Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ). 2 2 − − km , and 2 4 26 g m 10 ± × 1 Discussions − ) than along the 2 − Biogeosciences Mayr.) aboveground , M. C. Mack km 25 yr, 271 2 4 ) biomass ≤ 0.3 fires yr 10 erences in post-fire density ± × ff 1 0.9) with field measurements − 0.3 ≈ = 2 r Larix cajanderi 1.0 fires yr , H. D. Alexander 1 ± 1.0 7556 7555 = 792 yr, fire density = Larix cajanderi , M. M. Loranty area of open forest in far northeastern Siberia. In addi- 1 32), though was highly variable. The high variability was 2 = n , 2 ects of fire size and topography on post-fire larch aboveground − 110 yr, fire density ff = SE]) and decreased with increasing elevation and northwardly as- ± , P. S. A. Beck 1 100 g m 1 ± 66 [mean = This discussion paper is/has been under review for the journal Biogeosciences (BG). Please refer to the corresponding final paper in BG if available. The Woods Hole Research Center,University 149 of Woods Florida, Hole Department Road, of Falmouth, MA Biology, P.O. Box 02540-1644, 118525, USA Gainesville, FL 32611, USA of tree regrowth. Neitherlarch aboveground fire biomass. Fire size activity nor wastains considerably latitude (fire higher rotation were in the significant Kolymaforest-tundra Moun- predictors border (fire of rotation post-fire The MODIS burnedfrom area 2000–2007 maps by underestimated 40 %.olution the Tree satellite total shadows imagery area mapped were jointlyof burned strongly forest using in associated structure, high this which ( and permittedregional region medium extent. spatial res- Better extrapolation understanding of of aboveground biomass forest biomass to distribution, a disturbances, and ground biomass tendedn to be lowpect. during Larch early aboveground succession biomass38 yr, ( tended 746 tonot be associated higher with topography during and potentially mid-succession reflected (33– di analyze post-fire accumulation of Cajanderbiomass larch for ( a 100tion 000 km to examining e biomass, we assessed regionalburned area fire maps rotation generated andwe from mapped density, 116 MODIS as fire satellite scar wellaboveground imagery. perimeters biomass as Using that by dated linking performance Landsat field ca. imagery, of biomass 1969–2007.synergistically measurements We to then tree mapped from shadows larch mapped WorldView-1 and Landsat 5 satellite imagery. Larch above- Climate change and land-useSiberian activities boreal are increasing forest, yet fireficult the activity to across climate quantify much feedbacks due of fromregimes, to the forest and limited disturbances post-disturbance information ecosystem remain on recovery. dif- Our forest primary biomass objective distribution, here was disturbance to Abstract Biogeosciences Discuss., 9, 7555–7600, 2012 www.biogeosciences-discuss.net/9/7555/2012/ doi:10.5194/bgd-9-7555-2012 © Author(s) 2012. CC Attribution 3.0 License. Cajander larch ( distribution, fire regime and post-fire recovery in northeastern Siberia L. T. Berner S. J. Goetz 1 2 Received: 23 May 2012 – Accepted: 14Correspondence June to: 2012 L. – T. Published: Berner 25M. ([email protected]), June M. P. S. 2012 Loranty A. ([email protected]), Beck H.M. ([email protected]), D. C. Alexander Mack (hdalexander@ufl.edu), (mcmack@ufl.edu), and S. J.Published Goetz by ([email protected]) Copernicus Publications on behalf of the European Geosciences Union. 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | orts have relied ff 7558 7557 ect of these feedbacks on the climate system remains poorly ff C, respectively (Groisman and Soja, 2009), and climate models ◦ spp.) dominated boreal forests of Siberia (Groisman et al., 2007). ect forest carbon pools, surface energy budgets, and hydrologic pro- ff C increase in mean annual temperature across much of the region by ◦ Larix C and 1.35 , which can block seedling establishment and cause larch forests to convert to ◦ ff ects of individual fires depends largely on fire severity. (Goodale et al., 2002), the strength of this terrestrial carbon sink has weakened ff 2 Given Russia’s size, low population density, and logistical challenges associated with Fire is a dominant control on stand structure and composition in Siberian forests, with et al., 2005, 2008) scales has also been carried out, though these e cally monitored and selectively suppressed fireslands; across however, economic approximately instability 60 in % the ofsuppression 1990s forested and and early monitoring 2000s activities led (Sukhinin tolands et a of al., reduction Siberia 2004). fire have The always northern fallenabout outside open of fire wood- the regime protected in zone and thissatellite thus expansive little imagery region is has (Sofronov known and helpedthe Volokitina, shed 2010). country; light Analysis however, of on regional(Kovacs spatial fire et and mapping al., temporal has 2004; fireping primarily George dynamics at focused et across national on al., (Soja central 2006; et Siberia Sofronov al., and 2004, Volokitina, 2006; 2010). Sukhinin Fire et map- al., 2004) to supranational (Roy and associated floristic diversity (Zyryanovathe et a al., 2010; Schulze et al., 2012), though accessing fires, there is limitedity information (Conard on and fire Ivanova, location, 1997; frequency, size, Sukhinin and et sever- al., 2004). The federal government histori- which is the interval of timenorthern between larch successive disturbances, forests, though averages around exhibits 80 considerable(Furyaev yr topographic et in and al., regional 2001; variability Kharuk etfire al., prior 2011). to If reaching successive larch maturitycan cohorts and convert are outside to destroyed seed by non-arboreal sourcesvery vegetation are infrequent (Sofronov fires not in and available, northern then Volokitina, Siberiaand 2010). forests can du Alternatively, lead to the long-termtundra accumulation (Sofronov of moss and Volokitina, 2010). Periodic fires thus help maintain larch forests lishment and biomassperiodicity accumulation and depends severity ontions (Furyaev non-linear (James, et interactions 2011; al., Lloyd amonget et 2001; fire al., al., Schulze 2010; 2011), et Zyryanova siteSofronov et al., and micro-topography al., Volokitina, and 2012), 2010). 2010), permafrost Fire climate and frequency (Koike condi- (e.g. availability fire of rotation seeds or (Abaimov fire return et interval), al., 2000; particularly in the northeastern permafrost zone (Kajimoto et al., 2010). Larch reestab- in Siberia’s larch forestsand depends site-level on conditions, characteristics but of details the of fire these regime, interactions tree are biology not fully understood, will likely continuether to increase diminish fire because activityfire rising (Stocks regime air will et a temperatures al.,cesses, are 1998). yet expected Climate-induced the to intensification netunderstood of fur- (Goetz e the et al., 2007; Bonan, 2008). stand-replacing fires punctuating theet al., start 2001; and Schulze end et of al., successional 2012). cycles The accumulation (Furyaev of carbon after fire disturbance of the larch ( While the boreal forestCO biome of northern Eurasiaover the acts last as decade ain due net to decomposition sink increased of for fire soil atmospheric emissions organic and matter warming-induced (Hayes increases et al., 2011). The strength of the sink Forests in the Russianlargest Federation vegetation cover approximately carbon 800 pool millionet ha outside al., and of 2007) contain the which, the tropics alongponent (Goodale with of et their Earth’s al., low 2002; surface climate1880s, albedo, Houghton system make average (Bonan, them winter an 2008). important andrisen Since com- summer 2 instrumentation began air in temperaturespredict the a across 3–7 northernthe Eurasia end have of the 21stin century precipitation, (IPCC, have 2007). resulted Higher in temperatures, drier with conditions little and or increased no risk change of fire across much backs associated with landscape-scale forest disturbances in northern Eurasia. 1 Introduction post-disturbance recovery is needed to improve predictions of the net climatic feed- 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C, ◦ 12.5 − C increase in ◦ of the Kolyma River Betula divaricata and 2 C. Precipitation aver- ◦ 13 + orts to model forest biomass ), birch ( ff of the Kolyma River watershed C to ◦ (Kajimoto et al., 2010). Trees can 2 2 40 − − 7560 7559 of wind-dispersed seeds (Abaimov, 2010). Forest 2 − and total summer evaporation is twice as high as summer pre- 1 − ected by fire size and topography. To accomplish this, we conducted ff Salix pulchra, S. alaxensis, and S. glauca 10 m tall and AGB rarely exceeds 10 kg m < Cajander larch is the only tree species in our study area and dominates the per- Our primary objective here was to improve our understanding of landscape-level For similiar reasons that Russia’s fire regimes are not well understood, there is con- Alexander et al., 2012).ited Deciduous to, willow shrubs ( in the region include, though are not lim- mafrost zone from theEast Lena (Krestov, 2003; River Abaimov, in 2010). Cajander Centralneedleleaf larch conifer Yakutia are to capable a of the shade-intolerant growing deciduous growing Sea on seasons of permafrost (Abaimov and Okhost et withstanding in al.,erally very the 2000). short, Far In cool northeasternannually Siberia produce these trees up are tosuccession gen- 65 after kg ha stand replacingfollowed fires by generally larch involves a re-achieving multi-decadal dominance shrub 20–50 yr stage, after fire (Zyryanova et al., 2007; ages 200–215 mm yr cipitation (Corradi etmean al., annual 2005). air Global temperaturegion climate and by models the a end predict 15–20 of % a the increase 21st 3–7 in century precipitation (IPCC, 2007). across the re- 2 Materials and methods 2.1 Study area The study areain covered far approximately northeastern 100 Russian 000 km (Fig.and has 1). a The cold, region dry is andthough underlain continental average climate. by Annual daily continuous air permafrost temperatures temperature averages range from imagery. We also devised aments of technique AGB to to tree map shadows regionalmedium that (30 were AGB m) synergistically by spatial mapped resolution linking using satellite high fieldrelationships imagery. (50 In among measure- cm) addition and tree to shadows AGB, and we canopy assessed cover, the tree height, and tree density. burned area product (MCD45A1), based on fires mapped from 30 m resolution Landsat fire-size distribution, as well as evaluating regional performance of the global MODIS carbon cycling dynamics followingSiberia. fire We focused in on thedrive quantifying Cajander much aboveground of larch tree the forests biomasstrees landscape-level of (AGB) in variability because northeastern this in trees region AGB2012). We in can examined this account trajectories ecosystem. of forcumulation larch For was over AGB instance, a accumulation 95 % following firea of and geospatial total how analysis ac- stand using AGBand satellite fire (Alexander imagery scars et from (ca. multiple 1969–2007) al., watershed. sensors across Secondary approximately to 100 map 000 objectives km larch included AGB quantifying fire rotation, fire density, and in the boreal biomeQuantifying (Houghton climate et impacts associated al., with 2007; forestunderstanding disturbances Fuchs forest in et Siberia biomass al., necessitates distribution, 2009;ecosystem disturbance Leboeuf recovery; regimes, however, et there and is al., post-disturbance considerable 2012). uncertainty in each of these areas. siderable uncertainty in the magnitude andPublished distribution estimates of of Russia’s forest biomass carbon in stocks. al., Russia’s 2002; forests Houghton range et from al., 2007). 46–148ing Pg Satellite analysis of (Goodale has et the helped distribution improve ourbiomass, of understand- it forest biomass; is however, necessary sincelinked to no with derive satellites field surrogate directly measurementsdistribution measure variables (Baccini by from et linking al., satellite 2012). field data E and that satellite can measurements be have met with mixed success from coarse resolution imagery acrossusing broad higher resolution spatial data scales sets requireare (e.g. external Landsat), often validation though quite these limited independent inobserving fire availability records (Roy satellites et are al., powerful 2008). toolsdynamics In in that spite remote can of regions help their such limitations, improve as earth our Russia’s understanding boreal of forest. fire on medium to coarse resolution imagery (e.g. MODIS and AVHRR). Fire maps created 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Ledum de- ) (Petrovsky and Korol- erence vegetation index ff ), dwarf Labrador tea ( ort to improve TCC estimates along ff ) across eastern Siberia because there Pyrola grandiflora resolution from climate station data. As a ◦ 7562 7561 while evergreen shrubs include Siberian dwarf 47). Landsat data are not available from 1975–1995 = Vaccinium vitis-idaea n 30 m) digital elevation model (DEM) generated by the ∼ using 30 m resolution Landsat images (WRS-2, path 104, (Fig. 2b and c). We mapped AGB and fire scars across 2 ◦ , were acquired at local noon on 21 April 2009, with an average Landsat blackout period 2 Alnus fruticosa), ) from 2010 are described in (Alexander et al., 2012), while 2011 data field pumila), cowberry ( 12) or 1995–2007 ( ), and large-flowered wintergreen ( = ) and alder ( n Pinus nadir view angle of 2 Fire scar mapping was based on 59 Landsat scenes that were acquired either 1972– The five ancillary geospatial data sets included models of topography, burned area, ff inventoried eight sites, though did not ascertain the time since last fire. Both estimates 2.3 Field measurements of aboveground biomass Fieldwork was conducted nearstudy Cherskii, area in also theOkrug Sakha included and Republic portions the (Yakutia), though Magadan of Oblastduring the the (Fig. July 2010 2a). more and Field mountainous 2011 databiomass at (AGB Chukotka used 25 in Autonomous sites. this Field studywere estimates were of collected collected Cajander by larch Bunn aboveground 17 sites and that Frey burned (unpublished). between Alexander five et and 205 al. yr (2012) ago, while inventoried Bunn and Frey (unpublished) product (MODIS VCF v5;canopy MOD44B) cover (Hansen (TCC) et at al., 250 mson 2003), resolution, et which and al. mapped the (2011) global relatedthe tree from TCC circumpolar map MODIS taiga-tundra generated ecotone. VCF by (v4) Ran- in an e lites (Roy etof al., temperature 2005, and 2008). precipitationClimate The that Research gridded were Unit produced climate (CRU byproxy data v3.1) the for included at University vegetation monthly 0.5 of productivity,(NDVI) East averages data we set Anglia generated used from adata Advanced by Very normalized NASA’s High Global Resolution di Inventory Radiometer Modeling(Tucker (AVHRR) and et Mapping al., Studies 2005). (GIMMS v3G)present. This project 8 The km resolution final biweekly two data data set sets runs from included 1981 the through MODIS vegetation continuous fields Japanese Ministry of Economy, Trade, andborne Industry Thermal and NASA Emission from and2011). Advanced Reflection Space- The Radiometer burned data area (ASTER databurned GDEM set, area v2)(Meyer, which spanned product 2000–2007,Spectroradiometer (MCD43A1 was (MODIS) the v5) sensors 500 m generated carried monthly from aboard Moderate NASA’s Resolution Aqua Imaging and Terra satel- set was the 1 arc-second ( climate, vegetation productivity, and tree canopy cover (Table 1). The topography data 1974 ( (hereafter refered to as were no operational groundimages receiving available stations. for the Furthermore,until southernmost 1999. there The quarter were Landsat images no of thatand we the Landsat September, used preferentially study to under map area fires cloud-freeWhile were (path conditions WorldView-1 acquired and 104, late between Landsat June in row reflectance theaugmented 14) data the growing formed analysis season. the with basis ancillary of geospatial our datasets. analysis, we rows 11–14; Table 1; Fig.Earth 2a Resources and Observation d) provided andfour by Science along-track the Center. Landsat United Biomass Stated 5recent mapping Geological scenes cloud- was Survey and acquired based smoke-free on onextent images of 26 available the July for study 2007. the area. region These and were defined the the most spatial Our analysis made useof of satellites reflectance (Table 1). data As from partWorldView-1 the of images WorldView-1 the with and biomass a mapping Landsat nadirUniversity process, spatial series we of resolution used of Minnesota panchromatic 50 Polar cmapproximately 2150 Geospatial that km were Center. provided The byo the seven images, whichapproximately covered 100 000 km pine( cumbens eva, 1979). 2.2 Satellite and geospatial data B. exilis 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | LS data non- ) that LS WV and classes (0– WV er zone centered on ff tree shadow for 500 points not used . Landsat preprocessing ◦ We defined this value as ered one pixel outward to WV ff 45 ) using a regression equation < LS or ◦ by TSF LS 270 > tree shadow. , we randomly selected 2500 points that 500) a Random Forest model that predicted WV . 7564 7563 = field n measurements from 25 sites. We calculated mean if the digital number (DN) was between 30 and 70. field and AGB . See Alexander et al. (2012) for more details. and aspect either 16.7 m). It was not necessary to adjust the georeferencing LS ◦ field = ) as a surrogate for AGB and then using these TSF map into a map of AGB (AGB 15 standard error) within a 30 m diameter bu WV > LS ± tree shadow ( ciently process large data sets, has low bias, and tends not to over 2000) and evaluate ( ffi WV = n . The tree shadow map was then aggregated to 30 m resolution to match 30 m pixel that was classified as were derived from allometric equations that related diameter at breast height × and TSF field . Following the calculation of TSF from the Landsat data. LS WV LS We used a combination of Landsat reflectance values and band metrics (e.g. NDVI) Individual trees and tree shadows contrasted sharply with the bright, snow-covered The WorldView-1 images came orthorectified and calibrated to top of atmosphere re- TSF cision trees are constructedmined using based bootstrap upon sampling, “votes” withtechnique cast the can by final e each prediction treefit deter- (Breiman, models 2001). (Breiman, This machine 2001).2002) learning We in used R the (Rpredictor randomForest Development variables package Core randomly (Liaw Team, selectedperformance 2011) and and was to Wiener, tested evaluated build by atin 500 regressing model each decision TSF construction. node trees, The with ofthat calibrated we five each Random then compared tree. Forest with model Model AGB yielded a map of TSF used to train ( TSF as predictor data forRandom the Forests are Random a Forest form model of (See classification Table and 3 regression analysis in where Results many for de- details). classified pixels as The DN threshold resultedtree in shadow a 50 cm resolutionthe binary footprint map of of theeach Landsat 30 mosaic. AggregationTSF involved calculating thewere percent spaced at of least 301 m %, apart 1–30 %, and and which 30–100 were %). stratified The by underlying three TSF pixel TSF values were then extracted and covered an areatransformed about the 50 TSF timesderived that by comparing of TSF the WorldView-1 scenes. Weland then surface in linearly the WorldView-1 images. After testing a series of threshold values, we to train a Random Forest model to predict TSF across a Landsat mosaic (TSF map AGB across theshadow study fraction (TSF area. This involved first using WorldView-1 to map tree order polynomial (RMSE of the single non-contiguous WorldView-1 sceneter (Fig. mask 2b). and We the applied the topographyurban shadow Landsat zones wa- mask from and the then analysis. digitized and removed two2.5 small Aboveground biomass mapping We used a combination of multi-sensor satellite imagery and field measurements to resulted in a radiometrically calibratedareas image shaded mosaic by clouds void of and mountains. water bodies, clouds and flectance. We mosaiced the six1 contiguous mosaic images, then to co-registered the theidentifying Landsat WorldView- 50 mosaic matching to points ensure and proper then geographical warping overlap. the This WorldView-1 involved mosaic using a first- ance to top ofVisual atmosphere Information reflectance Solutions, using v4.8). the We thenows, Landsat masked and calibration water tool bodies, steep clouds, in north cloudWater ENVI shad- facing bodies (ITT slopes were where identifiedrithm shadows using with obscured a a underlying 30-class minimum vegetation. ensure isodata class-size complete unsupervised of removal. clustering 20 Cloudsexcluded. algo- pixels and Shadowed and cloud northern then shadows slopes were bu ing were manually areas removed delineated with based and slope on topography by select- or basal diameter to AGB 2.4 Satellite image preprocessing The four Landsat scenes used for biomass mapping were converted from surface radi- of AGB 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). . We SWIR LS R and both + NIR field R )/( SWIR N). We also used fire R ◦ − into a map of AGB LS NIR R ( = ) and NBR 7566 7565 red with field measurements of canopy cover, tree R + WV NIR R with the MODIS tree canopy cover products by resam- )/( LS and TSF red R LS − ected regrowth. Our analysis was restricted to fires that burned ff NIR . The regression intercept was forced through zero so that treeless R ( WV = N) and the lowland forest-tundra zone (67.0–69.5 ◦ and TSF N, WRS-2 path 104, rows 11–13). Due to greater availability of Landsat imagery ◦ LS erences in fire activity between the open larch forests of the Kolyma Mountains After mapping and dating fire scars, we derived descriptors of the fire regime (fire For each fire scar, we determined the period of time during which the fire occurred by ff evation, or aspect a ca. 1969–2005 because the AGB model predicted erroneously high values for fires that (64.5–67.0 scars from 2000–2007 toburned, examine number the of fires, associationsand and among precipitation). mean annual fire fire size) activity and climate (area (mean2.7 monthly temperature Accumulation of aboveground biomass followingTo fire assess larch regrowth following fire, wein quantified time AGB across since fire scars last that fire ranged from 3–38 yr and then examined whether fire size, latitude, el- fire rotation and density69.5 across the northern threeand quarters to the for MODIS ca. burned area 1969–2007complete product, from (66.0– our 2000–2007. As fire such, record we wasdi used most fire accurate scars and from spatially this time period to examine rotation is the averageto number of the years area necessaryof of to the burn interest landscape an and area thatequivalent is equivalent to burned in calculated fire during size return by that interval,between dividing period which two is the successive (Heinselman, a fire 1973). time point-specific events,probability Fire when period estimate of every rotation of burning. by point the is Fire on the averagearea density time the fraction is over landscape simply has which the an theyincomplete annual equal across occurred. number the of Since southern fires the quarter divided spatial of by the the coverage study of area Landsat prior to imagery 1999, was we calculated during the Landsat blackout period,estimated but that were they not occurred visible during in the post-1995 1980s. NIR imagery, we rotation, fire density, andamong fire fire size activity distribution) and and both then mean examined monthly the air relationships temperatures and precipitation. Fire estimated the fires had occurred anytime during the preceding 6 yr. If fires occurred generally visible in NIRin the for earliest about available six imagery, or years immediately following after fire. the For Landsat blackout fresh period, fire we scars visible perimeters to align with the Landsat 5 image usedrecording to the map year biomass. of thebetween pre-fire 2000–2007, and we post-fire used the Landsatthe MODIS images. year burned For of area fires burn when product that possible. (MCD45A1)by occurred We to using dated identify large GIMMS-NDVI fires to that identifyto occurred between when surrounding 1981–2000 NDVI areas. rapidly We droppedand estimated when and the it stayed burn was low not year relative possible when to smoke date trails fires based were on not MODIS visible or GIMMS. Fire scars were ical order. We used false-color images,NDVI along to with identify the fire normalized scars. The burnproved band ratio useful combinations (NBR) 7-5-2 and for (L5 identifying andlated L7) fire and as scars, 3-4-2 NDVI (L1–L3) asWe were hand-digitized the fire band scar metrics,ing perimeters which on at the were a fire calcu- scale size of and 1 perimeter : 150 complexity, 000 and or then 1 spatially : 60 adjusted 000, the depend- fire scar pling all to 500 m resolutionWe and also running Pearson’s compared correlations TSF on 5000height random and points. tree density using Pearson’s correlations. 2.6 Fire scar mapping The 59 Landsat images(path were 104, rows first 11–14). Each sorted stack of into images four was then regions visually inspected based in on chronolog- the WRS-2 grid intercept forced through zeroTSF to evaluate theareas, relationship such between as AGB alpine orsion tundra, equation could was be modeled then as usedcompared having to this no transform tree map the AGB. of map The of AGB regres- TSF the mid-point of each transect and then used least-squared linear regression with the 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | LS N, field ◦ was ects 0.04, 0.65) ff − LS ≈ N). The ◦ = r , Fig. 3a) 2 r − 69 ∼ 722 g m 500). Compared to = = n . Aboveground biomass 2 − , Fig. 3b). Diagnostic plots 25 yr) and mid-successional 2 − ≤ 0.77) and exhibited the weak- 16.4 %, − = 0.90, RMSE in areas of relatively dense forest = = map showed that it agreed relatively r 622 g m 2 LS r LS ( = 0.40; Table 2). We found that TSF WV ≈ 0.01, RMSE r 7568 7567 , TSF from both sensors exhibited strong corre- p < , as these inputs yielded the largest mean decrease 0.91, RMSE field LS showed similar patterns to tree canopy cover mapped = 0.72, 2 0.88), moderate correlations with tree density ( and both TSF LS r ( = ≈ 2 r than any individual Landat band or band metric used as an r field LS the residuals were randomly distributed and homoscedastic, field across only 25 % of the landscape (Fig. 5). We found that the , though ranged from 0.3 to 6703 g m LS 2 map ( 2 − − WV N) to around 400 m a.s.l. along the forest-tundra border ( the residuals were skewed towards lower values. In addition to show- ◦ 65 WV ect to correct for repeated sampling within individual fires. Aboveground ∼ ff 0.33). The Random Forest identified Landsat’s SWIR and red bands as the = 0.82 and 0.77) and red bands (band 3, map captured the altitudinal transition from forest to alpine, as well as the lat- , the model tended to under-predict TSF r − LS = WV r Larch stands are very heterogeneously distributed across the forest-tundra ecotone landscape distribution of AGB from MODIS imagery, though our map captured much finer spatial variability in forest in far northeastern Siberia, withof the climate distribution of and AGB pastwhile reflecting fires the altitudinal combined (Fig. a tree 4a).Mountains line Latitudinal ( decreased tree from lineAGB occurred around at 780 m around a.s.l.itudinal 69.07 in transition the from central forestaveraged Kolyma 1156 to g m tundra. Inexceeded 1882 areas g m with no recent fire activity, AGB in node impurity (sensuments Breiman with tree (2001), shadows Table mapped 3).a at By a broad high linking regional spatial field resolution extent,across biomass and we then the measure- modeled were study across area. able to characterize the spatial distribution of AGB lations with canopy cover ( and weak correlations witha tree better height proxy ( for AGB input for the Randomwas Forest most model strongly (Table correlated 3).5, with Of Landsat’s the short-wave Random infraredest Forest (SWIR, correlations inputs, bands with AGB 7 the and vegatationNDVI: indices (Soil Adjusted Vegetation Index: most important predictors of TSF relationship between AGB and model-predicted TSF showed that for TSF while for TSF ing strong relationships with AGB TSF and over-predict it in some areas of sparse tree cover. We observed a strong linear statistically evaluate whether landscape position influenced post-fire AGB production. 3 Results 3.1 Mapping and distribution of larchValidation aboveground of biomass the Random Forest-generated TSF well with the TSF a random e biomass values were log-transformed prior toassumptions inclusion of in the data mixed normality models toincluded and meet randomly elevation, the distributed aspect, residuals. firecompared Full against age, models, simpler which and models using their Akaike interactions, Information Criterion were (Akaike, fitted 1974) to first and then least-squared multiple linear regressiontheir to interactions determine with fire whethermulation age fire within an could size, individual predict latitude, firemicroclimate, median scar and we could fire used vary scar linear depending mixed AGB. onaspect models Since on landscape to rates position AGB investigate of and accu- the AGB controlswas accumulation of for cosine-transformed early elevation to and and quantify mid-successionalThe fire the scars. models degree Aspect were of calibrated northwardand using included topographical 9750 fire exposure. grid identity cells (i.e. randomly a sampled variable within denoting fire to scars which fire a sample belonged) as AGB accumulated linearly through time,median we fire used scar piecewise linear AGB regression asvia to a rlm model function in of the time.mate Robust R the linear MASS AGB regression, accumulation package as rate (Venables(33–38 implemented for yr) and early fire Ripley, successional 2002), ( scars. was There then were used no to fires esti- in the 26 to 32 age range. We then used had burned within two years of the Landsat data acquisitions. To determine whether 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of the 1.0 vs. 2 C) were (14.1 %) occurred standard ± ◦ 2 ± ( LS 2 C higher than ◦ ; Fig. 7), with the 2 5000) and MODIS erences. 64 km ff = ± exhibited moderately n ) and the annual area 68) of these fires, while 33 km LS 1 − = = . Relative to our estimate, 2 0.83). January was the only n C) and 2003 (14.0 0.001, = ◦ ). The large burn years of 2001 r 1 − p < 3 fires yr yr (median = 2 2 0.72), log10-trasformed area burned 0.76, , respectively. Fire rotation and density = 4) of fire scars, the potential dating error 2 = r − = r 119 km burned) contrasted sharply with the 2004– n = km 7570 7569 2 4 N), we estimated fire activity using data covering 8). Mean July air temperatures exhibited strong 10 C). Summer (July–August) precipitation in 2001 ◦ ◦ = × n 1 0.5 − (median 2 ± 5000). Forest patches of relatively high AGB ), highlighting pronounced regional di 2 0.05, = erences in fire activity (Table 4). Across the northern three − n = ff α km 4 ) were responsible for 78 % of the total area burned. Annual area 10 2 0.001, × erences between the Kolyma Mountains and the forest-tundra bor- 1 ff − p < 44) of the fires we estimated the potential dating error to vary from half a 150 km = burned) and 2003 (2296 km > 0.81, n 2 9.0 cm). July is the hottest month of the year and from 2000–2007 was the = ± r 0.3 fires yr 0.87), and log10-trasformed mean annual fire size ( ± = We identified 59 fires in Landsat scenes that cumulatively burned 4731 km We quantified fire rotation and density using the fire scars mapped from Landsat and Though there was some uncertainty in dating fire scars, fire activity exhibited con- r that were cumulatively responsible for 96 % of the total area burned that we mapped large regional di der. From 2000–2007, firethe rotation lowlands was seven (110 times vs.0.3 shorter 792 yr) in and the mountains fire than density in was threestudy area times between 2000–2007. greater The MODIS (1.0 of burned area these product fires detected 44 and (74.6MODIS %) mapped underestimated a the total total areaunderestimating area burned the of burned area 2859 by km burned about by 40 each %, fire. primarily Importantly, as MODIS a detected result the of fires quarters of the study area (66.0–69.5 both ca. 1969–2007 and 2000–2007,fire which density showed to fire rotation bewere to 0.3–0.2 both be fires yr about 279–397 yr one and thirdcalculated using less 2000–2007 when data. Examining calculated fire using activity ca. over this 1969–2007 broad data extent masked than when single largest fire accountinglargest for fires 17 % accounted for of about the 50fires % area of (fires burnt the total over burned the area,burned, 38 while yr number the period. of largest 25 The fires, %with ten and of fire July size air all temperatures,hottest varied with Julys widely two over and the of were 109 the yr positively record. largest associated burn yearsthen coinciding examined with regional the di two ( month during which fire activityhowever, and we monthly are precipitation inclined were to significantlyin believe correlated; January that these generally were accounting falseover for positives five less due orders than to of 7 precipitation magnitude, % from of 0.06–2570 km the annual total. Fire size varied correlations with annual number of fires ( only month during whichsignificant air correlations temperature ( and annual fire activity exhibited statistically burned ranged from 0–2296 km (1762 km 2007 period, during which theerror). annual Mean area daily burned averagedthe July 111 highest air reported temperatures inthe the in CRU 2004–2007 2001 record average (1901–2009) (14.4 (11.9 and(79.6 cm) were about and 2 2003(108.2 (93.9 cm) was about 20 % lower than the 2004–2007 average ranged from three to eightfires years. and Due area to burned, potentialthat as dating are errors, shown most the accurate in annual from Fig. number 2000–2007. 6, of should besiderable viewed interannual as best variability. approximations fire Excluding occurrence the ranged Landsat from blackout period, 0–22 the fires annual (median 3.2 Fires in the northeastern KolymaWe River mapped watershed 116 fires thatof burned the between northeastern ca. portion 1969–2007 of across thefident 14 Kolyma 778 in River km watershed having (Figs. correctly 4b and identifiedfor 6). the 38 We % are burn ( con- year foryear 59 to % two ( years. For the remaining 3 % ( strong correlations with both MODISTCC VCF ( ( along upland hill slopes,Kolyma Lowlands. between Within braided the streams,reflected forested the and influence zone, on of stand elevated past biomass fires. hills and in distribution the strongly wet distribution than did the MODIS products. Our map of AGB 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 to − 2 − (Kajimoto 66) or mid- 2 45 g m ) across the − 2 = ± only under fa- − n 2 25 yr), AGB av- − ≤ 25 yr, ≤ 1200 g m ∼ , while the largest fire missed 2 inter-quartile range). Larch AGB was ± 0.001) and with northward topographical < 7572 7571 age · ). During early succession ( (median 5 2 0.05). 10 − standard error), but ranged from 31 elevation × ± p p > 32) and we estimated the rate of AGB accumulation to 66, 32) stands, as assessed using multiple linear regression. interquartile range). The rate of AGB accumulation dur- = = 0.03), yet in mid-successional stands larch AGB did not dif- 0.05) of median larch AGB for early ( = 2000 g m ± n 66). There were no fires in the 26–32 yr age range, which vs. 162 ( n = n ( ± = 2 5 . Across these mid-successional stands, larch AGB ranged from p > 2 − n 1 age − 10 − · , 1 yr × − (median 2 yr − to 2212 2 2 26 g m 100 g m − 2 . Visual comparison of the fire maps showed that almost all of the MODIS − − 2 ± cos-aspect ± p 67 g m ± 5.4 g m 499 g m 46 g m ± ± ± The spatial distribution of larch AGB was relatively well captured by the two MODIS- Accumulation of larch AGB after fire was not associated with fire size or latitude, (Hansen et al., 2003) andcoverage, biome-level moderate (Ranson et spatial al., resolution, 2011) extents. and The quantification broad spatial of tree cover makes these in Siberia’s northern permafrost zone AGBwhile in in larch southern stands regions rarely exceeds AGBet 10 in 000 g mature al., m stands 2010). often exceedsslopes, Areas 15 000 though of g m also highelevated in AGB ridges fire-protected tended in the corridors to wet formed lowland occur areas. by on mountain lowderived streams elevation tree mountain and canopy valley on coverland products cover maps, that these we products examined. provide continuous Unlike estimates traditional of categorical tree cover at global al., 2007) and necessitatescal the techniques development that of can satellite-derived(Ranson be datasets et used and al., to analyti- 2011). map Cajanderforest-tundra and larch ecotone detect AGB in tended changes northeastern to in Siberiavorable be forest and site low exceeded cover conditions ( 6000 and over g in time m the absence of recent fire. Previous work has shown that fer discernibly with topography ( 4 Discussion 4.1 Distribution and mapping of larchUnderstanding aboveground biomass the currentforest-tundra magnitude ecotone and is distribution important of given ecosystem forest feedbacks biomass to along climate the (Goetz et significant predictors ( successional (33–38 yr, The mixed linear model showedlation that decreased for with early elevation successional ( standsexposure larch ( AGB accumu- northwardly aspect. Neither fire size, latitude, nor their interactions with fire age were though in the early successional stands it declined with both increased elevation and appeared to becreased an considerably. For important mid-successional transitionaleraged stands 746 period that after werebe 33–38 which yr 138 AGB old, AGB accumulation126 av- in- generally low duringsites the 33–38 yr first following fire; 25 however, yrshowed regrowth little was and highly AGB variable accumulation then and after some nearly exhibited fire four scars rapid decades. accumulation at some tion of timeeraged (148 271 892 ing early succession appeared minimal(5.9 when assessed using robust linear regression areas. 3.3 Accumulation of larch aboveground biomassFor after fire the 98AGB fires was strongly thatto non-linear burned time through was between confirmed timeear ca. by (Fig. regression, piecewise 1969–2005, 8). using linear a the regression. This34 yr In two-segment non-linear reduced accumulation comparison regression the AGB to of with residual response a variance a larch simple when breakpoint lin- modeling set median between fire-level 26– AGB as a func- from Landsat. The smallestwas 41.1 fire km detected wassingle-pixel or 1.7 km small pixel-clusteridentified burns in were the not MODIS actuallytured fires. product the central The tended region larger to of burn the be Landsat-mapped scars conservative fires, though areal missed estimates peripheral burned that cap- 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.71) = ected by r ff 0.87), who compared tree = r 0.54), yet neither correlation was = r 7574 7573 orts (Goetz et al., 2009). Here we used a multi-sensor ff 0.91) than did any of the individual Landsat predictor variables 0.82). The applicability of reflectance data in the SWIR and red = − r 0.77 to − = between spatial resolution and extent. The high resolution imagery made it r ff erence between sensors was notable in correlation with tree density. Tree den- ff 0.04 to 0.82). The Random Forest model identified the SWIR and red bands as orts to map attributes of forest structure across broad spatial extents can be im- ff = | E Tree shadows mapped from high-resolution satellite images have exhibited modest Mapping forest biomass in the boreal biome using satellite imagery has met with r | productivity of understory vegetation (shrubs, herbs, mosses, and lichens), which were the variables of greatesttally, importance these variables for also predicting exhibited treeof the shadow AGB strongest ( fraction correlations with and, fieldbands inciden- measurements to mapping forestFuchs et AGB al., has 2009; previously Bacciniing et been biomass al., due demonstrated 2012). to (Steininger, Reflectance shadowsal., in generated 2000; 2012). SWIR by Similiar decreases heterogeneity with in to increas- standSAVI) Fuchs showed structure et little (Baccini al. et to (2009), noof we relationship productivity found with and that tree standing vegetation AGB, biomass. indices potentially Furthermore, (NDVI due and vegetation to indices the are decoupling a the resulting maps toon train statistical coarser models resolution to multi-spectral predicting satellite the the data. attributes Tree Random of shadow interestments Forest fraction of based model predicted larch AGB us- exhibited ( ( a stronger relationship with field measure- than with tree shadows mappedas from strong Landsat as 5 that ( density reported derived by from Greenberg photo etWe al. found interpretation that (2005) with in ( treegood the proxy shadows open for mapped larch AGB forests from anddensity. of canopy IKONOS. cover, northeastern though Siberia did tree not shadows as were closely reflect a proved tree by height first or mapping forest structure at a higher spatial resolution and then using relationships with stand-level AGBWhile than the with correlations tree among heightWorldView-1 forest (Goetz and attributes Landsat and and 5 Dubayah, tree were 2011). the shadow quite di fraction similar for mapped AGB, from canopysity cover, and correlated tree more height, strongly with tree shadows mapped from WorldView-1 ( Similarly, light detection and ranging (LiDAR) measurements generally show stronger closely related to tree shadow fraction than do other measurements of forest structure. to strong relationships withdeciduous forests a (Greenberg number et ofWe al., found structural 2005; that field Leboeuf attributes measurements et inwith of al., tree AGB both shadow and 2012; fraction, coniferous canopy Wolter while cover tree and shadows. et were height Leboeuf strongly al., and et associated density 2012). al. were (2012) notous as also forests closely of observed related northeastern that with Canada treebasal was shadow area more fraction than closely in it associated the with wasand conifer- tree with distribution volume crown are and closure all or factors treeshadow that cast height. contribute by Tree to height, that AGB diameter, group in density of a trees. forest As stand, such, as it well is as not to surprising the that AGB appears more relied upon assigning field AGBal., measurements 2009; to thematic Baccini land etimagery cover al., types we (Goetz 2012). were et able By towith mapping derive field tree a measurements surrogate shadows to for model from larch the multi-sensor AGB regional and satellite forest then biomass use distribution. this surrogate and improve biomass mapping e approach based on high andtrade-o medium-resolution optical imagery to helppossible ameliorate to the map aability surrogate in for AGB, AGB while incorporating (i.e. medium treeacross resolution shadows) imagery an allowed and area us capture approximately to model 50 fineDirectly AGB times spatial mapping the vari- AGB size by imaged linkingquantify by finer field the spatial and high variability satellite resolution in measurements AGB sensor. than made would it have possible been possible to had the analysis derived datasets to betterin understand the multi-decadal boreal biome changes (Berner in et vegetation al., dynamics 2011). mixed success (Houghton etsurements al., from 2007; multiple Leboeuf satellites et can al., help 2012), overcome though limitations combining of mea- individual sensors products important tools that can be used with in situ measurements and other satellite- 5 5 25 15 20 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 km 4 for Siberian 0.8 fires per 2 ± km 4 25) owing to logis- 0.3 fires per 10 = in the Kolyma Moun- ± n 2 ered sevenfold between ff km 4 357 yr) from AVHRR satellite = 1.0 fires per 10 ± 7576 7575 N) in central Siberia (Kharuk et al., 2008, 2011), ◦ 64 ect the magnitude and distribution of larch biomass ∼ ff ected AGB estimates. In spite of these limitations and po- ff N) was slightly greater than the fire return interval determined ◦ orts and warrants further attention. ff 50 yr) (Kharuk et al., 2011). Our fire rotation estimate for the mountain ; however, we encountered a number of challenges and potential sources ± 2 erent threshold value used for mapping tree shadows from the WorldView-1 ff (Mollicone et al., 2006). Since annual fire counts in Siberia tend to decrease 200 2 = km 4 Mean annual fire density has been estimated to be 2.0 fires per 10 Using field and multi-satellite data we estimated larch AGB across approximately moving northward (Soja et al., 2004), our results and those of Mollicone et al. (2006) potentially due to slightly coolerscape. temperatures and less human disturbance of the land- mountain-tundra open forestsstudy (Valendik, area 1996; annual fire Furyaev density between etalong 2000–2007 averaged the al., 0.3 forest-tundra 2001), ecotonetains. though and Across in 1.0 intact our tractsas of calculated central using MODIS and10 satellite southern imagery taiga, from average 2002–2005, annual was fire 1.8 density, data (Soja et al., 2006). Furthermore,of they are fire similar return to dendrochronologicalmean interval estimates along the forest-tundralarch forests ecotone (64.5–67 in centralfor larch-dominated Siberia communities ( (130–350 yr, 397 yr, respectively. Subdividing the ecotonenorthern into lowland southern forest-tundra mountain larchthese revealed forests two that and zones fire (110 rotationobserved vs. di across 792 yr). Siberia, Thisnorth with spatial fire (Furyaev pattern return et ofwith interval fire al., both activity generally 2001; is summer increasing Soja similar temperaturesestimates from et to and south of that al., AGB to fire 2004; (FuryaevSiberia’s rotation et Kharuk northern are al., et sparse similar 2001). al., forests Our to 2011) (227–556 ecotone-level yr, the and mean mean correlated fire return interval calculated for al., 2012). Although ourbiomass principal recovery, purpose we also forof used mapping the the fires regional mapped was fire to fireproduct. scars quantify regime Based to post-fire and on provide to2007, an analysis evaluate we initial performance estimated of overview of the satellite the fire imagery rotation MODIS across from burned the area 1972–2007 forest-tundra and ecotone to from be 2000– 279 and characterize the carbon implications of forest recovery from disturbance (Goetz et It is importantsatellite-based approaches to so as develop to better means inform fire management for decisions and quantifying better and monitoring fire activity using to help understand factorsalong that a the forest-tundra ecotonea in multi-sensor northeastern technique Siberia. forto In regional other addition mapping forest of to ecosystems, AGB demonstrating examines that our how can study the potentially structural contributes attributes be tobased of applied on a forests tree growing can shadows. be body mapped of from literature satellite that images 4.2 Fire regime along the forest-tundra ecotone in northeastern Siberia tion of a di imagery could have alsotential a sources of error, weand observed field strong measurements of relationships larch amongof AGB, tree as larch shadow well fraction as AGB favorable and agreement among independent our tree map canopy cover estimates. Our map can be used tical challenges, which is2009; common in Schulze studies et of al.,and Siberian 2012). forest Another 2011, biomass issue but (Fuchs was persistentsat et that al., cloud 5 fieldwork cover and was conducted in WorldView-1sites in images the were 2010 from region not 2007 made disturbedsets and in it was 2009, also the necessary challenging respectively intervening tolap and (we years). between use necessitated note Co-registration data considerable Land- the sets. of care Usingunderestimating to field geospatial tree AGB ensure data shadows in proper to high over- map density forest stands AGB because might of have overlapping resulted shadows. in Selec- focused on the red andbiomass SWIR mapping wavelengths e shows considerable promise for improving 100 000 km of error. We had a limited number of field AGB measurements ( not included in our AGB estimates. Linking high and medium resolution satellite data 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 2 km 4 15 km < erences in fire ff . Average annual fire density in the Kolyma 7578 7577 2 only during the abnormally hot summer of 2001, km 2 4 km 4 erences human and lightning ignitions (Conard and Ivanova, ff culties in constructing the database. The principle source of uncertainty ffi While our newly created fire scar database allowed us to estimate elements of the The potential for climate-driven intensification of the fire regime highlights the need While fire activity varied across the landscape, we also observed considerable in- we were more likely to detect these fires following the resumption of satellite imaging. In approximately date many of therecord. We larger likely fires underestimated during both this thethis total period period, number using of which the fires meansoverestimated GIMMS and fire area NDVI that burned rotation. we during Nonetheless, fire consequently2007) rotation underestimated was across shorter the the full than fireexpected record when the density (ca. calculated fire and 1969– rotation only calculatedactivity for for the has the generally period most been 2000 recentand increasing period and 2003 across to were 2007. extreme be Siberia We fire shorter since had yearslikely since (Soja the biased et fire 1980s the al., and 2007). fire The both record 20 yr 2001 towards gap large in fires Landsat imagery and fires with minimal regrowth, since fire regime and partiallynumber evaluate of the di MODIS burnedstemmed area from product, the we 20 encountered yrto a gap detect in Landsat and data. date This fires gap that in imagery occurred hindered between our 1975–1995, ability though we were able to mated the area burned by mostsible fires, it for detected the 96 % fires that ofwere were likely cumulatively the respon- to total escape detection, area whileThe burned small underestimation false-positive of that fires burned we were area relatively is mappedtion common. likely (500 from a m), result persistent Landsat. of cloud Fires the cover,false MODIS and positives product the (Roy pixel hot-spot et resolu- detection al., 2005, techniqueful 2008). for for We screening identifying found the the year MODIS innortheastern burned which area Siberia a product it Landsat-mapped use- underestimated fire fire occurred, sizes though note and that consequently in total area burned. for regular monitoring of fireproduct activity, (Roy such et al., as 2005, that 2008).to provided We compared by fire MODIS the burned scars MODIS area mapped maps burnedmately (2000–2007) from area 75 % Landsat of imagery fires in andprincipally the region, by found missing though that underestimated peripheral the burned MODIS total areas. detected area Though burned MODIS approxi- by considerably 40 underesti- %, the coming century (IPCCregion (Stocks 2007), et will al., 1998). likely lead to increased fire activity across the 2003, which coincided withperienced the a two record-breaking heat largest wave burnan in 2003 years extreme (IPCC, that fire 2007) we season thatlarch-dominated was recorded. across forests associated Eurasia of Siberia with central ex- (Soja Siberiatemperature et have deviations previously al., (Kharuk been 2007). et linked al., with Extremepressures 2008). summer fire During air at the events northern positive in phase mid-latitudespositive the of association the enhance between AO, high the annual burned dryingSiberia area (Balzter of and et summer phytomass al., temperatures and 2005). in Increased central lead summer to air temperatures, a as are projected for Oscillation, AO) (Balzter etand al., fuel 2005) moisture on (Bartsch summerdriver et of temperatures al., fire (Kharuk 2009). activity etsociated (Stocks Consistent al., with et with air 2011) al., weather temperatures 1998), during being thearea, we an hottest found the months important that of two fire the hottest year activity (July). Julys was In over positively our the as- study 1901–2009 CRU data set occurred in 2001 and 1992 through 2001counts (DeWilde are and driven Chapin, by1997; 2006). di Kovacs et These al., regional 2004; Mollicone(Furyaev di et et al., al., 2006), 2001; vegetation Sofronov fuel andweather load Volokitina, that 2010), and and promotes flammability the the frequencyKharuk outbreak of et hot of al., and fire 2011). dry (Balzter et al., 2005;terannual Bartsch variability, et which al., previous 2009; work(Mollicone in et al., Siberia 2006) has and attributed to to the impact human of activities hemispheric climate conditions (e.g. Arctic together suggest that the estimated averagefor annual mountain-tundra fire open density forests is of probably 2.0tains too fires exceeded high. per Fire 2.0 10 density fires in perwhen the Kolyma 10 it Moun- peaked atMountains was 3.4 about fires 1/5 per that 10 reported for boreal spruce sites in Interior Alaska from 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 − 100 g m ± 3, Alexander et al., = erences in latitude af- n ff , 2 − 32), and errors in the AGB = n ect patterns of forest regrowth ff ´ alez et al., 2009), we quantified 497 g m ects due to the relatively short time ± ff ) were found to stay largely within 20 m 7580 7579 Larix laricinia ) and the modest climatic gradient that this encompassed. ◦ ected rates of post-fire larch AGB accumulation. To our knowledge, the 5 ff ∼ 33) was also similar to field observations (876 While topographically-dictated site micro-climates a = the four years immediately followingTrees that fire, suggesting survive a a rangeSofronov fire in and often post-fire Volokitina, exhibit tree 2010)layers a survival. and where period produce they of seeds are that rapid(Sofronov more and fall growth likely Volokitina, upon (Furyaev 2010). to thinner If et successfully long-rangeCajander soil al., germinate seed larch organic dispersal 2001; forests and is is become dependent limitedunburned upon and established trees recovery patches that of within survived a theseed fire, fire along availability with scar, by remnant then reducingdispersal an tree and increase the survival. in role More it fire work plays severity in is might forest needed recovery reduce after to fire. understand seed be ruled out, theAGB lack accumulation of might association mean betweenated that by fire regrowth surviving size is trees and moreneighboring within subsequent unburned the dependent rates stands. fire upon While of scar seeds fires larch (Abaimov than can gener- et on cause al., seeds high 2000; tree blown Zyryanova mortality into et in the al., larch fire 2007), forests scar we observed from a six-fold range in AGB in itina, 2010). Weoccur had more predicted slowly thatseed than larch sources, AGB after which accumulation small wouldAGB after fires lower accumulation. large due the We fires to density foundfire would of no a size tree evidence, possible a regrowth however, increasemaximum to and seed in support thus dispersal proximity the range subsequent to lated for hypothesis Cajander species that larch in is Northof unknown, America though the ( seeds seed of tree a re- (Brown et al., 1988). Though long-distance seed dispersal cannot fected larch AGB accumulationlatitudes rates, ( potentially due to the relatively limited rangeafter in fire, characteristics of theimpact initial fire the disturbance trajectory (e.g. fire of size forest or regrowth severity) can (Zyryanova also et al., 2007; Sofronov and Volok- model. We found no evidence to support the hypothesis that di It is possible that weperiod were examined not (6 yr), able the to limited detect number the of e fire scars ( provided the best site conditions forsuccession, tree growth and and likely biomass across accumulation during thethicker early on full low successional elevation, south-facing cycle. slopes Soils(Yanagihara than et tend on high to al., elevation, be 2000; north-facing warmer slopes Noetzlitrogen and et availability al., (Yanagihara et 2007), which al.,both increases higher 2000; net soil Matsuura photosynthetic respiration and rates and andThough Hirobe, annual larch ni- 2010) larch AGB shoot and was growth highly leads (Koike variableear et to during al., model mid-succession 2010). found (33–38 yr), no the evidence mixed lin- to support that elevation or aspect drove this variability. succession is consistent with thelarch in AGB, and situ low observations aboveground of net20 minimal primary yr. larch productivity Our (ANPP) recruitment, at low estimate sitesn younger of than larch AGB in2012) stands and that supports were theter fire 33–38 notion yr (Zyryanova et that old al., larch 2007, (746 2010). tend We to found that re-achieve low dominance elevation, south-facing 20–50 slopes yr af- While most satellite analyses ofassess forest regrowth the after recovery fire of have functional(Hicke used NDVI attributes et as (e.g. al., a leaf proxy 2003; to areathe Goetz recovery or of et net a al., primary structural 2006;AGB attribute productivity) during across Cuevas-Gonz early 98 and fire mid-succession scars.mulation by Our mapping after resulting larch fire estimates agreed ofal. well landscape-level (2012). larch with Our AGB stand-level accu- finding measurements that made larch by AGB Alexander tended et to be low and change little during early it possible for us toestimates not of only fire assess regime forestfor characteristics regrowth an and following area fire, evaluate that but the has also MODIS not derive burned been best- area well product studied. 4.3 Accumulation of larch aboveground biomass following fire spite of these limitations, the fire scar record that we created from Landsat data made 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 LS − culty of ffi ect fuel loads, ff ect rates of re- ff ects related to fire ect permafrost and ff ff 25 yr), then increased ≤ 7582 7581 0.91), the linear model RMSE was 622 g m ected by ongoing climatic changes that are likely = ff , 1974. 2 r ected our estimates of AGB accumulation. ff ected our estimates of both larch AGB and rates of post-fire AGB This work was supported by grants to SG and MM from the NASA ff ect post-fire biomass accumulation. For instance, how does fire sever- ff doi:10.1109/TAC.1974.1100705 ect regrowth density and subsequent trajectories of AGB accumulation? What fac- ff Ecosystems: Siberian Larch Forests,Kajimoto, edited T., by: and Osawa, Wein, A., R. W., Zyryanova, Springer, O. New A., York, 41–55, Matsuura, 2010. Y., 723, tems of the Cryolithicrogenic Zone Changes, of Eurasian Siberia: Journal Regional of Forest Features, Research, Mechanisms 1, of 1–10, Stability 2000. and Py- There are a number of unanswered questions related to how fire characteristics and Our study quantified post-fire forest recovery and examined the roles of topography Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716– Abaimov, A. P.: Geographic Distribution and Genetics of Siberian Larch Species, in: Permafrost Abaimov, A. P.,Zyryanova, O. A., Prokushkin, S. G., Koike, T., and Matsuura, Y.: Forest Ecosys- References Acknowledgements. Carbon Cycle and(NA08OAR4310526), NSF Ecosystems International Polar program Yearto (0732954) (NNX08AG13G), Andrew and Bunn NSF NOAA and OPP Karen Global (76347). Frey Thanks for Carbon assistance with Program field data collection. disturbance, particularly in the relatively poorly studied regions of northern Eurasia. early succession, whilegrowth. neither Field fire measurements size oftree forest nor shadows structure latitude synergistically (e.g. appeared AGB, mappedagery, canopy to using permitted cover), a high spatial linked and extrapolation with with of medium a forest resolution high structure degree satellite ofysis variables im- accuracy. techniques to Similar are derivation regional needed of extents forregime data characterizing sets dynamics, forest and and biomass application the distribution, ofimprove response prediction disturbance anal- of of the ecosystems net climatic to feedbacks disturbance associated with events landscape in scale order forest to pansive biome. We found thatfirst the few AGB years of after larchest forests fire, AGB varied suggesting accumulation considerably variability was duringrapidly in the minimal post-fire during during tree early mid-successional mortality, successiongrowth (33–38 yr) and ( decreased that as with for- larch increased resumed elevation dominance. and Forest northwardly re- aspect, at least during forest C pools, fire dynamics and post-fire forest recovery across this remote and ex- feedback in boreal Siberia willmate, necessitate fire understanding regimes, the and relationships vegetation among dynamics, cli- yet considerable uncertainty remains in Future research is needed to addressfactors governing these the and loss other and questions accumulation relatedern of to Siberia. carbon disturbance in the larch forests of northeast- 5 Conclusions Siberian boreal forests are stronglyto a result in large feedbacks to the global climate system. Quantifying the net climate perimeter impact spatial variability invegetation forest productivity recovery? How (“greening”), might as increasesments in in have regional recent been decades (Beck observedfire and Goetz, from activity, 2011; and satellite Berner post-fire et measure- the al., regrowth? 2011), vast Furthermore, a pools of how organic do carbon fires stored a within these frozen soils (Zimov et al., 2009)? error could have a accumulation, as well assize hindered and our topography. Additionally, ability errors in tofire dating and detect fire could potential scars have increased e also with a years since site conditions a ity a tors govern fire-induced tree mortality and how do surviving trees and proximity to fire and fire size in shapingtainty. Primary regrowth trajectories, among these but were there potential wereprecisely errors several dating in sources our fire of AGB scars. model uncer- Though andand the we field di observed measurements a strong ofwhich relationship AGB was between in ( TSF the range of AGB values that we observed across fire scars. This model 5 5 25 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erences, ff , 2012. , 2009. ects of Fire and ff doi:10.1890/1051- , 2011. , 2008. , 2009. , 2011. , 1997. erence water index, Remote Sens. , 2012. ff , 2006. doi:10.1029/2011jg001733 doi:10.1186/1750-0680-4-2 , 2007. 7584 7583 , 2005. , 2005. , 2002. , 2009. doi:10.1126/science.1155121 doi:10.1029/2010jg001475 , 2006. doi:10.1088/1748-9326/6/4/045501 doi:10.1016/j.rse.2008.07.017 doi:10.1038/nclimate1354 , 2005. doi:10.1016/j.rse.2006.05.015 , 2011. doi:10.1016/s0269-7491(97)00140-1 doi:10.1088/1748-9326/2/4/045031 doi:10.1029/2005GL022526 doi:10.1016/j.rse.2005.02.015 and Sun, M.: Mappingson and of monitoring methods, carbon Carbon stocks Balance with Manage., satellite 4, observations: 2 a , S., compari- O’Halloran, T., Harmon,chke, M., E. Meddens, S.: A. Observations J.in and H., North assessment Pfeifer, America, of E. J. forest M., Geophys. carbon Mildrexler, Res., dynamics D., 117, following and G02022, disturbance Kasis- C., Kohlmaier, G. H., Kurz, W.,est Liu, S., carbon Nabuurs, sinks G.-J., Nilsson, in0761(2002)012[0891:fcsitn]2.0.co;2 S., the and Northern Shvidenko, A. Hemisphere, Z.: Ecol. For- Appl., 12, 891–899, tural parameters using hyperspatial image analysis, Remote Sens. Environ., 97, 15–25, Environ., 104, 346–359, suring and monitoringdoi:10.4155/cmt.11.18 forest carbon stocks and change,responses to Carbon recent Manage., climate changetions 2, and and fire model 231–244, disturbance results at contrasting northern2, northern 045031, high Eurasia latitudes: and North observa- America, Environ. Res. Lett., Climate on Successions andForest Structural Res., Changes 2, in 1–15, the 2001. Siberian Boreal Forest, EurasianCentral J. Siberia using a modification of the normalised di ment of the SiberianSens. forest Environ., tundra: 113, Combining 518–531, satellite imagery and field inventory, Remote doi:10.1007/s10021-006-0095-0 Fire Regime Characteristics onPollut., Emissions 98, and 305–313, Global Carbon Balance Estimates, Environ. exchange of adoi:10.1111/j.1365-2486.2005.01023.x north-east Siberian tussock tundra, Glob. Change Biol., 11, 1910–1925, actions among Fuels, Ignition Sources, and Fire Suppression, Ecosystems, 9, 1342–1353, Survival of Larix Laricina18, (DuRoi) 306–314, 1988. Koch K. in the Tanana Valley, Alaska, Can. J. For. Res., A., Nilsson, S., Sukhinin,tion A., pattern Onuchin, on A., interannual andL14709, forest Schmullius, fire C.: variability Impact in of Central the Siberia, Arctic Geophys. Oscilla- anomalies Res. on Lett., forest 32, fires indoi:10.1088/1748-9326/4/4/045021 Siberia observed from satellites, Environ. Res. Lett., 4,ductivity 045021, changes between 1982Environ. Res. and Lett., 2008: 6, 045501, ecological variability and regionaland satellite di vegetation indices: Correlations andJ. trends Geophys. in Res., Russia 116, and G01015, Canada (1982–2008), forests, Science, 320, 1444–1449, Beck, P. S. A., Dubayah, R.,bon Friedl, dioxide M. emissions A., from Samanta, tropical S.,Climate deforestation and Change, improved Houghton, by 2, carbon- R. 182–185, density A.: maps, Estimated Nature car- mulation patterns during post-fireSiberia, succession Ecosystems, in in press, Cajander 2012. larch (Larix cajanderi) forests of Goodale, C. L., Apps, M. J., Birdsey, R. A., Field, C. B., Heath, L. S., Houghton, R. A., Jenkins, J. Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., Houghton,Goetz, R. S. A., J., Bond-Lamberty, B., Law, B. E., Hicke, J. A., Huang, C., Houghton, R. A., McNulty, Greenberg, J. A., Dobrowski, S. Z., and Ustin, S. L.: Shadow allometry: Estimating tree struc- Goetz, S. J., Mack, M. C., Gurney, K. R., Randerson, J. T., and Houghton, R. A.: Ecosystem George, C., Rowland, C., Gerard, F., and Balzter, H.: Retrospective mapping of burntGoetz, areas S. in and Dubayah, R.: Advances in remote sensing technology and implications for mea- Furyaev, V. V., Vaganov, E. A., Tchebakova, N. M., and Valendik, E. N.: E Fuchs, H., Magdon, P., Kleinn, C., and Flessa, H.: Estimating aboveground carbon in a catch- Corradi, C., Kolle, O., Walter, K., Zimov, S. A., and Schulze, E. D.: Carbon dioxide and methane DeWilde, L. o. and Chapin, F.: Human Impacts on the Fire Regime of Interior Alaska: Inter- Conard, S. G. and Ivanova, G. A.: Wildfire in Russian Boreal Forests – Potential Impacts of Brown, K. R., Zobel, D. B., and Zasada, J. C.: Seed Dispersal, Seedling Emergence, and Early Balzter, H., Gerard, F. F., George, C. T., Rowland, C. S., Jupp, T. E., McCallum, I., Shvidenko, Beck, P. S. A. and Goetz, S. J.: Satellite observations of high northernBerner, latitude L. vegetation T., Beck, pro- P.S. A., Bunn, A. G., Lloyd, A. H., and Goetz, S. J.: High-latitudeBonan, tree growth G. B.: Forests and climateBreiman, change: L.: forcings, Random feedbacks, Forests, Mach. and Learn., the 45, 5–32, climate 2001. benefits of Bartsch, A., Balzter, H., and George, C.: The influence of regional surface soil moisture Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Alexander, H. D., Mack, M. C., Goetz, S., Loranty, M. M., and Beck, P. S. A.: Carbon accu- 5 5 20 30 25 15 10 30 25 15 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , 2009. , 2012. doi:10.1175/1087- doi:10.1111/j.1365- , 2011. , 2008. , 1973. , 2007. , 2004. doi:10.1088/1748-9326/6/4/045208 doi:10.1088/1748-9326/4/4/045002 2.0.co;2 > sink weakening?, Global Biogeochem. Cy., 25, doi:10.1016/j.foreco.2011.11.008 2 7586 7585 doi:10.1029/2007gl032291 , 2003. 1:trottm < , 2011. , 2007. doi:10.1016/j.foreco.2011.04.031 2.0.co;2 doi:10.1016/0033-5894(73)90003-3 > , 2006. doi:10.1088/1748-9326/2/4/045032 , 2011. 0001:gptcaa < doi:10.1029/2010gb003813 doi:10.1175/1087-3562(2004)8 doi:10.1038/440436a 437, 2486.2010.02360.x in Siberian Permafrost Region,by: in: Osawa, A., Permafrost Zyryanova, Ecosystems: O.York, A., Siberian 149–163, Matsuura, 2010. Larch Y., Kajimoto, Forests, T., and edited Wein, R. W., Springer, New NASA Land Processes DistributedTeam, 26, Active 2011. Archive Center Joint Japan-US ASTER Science 2002. mate warming in the Siberian taiga, Glob. Change Biol., 17, 1935–1945, timation of northeastern Canadianmethod, Forest forests Ecol. using Manage., QuickBird 266, imagery 66–74, and a shadow fraction tation of Northeast Asia,2003. edited by: Kolbek, J., Srutek, M., and Box, E., Kluwer, Dordrecht, zone, Geophys. Res. Lett., 35, L01402, thetic Characteristics of Trees andin Shrubs Central Siberia, Growing in: on Permafrost theZyryanova, Ecosystems: O. North- Siberian A., and Larch Matsuura, Forests, South-Facing Y., Kajimoto, edited Slopes 2010. T., by: and Osawa, Wein, A., R. W., Springer, New York, 273–287, Product and Anthropogenic Features1–25, in the Central Siberian Landscape, Earth Interact., 8, Larch Forest Ecosystems, in:Osawa, A., Permafrost Zyryanova, Ecosystems: O. SiberianYork, 99–122, A., Larch 2010. Matsuura, Forests, Y., edited Kajimoto, T., by: and Wein, R. W., Springer, New dominated communities, Environ. Res. Lett., 6,2011. 045208, and Siberian spruce (Piceaand Management, obovata) 262, in 629–636, northern Mongolia’s boreal forest, Forest Ecology the Fourth Assessment Reportbridge, 2007. of the Intergovernmental Panel on Climate Change, Cam- Mapping Russian forest biomassRes. Lett., with 2, data 045032, from satellites and forest inventories, Environ. pedient research, Environ. Res. Lett., 4, 045002, R. A.: Global Percent TreeMODIS Cover Vegetation at Continuous a Fields Spatial Algorithm, Resolution Earth of Interact., 7, 500 1–15, Meters: First Results of the Is the northern high-latitude land-basedGB3018, CO Quaternary Res., 3, 329–382, mentova, N. S.,sandersson, Whitfield, H., P. H., Mescherskaya,Northern Førland, A. Eurasia: E., V., Changes during and Hannsen-Bauer,doi:10.1016/j.gloplacha.2006.07.029 the I., Karl, 20th Tuomenvirta, T. century, H., Global R.: Planet. Alek- Potential Change, forest 56, 371–386, fire danger over 3562(2003)007 Mollicone, D., Eva, H. D., and Achard, F.: Human role in Russian wild fires, Nature, 440, 436– Meyer, D.: ASTER Global Digital Elevation Model Version 2 – Summary of Validation Results, Matsuura, Y. and Hirobe, M.: Soil Carbon and Nitrogen, and Characteristics of Soil Active Layer Lloyd, A. H., Bunn, A. G., and Berner, L.: A latitudinal gradient in tree growth response to cli- Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, Leboeuf, A., Fournier, R. A., Luther, J. E., Beaudoin, A., and Guindon, L.: Forest attribute es- Krestov, P. V.: Forest Vegetation of Easternmost Russia (Russian Far East), in: Forest Vege- Kovacs, K., Ranson, K. J., Sun, G., and Kharuk, V. I.: The Relationship of the Terra MODIS Fire Kharuk, V. I., Ranson, K. J., and Dvinskaya, M. L.: Wildfires dynamic in the larch dominance Koike, T., Mori, S., Zyryanova, O. A., Kajimoto, T., Matsuura, Y., and Abaimov, A. P.: Photosyn- Kajimoto, T., Osawa, A., Usoltev, V. A., and Abaimov, A. P.:Biomass and Productivity of Siberian Kharuk, V. I., Ranson, K. J., Dvinskaya, M. L., and Im, S. T.: Wildfires in northern Siberian larch James, T. M.: Temperature sensitivity and recruitment dynamics of Siberian larch (Larix sibirica) IPCC: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to Houghton, R. A., Butman, D., Bunn, A. G., Krankina, O. N., Schlesinger, P., and Stone, T. A.: Hayes, D. J., McGuire, A. D., Kicklighter, D. W., Gurney, K. R., Burnside, T. J., and Melillo, J. M.: Groisman, P. and Soja, A. J.: Ongoing climatic change in Northern Eurasia: justification for ex- Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Carroll, M., Dimiceli, C., and Sohlberg, Heinselman, M. L.: Fire in the virgin forests of the Boundary Waters Canoe Area, Minnesota, Groisman, P. Y., Sherstyukov, B. G., Razuvaev, V. N., Knight, R. W., Enloe, J. G., Strou- 5 5 30 25 20 15 10 30 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , 2009. Larix gmelinii , 2006. doi:10.5194/bg-9-1405- doi:10.1016/j.rse.2004.08.011 doi:10.1080/01431160500168686 , 2008. doi:10.1029/2008gl036332 , 2004. , 2012. , 2007. ¨ upke, N., Ziegler, W., Achard, F., Mund, M., 7588 7587 doi:10.1007/s11027-006-1009-3 , 2005. , 2011. doi:10.1016/j.rse.2008.05.013 doi:10.1029/2006jf000545 doi:10.1016/j.rse.2012.01.008 doi:10.1080/01431160310001609725 ected area mapping using MODIS time series data, Remote Sens. Environ., 97, 137– ff , 2012. doi:10.1016/j.rse.2005.04.007 Vegetation After Fire Disturbance, in: Permafrostby: Ecosystems: Siberian Osawa, A., Larch Forests, Zyryanova, edited O.York, A., 83–96, Matsuura, 2010. Y., Kajimoto, T., and Wein, R. W., Springer, New F., and Zyryanov, V. I.: The Structural(Rupr.) and Rupr. Biodiversity Forests, after Northeastern Fire2007. Disturbance Asia in Eurasian Journal of Forest Research, 10, 19–29, Carbon storage in permafrostglobal and carbon budget, soils Geophys. of Res. the Lett., 36, mammoth L02502, tundra-steppe biome: Role in the Zyryanova, O. A., Prokushkin, A.Rate S., on Prokushkin, the Contrasting S. North- G., andEurasian and South-Facing Journal Abaimov, Slopes A. of of P.: a Forest Soil Larch Research, Respiration Forest 1, in 19–29, Central 2000. Siberia, Prokushkin, A., and Scherbina,fire S.: versus Factors growth promoting performanceevergreen and larch and deciduous dominance implications conifers, in Biogeosciences,2012 for 9, central carbon 1405–1421, Siberia: dynamics at the boundarymafrost of Ecosystems: Siberian Larchsuura, Forests, Y., edited Kajimoto, T., by: and Osawa, Wein, R. A., W., Zyryanova, Springer, O. New A., York,Return 59–83, Mat- Intervals 2010. as IndicatorsStrategies of for Change Global in Change, Siberia 11, (1995–2002), 75–96, Mitigation and Adaptation derived fire frequency, distribution1939–1960, and area burned in Siberia,from Int. Brazil and J. Bolivia, Remote Int. J. Sens., Remote 25, Sens., 21, 1139–1157, 2000. 2002. shadows on snow for estimatingron., 121, forest 69–79, basal area using Landsat data, Remote Sens. Envi- Environ., 112, 3690–3707, E. J., Loboda, T., Conrad, S.O. G., Romasko, A.: V. I., AVHRR-based Pavlichenko, E. mapping A.,carbon Miskiv, of cycle S. I., studies, fires and Remote Slinkina, in2004. Sens. Russia: Environ., New 93, products 546–564, for fire managementleous, and N.: An extended AVHRRetation 8-km NDVI NDVI data, dataset Int. compatible J.2005. with Remote MODIS Sens., and 26, SPOT 4485–4498, veg- fire-a 162, uct – Global evaluation by comparison with the MODIS active fire product, Remote Sens. Lawrence, K., Hartley, G. R.,fire Mason, potential J. A., in and Russia McKenney, and D. W.: Canada Climate boreal change forests, and Climatic forest Change, 38, 1–13, 1998. lar taiga–tundra ecotone withdoi:10.1016/j.rse.2011.09.006 MODIS tree cover, Remote Sens. Environ., 115, 3670–3680, dation for Statistical Computing, Vienna, 2011. 1979. and evolution of permafrost temperatures inRes., idealized high-mountain 112, topography, J. F02S13, Geophys. Zyryanova, O. A., Abaimov, A. P., Bugaenko, T. N., and Bugaenko, N. N.: Recovery of Forest Zyryanova, O. A., Yaborov, V. T., Tchikhacheva, T. I., Koike, T., Makoto, K., Matsuura, Y., Satoh, Zimov, N. S., Zimov, S. A., Zimova, A. E., Zimova, G. M., Chuprynin, V. I., and Chapin, F. S.: Yanagihara, Y., Koike, T., Matsuura, Y., Mori, S., Shibata, H., Satoh, F., Masuyagina, O. V., Schulze, E.-D., Wirth, C., Mollicone, D., von L Soja, A., Shugart, H., Sukhinin, A., Conard, S., and Stackhouse, P.:Satellite-Derived Mean Fire Soja, A. J., Sukhinin, A. I., Cahoon, D. R., Shugart, H. H., and Stackhouse, P. W.: AVHRR- Sofronov, M. A. and Volokitina, A. V.: Wildfire Ecology in Continuous Permafrost Zone, in: Per- Steininger, M. K.: Satellite estimation of tropical secondary forest above-ground biomass: data Venables, W. N. and Ripley, B. D.: Modern Applied Statistics withWolter, S, P. T., 4 Berkley, ed., E. Springer, New A., York, Peckham, S. D., Singh, A., and Townsend, P. A.: Exploiting tree Tucker, C., Pinzon, J., Brown, M., Slayback, D., Pak, E., Mahoney, R., Vermote, E., and El Sa- Valendik, E. N.: Ecological aspects of forest fires in Siberia, Sib. Ecol. J., 1, 1–8, 1996. Sukhinin, A. I., French, N. H. F., Kasischke, E. S., Hewson, J. H., Soja, A. J., Csiszar, I. A., Hyer, Roy, D. P., Boschetti, L., Justice, C. O., and Ju, J.: The collection 5 MODIS burned area prod- Stocks, B. J., Fosberg, M. A., Lynham, T. J., Mearns, L., Wotton, B. M., Yang, Q., Jin, J.-Z., Roy, D. P., Jin, Y., Lewis, P. E., and Justice, C. O.: Prototyping a global algorithm for systematic Ranson, K. J., Montesano, P. M., and Nelson, R.: Object-based mapping of the circumpo- R Development Core Team: R: A Language and Environment for Statistical Computing, R Foun- Petrovsky, V. V. and Koroleva, T. M.: On the flora Kolyma River Delta, Botanical J. , 64, 19–39, Noetzli, J., Gruber, S., Kohl, T., Salzmann, N., and Haeberli, W.: Three-dimensional distribution 5 5 30 25 20 25 30 15 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) r cients ( 3 3 ffi 10 10 – × × ) (m) n 0.41 25). Correlation coe = + n 0.920.890.71 0.91 0.88 0.54 1999–20042006, 2007 26 30 2000-2005 1909–2007 WorldView-1 Landsat 5 7590 7589 22) and Landsat 5 ( = and PPT) Monthly 1284 55 n + T 0.05. α < Aboveground Biomass Canopy Cover Tree Density Tree Height 0.39 Stand Measurement Tree Shadow Fraction Landsat 1–3 MSSTotal 1972–1974 12 1972–2007 79 59 or Data Set Acquisition ( Tree Canopy Cover(MODIS) 2010Burned Area(MODIS)NDVI(GIMMS AVHRR) Composite 1CRU Climate ( Monthly 1981–2007 1 2000-2007 250 96 Biweekly 500 500 636 8 Satellite imagery and ancillary geospatial data sets used in analysis. Pearson’s correlations among field measurements of forest structure and tree shadow Mapping Landsat 5 TM 1995, 2005, 21 30 BiomassMapping WorldView-1 LandsatFire 5 Scar TM Landsat 7 ETM 21 April 2009Ancillary 26 July 2007Data Sets Digital Elevation Model 7 (ASTER) 4 Composite 0.5 30 1 2000–2008 30 Use Satellite Data Scenes Resolution Table 2. fraction mapped from WorldView-1 ( in bold are significant at Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 2 − km 4 10 × 1 − yr n 0.05. NA α < 40 621 50 411 40 307 92 183 141 167 129 733 342 318 316 155 478 976 of the Kolyma River watershed in far ) in Node Impurity r 2 ) with aboveground biomass and the mean 0.91 0.70 0.72 0.77 0.310.77 0.82 0.50 0.41 36 685 0.340.040.44 0.60 47 084 0.37 31 219 28 622 r − − − − − − − − − − − ) (%) (years) ( 2 7592 7591 ) (km 2 ) (km n Landsat 5VariableTree Shadow Fraction Band 1 Correlation with AGB Mean ( Decrease Band 5 Bands 7/4 Bands 7/5 NDVISAVI NBR PCA I PCA II PCA III 0.33 0.30 41 678 51 482 Band 2 Band 3 Band 4 Band 7 Bands 4/5 Summary of fire activity across 100 000 km Landsat 5 indices used as predictor variables with the Random Forest algorithm to forest-tundra2000–2007 mountain forest2000–2007 12 47 454 4277 57 [0, 58] 0.13 [0.00, 535 0.13] 792 [42, 601] 0.91 0.3 [0.0, 0.5] [0.07, 1.03] 110 1.0 [0.3, 1.1] Periodnorthern 3/4ca. 1969–2007 northern 3/42000–2007 70 ( 20 10 270 1482 263 185 [0, 145] 0.25 [0.00, 0.20] 0.36 397 0.3 [0.0, 0.6] 279 0.2 Region of StudyArea and Time Number of Total Area Fires Mean Annual Burned Area Fire Burned Fire Rotation Density Table 4. northeastern Siberia. Annual75th area percentiles burned to and show skewness. fire density are annual means, with 25th and decrease in node purity,vided a for Random each predictor Forest variable. generated Correlations metric in bold of are variable significant importance, at are pro- model tree shadow fraction. Pearson’s correlation ( Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (D) and (C) of open larch 2 WorldView-1 satellite imagery used in (B) 7594 7593 Landsat 5 and erence in spatial resolution between sensors, ff (A) Maps of the northeastern portion of the Kolyma River watershed showing the field Study area map of the lower Kolyma River watershed, which feeds into the East Siberian show close-up views for WorldView-1 and Landsat 5, respectively. the analysis. To illustrate the large di sampling locations, as well as the Fig. 2. Sea in the Russian Far East. The study area spanned approximately 100 000 km Fig. 1. forest that transitioned into tundra along the northern edge. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | above- (A) B 100 ●● and TSF is highly ) with tree shadow 80 field field 60 ●● ●● ●● ●● ●● 40 ●● ●● ●● ●● ●● 20 Landsat 5 satellite images. The points 0.91 ●● ●● = ●● ●● ●● 2 ●● AGB = TSF * 69 AGB r n = 25 p < 0.001 ●● (B) ●● ●● ●● Landsat 5 Tree Shadow Fraction (%) Fraction Shadow Landsat 5 Tree ●● ●● ●● ●● 0 A 7596 7595 ●● 100 0.001). burn scar perimeters for fires that occurred ca. 1969– ●● p < (B) 80 0.90, WorldView-1 and ●● ●● 60 ≈ ●● 2 ●● (A) r ●● 40 1 standard error). The relationship between AGB ± ●● 20 0.9 ●● = ●● ●● ●● 2 AGB = TSF * 58 AGB r n = 22 p < 0.001 ●● ●●

●● ●● ●● ●● ●● ●● ●●●● 0

WorldView−1 Tree Shadow Fraction (%) Fraction Shadow Tree WorldView−1

3000 1000 0 7000

5000 Maps of the northeastern portion of the Kolyma River watershed showing Comparison of aboveground biomass from field estimates (AGB

) m (g Biomass Aboveground Larch

2 − 2007. Both data sets were derived primarily from 30 m resolution Landsat imagery. ground biomass of Cajander larch and Fig. 4. Fig. 3. fraction (TSF) derived from represent site means ( significant for both sensors (

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Number of Fires of Number

Cumulative Probability Cumulative

25 20 15 10 5 0

0.75 0.50 0.25 0.00 1.00 ● ) 2 ● − ● 2005 ● ● 70007000 , with 50 % of areas having ● 2 ) in areas with no recent fire ● area burned − number of fires number ● LS 2000 ● ● ● ● ● 1995 50005000 2 2 km km 1990 7598 7597 1985 30003000 20 yrs ranged from 0.3 to 6703 g m Landsat Blackout fires detected: 22 LS 1980 burn area detected: 5115 mean annual burn area: 256 mean annual 10001000 1975 ● ● ● 00 ●

● .

Modeled Larch Aboveground Biomass (g m Modeled Larch Aboveground 1970 ● 2

1500 500 0 2500 −

2000 1500 1000 500 0 2500

Frequency

Area Burned (km Burned Area ) 2 Frequency and empirical cumulative distribution function plots of Cajander larch above- Estimates of the annual number of fires and area burned across the lower Kolyma River Fig. 6. watershed between ca. 1969considerably and among years, 2007. with The 2003 being annual an number exceptionally of large fire fires year and across area Siberia. burned varied Fig. 5. ground biomass modeled from Landsat 5activity. Across satellite the imagery landscape, (AGB AGB less than 748 g m Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

.

2 Cumulative Probability Cumulative

● ●

1 1

0.75 0.50 0.25 0.00 1.00 ● ● ● ● ● ● ● ● ● ● 1 1 1 1 1 1 1 1 1 1 ● ● ● 1 1 1 ● ● ● ● ● ● ● ● ● 1 1 1 1 1 1 1 1 1 3535 ● ● 1 1 ● ● ● ● ● ● 1 1 1 1 1 1 2500 303030 and 5 % greater than 400 km ● 1 2 2525 2000 ) ● ● ● 1 1 1 2 ● 1 ● 1 202020 1500 ● ● ● ● 1 1 1 1 7600 7599 Years Since Fire Years ● ● ● 1 1 1 ● ● ● 1 1 1 1515 ● ● 1 1 1000 ● ● ● ● 1 1 1 1 Area Burned (km 101010 ● 1 500 ● ● 1 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ● ● ● ● ● ● ● 1 1 1 1 1 1 1 55 ● ● ● ● ● ● ● ● ● ● 1 1 1 1 1 1 1 1 1 1

0

● ● 1 1

40 20 0 80 60

1000 500 0 3000 2500 2000 1500

Number of Fire Scars Fire of Number

Larch Aboveground Biomass (g m (g Biomass Aboveground Larch )

2 − Cajander larch aboveground biomass (AGB) across 98 fire scars that burned in the Frequency and empirical cumulative distribution function plots of fire scar size. Fires Fig. 8. lower Kolyma Riverlarch watershed between AGB in ca.x-error a 1969–2005. bars fire Each indicate the scar, point range while represents of uncertainty y-error the in bars median when the indicate fire 25th occurred. and 75th percentiles in AGB and Fig. 7. were generally quite small, with 75 % smaller than 150 km