table 3 in Results for details) RandomForests are a form of classification and regression analysiswhere many decision trees are constructed using bootstrapsampling with the final prediction determined based uponldquovotesrdquo cast by each tree (Breiman 2001) This machinelearning technique can efficiently process large datasets haslow bias and tends not to over fit models (Breiman 2001)We used the randomForest package (Liaw and Wiener 2002)

in R (R Development Core Team 2011) to build 500 de-cision trees with five predictor variables randomly selectedand tested at each node of each tree Model performance wasevaluated by regressing TSFLS by TSFWV for 500 points notused in model construction The calibrated Random Forestmodel yielded a map of TSFLS that we then compared withAGBfield measurements from 25 sites We calculated meanTSFLS and TSFWV (plusmn standard error) within a 30 m diam-eter buffer zone centred on the mid-point of each transectand then used least-squared linear regression with the inter-cept forced through zero to evaluate the relationship betweenAGBfield and both TSFLS and TSFWV The regression inter-cept was forced through zero so that treeless areas such asalpine or tundra could be modelled as having no tree AGBThe regression equation was then used to transform the mapof TSFLS into a map of AGBLS We compared this map ofAGBLS with the MODIS tree canopy cover products by re-sampling all to 500 m resolution and running Pearsonrsquos cor-relations on 5000 random points We also compared TSFLSand TSFWV with field measurements of canopy cover treeheight and tree density using Pearsonrsquos correlations

26 Fire scar mapping

The 59 Landsat images were first sorted into four regionsbased on the WRS-2 grid (path 104 rows 11ndash14) Each stackof images was then visually inspected in chronological order



We used false-colour images along with the normalised burnratio (NBR) and NDVI to identify fire scars The band com-binations 7-5-2 (L5 and L7) and 3-4-2 (L1-L3) proved usefulfor identifying fire scars as were the band metrics whichwere calculated as NDVI = (RNIR ndash Rred) (RNIR + Rred) andNBR = (RNIR ndash RSWIR) (RNIR + RSWIR) We hand-digitisedfire scar perimeters at a scale of 1 150 000 or 1 60 000 de-pending on the fire size and perimeter complexity and thenspatially adjusted the fire scar perimeters to align with theLandsat 5 image used to map biomass

For each fire scar we determined the period of time dur-ing which the fire occurred by recording the year of the pre-fire and post-fire Landsat images For fires that occurred be-tween 2000ndash2007 we used the MODIS burned area product(MCD45A1) to identify the year of burn when possible Wedated large fires that occurred between 1981ndash2000 by usingGIMMS-NDVI to identify when NDVI rapidly dropped andstayed low relative to surrounding areas We estimated theburn year when smoke trails were not visible and when itwas not possible to date fires based on MODIS or GIMMSFire scars were generally visible in NIR for about six yearsfollowing fire For fresh fire scars visible in the earliest avail-able imagery or immediately after the Landsat blackout pe-riod we estimated the fires had occurred anytime during thepreceding 6 yr If fires occurred during the Landsat blackoutperiod but were not visible in post-1995 NIR imagery weestimated that they occurred during the 1980s

After mapping and dating fire scars we derived descrip-tors of the fire regime (fire rotation fire density and fire sizedistribution) and then examined the relationships among fireactivity and both mean monthly air temperatures and precipi-tation Fire rotation is the average number of years necessaryto burn an area equivalent in size to the area of interest and iscalculated by dividing the time period by the fraction of thelandscape that burned during that period (Heinselman 1973)Fire rotation is equivalent to fire return interval which is apoint-specific estimate of the average time between two suc-cessive fire events when every point on the landscape hasan equal probability of burning We calculated fire densityas the number of fire scars formed in a given year dividedby the area over which they occurred Defining fire densitybased on fire scars likely underestimates the true number ofdiscrete fire events as large fire scars in Siberia sometimesresult from multiple fire events merging during the fire sea-son (Loboda and Csiszar 2007) Since the spatial coverageof Landsat imagery was incomplete across the southern quar-ter of the study area prior to 1999 we calculated fire rotationand density across the northern three quarters for c 1966ndash2007 (660ndash695 N WRS-2 path 104 rows 11ndash13) In bas-ing these calculations on the c 1966ndash2007 time period weare assuming that fires visible in the 1972 imagery occurredwithin the preceding six years Due to greater availabilityof Landsat imagery and to the MODIS burned area prod-uct our fire record was most accurate and spatially completefrom 2000ndash2007 As such we used fire scars from this time

period to examine differences in fire activity between theopen larch forests of the Kolyma Mountains (645ndash670 N)and the lowland forest-tundra zone (670ndash695 N) We alsoused fire scars from 2000ndash2007 to examine the associationsamong annual fire activity (area burned number of fires andmean fire size) and climate (mean monthly temperature andprecipitation)

27 Accumulation of aboveground biomassfollowing fire

To assess larch regrowth following fire we quantified AGBacross fire scars that ranged in time since last fire from 3ndash38 yr and then examined whether fire size latitude eleva-tion or aspect affected regrowth Our analysis was restrictedto fires that burned c 1969ndash2005 because the AGB modelpredicted erroneously high values for fires that had burnedwithin two years of the Landsat data acquisitions Fire scarsthat were visible in the NIR band in the 1972 imagery weredated as having occurred at the earliest in 1969plusmn 3 yr Todetermine whether AGB accumulated linearly through timewe used piecewise linear regression to model median firescar AGB as a function of time Robust linear regressionas implemented via rlm in the R MASS package (Venablesand Ripley 2002) was then used to estimate the AGB ac-cumulation rate for early successional (le 25 yr) and mid-successional (33ndash38 yr) fire scars There were no fires inthe 26 to 32 age range We then used least-squared mul-tiple linear regression to determine whether fire size lati-tude and their interactions with fire age could predict me-dian fire scar AGB Since AGB accumulation within an indi-vidual fire scar could vary depending on landscape positionand microclimate we used linear mixed models to investi-gate the controls of elevation and aspect on rates of AGB ac-cumulation for early and mid-successional fire scars Aspectwas cosine-transformed to quantify the degree of northwardtopographical exposure The models were calibrated using9750 grid cells randomly sampled within fire scars and in-cluded fire identity (ie a variable denoting to which fire asample belonged) as a random effect to correct for repeatedsampling within individual fires Aboveground biomass val-ues were log-transformed prior to inclusion in the mixedmodels to meet the assumptions of data normality and ran-domly distributed residuals Full models which included el-evation aspect fire age and their interactions were fitted firstand then compared against simpler models using Akaike In-formation Criterion (Akaike 1974) to statistically evaluatewhether landscape position influenced post-fire AGB pro-duction



3 Results

31 Mapping and distribution of larchaboveground biomass

Validation of the Random Forest-generated TSFLS mapshowed that it agreed relatively well with the TSFWV map(r2

= 072p lt 001 RMSE = 164 n = 500) Comparedto TSFWV the model tended to under-predict TSFLS in ar-eas of relatively dense forest and over-predict it in someareas of sparse tree cover We observed a strong linear re-lationship between AGBfield and both TSFWV (r2

= 090RMSE = 722 g mminus2 Fig 3a) and model-predicted TSFLS(r2

= 091 RMSE = 622 g mminus2 Fig 3b) Diagnostic plotsshowed that for TSFLS the residuals were randomly dis-tributed and homoscedastic while for TSFWV the residualswere skewed towards lower values In addition to showingstrong relationships with AGBfield TSF from both sensorsexhibited strong correlations with canopy cover (r asymp 088)moderate correlations with tree density (r asymp 065) and weakcorrelations with tree height (r asymp 040 Table 2) We foundthat TSFLS was a better proxy for AGBfield than any indi-vidual Landat band or band metric used as an input for theRandom Forest model (Table 3) Of the Random Forest in-puts AGBfield was most strongly correlated with Landsatrsquosshort-wave infrared (SWIR bands 7 and 5r = minus082 and077) and red bands (band 3r = minus077) and exhibited theweakest correlations with the vegetation indices (Soil Ad-justed Vegetation Indexr = minus004 NDVI r = 033) TheRandom Forest identified Landsatrsquos SWIR and red bandsas the most important predictors of TSFLS as these inputsyielded the largest mean decrease in node impurity (sensuBreiman 2001 Table 3) By linking field biomass measure-ments with tree shadows mapped at a high spatial resolutionand then modelled across a broad regional extent we wereable to characterise the spatial distribution of AGB acrossthe study area

Larch stands are very heterogeneously distributed acrossthe forest-tundra ecotone in far northeastern Siberia with thedistribution of AGB reflecting the combined influences of cli-mate topography and past disturbances (Fig 4a) Latitudinaltree line occurred at around 6907 N while altitudinal treeline decreased from around 780 m asl in the central KolymaMountains (sim 65 N) to around 400 m asl along the forest-tundra border (sim 69 N) The AGBLS map captured the alti-tudinal transition from forest to alpine as well as the latitu-dinal transition from forest to tundra In areas with no recentfire activity AGBLS averaged 1156 g mminus2 though rangedfrom 03 to 6703 g mminus2 Aboveground biomass exceeded1882 g mminus2 across only 25 of the landscape (Fig 5) Wefound that the landscape distribution of AGBLS showed sim-ilar patterns to tree canopy cover mapped from MODIS im-agery though our map captured much finer spatial variabilityin forest distribution than did the MODIS products Our mapof AGBLS exhibited moderately strong correlations with both

Table 2Pearsonrsquos correlations among field measurements of foreststructure and tree shadow fraction mapped from WorldView-1 (n =

22) and Landsat 5 (n = 25) Correlation coefficients (r) in bold aresignificant atα lt 005

Stand Measurement Tree Shadow Fraction

WorldView-1 Landsat 5

Aboveground Biomass 092 091Canopy Cover 089 088Tree Density 071 054Tree Height 039 041

0 20 40 60 80 100

010

0030

0050

0070

00

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)

WorldViewminus1 Tree Shadow Fraction ()

AGB = TSF 58r2 = 09n = 22p lt 0001

A

0 20 40 60 80 100

AGB = TSF 69r2 = 091n = 25p lt 0001

B

Landsat 5 Tree Shadow Fraction ()

Fig 3 Comparison of aboveground biomass from field estimates(AGBfield) with tree shadow fraction (TSF) derived from(a)WorldView-1 and(b) Landsat 5 satellite images The points rep-resent site means (plusmn1 standard error) The relationship betweenAGBfield and TSF is highly significant for both sensors (r2

asymp 090p lt 0001)

MODIS VCF (r = 076 p lt 0001n = 5000) and MODISTCC (r = 081 p lt 0001 n = 5000) Forest patches ofrelatively high AGBLS occurred along upland hill slopesbetween braided streams and on elevated hills in the wetKolyma Lowlands Within the forested zone stand biomassand distribution strongly reflected the influence of past fires

32 Fires in the northeastern kolyma river watershed

We mapped 116 fires that burned between c 1966ndash2007across 14 778 km2 (141 ) of the northeastern portion of theKolyma River watershed (Figs 4b and 6) We are confidentin having correctly identified the burn year for 59 (n = 68)of these fires while for 38 (n = 44) of the fires we esti-mated the potential dating error to vary from half a year totwo years For the remaining 3 (n = 4) of fire scars thepotential dating error ranged from three to eight years Dueto potential dating errors the annual number of fires and areaburned as shown in Fig 6 should be viewed as best approx-imations that are most accurate from 2000ndash2007

Though there was some uncertainty in dating fire scarsfire activity exhibited considerable interannual variability



Table 3Landsat 5 indices used as predictor variables with the Ran-dom Forest algorithm to model tree shadow fraction Pearsonrsquos cor-relation (r) with aboveground biomass and the mean decrease innode purity a Random Forest generated metric of variable impor-tance are provided for each predictor variable Correlations in boldare significant atα lt 005

Landsat 5 Correlation Mean decreasevariable with AGB (r) in node impurity

Tree shadow fraction 091 NABand 1 minus070 141 167Band 2 minus072 129 733Band 3 minus077 342 318Band 4 minus031 36 685Band 5 minus077 316 155Band 7 minus082 478 976Bands 45 050 40 621Bands 74 minus041 50 411Bands 75 minus034 47 084NDVI 033 41 678SAVI minus004 31 219NBR 044 40 307PCA I minus060 92 183PCA II minus037 28 622PCA III 030 51 482

Excluding the Landsat blackout period the annual fireoccurrence ranged from 0ndash22 fires (median = 3 fires yrminus1)

and the annual area burned ranged from 0ndash2296 km2

(median = 119 km2 yrminus1) The large burn years of 2001(1762 km2 burned) and 2003 (2296 km2 burned) contrastedsharply with the 2004ndash2007 period during which the an-nual area burned averaged 111plusmn 64 km2 (plusmn standard error)Mean daily July air temperatures in 2001 (144C) and 2003(140C) were the highest reported in the CRU record (1901ndash2009) and were about 2C higher than the 2004ndash2007 aver-age (119plusmn 05C) Summer (JulyndashAugust) precipitation in2001 (796 cm) and 2003 (939 cm) was about 20 lowerthan the 2004ndash2007 average (1082plusmn 90 cm) July is thehottest month of the year and from 2000ndash2007 was the onlymonth during which air temperature and annual fire activityexhibited statistically significant correlations (α = 005n =

8) Mean July air temperatures exhibited strong correlationswith annual number of fires (r = 072) log10-transformedarea burned (r = 087) and log10-transformed mean an-nual fire size (r = 083) January was the only month dur-ing which fire activity and monthly precipitation were sig-nificantly correlated however we are inclined to believe thiswas a false positives due to precipitation in January gener-ally accounting for less than 7 of the annual total Fire sizevaried over five orders of magnitude from 006ndash2570 km2

(median = 33 km2 Fig 7) with the single largest fire scar ac-counting for 17 of the area burned over the 38 yr periodThe ten largest fires accounted for about 50 of the totalburned area while the largest 25 of fires (firesgt 150 km2)

Fig 4 Maps of the northeastern portion of the Kolyma River wa-tershed showing(a) aboveground biomass of Cajander larch and(b) burn scar perimeters for fires that occurred c 1969ndash2007 Bothdatasets were derived primarily from 30 m resolution Landsat im-agery

were responsible for 78 of the total area burned Annualarea burned number of fires and fire size all varied widelyand were positively associated with July air temperatureswith two of the largest burn years coinciding with the twohottest Julys over the 109 yr record

We quantified fire rotation and density using the fire scarsmapped from Landsat and then examined regional differ-ences in fire activity (Table 4) Across the northern threequarters of the study area (660ndash695 N) we estimated fireactivity using data covering both c 1966ndash2007 and 2000ndash2007 which showed fire rotation to be 301ndash397 yr and firedensity to be 03ndash02 fires yrminus1

times104 kmminus2 respectively Firerotation and density were both about one third less when cal-culated using c 1966ndash2007 data than when calculated using2000ndash2007 data Examining fire activity over this broad ex-tent masked large regional differences between the KolymaMountains and the forest-tundra border From 2000ndash2007fire rotation was seven times shorter in the mountains thanin the lowlands (110 vs 792 yr) and fire density was threetimes greater (10plusmn10 vs 03plusmn03 fires yrminus1

times104 kmminus2)highlighting pronounced regional differences

We identified 59 fires in Landsat scenes that cumulativelyburned 4731 km2 of the study area between 2000ndash2007 TheMODIS burned area product detected 44 (746 ) of thesefires and mapped a total area burned of 2859 km2 Relativeto our estimate MODIS underestimated the total area burned



Table 4 Summary of fire activity across 100 000 km2 of the Kolyma River watershed in far northeastern Siberia Annual area burned andfire density are annual means with 25th and 75th percentiles to show skewness

Region of Study Area Number of Fires Total Area Burned Mean Annual Area Burned Fire Fireand Time Period Rotation Density

(n) (km2) (km2) ( ) (years) (n yrminus1times 104 kmminus2)

northern 34c 1966ndash2007

70 10 270 245 033 301 02

northern 342000ndash2007

20 1482 185 [0 145] 025 [000 020] 397 03 [00 06]

forest-tundra2000ndash2007

12 454 57 [0 58] 013 [000 013] 792 03 [00 05]

mountain forest2000ndash2007

47 4277 535 [42 601] 091 [007 103] 110 10 [03 11]

0 1000 3000 5000 7000

050

015

0025

00

Modeled Larch Aboveground Biomass (g mminus2)

Fre

quen

cy

0 1000 3000 5000 7000

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity

Fig 5 Frequency and empirical cumulative distribution functionplots of Cajander larch aboveground biomass modelled from Land-sat 5 satellite imagery (AGBLS) in areas with no recent fire activityAcross the landscape AGBLS ranged from 03 to 6703 g mminus2 with50 of areas having less than 748 g mminus2

by about 40 primarily as a result of underestimating thearea burned by each fire Importantly MODIS detected thefires that were cumulatively responsible for 96 of the to-tal area burned that we mapped from Landsat The smallestfire detected was 17 km2 while the largest fire missed was411 km2 Visual comparison of the fire maps showed thatalmost all of the MODIS single-pixel or small pixel-clusterburns were not actually fires The larger burn scars identifiedin the MODIS product tended to be conservative areal esti-mates that captured the central region of the Landsat-mappedfires though missed peripheral burned areas

33 Accumulation of larch aboveground biomassafter fire

For the 98 fires that burned between c 1969ndash2005 the accu-mulation of larch AGB was strongly nonlinear through time(Fig 8) This nonlinear AGB response to time was confirmed

by piecewise linear regression In comparison to a simplelinear regression using a two-segment regression with abreakpoint set between 26ndash34 yr reduced the residual vari-ance when modelling median fire-level AGB as a functionof time (148times 105 vs 162times 105) During early succession(le 25 yr) AGB averaged 271plusmn26 g mminus2 (n = 66plusmn standarderror) but ranged from 31plusmn 45 g mminus2 to 892plusmn 499 g mminus2

(medianplusmn interquartile range) The rate of AGB accumu-lation during early succession appeared minimal when as-sessed using robust linear regression (59plusmn 54 g mminus2 yrminus1n = 66) There were no fires in the 26ndash32 yr age rangewhich appeared to be an important transitional period afterwhich AGB accumulation increased considerably For mid-successional stands that were 33ndash38 yr old AGB averaged746plusmn 100 g mminus2 (n = 32) and we estimated the rate of AGBaccumulation to be 138plusmn 67 g mminus2 yrminus1 Across these mid-successional stands larch AGB ranged from 126plusmn 46 g mminus2

to 2212plusmn2000 g mminus2 (medianplusmn inter-quartile range) LarchAGB was generally low during the first 25 yr and then exhib-ited rapid accumulation at some sites 33ndash38 yr following firehowever regrowth was highly variable and some fire scarsshowed little AGB accumulation after nearly four decades

Accumulation of larch AGB after fire was not associatedwith fire size or latitude though in the early successionalstands it declined with both increased elevation and north-wardly aspect Neither fire size latitude nor their interac-tions with fire age were significant predictors (p gt 005)of median larch AGB for early (le 25 yr n = 66) or mid-successional (33ndash38 yrn = 32) stands as assessed usingmultiple linear regression The mixed linear model showedthat for early successional stands larch AGB accumula-tion decreased with elevation (pelevationmiddotagelt 0001) and withnorthward topographical exposure (pcos-aspectmiddotage= 003) yetin mid-successional stands larch AGB did not differ dis-cernibly with topography (p gt 005)



050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22

burn area detected 5115 km2

mean annual burn area 256 km2

number of firesarea burned

Fig 6 Estimates of the annual number of fires and area burnedacross the lower Kolyma River watershed between c 1969 and2007 The annual number of fires and area burned varied consider-ably among years with 2003 being an exceptionally large fire yearacross Siberia

020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity

Fig 7 Frequency and empirical cumulative distribution functionplots of fire scar size Fires were generally quite small with 75 smaller than 150 km2 and 5 greater than 400 km2

4 Discussion

41 Distribution and mapping of larchaboveground biomass

Understanding the current magnitude and distribution of for-est biomass along the forest-tundra ecotone is importantgiven ecosystem feedbacks to climate (Goetz et al 2007)and necessitates the development of satellite-derived datasetsand analytical techniques that can be used to map and detectchanges in forest cover over time (Ranson et al 2011) Ca-jander larch AGB tended to be low (sim 1200 g mminus2) acrossthe forest-tundra ecotone in northeastern Siberia and ex-ceeded 6000 g mminus2 only under favourable site conditions andin the absence of recent fire Previous work has shown thatin Siberiarsquos northern permafrost zone AGB in larch standsrarely exceeds 10 000 g mminus2 while in southern regions AGB

in mature stands often exceeds 15 000 g mminus2 (Kajimoto etal 2010) Areas of high AGB tended to occur along low el-evation mountain valley slopes though also in fire-protectedcorridors formed by mountain streams and on elevated ridgesin the wet lowland areas

The spatial distribution of larch AGB was relatively wellcaptured by the two MODIS-derived tree canopy cover prod-ucts that we examined Unlike traditional categorical landcover maps these products provide continuous estimates oftree cover at global (Hansen et al 2003) and biome-level(Ranson et al 2011) extents The broad spatial coveragemoderate spatial resolution and quantification of tree coveralong a continuous gradient makes these products importanttools that can be used with in situ measurements and othersatellite-derived datasets to better understand multi-decadalchanges in vegetation dynamics in the boreal biome (Berneret al 2011)

Mapping forest biomass in the boreal biome using satelliteimagery has met with mixed success (Houghton et al 2007Leboeuf et al 2012) though combining measurements frommultiple satellites can help overcome limitations of individ-ual sensors and improve biomass mapping efforts (Goetz etal 2009) Here we used a multi-sensor approach based onhigh and medium-resolution optical imagery to help amelio-rate the trade-off between spatial resolution and extent Thehigh resolution imagery made it possible to map a surrogatefor AGB (ie tree shadows) and capture fine spatial variabil-ity in AGB while incorporating medium resolution imageryallowed us to model AGB across an area approximately 50times the size imaged by the high resolution sensor Directlymapping AGB by linking field and satellite measurementsmade it possible to quantify finer spatial variability in AGBthan would have been possible had the analysis relied uponassigning field AGB measurements to thematic land covertypes (Goetz et al 2009 Baccini et al 2012) By mappingtree shadows from multi-sensor satellite imagery we wereable to derive a surrogate for larch AGB and then use thissurrogate with field measurements to model the regional for-est biomass distribution

Tree shadows mapped from high-resolution satellite im-ages have exhibited modest to strong relationships with anumber of structural attributes in both coniferous and decid-uous forests (Greenberg et al 2005 Leboeuf et al 2012Wolter et al 2012) We found that field measurements ofAGB and canopy cover were strongly associated with treeshadow fraction while tree height and density were not asclosely related with shadows Leboeuf et al (2012) also ob-served that tree shadow fraction in the coniferous forestsof northeastern Canada was more closely associated withtree volume and basal area than it was with crown closureor tree height Tree height diameter density and distribu-tion are all factors that contribute to AGB in a forest standas well as to the shadow cast by that group of trees Assuch it is not surprising that AGB appears more closely re-lated to tree shadow fraction than do other measurements



1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)

Years Since FireFig 8 Cajander larch aboveground biomass (AGB) across 98 firescars that burned in the lower Kolyma River watershed between c1969ndash2005 Each point represents the median larch AGB in a firescar while y-error bars indicate 25th and 75th percentiles in AGBand x-error bars indicate the range of uncertainty in when the fireoccurred

of forest structure Similarly light detection and ranging(LiDAR) measurements generally show stronger relation-ships with stand-level AGB than with tree height (Goetzand Dubayah 2011) While the correlations among forest at-tributes and tree shadow fraction mapped from WorldView-1 and Landsat 5 were quite similar for AGB canopy coverand tree height the difference between sensors was no-table in correlation with tree density Tree density correlatedmore strongly with tree shadows mapped from WorldView-1(r = 071) than with tree shadows mapped from Landsat 5(r = 054) yet neither correlation was as strong as that re-ported by Greenberg et al (2005) (r = 087) who comparedtree density derived from photo interpretation with tree shad-ows mapped from IKONOS We found that in the open larchforests of northeastern Siberia tree shadows were a goodproxy for AGB and canopy cover though did not as closelyreflect tree height or density

Efforts to map attributes of forest structure across broadspatial extents can be improved by first mapping forest struc-ture at a higher spatial resolution and then using the result-ing maps to train statistical models to predict the attributesof interest based on coarser resolution multi-spectral satellitedata Tree shadow fraction predicted using the Random For-est model exhibited a stronger relationship with field mea-surements of larch AGB (r = 091) than did any of the indi-vidual Landsat predictor variables (|r| = 004 to 082) TheRandom Forest model identified the SWIR and red bandsas the variables of greatest importance for predicting treeshadow fraction and incidentally these variables also ex-hibited the strongest correlations with field measurements ofAGB (r = minus077 tominus082) The applicability of reflectancedata in the SWIR and red bands to mapping forest AGB

has previously been demonstrated (Steininger 2000 Fuchset al 2009 Baccini et al 2012) Reflectance in SWIR de-creases with increasing biomass due to shadows generatedby heterogeneity in stand structure (Baccini et al 2012) Si-miliar to Fuchs et al (2009) we found that vegetation in-dices (NDVI and SAVI) showed little to no relationship withtree AGB potentially due to the decoupling of productiv-ity and standing biomass Furthermore vegetation indicesare affected by productivity of understory vegetation (shrubsherbs mosses and lichens) which were not included in ourAGB estimates Linking high and medium resolution satel-lite data focused on the red and SWIR wavelengths showsconsiderable promise for improving biomass mapping effortsand warrants further attention

Using field and multi-satellite data we estimated larchAGB across approximately 100 000 km2 however we en-countered a number of challenges and potential sources oferror We had a limited number of field AGB measurements(n = 25) owing to logistical challenges which is commonin studies of Siberian forest biomass (Fuchs et al 2009Schulze et al 2012) Another issue was that fieldwork wasconducted in 2010 and 2011 but persistent cloud cover in theregion made it necessary to use Landsat 5 and WorldView-1 images from 2007 and 2009 respectively (we note thefield sites were not disturbed in the intervening years) Co-registration of geospatial datasets was also challenging andnecessitated considerable care to ensure proper overlap be-tween datasets Using tree shadows to map forest AGBmight have resulted in underestimating AGB in high densitystands because of overlapping shadows Comparison of theWorldView-1 images with digital photographs taken acrossthe study area revealed that when the WorldView-1 imageswere acquired snow tended to mask much of the low staturebirch and willow shrub cover found in upland forests whilein some riparian areas tall willow and alder protruded abovethe snow Though many of these tall riparian shrubs were ex-cluded from the analysis as part of our liberal water mask insome instances the shadows generated by these shrubs wereincluded in the tree shadow maps This likely lead to an overestimation of larch AGB in riparian areas Additional workis needed to understand the contribution of shrubs to carbonpools in this region Selection of a different threshold valueused for mapping tree shadows from the WorldView-1 im-agery could have also affected AGB estimates In spite ofthese limitations and potential sources of error we observedstrong relationships among tree shadow fraction and fieldmeasurements of larch AGB as well as favourable agree-ment among our map of larch AGB and independent treecanopy cover estimates Our map can be used to help un-derstand factors that affect the magnitude and distribution oflarch biomass along the forest-tundra ecotone in northeast-ern Siberia In addition to demonstrating a multi-sensor tech-nique for regional mapping of AGB that can potentially beapplied to other forest ecosystems our study contributes to agrowing body of literature that examines how the structural



attributes of forests can be mapped from satellite imagesbased on tree shadows

42 Fire regime along the forest-tundra ecotone innortheastern siberia

It is important to develop means for quantifying and moni-toring fire activity using satellite-based approaches so as tobetter inform fire management decisions and better charac-terise the carbon implications of forest recovery from distur-bance (Goetz et al 2012) Although our principal purposefor mapping fires was to quantify post-fire biomass recov-ery we also used the mapped fire scars to provide an initialoverview of the regional fire regime and to evaluate perfor-mance of the MODIS burned area product Based on anal-ysis of satellite imagery from 1972ndash2007 and from 2000ndash2007 we estimated the fire rotation across the forest-tundraecotone to be 301 and 397 yr respectively Subdividing theecotone into southern mountain larch forests and northernlowland forest-tundra revealed that fire rotation differed sev-enfold between these two zones (110 vs 792 yr) This spa-tial pattern of fire activity is similar to that observed acrossSiberia with fire return interval generally increasing fromsouth to north (Furyaev et al 2001 Soja et al 2004 Kharuket al 2011) and correlated with both summer temperaturesand AGB (Furyaev et al 2001) Our ecotone-level estimatesof fire rotation are similar to the mean fire return intervalcalculated for Siberiarsquos northern sparse forests (227ndash556 yrmean = 357 yr) from AVHRR satellite data (Soja et al 2006)Furthermore they are similar to dendrochronological esti-mates of fire return interval along the forest-tundra ecotonein central Siberia (130ndash350 yr mean = 200plusmn 50 yr) (Kharuket al 2011) Our fire rotation estimate for the mountainlarch forests (645ndash67 N) was slightly greater than the firereturn interval determined for larch-dominated communities(sim 64 N) in central Siberia (Kharuk et al 2008 2011) po-tentially due to slightly cooler temperatures and less humandisturbance of the landscape

Mean annual fire density has been estimated to be 20 firesper 104 km2 for Siberian mountain-tundra open forests (Val-endik 1996 Furyaev et al 2001) though in our study areaannual fire density between 2000ndash2007 averaged 03plusmn 03fires per 104 km2 along the forest-tundra ecotone and 10plusmn

10 fires per 104 km2 in the Kolyma Mountains Across in-tact tracts of central and southern taiga average annual firedensity as calculated using MODIS satellite imagery from2002ndash2005 was 18plusmn 08 fires per 104 km2 (Mollicone etal 2006) Since annual fire counts in Siberia tend to de-crease moving northward (Soja et al 2004) our results andthose of Mollicone et al (2006) together suggest that the esti-mated average annual fire density of 20 fires per 104 km2 formountain-tundra open forests is probably too high Fire den-sity in the Kolyma Mountains exceeded 20 fires per 104 km2

only during the abnormally hot summer of 2001 when itpeaked at 34 fires per 104 km2 Average annual fire den-

sity in the Kolyma Mountains was about 15 that reportedfor boreal spruce sites in Interior Alaska for 1992ndash2001(DeWilde and Chapin 2006) These regional differences infire counts are driven by differences human and lightningignitions (Conard and Ivanova 1997 Kovacs et al 2004Mollicone et al 2006) vegetation fuel load and flammabil-ity (Furyaev et al 2001 Sofronov and Volokitina 2010)and the frequency of hot and dry weather that promotes theoutbreak of fire (Balzter et al 2005 Bartsch et al 2009Kharuk et al 2011)

While fire activity varied across the landscape we also ob-served considerable interannual variability which previouswork in Siberia has attributed to human activities (Molliconeet al 2006) and to the impact of hemispheric climate condi-tions (eg Arctic Oscillation (AO)) (Balzter et al 2005) onsummer temperatures (Kharuk et al 2011) and fuel moisture(Bartsch et al 2009) Consistent with weather being an im-portant driver of fire activity (Stocks et al 1998) we foundthat fire activity was positively associated with air temper-atures during the hottest months of the year (July) In ourstudy area the two hottest Julys over the 1901ndash2009 CRUdataset occurred in 2001 and 2003 which coincided with thetwo largest burn years that we recorded Eurasia experienceda record-breaking heat wave in 2003 (IPCC 2007) that wasassociated with an extreme fire season across Siberia (Soja etal 2007) Extreme fire events in the larch-dominated forestsof central Siberia have previously been linked with sum-mer air temperature deviations (Kharuk et al 2008) Dur-ing the positive phase of the AO high pressures at northernmid-latitudes enhance the drying of phytomass and lead toa positive association between annual burned area and sum-mer temperatures in central Siberia (Balzter et al 2005) In-creased summer air temperatures as are projected for thecoming century (IPCC 2007) will likely lead to increasedfire activity across the region (Stocks et al 1998)

Obtaining an accurate picture of fire activity across Siberianecessitates the use of satellite data (Soja et al 2004) how-ever systematic mapping of fires across broad spatial ex-tents is not a trivial undertaking due to the necessity of us-ing medium to coarse resolution datasets the presence ofthick clouds and smoke and temporal changes in the op-tical characteristics of burned areas (Roy et al 2008) Inour study area MODIS considerably underestimated the to-tal area burned in comparison to that mapped using Land-sat though MODIS did detect the fires that were responsiblefor the vast majority of the area burned The underestima-tion of burned area is likely a result of the MODIS productpixel resolution (500 m) persistent cloud cover and the hot-spot detection technique for screening false positives (Roy etal 2005 2008) We found the MODIS burned area productuseful for identifying the year in which a Landsat-mappedfire occurred though note that in northeastern Siberia it un-derestimated fire sizes and consequently total area burned

While our newly created fire scar database allowed us toestimate elements of the fire regime and partially evaluate



the MODIS burned area product we encountered a num-ber of difficulties in constructing the database The principlesource of uncertainty stemmed from the 20 yr gap in Landsatdata This gap in imagery hindered our ability to detect anddate fires that occurred between 1975ndash1995 though we wereable to approximately date many of the larger fires duringthis period using the GIMMS NDVI record We likely un-derestimated both the total number of fires and area burnedduring this period which means that we consequently un-derestimated the fire density and overestimated fire rotationNonetheless fire rotation across the full record (c 1966ndash2007) was shorter than when calculated only for the period2000 and 2007 We had expected the fire rotation calculatedfor the most recent period to be shorter since fire activityhas generally been increasing across Siberia since the 1980sand both 2001 and 2003 were extreme fire years (Soja etal 2007) The 20 yr gap in Landsat imagery likely biasedthe fire record towards large fires and fires with minimal re-growth since we were more likely to detect these fires fol-lowing the resumption of satellite imaging In spite of theselimitations the fire scar record that we created from Landsatdata made it possible for us to not only assess forest regrowthfollowing fire but also derive best-estimates of fire regimecharacteristics and evaluate the MODIS burned area productfor an area that has not been well studied Future researchon the regional fire regime could include a more exhaustiveevaluation of burned area products as well as investigationof the historical fire frequency using tree ring data

43 Accumulation of larch aboveground biomassfollowing fire

While most satellite analyses of forest regrowth after firehave used NDVI as a proxy to assess the recovery of func-tional attributes (eg leaf area or net primary productivity)(Hicke et al 2003 Goetz et al 2006 Cuevas-Gonzalez etal 2009) we quantified the recovery of a structural attributeduring early and mid-succession by mapping larch AGBacross 98 fire scars Our resulting estimates of landscape-level larch AGB accumulation after fire agreed well withstand-level measurements made by Alexander et al (2012)Our finding that larch AGB tended to be low and change lit-tle during early succession is consistent with the in situ ob-servations of minimal larch recruitment low larch AGB andlow aboveground net primary productivity (ANPP) at sitesyounger than 20 yr Our estimate of larch AGB in stands thatwere 33ndash38 yr old (746plusmn 100 g mminus2 n = 33) was also sim-ilar to field observations (876plusmn 497 g mminus2 n = 3 Alexan-der et al 2012) and supports the notion that larch tendto re-achieve dominance 20ndash50 yr after fire (Zyryanova etal 2007 2010) We found that low elevation south-facingslopes provided the best site conditions for tree growth andbiomass accumulation during early succession and likelyacross the full successional cycle Soils tend to be warmerand thicker on low elevation south-facing slopes than on

high elevation north-facing slopes (Yanagihara et al 2000Noetzli et al 2007) which increases soil respiration and ni-trogen availability (Yanagihara et al 2000 Matsuura and Hi-robe 2010) and leads to both higher net photosynthetic ratesand annual larch shoot growth (Koike et al 2010) Thoughlarch AGB was highly variable during mid-succession (33ndash38 yr) the mixed linear model found no evidence to supportthat elevation or aspect drove this variability It is possiblethat we were not able to detect the effects due to the rela-tively short time period examined (6 yr) the limited numberof fire scars (n = 32) and potential errors in the AGB andelevatioan models We found no evidence to support the hy-pothesis that differences in latitude affected larch AGB accu-mulation rates potentially due to the relatively limited rangein latitudes (sim 5) and the modest climatic gradient that thisencompassed

While topographically-dictated site micro-climates affectpatterns of forest regrowth after fire characteristics of theinitial fire disturbance (eg fire size or severity) can also im-pact the trajectory of forest regrowth (Zyryanova et al 2007Sofronov and Volokitina 2010) We had predicted that larchAGB accumulation after large fires would occur more slowlythan after small fires due to a possible increase in proximityto seed sources which would lower the density of tree re-growth and thus subsequent AGB accumulation We foundno evidence however to support the hypothesis that fire sizeaffected rates of post-fire larch AGB accumulation To ourknowledge the maximum seed dispersal range for Cajan-der larch is unknown though seeds of a related species inNorth America (Larix laricinia) were found to stay largelywithin 20 m of the seed tree (Brown et al 1988) Thoughlong-distance seed dispersal cannot be ruled out the lack ofassociation between fire size and subsequent rates of larchAGB accumulation might mean that regrowth is more de-pendent upon seeds generated by surviving trees within a firescar than on seeds blown into a fire scar from neighbouringunburned stands While fires can cause high tree mortality inlarch forests (Abaimov et al 2000 Zyryanova et al 2007)we observed a six-fold range in AGB in the four years imme-diately following fire suggesting a range in post-fire tree sur-vival Trees that survive a fire often exhibit a period of rapidgrowth (Furyaev et al 2001 Sofronov and Volokitina 2010)and produce seeds that fall upon thinner soil organic layerswhere they are more likely to successfully germinate and be-come established (Sofronov and Volokitina 2010) If long-range seed dispersal is limited and recovery of Cajander larchforests is dependent upon trees that survived a fire alongwith remnant unburned patches within the fire scar then anincrease in fire severity might reduce seed availability by re-ducing tree survival More work is needed to understand seeddispersal and the role it plays in forest recovery after fire

Our study quantified post-fire forest recovery and exam-ined the roles of topography and fire size in shaping regrowthtrajectories but there were several sources of uncertaintyPrimary among these were potential errors in our AGB model



and the difficulty of precisely dating fire scars Though weobserved a strong relationship between TSFLS and field mea-surements of AGB (r2

= 091) the linear model RMSE was622 g mminus2 which was in the range of AGB values that we ob-served across fire scars This model error could have affectedour estimates of both larch AGB and rates of post-fire AGBaccumulation as well as hindered our ability to detect poten-tial effects related to fire size and topography Additionallyerrors in dating fire scars increased with years since fire andcould have also affected our estimates of AGB accumulation

There are a number of unanswered questions related tohow fire characteristics and site conditions affect post-firebiomass accumulation For instance how does fire severityaffect regrowth density and subsequent trajectories of AGBaccumulation What factors govern fire-induced tree mortal-ity and how do surviving trees and proximity to fire perimeterimpact spatial variability in forest recovery How might in-creases in regional vegetation productivity (ldquogreeningrdquo) ashave been observed from satellite measurements in recentdecades (Beck and Goetz 2011 Berner et al 2011) affectfuel loads fire activity and post-fire regrowth Furthermorehow do fires affect permafrost and the vast pools of organiccarbon stored within these frozen soils (Zimov et al 2009)Future research is needed to address these and other ques-tions related to disturbance factors governing the loss andaccumulation of carbon in the larch forests of northeasternSiberia

5 Conclusions

Siberian boreal forests are strongly affected by ongoing cli-matic changes that are likely to result in large feedbacks tothe global climate system Quantifying the net climate feed-back in boreal Siberia will necessitate understanding the rela-tionships among climate fire regimes and vegetation dynam-ics yet considerable uncertainty remains in forest C poolsfire dynamics and post-fire forest recovery across this re-mote and expansive biome We found that the AGB of larchforests varied considerably during the first few years afterfire suggesting variability in post-fire tree mortality LarchAGB accumulation was minimal during early succession(le 25 yr) and then increased rapidly during mid-successional(33ndash38 yr) as larch resumed dominance Larch regrowth de-creased with increased elevation and northwardly aspect atleast during early succession while neither fire size nor lat-itude appeared to affect rates of regrowth Field measure-ments of forest structure (eg AGB canopy cover) linkedwith tree shadows synergistically mapped using high andmedium resolution satellite imagery permitted spatial ex-trapolation of forest structure variables to regional extentswith a high degree of accuracy Similar derivation of datasetsand application of analysis techniques are needed for charac-terising forest biomass distribution disturbance regime dy-namics and the response of ecosystems to disturbance events

in order to improve prediction of the net climatic feedbacksassociated with landscape scale forest disturbance particu-larly in the relatively poorly studied regions of northern Eura-sia

AcknowledgementsThis work was supported by grants to SGand MM from the NASA Carbon Cycle and Ecosystems pro-gramme (NNX08AG13G) NOAA Global Carbon Programme(NA08OAR4310526) NSF International Polar Year (0732954) andNSF OPP (76347) Thanks to Andrew Bunn and Karen Frey aswell as to the students of the POLARIS project for larch biomassdata Thanks also to two anonymous reviewers who providedconstructive feedback on the previous version of this manuscript

Edited by P Stoy

References

Abaimov A P Geographic Distribution and Genetics of SiberianLarch Species in Permafrost Ecosystems Siberian LarchForests edited by Osawa A Zyryanova O A Matsuura YKajimoto T and Wein R W Springer New York 41ndash55 2010

Abaimov A P Zyryanova O A Prokushkin S G Koike Tand Matsuura Y Forest Ecosystems of the Cryolithic Zone ofSiberia Regional Features Mechanisms of Stability and Pyro-genic Changes Eur J Forest Res 1 1ndash10 2000

Akaike H A new look at the statistical model iden-tification IEEE T Automat Contr 19 716ndash723doi101109TAC19741100705 1974

Alexander H Mack M Goetz S Loranty M Beck P EarlK Zimov S Davydov S and Thompson C Carbon Ac-cumulation Patterns During Post-Fire Succession in CajanderLarch (Larix cajanderi) Forests of Siberia Ecosystems 1ndash18doi101007s10021-012-9567-6 2012

Baccini A Goetz S J Walker W S Laporte N T SunM Sulla-Menashe D Hackler J Beck P S A DubayahR Friedl M A Samanta S and Houghton R A Es-timated carbon dioxide emissions from tropical deforesta-tion improved by carbon- density maps Nature 2 182ndash185doi101038nclimate1354 2012

Balzter H Gerard F F George C T Rowland C S JuppT E McCallum I Shvidenko A Nilsson S Sukhinin AOnuchin A and Schmullius C Impact of the Arctic Oscillationpattern on interannual forest fire variability in Central SiberiaGeophys Res Lett 32 L14709doi1010292005gl0225262005

Bartsch A Balzter H and George C The influence of re-gional surface soil moisture anomalies on forest fires inSiberia observed from satellites Environ Res Lett 4 045021doi1010881748-932644045021 2009

Beck P S A and Goetz S J Satellite observations of high north-ern latitude vegetation productivity changes between 1982 and2008 ecological variability and regional differences EnvironRes Lett 6 045501doi1010881748-932664045501 2011

Berner L T Beck P S A Bunn A G Lloyd A H and GoetzS J High-latitude tree growth and satellite vegetation indicesCorrelations and trends in Russia and Canada (1982ndash2008) JGeophys Res 116 G01015doi1010292010jg001475 2011



Bonan G B Forests and climate change forcings feedbacksand the climate benefits of forests Science 320 1444ndash1449doi101126science1155121 2008

Breiman L Random Forests Mach Learn 45 5ndash32 2001Brown K R Zobel D B and Zasada J C Seed Disper-

sal Seedling Emergence and Early Survival of Larix Laricina(DuRoi) Koch K in the Tanana Valley Alaska Can J ForestRes 18 306ndash314 1988

Conard S G and Ivanova G A Wildfire in Russian BorealForestsmdashPotential Impacts of Fire Regime Characteristics onEmissions and Global Carbon Balance Estimates Environ Pol-lut 98 305ndash313doi101016s0269-7491(97)00140-1 1997

Corradi C Kolle O Walter K Zimov S A and SchulzeE D Carbon dioxide and methane exchange of a north-eastSiberian tussock tundra Glob Change Biol 11 1910ndash1925doi101111j1365-2486200501023x 2005

Cuevas-Gonzalez M Gerard F Balzter H and RianO DAnalysing forest recovery after wildfire disturbance in borealSiberia using remotely sensed vegetation indices Glob ChangeBiol 15 561ndash577 doi101111j1365-2486200801784x2009

DeWilde L O and Chapin F Human Impacts on the FireRegime of Interior Alaska Interactions among Fuels Igni-tion Sources and Fire Suppression Ecosystems 9 1342ndash1353doi101007s10021-006-0095-0 2006

Fuchs H Magdon P Kleinn C and Flessa H Estimatingaboveground carbon in a catchment of the Siberian forest tundraCombining satellite imagery and field inventory Remote SensEnviron 113 518ndash531doi101016jrse200807017 2009

Furyaev V V Vaganov E A Tchebakova N M and ValendikE N Effects of Fire and Climate on Successions and StructuralChanges in the Siberian Boreal Forest Eur J Forest Res 2 1ndash15 2001

George C Rowland C Gerard F and Balzter H Retrospectivemapping of burnt areas in Central Siberia using a modification ofthe normalised difference water index Remote Sens Environ104 346ndash359doi101016jrse200605015 2006

Goetz S and Dubayah R Advances in remote sensing tech-nology and implications for measuring and monitoring forestcarbon stocks and change Carbon Management 2 231ndash244doi104155cmt1118 2011

Goetz S J Mack M C Gurney K R Randerson J Tand Houghton R A Ecosystem responses to recent climatechange and fire disturbance at northern high latitudes observa-tions and model results contrasting northern Eurasia and NorthAmerica Environ Res Lett 2 045031doi1010881748-932624045031 2007

Goetz S J Baccini A Laporte N T Johns T Walker W Kell-ndorfer J Houghton R A and Sun M Mapping and moni-toring carbon stocks with satellite observations a comparison ofmethods Carbon Balance Manag 4doi1011861750-0680-4-2 2009

Goetz S J Bond-Lamberty B Law B E Hicke J A HuangC Houghton R A McNulty S OrsquoHalloran T Harmon MMeddens A J H Pfeifer E M Mildrexler D and KasischkeE S Observations and assessment of forest carbon dynamicsfollowing disturbance in North America J Geophys Res 117G02022doi1010292011jg001733 2012

Goodale C L Apps M J Birdsey R A Field C BHeath L S Houghton R A Jenkins J C KohlmaierG H Kurz W Liu S Nabuurs G-J Nilsson Sand Shvidenko A Z Forest carbon sinks in the North-ern Hemisphere Ecol Appl 12 891ndash899doi1018901051-0761(2002)012[0891fcsitn]20co2 2002

Greenberg J A Dobrowski S Z and Ustin S L Shadowallometry Estimating tree structural parameters using hyper-spatial image analysis Remote Sens Environ 97 15ndash25doi101016jrse200502015 2005

Groisman P and Soja A J Ongoing climatic change in NorthernEurasia justification for expedient research Environ Res Lett4 045002doi1010881748-932644045002 2009

Groisman P Y Sherstyukov B G Razuvaev V N KnightR W Enloe J G Stroumentova N S Whitfield P HFoslashrland E Hannsen-Bauer I Tuomenvirta H Aleksander-sson H Mescherskaya A V and Karl T R Poten-tial forest fire danger over Northern Eurasia Changes dur-ing the 20th century Global Planet Change 56 371ndash386doi101016jgloplacha200607029 2007

Hansen M C DeFries R S Townshend J R G Car-roll M Dimiceli C and Sohlberg R A Global Per-cent Tree Cover at a Spatial Resolution of 500 Me-ters First Results of the MODIS Vegetation ContinuousFields Algorithm Earth Interact 7 1ndash15doi1011751087-3562(2003)007iexcl0001GPTCAAiquest20CO2 2003

Hayes D J McGuire A D Kicklighter D W Gurney K RBurnside T J and Melillo J M Is the northern high-latitudeland-based CO2 sink weakening Global Biogeochem Cy 25GB3018doi1010292010gb003813 2011

Heinselman M L Fire in the virgin forests of the BoundaryWaters Canoe Area Minnesota Quaternary Res 3 329ndash382doi1010160033-5894(73)90003-3 1973

Hicke J A Asner G P Kasischke E S French N H F Ran-derson J T James Collatz G Stocks B J Tucker C J LosS O and Field C B Postfire response of North American bo-real forest net primary productivity analyzed with satellite obser-vations Glob Change Biol 9 1145ndash1157doi101046j1365-2486200300658x 2003

Houghton R A Butman D Bunn A G Krankina O NSchlesinger P and Stone T A Mapping Russian forestbiomass with data from satellites and forest inventories EnvironRes Lett 2 045032doi1010881748-932624045032 2007

IPCC Climate Change 2007 The Physical Science Basis Contribu-tion of Working Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change Cambridge 747ndash845 2007

James T M Temperature sensitivity and recruitment dynamics ofSiberian larch (Larix sibirica) and Siberian spruce (Picea obo-vata) in northern Mongoliarsquos boreal forest Forest Ecol Manag262 629ndash636doi101016jforeco201104031 2011

Kajimoto T Osawa A Usoltev V A and Abaimov A PBiomass and Productivity of Siberian Larch Forest Ecosystemsin Permafrost Ecosystems Siberian Larch Forests edited byOsawa A Zyryanova O A Matsuura Y Kajimoto T andWein R W Springer New York 99ndash122 2010

Kharuk V I Ranson K J and Dvinskaya M L Wildfires dy-namic in the larch dominance zone Geophys Res Lett 35L01402doi1010292007gl032291 2008



Kharuk V I Ranson K J Dvinskaya M L and Im S T Wild-fires in northern Siberian larch dominated communities EnvironRes Lett 6 045208doi1010881748-932664045208 2011

Koike T Mori S Zyryanova O A Kajimoto T Matsuura Yand Abaimov A P Photosynthetic Characteristics of Trees andShrubs Growing on the North- and South-Facing Slopes in Cen-tral Siberia in Permafrost Ecosystems Siberian Larch Forestsedited by Osawa A Zyryanova O A Matsuura Y KajimotoT and Wein R W Springer New York 273ndash287 2010

Kovacs K Ranson K J Sun G and Kharuk V I The Relation-ship of the Terra MODIS Fire Product and Anthropogenic Fea-tures in the Central Siberian Landscape Earth Interact 8 1ndash25doi1011751087-3562(2004)8iexcl1TROTTMiquest20CO2 2004

Krestov P V Forest Vegetation of Easternmost Russia (RussianFar East) in Forest Vegetation of Northeast Asia edited by Kol-bek J Srutek M and Box E 28 Kluwer Dordrecht 93ndash1802003

Leboeuf A Beaudoin A Fournier R Guindon L LutherJ and Lambert M A shadow fraction method for map-ping biomass of northern boreal black spruce forests usingQuickBird imagery Remote Sens Environ 110 488ndash500doi101016jrse200605025 2007

Leboeuf A Fournier R A Luther J E Beaudoin Aand Guindon L Forest attribute estimation of north-eastern Canadian forests using QuickBird imagery and ashadow fraction method Forest Ecol Manag 266 66ndash74doi101016jforeco201111008 2012

Liaw A and Wiener M Classification and Regression by random-Forest R News 2 18ndash22 2002

Lloyd A H Bunn A G and Berner L A latitudinal gradi-ent in tree growth response to climate warming in the Siberiantaiga Glob Change Biol 17 1935ndash1945doi101111j1365-2486201002360x 2011

Loboda T V and Csiszar I A Reconstruction of fire spreadwithin wildland fire events in Northern Eurasia from theMODIS active fire product Global Planet Change 56 258ndash273doi101016jgloplacha200607015 2007

Matsuura Y and Hirobe M Soil Carbon and Nitrogen and Char-acteristics of Soil Active Layer in Siberian Permafrost Regionin Permafrost Ecosystems Siberian Larch Forests edited byOsawa A Zyryanova O A Matsuura Y Kajimoto T andWein R W Springer New York 149ndash163 2010

Meyer D Tachikawa T Kaku M Iwasaki A Gesch DOimoen M Zheng Z Danielson J Krieger T Curtis WHaase J Abrams M Crippen R and Carabaja C ASTERGlobal Digital Elevation Model Version 2 ndash Summary of Valida-tion Results Japan-US ASTER Science Team 1ndash26 2011

Mollicone D Eva H D and Achard F Human role in Russianwild fires Nature 440 436ndash437doi101038440436a 2006

Noetzli J Gruber S Kohl T Salzmann N and Haeberli WThree-dimensional distribution and evolution of permafrost tem-peratures in idealized high-mountain topography J GeophysRes 112 F02S13doi1010292006jf000545 2007

Petrovsky V V and Koroleva T M On the flora Kolyma RiverDelta Bot J Linn Soc 64 19ndash39 1979

R Development Core Team R A Language and Environment forStatistical Computing R Foundation for Statistical ComputingVienna 2011

Ranson K J Montesano P M and Nelson R Object-based mapping of the circumpolar taigandashtundra ecotone withMODIS tree cover Remote Sens Environ 115 3670ndash3680doi101016jrse201109006 2011

Roy D P Jin Y Lewis P E and Justice C O Prototyping aglobal algorithm for systematic fire-affected area mapping usingMODIS time series data Remote Sens Environ 97 137ndash162doi101016jrse200504007 2005

Roy D P Boschetti L Justice C O and Ju J The collection 5MODIS burned area product ndash Global evaluation by comparisonwith the MODIS active fire product Remote Sens Environ 1123690ndash3707doi101016jrse200805013 2008

Schulze E-D Wirth C Mollicone D von Lupke N ZieglerW Achard F Mund M Prokushkin A and Scherbina SFactors promoting larch dominance in central Siberia fire versusgrowth performance and implications for carbon dynamics at theboundary of evergreen and deciduous conifers Biogeosciences9 1405ndash1421doi105194bg-9-1405-2012 2012

Sofronov M A and Volokitina A V Wildfire Ecology in Continu-ous Permafrost Zone in Permafrost Ecosystems Siberian LarchForests edited by Osawa A Zyryanova O A Matsuura YKajimoto T and Wein R W Springer New York 59ndash83 2010

Soja A J Sukhinin A I Cahoon D R Shugart H H andStackhouse P W AVHRR-derived fire frequency distributionand area burned in Siberia Int J Remote Sens 25 1939ndash1960doi10108001431160310001609725 2004

Soja A Shugart H Sukhinin A Conard S and StackhouseP Satellite-Derived Mean Fire Return Intervals as Indicatorsof Change in Siberia (1995ndash2002) Mitig Adapt Strat GlobChange 11 75ndash96doi101007s11027-006-1009-3 2006

Soja A J Tchebakova N M French N H F Flan-nigan M D Shugart H H Stocks B J SukhininA I Parfenova E I Chapin F S and Stackhouse PW Climate-induced boreal forest change Predictions ver-sus current observations Global Planet Change 56 274ndash296doi101016jgloplacha200607028 2007

Steininger M K Satellite estimation of tropical secondary forestabove-ground biomass data from Brazil and Bolivia Int J Re-mote Sens 21 1139ndash1157 2000

Stocks B J Fosberg M A Lynham T J Mearns L WottonB M Yang Q Jin J-Z Lawrence K Hartley G R MasonJ A and McKenney D W Climate change and forest fire po-tential in Russia and Canada boreal forests Climatic Change 381ndash13 1998

Sukhinin A I French N H F Kasischke E S Hewson J HSoja A J Csiszar I A Hyer E J Loboda T Conrad S GRomasko V I Pavlichenko E A Miskiv S I and SlinkinaO A AVHRR-based mapping of fires in Russia New productsfor fire management and carbon cycle studies Remote Sens En-viron 93 546ndash564doi101016jrse200408011 2004

Tucker C Pinzon J Brown M Slayback D Pak E Ma-honey R Vermote E and El Saleous N An extendedAVHRR 8-km NDVI dataset compatible with MODIS and SPOTvegetation NDVI data Int J Remote Sens 26 4485ndash4498doi10108001431160500168686 2005

Valendik E N Ecological aspects of forest fires in SiberiaSiberian J Ecol 1 1ndash8 1996

Venables W N and Ripley B D Modern Applied Statistics withS 4 Edn Springer New York 139ndash178 2002



Wolter P T Berkley E A Peckham S D Singh A andTownsend P A Exploiting tree shadows on snow for estimat-ing forest basal area using Landsat data Remote Sens Environ121 69ndash79doi101016jrse201201008 2012

Yanagihara Y Koike T Matsuura Y Mori S Shibata H SatohF Masuyagina O V Zyryanova O A Prokushkin A SProkushkin S G and Abaimov A P Soil Respiration Rateon the Contrasting North- and South-Facing Slopes of a LarchForest in Central Siberia Eur J Forest Res 1 19ndash29 2000

Zimov N S Zimov S A Zimova A E Zimova G MChuprynin V I and Chapin F S Carbon storage in permafrostand soils of the mammoth tundra-steppe biome Role in the

global carbon budget Geophys Res Lett 36 L02502doi1010292008gl036332 2009

Zyryanova O A Yaborov V T Tchikhacheva T I KoikeT Makoto K Matsuura Y Satoh F and Zyryanov V IThe Structural and Biodiversity after Fire Disturbance inLarixgmelinii (Rupr) Rupr Forests Northeast Asia Eur J ForestRes 10 19ndash29 2007

Zyryanova O A Abaimov A P Bugaenko T N and BugaenkoN N Recovery of Forest Vegetation After Fire Disturbance inPermafrost Ecosystems Siberian Larch Forests edited by Os-awa A Zyryanova O A Matsuura Y Kajimoto T and WeinR W Springer New York 83ndash96 2010



the larch (Larix spp) dominated boreal forests of Siberia(Groisman et al 2007) While the boreal forest biome ofnorthern Eurasia acts as a net sink for atmospheric CO2(Goodale et al 2002) the strength of this terrestrial car-bon sink has weakened over the last decade due to increasedfire emissions and warming-induced increases in decomposi-tion of soil organic matter (Hayes et al 2011) The strengthof the sink will likely continue to diminish because risingair temperatures are expected to further increase fire activ-ity (Stocks et al 1998) Climate-induced intensification ofthe fire regime will affect forest carbon pools surface energybudgets and hydrologic processes yet the net effect of thesefeedbacks on the climate system remains poorly understood(Goetz et al 2007 Bonan 2008)

Fire is a dominant control on stand structure and compo-sition in Siberian forests with stand-replacing fires punc-tuating the start and end of successional cycles (Furyaevet al 2001 Schulze et al 2012) The accumulation ofcarbon after fire disturbance in Siberiarsquos larch forests de-pends on characteristics of the fire regime tree biology andsite-level conditions but details of these interactions arenot fully understood particularly in the northeastern per-mafrost zone (Kajimoto et al 2010) Larch reestablishmentand biomass accumulation depends on nonlinear interactionsamong fire periodicity and severity (Furyaev et al 2001Schulze et al 2012) climate conditions (James 2011 Lloydet al 2011) site micro-topography and permafrost (Koike etal 2010 Zyryanova et al 2010) and availability of seeds(Abaimov et al 2000 Sofronov and Volokitina 2010) Firefrequency (eg fire rotation or fire return interval) whichis the interval of time between successive disturbances av-erages around 80 yr in northern larch forests though exhibitsconsiderable topographic and regional variability (Furyaev etal 2001 Kharuk et al 2011) If successive larch cohorts aredestroyed by fire prior to reaching maturity and outside seedsources are not available then forests can convert to non-arboreal vegetation (Sofronov and Volokitina 2010) Alter-natively very infrequent fires in northern Siberia can leadto the long-term accumulation of moss and duff which canblock seedling establishment and cause larch forests to con-vert to tundra (Sofronov and Volokitina 2010) Periodic firesthus help maintain larch forests and associated floristic di-versity (Zyryanova et al 2010 Schulze et al 2012) thoughthe affects of individual fires depends largely on fire severity

Given Russiarsquos size low population density and logisti-cal challenges associated with accessing fires there is lim-ited information on fire location frequency size and severity(Conard and Ivanova 1997 Sukhinin et al 2004) The fed-eral government historically monitored and selectively sup-pressed fires across approximately 60 of forested landshowever economic instability in the 1990s and early 2000sled to a reduction fire suppression and monitoring activi-ties (Sukhinin et al 2004) The northern open woodlandsof Siberia have always fallen outside of the protected zoneand thus little is known about fire regime in this expansive

region (Sofronov and Volokitina 2010) Analysis of satelliteimagery has helped shed light on spatial and temporal firedynamics across the country however regional fire mappinghas primarily focused on central Siberia (Kovacs et al 2004George et al 2006 Sofronov and Volokitina 2010) Firemapping at national (Soja et al 2004 Sukhinin et al 2004Soja et al 2006) to supranational (Roy et al 2005 2008)scales has also been carried out though these efforts reliedon medium to coarse resolution imagery (eg MODIS andAVHRR) Fire maps created from coarse resolution imageryacross broad spatial scales require external validation usinghigher resolution datasets (eg Landsat) though these in-dependent fire records are often quite limited in availability(Roy et al 2008) In spite of their limitations Earth observ-ing satellites are powerful tools that can help improve our un-derstanding of fire dynamics in remote regions such as Rus-siarsquos boreal forest

For similar reasons as Russiarsquos fire regimes are not wellunderstood there is considerable uncertainty in the magni-tude and distribution of Russiarsquos forest carbon stocks Pub-lished estimates of biomass in Russiarsquos forests range from46ndash148 Pg (Goodale et al 2002 Houghton et al 2007)Satellite analysis has helped improve our understanding ofthe distribution of forest biomass however since no satel-lites directly measure biomass it is necessary to derivesurrogate variables from satellite data that can be linkedwith field measurements (Baccini et al 2012) Efforts tomodel forest biomass distribution by linking field and satel-lite measurements have met with mixed success in the bo-real biome (Houghton et al 2007 Fuchs et al 2009Leboeuf et al 2012) Quantifying climate impacts associ-ated with forest disturbances in Siberia necessitates under-standing forest biomass distribution disturbance regimes andpost-disturbance ecosystem recovery however there is con-siderable uncertainty in each of these areas

Our primary objective here was to improve our under-standing of landscape-level carbon cycling dynamics follow-ing fire in the Cajander larch forests of northeastern SiberiaWe focused on quantifying aboveground tree biomass (AGB)because trees drive much of the landscape-level variabilityin AGB in this ecosystem For instance trees in this regioncan account for over 95 of total stand AGB (Alexander etal 2012) We examined trajectories of larch AGB accumu-lation following fire and how accumulation was affected byfire size and topography To accomplish this we conducteda geospatial analysis using satellite imagery from multiplesensors to map larch AGB and fire scars (c 1966ndash2007)across approximately 100 000 km2 of the Kolyma River wa-tershed Secondary objectives included quantifying fire rota-tion fire density and fire-size distribution as well as evaluat-ing regional performance of the global MODIS burned areaproduct (MCD45A1) (Roy et al 2005 2008) based on firesmapped from 30 m resolution Landsat imagery We also de-vised a technique to map regional AGB by linking field mea-surements of AGB to tree shadows that were synergistically





21 Study area




























DataAcquisition

Scenes(n)

Resolution(m)

BiomassMapping


21 April 200926 July 2007

74

0530

Fire ScarMapping


1999ndash20041995 20052006 2007

2621

3030



AncillaryDatasets



1 30



11

250500

Burned Area(MODIS)


96 500

NDVI(GIMMS AVHRR)


636 8times 103


1284 55times 103
















3 Results












0 20 40 60 80 100

010

0030

0050

0070

00

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)


AGB = TSF 58r2 = 09n = 22p lt 0001

A

0 20 40 60 80 100

AGB = TSF 69r2 = 091n = 25p lt 0001

B



asymp 090p lt 0001)



























70 10 270 245 033 301 02


20 1482 185 [0 145] 025 [000 020] 397 03 [00 06]


12 454 57 [0 58] 013 [000 013] 792 03 [00 05]


47 4277 535 [42 601] 091 [007 103] 110 10 [03 11]

0 1000 3000 5000 7000

050

015

0025

00


Fre

quen

cy

0 1000 3000 5000 7000

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity











050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22





020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity


4 Discussion









1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References


















































































21 Study area




























DataAcquisition

Scenes(n)

Resolution(m)

BiomassMapping


21 April 200926 July 2007

74

0530

Fire ScarMapping


1999ndash20041995 20052006 2007

2621

3030



AncillaryDatasets



1 30



11

250500

Burned Area(MODIS)


96 500

NDVI(GIMMS AVHRR)


636 8times 103


1284 55times 103
















3 Results












0 20 40 60 80 100

010

0030

0050

0070

00

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)


AGB = TSF 58r2 = 09n = 22p lt 0001

A

0 20 40 60 80 100

AGB = TSF 69r2 = 091n = 25p lt 0001

B



asymp 090p lt 0001)



























70 10 270 245 033 301 02


20 1482 185 [0 145] 025 [000 020] 397 03 [00 06]


12 454 57 [0 58] 013 [000 013] 792 03 [00 05]


47 4277 535 [42 601] 091 [007 103] 110 10 [03 11]

0 1000 3000 5000 7000

050

015

0025

00


Fre

quen

cy

0 1000 3000 5000 7000

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity











050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22





020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity


4 Discussion









1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References































































































DataAcquisition

Scenes(n)

Resolution(m)

BiomassMapping


21 April 200926 July 2007

74

0530

Fire ScarMapping


1999ndash20041995 20052006 2007

2621

3030



AncillaryDatasets



1 30



11

250500

Burned Area(MODIS)


96 500

NDVI(GIMMS AVHRR)


636 8times 103


1284 55times 103
















3 Results












0 20 40 60 80 100

010

0030

0050

0070

00

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)


AGB = TSF 58r2 = 09n = 22p lt 0001

A

0 20 40 60 80 100

AGB = TSF 69r2 = 091n = 25p lt 0001

B



asymp 090p lt 0001)



























70 10 270 245 033 301 02


20 1482 185 [0 145] 025 [000 020] 397 03 [00 06]


12 454 57 [0 58] 013 [000 013] 792 03 [00 05]


47 4277 535 [42 601] 091 [007 103] 110 10 [03 11]

0 1000 3000 5000 7000

050

015

0025

00


Fre

quen

cy

0 1000 3000 5000 7000

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity











050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22





020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity


4 Discussion









1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References
























































































3 Results












0 20 40 60 80 100

010

0030

0050

0070

00

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)


AGB = TSF 58r2 = 09n = 22p lt 0001

A

0 20 40 60 80 100

AGB = TSF 69r2 = 091n = 25p lt 0001

B



asymp 090p lt 0001)



























70 10 270 245 033 301 02


20 1482 185 [0 145] 025 [000 020] 397 03 [00 06]


12 454 57 [0 58] 013 [000 013] 792 03 [00 05]


47 4277 535 [42 601] 091 [007 103] 110 10 [03 11]

0 1000 3000 5000 7000

050

015

0025

00


Fre

quen

cy

0 1000 3000 5000 7000

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity











050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22





020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity


4 Discussion









1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References




































































































70 10 270 245 033 301 02


20 1482 185 [0 145] 025 [000 020] 397 03 [00 06]


12 454 57 [0 58] 013 [000 013] 792 03 [00 05]


47 4277 535 [42 601] 091 [007 103] 110 10 [03 11]

0 1000 3000 5000 7000

050

015

0025

00


Fre

quen

cy

0 1000 3000 5000 7000

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity











050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22





020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity


4 Discussion









1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References
















































































050

010

0015

0020

0025

00

Are

a B

urne

d (k

m2 )

05

1015

2025

1970 1975 1980 1985 1990 1995 2000 2005

Num

ber

of F

ires

Landsat Blackout

20 yrs

fires detected 22





020

4060

80

0 500 1000 1500 2000 2500

Area Burned (km2)

Num

ber

of F

ire S

cars

000

025

050

075

100

Cum

ulat

ive

Pro

babi

lity


4 Discussion









1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References
















































































1111

1

1

11

1

1111

111

11

1

1

111 1

1

1

1

1 111 1

111

1

1

1

111

11

1

1

1

1

1

1 11

1

11

1

1

11

1

11 1

11

11

1 1

1

1

1

1

1

11

11

1 11

1

11 1

1 1

111

1

11

1

1

1

1

11

5 10 15 20 25 30 35

5 10 15 20 25 30 35

050

010

0015

0020

0025

0030

00

10 20 30

Larc

h A

bove

grou

nd B

iom

ass

(g m

minus2)































5 Conclusions




Edited by P Stoy

References







































































































5 Conclusions




Edited by P Stoy

References















































































cajander larch (larix cajanderi) biomass distribution, ﬁre ......l. t. berner et al.: cajander...

Documents