Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , and 1 erent proportions. Accuracy 10 pixels indicated an overall ff , A. F. Sabrekov 2 × 20149 20150 emissions from the entire region is expected. , E. D. Lapshina 4 1,2,3 , M. V. Glagolev 4 1,* This discussion paper is/has beenPlease under refer review to for the the corresponding journal final Biogeosciences paper (BG). in BG if available. Tomsk State University, Tomsk, Russia Yugra State University, Khanty-Mansyisk, Russia Moscow State University, Moscow, Russia National Institute for Environmental Studies, Tsukuba, Japan previously published as I. E. Kleptsova S. S. Maksyutov 1 2 3 4 * Received: 5 November 2015 – Accepted: 25Correspondence November to: 2015 I. – E. Published: Terentieva 16 ([email protected]) December 2015 Published by Copernicus Publications on behalf of the European Geosciences Union. Abstract High latitude wetlands arethese important environments sink for carbon understanding and emit climatelandscapes methane. change Fine pose scale risks challenges heterogeneity because of for wetland producingon the point greenhouse observations. gas To reduce fluxlands uncertainties inventories and based at water the bodies regionalusing in scale, a the we supervised taiga mapped classification zone wet- on of of high-resolution Landsat West images imagery. Siberia The and onfication training field scheme a dataset data was scene-by-scene was that aimed basis based were atecosystem collected methane types inventory at composing applications 28 9 andassessment test included wetland based areas. 7 complexes Classi- on wetland in 1082 di validation polygons of 10 map accuracy of 79 %.to The total be area 52.4 Mha of the orin wetlands 4–12 % and WS’s water of taiga, bodies the occupying was(23 global %), estimated 33 wetland % ridge-hollow-lake area. complexes of Ridge-hollow (16open complexes the %), prevail bogs open domain, (5 fens %), followed (8 patterned byronments %), are palsa fens forested dominant (4 complexes bogs %), among (7 the %), or andthe wetland area. swamps “ryams” ecosystems, Because (4 while %). of fens the Varioussiderable cover significant oligotrophic only update revaluation 14 envi- in % of the of wetland theA ecosystem new total coverage, Landsat-based a CH map con- ofof WS’s taiga coarse-resolution wetlands global provides land a cover benchmark products for and validation wetland datasets in high latitudes. 1 Introduction High latitude wetlands are importantthey provide for long understanding term climate storage change ofSiberia carbon mechanism (WS) and as emit is significant amount thelatitudes of experiencing world’s methane. accelerated West largest rate high-latitude of climate wetland change system (Solomon situating et al., in 2007). the high High resolution wetland mapping inSiberian West taiga zone for methane emission inventory I. E. Terentieva Biogeosciences Discuss., 12, 20149–20178, 2015 www.biogeosciences-discuss.net/12/20149/2015/ doi:10.5194/bgd-12-20149-2015 © Author(s) 2015. CC Attribution 3.0 License. 5 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff erent wetlands and ff 20152 20151 cient accuracy of WS wetland maps with the best ffi N. Because of its great expanse and flat topography, the vegetation ◦ E to 53–73 ◦ Our long-term goal is to develop a multi-scale approach for mapping Russian wet- Modellers should be able to draw upon a global version of the high-resolution map It was found both at global and regional scales that poorly constrained estimates of Present land cover products failed to capture the fine-scale spatial variability within their linkage with otherparameter land upscaling units; and and the third, model provide assessment. the foundation for environmental 2 Materials and methods 2.1 Study region The West Siberian Lowland is athe geographical west region and of Russia the bordered Yenisey62–89 River by the in Urals the in east; the region covers 275 Mha stretching from cover of the Lowlandthe has taiga clear zone is latitudinal divided into zonation.and three According southern geobotanical taiga. to subzones: northern It Gvozdetsky taiga, corresponds160 (1968), Mha middle to taiga in the raised the stringevations central of bog part province 80 of and to covers theage 100 ma.s.l. about annual WS. precipitation rising It is about to is 450–5001963). about mm characterized The and 190 m by evaporation excess is in flat water 200–400 the mmfavourable supply topography (Rihter, conditions “Siberian with and for Uvaly” flat el- area. wetland topographytersheds Aver- formation. with and Large impeded floodplains, fraction drainage is of provides fers waterlogged. the from The the area, hydrographic northern including structuretypical and wa- of southern of this parts zone the dif- ofcomprise central the the flat WS. The zonal parts largest vegetation of peatlands and the are cover most watersheds vast where, territories together (Solomeshch, with 2005). forests, Com- they lands using Landsat imagerymeet with the needs a of resolution landemissions of process from modelling 30 peatlands. m and In other so thistarget applications study, that for the concerning the the WS methane land taiga cover results classification zonewere due can was to threefold: chosen better the first, as abundance develop of the a wetlands. primary tural The consistent components; objectives land second, cover understand of the peatland spatial classes distribution and of its di struc- ent environmental parameters could potentiallywetland be maps applied (Bohn have et been al.,tic used 2015). classification Various to schemes define ingeneralization the aggregate of wetland to classes extent limited diminish inthat or their WS, distinguishes no applicability. however several The ground simplis- vegetation only truth andwas peatland data microtopography developed classes typology and at and map strong thePeregon their et State mixtures al. Hydrological (2005) digitized Institute andcoverage complemented of (SHI) this wetland by map structural by Romanova components estimatinghigh-resolution or et the images wetland fractional for al. ecosystems five using (1977). test Landsat sites.erage data However, and the and limited coarse amount resolution introduce of(Kleptsova large fractional et uncertainties cov- al., in scaled-up 2012). estimations that not only delineates wetlands but also identifies the major sub-types to which di agreement of only(Sheng 56 et % al., for 2004).area the when Coarse-resolution the products high-resolution water tend tableconclusion WS is that to surface Peatland a underestimate is few Database not the centimetres2003; saturated below Krankina wetland (WSLPD) with the et water al., moss (Saarnio 2008). cover, et resulting al., in 1997; the Friborg et al., wetland and lake area ishouse a gas major budget uncertainty (Melton inFine predicting et scale current heterogeneity al., and of 2013; WS future2010; wetland of Turetsky landscapes et Bridgham green- (Bohn al., et et 2014; al., al.,emissions 2007; Petrescu (Glagolev 2013) Eppinga et et et pose al., al., al., challenges 2011)2008) 2010). which and for are wetland producing based net inventories on primary large of production number methane (Peregon of et point al., wetlands scale because field measurements. mixed pixels greatlyand decrease Smith the (2007) accuracy mentioned of these insu products. Frey 5 5 10 15 20 25 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cult to overcome emerges even within ffi metric tons. 9 10 20154 20153 × orts (Gong et al., 2013). Due to the remoteness of WS, we ff erent years. Majority of the images were from 2007. ff Training data plays a critical role in the supervised classification technique. Repre- The overall work flow involves data pre-processing, training and test sample col- The scene selection procedure was facilitated because the possibility to adequately regional scale mapping e sentative data collection is the most time-consuming andhave a labour-intensive lack process of in As ground truth a information, result, which weresolution hampers were imagery training constrained available dataset in to construction. Google base Earth. training Our sample field selections knowledge mostly comprising on 8 years high- dated due to land cover changes,suitable so imagery they could were be used as found.due substitutes Landsat-7 to for images data places received gaps, where and after no Landsat-8 2003Finally, was we were launched not collected after used the 70 beginning compatibleseasons mapping vegetation in procedure. di scenes during the peak of the growing classified (Song et al., 2001).jection. All Because of the the vegetation imagesforests, of were meadows, re-projected the burned onto West areas, the Siberiandistinguished Albers agricultural plane from pro- fields, other includes etc., landscapes various usingcation. wetland types a We environments of thresholding used were method the to Green-Redmajority avoid Vegetation of Indices misclassifi- wetlands (Motohka and et forests;inundated al., the areas 2010) including 5th to water Landsat bodies. separate channel Thesefor thresholds was each were used scene empirically to determined by mask testingimages the various were most filtered candidate in values MATLAB in v.7.13classified (MathWorks) Quantum in to GIS. Multispec remove v.3.3 Masked random (Purdue noise Landsat Researchcation and Foundation) method. then using The a maximum supervised likelihood classifi- and algorithm was availability used in because ofbands almost its except robustness any the thermal image-processing infrared software band were (Lu used. and Weng, 2007). All lection, image classification onclasses a into scene-by-scene 9 basis, wetland the complexes, regroupingerage the and of estimation accuracy the of assessment. derived wetland Atmospheric ecosystem correctionprocess was fractional is not cov- unnecessary applied as because this long as the training data are derived from the image being prehensive synthesis of Russian literaturelands, regarding their development the and current sensitivity state toet of al. climatic the (2003). changes The WS was study made peat- summarizesvegetation, by information Kremenetski and about WS peatland geology, hydrology,that climate, zonation. the Basing mean on depthof of existing carbon peat Russian stored accumulation there data, in may the exceed authors WSL 54 found is 256 cm and the total amount especially in the peak ofthe the growing main season complication (July), for wasWS wetland identification. from the However, those low periods. accessibility Scenes collected of earlier good than quality the 2000s cloudless were images considered of out- smooth the slight inconsistencies betweenlapped images areas. by Ideally, specifying it training sites is in better over- to use data acquired in the same year or season, wetland types. In thisclass study, definition it within was scene-by-scene considered classificationof greatly that edge exceed the matching the and advantages disadvantages on processing of a slowness. consistency scene-by-scene Thus, in basis, our as entire conducted by analysis Giri was et performed al. (2011) and Gong et al. (2013). 2.2 Classification methodology No single classification algorithmproving can vegetation discrimination be and considered mapping;algorithms as hence, must optimal the be methodology use based ofareas for on advanced (Adam im- their classifier et suitability al.,many 2009). to satellite Because achieve scenes, mapping multi-scene certain mosaicking over objectives isgle large often in file landscapes used for specific typically to further group classification. involves scenes Thisand into approach a optimizes edge both sin- matching. the classification However,when process large applying multi-scene to mosaickingtral highly has gradients essential heterogeneous within drawback WS theof wetlands. file the (Homer It appropriate and creates scenesited. Gallant, a As with 2001), a variety similar especially result, of spectral when vegetation discrepancy the spec- and that number is hydrological di conditions is lim- 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 Landsat pixels or depressed × erences exist among the scenes, ff 2 Landsat pixels; × 20156 20155 emissions (Sabrekov et al., 2013). To yield reliable 4 flux and, secondarily, as a base to upscale other ecological functions. WS 4 ects can be slightly observed. Classified images and area calculations were ff parts of wetland complexesface; with WTLs above the average moss/vegetation sur- moss/vegetation cover; sphagnum vegetation cover; swamps; face. However, wetland ecosystems generally have sizes of approximately 1–10 m and The spectral classes that were discriminated during the supervised classification 3. “oligotrophic hollows”: depressed parts of bogs with WTLs beneath4. the“ridges”: average long and narrow elevated parts of wetland complexes with dwarf shrubs- 5. “ryams”: extensive pine-dwarf shrubs-sphagnum peatland areas; 6. “fens”: integrated class for various types7. of“palsa rich hillocks”: elevated fens, parts of poor palsa fens complexes with and permafrost below wooded the sur- 2. “waterlogged hollows”: water bodies fewer than 2 1. “Water”: all water bodies greater than 2 cannot be directlya distinguished few using exceptions. Landsat Whentive imagery the to objects the with resolution in 30The m cell the reflectance size, resolutions, scene measured they with become by mayamong the increasingly no various sensor classes smaller longer of can be rela- scene beand elements regarded treated as as Weng, weighted as individual 2007; by a their objects. Strahlertypology sum relative proportions et that of (Lu involves the al., 9 interactions 1986). mixed Therefore, “wetland complexes” we and developed then a estimated second the wetland fractional conditions, vegetation covers, etc. However, thesehomogeneous wetlands structural are composed elements of or relatively “wetlandfeatures ecosystems” and, with similar thus, environmental rates of CH upscaling, we assigned 7 wetland ecosystems in our classification scheme (Fig. 1): patch e 2.3 Wetland typology development As a starting pointquired. Congalton for et the al. mapping (2014)error procedure, showed contribution that and a the implementation proper priority. classificationposes Its classification scheme and development scheme has the relies the is on class highest was the re- separability initially study of pur- conceived the as input angional variables. CH advanced In technique our to case, improvewetlands wetland are the heterogeneous mapping estimation with highly of variable the water table re- levels (WTL), geochemical were generalized intoareas 9 was wetland minimized by complexes. collectingfactory Classification training results samples mismatch were from in achieved. overlapping Because areas overlapping temporal until di satis- combined using the GRASS moduleare in only Quantum GIS. one Wetlands or andrandom water image a noises. bodies Therefore, few that a Landsat simpleobjects. low pixels pass in filter was size applied exist, to eliminate and such some of these sites may be of fieldwork in West Siberia, whichtions, resulted field in an photos, extensive and dataset pH ofet botanical and descrip- al., electrical 2011). conductivity They datastarted from were 28 with used test mapping as sites scenes an (Glagolev extensively where additional available, ground source so truth of the data information.ance, and classification The high-resolution results continued processing images could through are be contiguouswith checked images poor for and ground quality ended truth assur- ficiently, at and we the auxiliary used less data criteriaselection: explored coverage. (i) similar the scenes To training to collect samples training thoseerogeneous must be areas data in homogeneous; are most land-cover (Gong avoided, mixtures ef- and and etsize (ii) het- with al., all an of 2013) average the sample for samples area of must training approximately be sample 100–200 at pixels. least 10 pixels in 5 5 20 25 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient detail ffi 20158 20157 2 Landsat pixels. These water bodies are repre- × size for each wetland complex depending on its heterogene- 2 Our peatland coverage is most similar to the estimate of 51.5 Mha (Peregon et al., As summarized by Sheng et al. (2004), the majority of previous local Russian stud- During wetland typology development, we were forced to make several assumptions. To estimate the fractional area coverage of the wetland ecosystems, we selected 8– 2009) by SHI map (Romanova etpeatland al., structures 1977). shows However, a that direct the comparison SHI between map the is too generalized without su probably inherited the drawbacksvey of database, the which original was(Ivanova Russian used and Federation as Novikova, 1976). Geological the This Sur- basisin database remote was for regions characterized the and by a existing a highable WS lack generalization wetlands level of peatland and with field only inventories peat data considers layers economically deeper valu- than 50 cm. on the wetland distributionis (Fig. not 3). feasible SHI for map mappingcause was and it based is monitoring on too wetland aerial costly vegetationbased photography, and which on classifications time-consuming tend a to to process regional identify (Adam scaleare many et ignored be- small al., in peatlands 2009). the and The more their satellite- generalized subgroups, SHI which map. However, the satellite classifications also ies estimated the extent of the entire WS’s mires to be much lower. These studies sented by wetland pools, waterloggedcomponents hollows of and RHLC. watercourses, The whichrivers” rest are class. structural of Third, the in waterplain this areas bodies study, we were were not only placed taken consider into into peatlands account the and aside “Lakes water from and bodies; misclassification flood- cases. 3 Results and discussion 3.1 Wetland map Using Landsat imagery, wetaiga developed zone a (Fig. high-resolution 2). wetland52.4 The inventory total Mha. of area West the of WS Siberianscale. wetlands taiga Based and wetlands on water bodies proved observationalpeatlands was to datasets, was estimated be assumed the to noticeable to global be Döll, even cover average 2004) from at of as 430 the inundated summarizedaccount (Cogley, global areas 1994) by for and to Melton approximately 1170 4–12 et Mha %than al. the of (Lehner total (2013); the and wetland areas therefore, total ofBay taiga 32.4, wetland 32, Lowland wetlands area. and (Cowell, 41 in Their Mha 1982)extent WS coverage in of China is and (Niu West larger et Siberia’s Alaska wetlands al., 2012), exceedset (Whitcomb Hudson the al., tropical et 2011), wetland emphasizing al., area the of 2009), considerable 43.9 ecological Mha respectively. role (Page The of the studied region. on field knowledge, we assumeddevelopment have that sizes all less of than the 2 water bodies that arose from peatland First, the wetland complexes werealong considered a individual objects, continuum, while have they vaguetems. actually borders lie Second, (Fig. 1) the and(rivers, floating classification creeks, ratios schemes of and wetland include large ecosys- all lakes) water are bodies, not although structural many components of wetlands. Based ity. High-resolution images correspondingv.3.3 to by these an areas unsupervised were classificationtem classified ratios method. in were Finally, averaged Multispec the among obtaineddescribed the wetland by test Peregon ecosys- sites. et This al. approachby (2005), “micro-landscape” is where elements similar the within to evaluation patterned the ofraphy. wetlands method the was area based fraction on occupied aerial photog- 27 test sites of 0.1–1 km area coverage of theassigned wetland wetland ecosystems complexes should within meet eachLandsat the images, of following criteria: and them (i) (ii) (Fig.described distinguishability abundance 1; by in in Table detail the 1). in WS The 1977; a taiga Romanova, number zone. 1985; of All Liss Russian et these studies2009; al., complexes (Katz Masing 2001; were and Lapshina, et Neishtadt, 2004; 1963;water al., Solomeshch, bodies. Walter, 2005; 2010) Usova, and encompass wooded, patterned, open wetlands and 5 5 20 25 15 25 10 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect that ff N), where ◦ N. Peatland ects is a for- ◦ ff erent wetlands facilitates ff erent climatic and geomor- ff N) has approximately 36 % mire cov- ◦ flux estimation from the domain (Kleptsova 4 20159 20160 erences (Sheng et al., 2004), leaving the discrepancy in ff erent types of RHCs cover better-drained gentle slopes. ff emissions from the whole region is expected. 4 N) divides the northern taiga into two lowland parts. Palsa hillocks ap- ◦ grids and calculated the wetland ecosystem to the total cell area ratios for ◦ 0.1 , the northern taiga corresponds to the maximal distribution of lakes and water- ff × N). Vast peatland systems are composed of raised bogs represented by ryams ◦ ◦ RHCs are the most abundant in the middle taiga (approximately 59–62 WS’s northern taiga (approximately 62–65 The individual wetland environments have a strongly pronounced latitudinal zonality The wetland extent reaches 28 % in WS’s southern taiga area (approximately 56– mires occupy 34 % ofand the have area. a Large convexThese wetland cupola environments have systems with peat commonly layers centres thatof cover that sphagnum are watersheds peat several are with meters the 3 deep smallhave and to addition are of strict 6 composed other m spatial plants. The higherrepresented regularities. wetland than ecosystems by Central here the RHLCs. plateau periphery. Di The depressions most with drained stagnant areasthe water are wetland’s are edges, dominated with by lowWooded ryams. lateral swamps usually water Poor surround flow and peatland and rich systems. relatively fens high develop nutrient availability. along 59 and RHCs with huge opensubzone and is patterned dominated fens by betweentaiga small them. wetlands and The have medium-sized eastern similar wetland part spatialof complexes. of regularities the The the fens as southern increases the stepwise middle here taiga; due however, the to area the abundance of carbonate soils and the ensues from the scene-by-scene classification technique is observed. Abrupt leaps and water bodies cover upcomplexes to that 70 are % divided of by theElevation” river (63.5 territory, valleys. forming Northward, several the huge slightlypear peatland-lake paludified in “Siberian the “Surgutskoe Polesye”of region the and “Siberian replace the Elevation” region ridges (Fig. and 4f). ryams to the north mapping and further understanding of theirTo linkage visualize with the each other regularities and of other0.1 land the units. wetland distribution, weeach divided grid the (Fig. entire 4) using area fractional into coverage data from Table 1. A slight patch e correspond to classification errors andbe indicate improved by less more accurate careful map image patches, acquisition. which can erage. Because of theruno abundance of precipitation, minimal evaporation, and scanty logged hollows, covering a thirdare occupied of by the the domain peatlanddred (Fig. system 4a kilometres “Surgutskoe and Polesye,” from which b). stretches east Vast for parts to a of hun- west the and zone is located between 61.5 and 63 the taiga domain into thelines). southern, The middle, knowledge and regarding northern the taiga spatial subzones distribution (Fig. of 2, di black within the studied region. Zonal borders stretch closely along latitude lines, subdividing tuitous cancellation of their di the spatial distributions. Thepurposes. latter In is the essential case for studyland environmental of map parameter WS’s led middle to upscaling a taiga, 130 we % found increase that in applying the CH the new wet- phologic conditions. Concerning theand wetland rivers” complex class), typology RHCs (excludinglowed prevail the by ryams in “Lakes (23 WS’s %),open taiga, RHLCs bogs (16.4 occupying %), (4.8 32.2 open %), % fensenvironments patterned of (8.4 are %), fens the dominant palsa among (3.9 domain, complexes %) the (7.6 fol- only wetland and %), ecosystems 14.3 % swamps (Table 2), (3.7 of while %). fens theregion, cover Various which wetlands. oligotrophic is Waterlogged similar hollows to and5 the % average open of estimation the water by boreal-Arctic Watts occupy domain etsummer was 7 al. % inundated season. (2014), with of surface who water the found during that the non-frozen et al., 2012) comparing withuation estimation of based the on total SHI map. CH Thus, a considerable reval- 3.2 Regularities of zonal distribution WS has a large variety of wetlands that developed under di delineate small gaps within contiguous peatlands. The net result of both e 5 5 15 20 25 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff 10 pixels er in their ff × erence in the global ff ort was exerted to map the individual ff 20162 20161 N and corresponds to the border between the ◦ ective in distinguishing similar environments that di ff ective use of remotely sensed data in per-pixel classification. ective way to represent map accuracy as the individual accuracies ff ff ecting the e ff Generally, we indicate a reasonable accuracy of 79 % for such a large and remote Among the classes, patterned fens and open bogs were classified with the lowest Wetland complexes within large wetland systems have high classification accuracies. images are not always e area. However, this value seems to be slightly overestimated, because high-resolution geneity. However, those small areas do not make a substantial di Swamps and palsa complexes haveincorrect very identification high in PA and non-wetland low areas.open UA, Palsa woodlands, which complexes were is with spectrally related pine closeWS. to to and Swamps their were lichen commonly confused vegetation withnized covering forests; mainly wide in by dry portions the periods, of theyof field can peat northern investigations be layers. based In recog- both on cases,higher the more accuracy typical accurate levels. microrelief wetland Lakes masks and andtral would rivers presence lead were separability to classified substantially of the best thewhen due represented class. to by the a They series high can ofnarrow spec- small necks be lakes on seldom or the waterlogged land. confused hollowsthe Floodplains that image with can are corresponds RHLCs, divided also to by be the especially RHCs classified most and as inundated ryams period lakes after were and snow accurately riversin melting identified when the (11 due study polygons). to region the and abundance their of high these spectral categories separability. producer’s accuracy (PA), whichless was areas, 62 so %. theywith Patterned were RHCs fens often due include misclassified to substantialshrub as the cover open tree- similar with fens. “ridge-hollow” sparse They structure. pines,and were Open increasing ryams. also the bogs Open confused frequency often fens ofues have have were misclassification higher tussock probably as user’s overestimated. High-resolution RHCs accuracy imageslevels, (UA) poorly which and reflect nutrient underrates PA; however, supply the these classification val- errors between open bogs and open fens. land-cover categories varied from 62areas to classified 99 the %, best withclassifications and the usually open lake occurred and bogs between river, neighbouring andin ryam, classes patterned their and with environmental fens greater RHC parameters, the similarities whichtions. most exhibit Some confused. only errors minor Mis- distortions occurred in(33 along polygons), map boundaries whose applica- and presence had were beenlem, associated recognized a by with Foody mixed (2002) pixels as a major prob- total. nutrient supply level. In contrast, our approach omits wetlandsparticularly below a high certain for size, the and smallest the objects. uncertainties Great are e southern and middle taiga zones (Fig. 4c and e). 3.3 Accuracy assessment The accuracy assessment wasthat based were on randomly spread 1082available over validation in the polygons Google WS Earth of taigawas as 10 zone. used ground We as truth used an information. high-resolution e of The images each confusion category are matrix plainly (Table describedof 3) along exclusion with (Congalton both the and errors Green, of inclusion 2008). and We errors found that the accuracies for di higher nutrient availability. Velichko et al. (2011)in provided evidence the of northern a vast half coldof desert of the WS during plain thebog was last dominated an areas glacial area extends period, near while of 59 the loess southernmost accumulation. part Now, the border between fen and wetlands that are abundant inagricultural the southern fields. We part tested of several thewas vegetation domain indices revealed due until to to the the produce Landsat prevalencethe the of thermal optimal soil band thresholding moisture. results Manyrelated due of to to the the its errors ability lack were to of also estimate ground disposed truth along data the and tundra worsened boundary, by the high landscape hetero- 5 5 25 15 20 10 15 20 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | flux upscaling purposes ects of dryer ecosystems 4 ff 20164 20163 flux upscaling because only wetland ecosystems with 4 erent vegetation types commonly possess the same spectral signa- ff The scarcity of reliable reference data and subsequent lack of consistency limit the Although the synergistic combination of optical and radar data is advantageous for New methodologies and protocols are needed to combine remotely sensed obser- accuracy of land cover informationGallant, that 2001). are derived The from use(Congalton satellite of imagery et (Homer ancillary al., and datasignificant 2014); can costs however, largely more regarding improve reliablestep data, the in classification local improving accuracy accuracy mapping knowledge, of should comesmost and maps rely ambiguous with on detailed wetland the landscapes field acquisition and of remote data. ground regions. The truth data next from the 4 Conclusions Boreal peatlands play acling major and role other in globalis carbon environmental constrained storage, processes, methane by but emissions, the betteromission water understanding inconsistent from) cy- many of representation global this of landdescribe role peatlands cover a maps on map (Krankina representing (or et the even al., state complete 2008). of In the this taiga study, wetlands we in WS during the peak of high water levels contribute to the regional flux, while the e station (Krasnov et al.,however, 2013) the and vast Mukhrino majority field ofare obtained station of data (Bleuten were special and not importance Filippov, published.waterlogged 2008); for These hollows the with measurements northern fluctuating water taiga regimes zone, cover where huge small areas. shallow lakes and accurately characterizing open water (Schroeder etsensing al., of 2010) and water wetlands, regimes the is remote tion. successful We only still lack when in inwetlands. situ situ measurements Simplistic data of are monitoring the available measurements water for table have calibra- dynamics been and extent made in at WS’s the Bakchar field result from PALSAR. The use ofwill additional be radar especially data to useful map for the CH most inundated areas (ryams, ridges and palsaet hillocks) al., can 2014). be neglected (Glagolev et al., 2011; Sabrekov riods, resulting in the transformationhollows. Thus, of RHCs oligotrophic or hollows patterned andareas fens fens can in into turn RHCs with into waterlogged brown RHLCflooding). Sphagnum because Swamps cover of commonly usually flooding dry turn (Fig. up into 1: aftertures waterlogged drought hollows periods, become after and similar their environmental to fea- of those the of Ob’ non-wetland and areas. Irtyshterannual Oppositely, Rivers the variability become huge also inundated floodplains occurs duringThe changes prolonged in in snowmelt WS water floods. level (Schroeder In- are et especially reasonable al., for 2010; CH Watts et al., 2014). vations to improve our abilitytypes or to other monitor characteristics continuous ofbest water opportunity wetland levels in environments or the (Kim distinguish next et few habitat years al., for 2009). routine Perhaps measurements the of inundated areas will ture in remotely sensedcontrast images between (Ozesmi vast and wetlandso Bauer, distinct systems 2002; that and Xie wetlands thein can et be which surrounding al.). adequately the forest However, identified the discrimination areasproblem by between (Sheng the is wetlands et summer usually and al., season forestsries 2004). images, of does On wetland not the complexes complicated impose contrary, by correctlychallenges. seasonal a Wetlands variations distinguishing serious remain become continuous one the se- of most the inundated largest after snow melting or long rainy pe- 3.4 Challenges and future prospects The spectral discrimination of wetlandtask types because in di complex environments is a challenging because the areal extent of methane-emittingfore, regions the across total the methane landscape, and emissions,depths there- is (Bohn a function et of al.,toring the 2007). spatial changes Watts distribution in of et surface waterof al. moisture table boreal-Arctic and (2014) wetlands temperature underscored to when themate. enhanced assessing importance greenhouse the of gas vulnerability emissions moni- under a shifting cli- 5 5 20 25 15 10 15 10 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | orts ff emissions from the 4 20166 20165 We thank Amber Soja for assisting in evaluating the initial version of the Our new Landsat-based map of WS’s taiga wetlands provides a benchmark for val- We estimate a map accuracy of 79 %, which is reasonably good for this large and re- identification and mapping of wetlanddoi:10.1007/s11273-009-9169-z, vegetation: a 2009. review, Wetl. Ecol. Manag., 18, 281–296, Field Station, in: Transactions of UNESCOics department of of environment Yugorsky and State global University climate “Dynam- NSU, change”, Novosibirsk, edited Russia, by: Glagolev, 208–224, M. 2008. V. and Lapshina, E. D., Friborg, T.: Methane emissions from western Siberianto wetlands: climate heterogeneity change, and Environ. Res. sensitivity Lett., 2, 045015, doi:10.1088/1748-9326/2/4/045015, 2007. Schroeder, R., Glagolev,Eliseev, A. M. V., Gallego-Sala, V., A.,Tian, Maksyutov, McDonald, K. H., S., C., Zhuang, Rawlins, Brovkin,methane M. Q., V., emissions A., and Riley, models Chen, W. Kaplan, J.,doi:10.5194/bgd-12-1907-2015, over G., Subin, 2015. West J. Z. Denisov, Siberia, O.: M., Biogeosciences S. WETCHIMP-WSL: Discuss., N., intercomparison 12, of 1907–1973, wetlands: biogeochemical, wetland microbial, and modelingGlob. perspectives Change from Biol., local 19, to 1325–1346, global, doi:10.1111/gcb.12131 scales, 2013. cover mapping: adoi:10.3390/rs61212070, 2014. review and uncertainty analysis,and Remote Practices, CRC Sensing, Press, Boca 6, Raton, Florida, 12070–12093, USA, 2008. Bibliography, Lands Directorate, Environment Canada, Burlington, Ontario, Canada, 1982. Resource contrast in patterned2344–2355, peatlands 2010. increases along a climatic gradient, Ecology, 91, References Adam, E., Mutanga, O., and Rugege, D.: Multispectral and hyperspectral remoteBleuten, W. sensing and Filippov, for I.: Hydrology of mire ecosystems in central West Siberia: the Mukhrino Bohn, T. J., Lettenmaier, D. P., Sathulur, K., Bowling, L. C., Podest, E.,Bohn, McDonald, T. K. J., C., Melton, and J. R., Ito, A., Kleinen, T., Spahni, R., Stocker, B. D., Zhang, B., Zhu, X., Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang,Cogley, J.: Q.: GGHYDRO: Methane global hydrographic emissions data,Congalton, from Peterborough, Ontario, Canada, R., 1994. Gu, J., Yadav,Congalton, R. K., G. and Green, Thenkabail, K.: Assessing the Accuracy P., ofCowell, Remotely D. and Sensed W.: Data: Earth Principles Sciences Ozdogan, of the M.: HudsonEppinga, Bay M. Lowland: B., Global Literature Rietkerk, M., Review Belyea, and land L. Annotated R., Nilsson, M. B., Ruiter, P. C. D., and Wassen, M. J.: reported here can beusing considered Landsat imagery, as with anresources the initial and general upscaling attempt goal the at of methaneresulting emissions supporting mapping from quantitative the Russian wetlands definitions and monitoring wetlands inland of of waters.map wetland wetland The can complexes be combined used with fortures the a to estimation new the and wetland regional spatialmap scale. extrapolation led of In to many a the ecosystemtimate significant case fea- change by study in Peregon of the et WS’set wetland al. taiga, ecosystem al., (2009), areas applying 2011). previously comparing a used to Thus, new in the a wetland es- our considerable methane revaluation inventory of (Glagolev the total CH idation of coarse-resolution globalmodel land performance cover in products highinventory latitudes. and but Classification for is scheme assessment applicable was for of oriented the global upscaling on of methane Acknowledgements. other environmental parameters. the growing season. Although Hansen andinteractive Loveland (2012) analyses highlighted will that no “per longer scene, still be find viable” for that global the land procedureneous cover is studies; landscapes quite however, and we suitable low for accessibility regional of mapping with good highly quality heteroge- cloudless images. The e mote area. The next step inground improving truth mapping quality data will from dependCorrectly the on the most distinguishing acquisition ambiguous wetland of wetland complexesability landscapes in with and their strongly water remote regimes, pronounced regions. remainsinstalling one seasonal water of level vari- the gauge largest network challenges. embracing There at is least a need most for abundant wetland types. manuscript. This study (researchUniversity grant Academic No D.I. 8.1.94.2015) Mendeleevported was Fund by supported Program the in by15-05-07622 The GRENE-Arctic 2014–2015. and Tomsk The 15-44-00091. project State study by was MEXT also Japan sup- and by RFBR, research projects No. entire region is expected. 5 5 25 10 15 20 30 10 20 25 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | data, Int. J. Remote Sens., 34, 2607–2654, + 20168 20167 Great Vasyugan mire foron estimation “Global of methane Warmingthe emission, and in: Changes the 1st of International the Human-Nature9 Conference March Terrestrial Dimension Ecosystem, 2012, Kyoto, with in Japan, 38–41, an Siberia: 2012. Emphasis Social on Water Adaptationthe Environments”, challenge to 7– of mapping1820, peatlands, doi:10.5194/bg-5-1809-2008 with 2008. remotely sensed data, Biogeosciences, 5,Schelevoi, 1809– V. D.: Automated complexdioxide “Flux-NIES” fluxes, for Atmospheric measurement and of Oceanic methane Optics, and 26, 1090–1097, carbon 2013. and Orlova, L.zonation, carbon A.: content Peatlands anddoi:10.1016/s0277-3791(02)00196-8, Late of 2003. Quaternary the history, Quaternary Western Sci. Siberian Rev., 22, lowlands:2004. 703–723, current knowledge on and wetlands, J. Hydrol., 296, 1–22,, doi:10.1016/j.jhydrol.2004.03.028 2004. sheva, T., and Shvedchikova, N.: MireImportance, Systems Grif of and West Co, Siberia Tula, and Russia, Its 584 Nature pp., Conservation 2001. for improvingdoi:10.1080/01431160600746456, 2007. classification performance,18, 397–433, Int. 2010. J.Avis, Remote C. A., Beerling,tenmaier, Sens., D. D. P., Riley, W. J., J., 28, Chen, Singarayer,van J. Bodegom, G., S., P. M., Subin, Eliseev, Kleinen, Z. 823–870, A. T., M., Yu,extent Z. Tian, V., and C., H., Denisov, wetland and Zürcher, S. Kaplan, methane S., J. N.,(WETCHIMP), Brovkin, modelling: O.: Biogeosciences, V., Present conclusions Hopcroft, 10, state from P. 753–788, of a O., doi:10.5194/bg-10-753-2013, global model 2013. wetland Let- inter-comparison project 80, 185–201, 2002. global vegetation and wetlandGlobal products Biogeochem. with Cy., a 21, new GB1016, ground-truth, doi:10.1029/2006gb002706 database 2007. for Westwhere Siberia, a sink is a source, Geophys. Res. Lett., 30,Status 2129,, doi:10.1029/2003GL017797 and 2003. distributiondata, of Global mangrove Ecol. Biogeogr., forests 20, of 154–159,, doi:10.1111/j.1466-8238.2010.00584.x the 2011. worldsion using from earth West Siberia observation9326/6/4/045214, mire satellite 2011. landscapes, Environ. Res. Lett., 6, 045214, doi:10.1088/1748- Li, C., Li, X., Fu,Zhao, W., Z., Liu, Zhang, C., Xu, H., Y., Zheng, Wang,Liu, Y., X., Ji, X., Cheng, L., Q., Shi, Zhang, Hu, Y., T.,Chen, Chen, L., Zhu, J., H., Yao, W., M., and Yan, Zhang, A., Chen, Chen, H., Guo, J.: Zhu, Y., J.,mapping Finer P., Yang, Yu, resolution L., results G., observation Wang, and Tang, with L., P., monitoring Landsat Xu,,doi:10.1080/01431161.2012.748992 of 2013. B., global TM land Giri, and cover: C., first ETM Clinton, N., Zhu, Z., Landsat data, Remote Sens. Environ., 122, 66–74,, doi:10.1016/j.rse.2011.08.024 2012. Landsat TM Land Cover Mapping, unpublished US Geologic SurveyRegime, report, Gidrometeoizdat, 2001. Leningrad, USSR, 448 pp., 1976. Moscow, Russia, 230–248, 1963. Integrated analysis of PALSAR/Radarsat-1 InSARof and absolute ENVISAT water altimeter level data changes2365, for in, doi:10.1016/j.rse.2009.06.014 mapping 2009. Louisiana wetlands, Remote Sens. Environ., 113, 2356– Kleptsova, I., Glagolev, M., Lapshina, E., and Maksyutov, S.: Landcover classification of the Krankina, O. N., Pflugmacher, D., Friedl, M., Cohen, W. B., Nelson, P., and Baccini, A.: Meeting Krasnov, O. A., Maksutov, S. S., Glagolev, M. V., Kataev, M. Y., Inoue,Kremenetski, G., K. Nadeev, V., Velichko, A. A. A., I., Borisova, and O. K., MacDonald, G. M., Smith, L. C., Frey, K. E., Lapshina, E.: Peatland Vegetation of South-East West Siberia,Lehner, TSU, B. Tomsk, Russia, and 296 Döll, pp., P.: Development and validationLiss, of O., a Abramova, L., global Avetov, database N., Berezina, of N., lakes, Inisheva, reservoirs L., Kurnishkova, T., Sluka, Z., Tolpy- Lu, D. and Weng, Q.:Masing, V., Botch, A M., and Läänelaid, survey A.: Mires of the ofMelton, former Soviet J. Union, image Wetl. R., Ecol. Manag., classification Wania, R., methods Hodson, E. and L., techniques Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Foody, G. M.: Status of land cover classification accuracy assessment, RemoteFrey, Sens. K. Environ., E. and Smith, L. C.: How well do we knowFriborg, T., northern Soegaard, H., land Christensen, cover?, T. R., Comparison Lloyd, C. of R., and four Panikov, N. S.:Giri, Siberian wetlands: C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T.,Glagolev, M., Masek, Kleptsova, J., I., Filippov, and I., Duke, Maksyutov, S., N.: and Machida, T.: Regional methane emis- Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Gvozdetsky, N.: Physiographic zoning ofHansen, USSR, M. MSU, C. Moscow, and Russia, Loveland, 576 T. R.: pp., A 1968. review of large area monitoringHomer, of C. land and cover Gallant, change A.: using Partitioning the Conterminous United StatesIvanova, into Mapping K. Zones for and Novikova, S.: WestKatz, N. Siberian and Peatlands, Neishtadt, Their M.: Peatlands, Structure in:Kim, and West J.-W., Hydrological Siberia, Lu, edited Z., by: Lee, Rihter, H., G. Shum, D., C. AS K., USSR, Swarzenski, C. M., Doyle, T. W., and Baek, S.-H.: 5 5 20 10 15 25 30 30 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ects?, ff flux upscaling, 4 20170 20169 emissions of boreal and arctic wetlands, Global Biogeochem. Cy., 24, emissions and production and oxidation potentials at microsites on an oligotrophic 4 4 Remote Sens. Environ., 75, 230–244, 2001. Remote Sens. Environ., 20, 121–139, 1986. Moore, T. R., Myers-Smith,pali, I. N., Tuittila, H., E. Nykanen,A S., synthesis H., Waddington, of J. Olefeldt, methane M.,Glob. D., emissions Change White, Rinne, Biol., from J. 20, J., 71 R., 2183–2197, Saarnio, northern,, doi:10.1111/gcb.12580 Wickland, 2014. S., temperate, K. and Shur- P., and subtropical Wilmking, wetlands, M.: tion in CH pine fen, Oecologia, 110, 414–422, 1997. from mires of the West Siberian taiga, Eurasian Soilsonal Sci., variability 46, as 1182–1193, a 2013. source of uncertainty in the West Siberian regional CH Environ. Res. Lett., 9, 045008, doi:10.1088/1748-9326/9/4/045008, 2014. pers, M.: Satellite microwave remoteopment sensing of of North coarse-resolution Eurasian productsture inundation dynamics: and radar devel- data, comparison Environ. with Res. Lett., high-resolution 5, synthetic 015003, aper- doi:10.1088/1748-9326/5/1/015003,Lee, 2010. M.,tory Beilman, ofdoi:10.1029/2003gb002190, the D. 2004. west W., Siberian and peatservation, Cambridge carbon Dubinin, University Press, pool, Cambridge, P.: 11–62, 2005. Global A Biogeochem.Zhenlin, C.: high-resolution Climate Cy., Change 18,tion 2007, GIS-based to The the GB3004, Physical Fourth Science Assessment inven- UK Basis, Report and Working of New Group the York, IPCC, I NY, USA, Cambridge Contribu- 2007. University Press, Cambridge, change detection using Landsat TM data: when and how to correct atmospheric e tation index for remote2010. sensing of vegetation phenology, Remote Sensing,Yang, 2, J., 2369–2387, Liu, C., Liu,Gao, S., X., Wang, Zhu, L., Li, W.,Guo, Z., Wang, Z., Yang, and W., Z., Gong, Wang, Qiao, P.: Mapping H., F.,Sci. wetland Zheng, Bull., Weng, changes Y., in 57, Y., Chen, China 2813–2823, Zhuang, Y., between Sheng,, doi:10.1007/s11434-012-5093-3 D., 1978 2012. Y., and Liu, 2008, J., Chinese Luo,381–402, Z., 2002. Cheng, X., peatland carbon pool, Glob. Change Biol., 17, 798–818, 2011. Application of the multi-scale remotewest sensing Siberian and wetlands, GIS Phyton, 45, to 543–550, mapping 2005. net primary productiontory in of wetland biomassBiogeo., and 113, G01007, net doi:10.1029/2007JG000441 , primary 2008. production in westernstructure Siberia, J. of Geophys. Res.- West9326/4/4/045014, 2009. Siberian wetlands, Environ. Res. Lett.,Corradi, 4, C. 045014,global A. doi:10.1088/1748- CH R.,doi:10.1029/2009gb003610, 2010. Parmentier, F. J. W., and Dolman,typology map of A. West Siberian lowland J.: scale 1 Modeling : 2500000Il’ina, GUGK, regional I., Leningrad, Russia, Lapshina, 1977. to Mahno, E., V., Science, Lavrenko, Novosibirsk, N., Russia, 138–160, Meltser, 1985. L., Romanove, E., Bogoyavlenskiy, M., and Turetsky, M. R., Kotowska, A., Bubier, J., Dise, N. B., Crill, P., Hornibrook, E. R., Minkkinen, K., Strahler, A. H., Woodcock, C. E., and Smith, J. A.: On the nature of models in remote sensing, Sabrekov, A., Glagolev, M., Kleptsova, I., Machida, T.,Sabrekov, and A. Maksyutov, F., S.: Runkle, B. Methane R. emission K., Glagolev, M. V., Kleptsova, I. E., and Maksyutov, S. S.: Sea- Schroeder, R., Rawlins, M. A., McDonald, K. C., Podest, E., Zimmermann,Sheng, R., Y., and Smith, L. Kuep- C., MacDonald, G. M., Kremenetski, K. V., Frey, K. E.,Solomeshch, A.: Velichko, The A. West Siberian A., Lowland, The World’s Largest Wetlands: EcologySolomon, and S., Con- Dahe, Q., Martin, M., Melinda, M., Kristen, A., Melinda, M. B., T., Henry, L. M., and Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., and Macomber, S. A.: Classification and Saarnio, S., Alm, J., Silvola, J., Lohila, A., Nykänen, H., and Martikainen, P. J.: Seasonal varia- Motohka, T., Nasahara, K. N., Oguma, H., and Tsuchida, S.: ApplicabilityNiu, of Z., green-red Zhang, vege- H., Wang, X., Yao, W., Zhou, D., Zhao, K., Zhao, H., Li, N., Huang, H., Li, C., Ozesmi, S. L. and Bauer, M. E.: Satellite remotePage, sensing S. of E., wetlands, Rieley, Wetl. J. Ecol. Manag., O., 10, andPeregon, A., Banks, Maksyutov, C. S., Kosykh, J.: N., Mironycheva-Tokareva, N., Global Tamura, M., and and regional Inoue, G.: importance of thePeregon, A., tropical Maksyutov, S., Kosykh, N. P., and Mironycheva-Tokareva, N. P.: Map-based inven- Peregon, A., Maksyutov, S., and Yamagata, Y.: AnPetrescu, image-based A. inventory M. of the R., spatial van Beek, L. P.Rihter, G. H., D.: West Siberia, vanRomanova, AS E., USSR, Bybina, Huissteden, Russia, R., Moscow, 1963. Golitsyna, J., E., Ivanova, G., Prigent, Usova,Romanova, L., E.: C., and Vegetation Trushnikova, cover L.: Sachs, of Wetland West Siberian T., Lowland, in: Peatland Vegetation, edited by: 5 5 25 30 10 15 20 10 15 20 25 30 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ecosystems Ry: 100 % F: 100 % R: 42 % OH: 58 % R: 31 % OH: 25 % WH: 31 % F: 13 % R: 28 % F: 72 % WH: 12 % OH: 37 % P: 51 % OH: 100 % F: 100 % W: 100 % 2 Landsat pixels, × erent West Siberian Mire Landscapes, ff 20172 20171 and are characterized by mosaic dwarfcover shrubs-sphagnum with vegetation sparse dwarf pine. This type consists of all water bodies larger than 2 Short descriptionDwarf shrubs-sphagnum communities with“ryams”) pine trees occupy (local the name mostheight – drained and parts crown ofangle. density the The are wetlands. peat The positivelyters surface pine high correlated is above with the usuallypurely WTL. the approximately depend Ryams slope several are on decime- typicalents. precipitation oligotrophic Their and mires next the that evolutionaryweaker atmospheric drainage type input is under RHC. of increased nutri- precipitation or RHC are dominant in theand WS hollows taiga depend zone. The on configurationof the of the slope ridges angle contiguous and areas. Wetland lows hydrological are RHCs conditions usually with arranged small, within the medium, class. and large hol- Wooded swamps develop in areas enrichedcan by be groundwater usually flow found and in riverthe valleys, young floodplains river farthest terraces and from partsdiverse the of in river floristic composition channels. and They have are prominent microtopography. extremely RHLCs develop from RHCs orter patterned stagnation or fens after under seasonal permanentdant flooding. in wa- RHLCs northern are taigas the andmay most occupy include abun- poorly drained the watersheds. presence They class of incorporates numerous two prolate types:eutrophic shallow 1) hollows. pools. with oligotrophic, The Patterned 2) fens with are meso- widelyspond or distributed to the within WS the type ofopen region. aapa fen They mires. environments Patterned corre- that fens alternate aretation composed with of cover narrow commonly ridges. Their includes vege- sedge-mossPalsa or sedge complexes communities. arehillocks patterned – bogs frost withmafrost. They heaves the appear with in presence the heights north of taiga of and palsa prevail 0.5–1 m northwards. that contain per- open rich and poor fenswith in WS higher taigas. mineral They are suppliessystems confined or along to along locations the peatland watercourses peripherywater and supplies. of The areas vegetation with large cover of richhigher peatland open ground fens productivity is and characterized by includesmosses. sedges, herbs, hypnum and brown which can be directly distinguished by Landsat images. Open wetlands Open bogs Open bogs are widespread along the periphery of wetland systems Water bodies Lakes and rivers Wetland complexes Wooded wetlands Pine-dwarf shrubs- sphagnum bogs (ryams) Patterned wetlands Ridge- hollow complexes (RHC) Wooded swamps Ridge- hollow-lake complexes (RHLC) Patterned fens Palsa com- plexes Open fens Open fens are the integral class that encompasses all varieties of Wetland types and fractional coverage of wetland ecosystems (Water – W, Water- Nestor-History, Saint-Petersburg, 83 pp., 2009. Smith, L. C.:,doi:10.1016/j.quaint.2011.01.013 2011. West Siberian Plain asworld, a Vegetatio, 34, 167–178, late 1977. glacial desert, Quatern.the boreal-Arctic: Int., potential impacts 237,075001, on, doi:10.1088/1748-9326/9/7/075001 45–53, 2014. regional methane emissions, Environ. Res. Lett.,tated 9, wetlands of Alaska54–72, 2009. using L-band radar satellite imagery, Can.Ecol.-UK, J. 1, Remote 9–23, Sens., 2008. 35, Walter, H.: The oligotrophic peatlands of WesternWatts, Siberia-the J. largest D., peino-helobiome Kimball, in the J. S., Bartsch, A., and McDonald, K. C.: Surface water inundation in Xie, Y., Sha, Z., and Yu, M.: Remote sensing imagery in vegetation mapping: a review, J. Plant Whitcomb, J., Moghaddam, M., McDonald, K., Kellndorfer, J., and Podest, E.: Mapping vege- Table 1. logged hollows – WH,hillocks Oligotrophic – P). hollows – OH, Ridges – R, Ryams – Ry, Fens – F, Palsa Usova, L.: Aerial Photography Classification of Di Velichko, A. A., Timireva, S. N., Kremenetski, K. V., MacDonald, G. M., and 5 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ,% 1 92 74 47 81 100 82 1038 3 74 73 2 113 97 38 54 82 2068 3 116 74 5 9 8 181 83 86 20174 20173 7 4 7 130 83 150 1 1 4 2 87 79 108 3 1 98 96 69 94 Area, Mha % Area, Mha % Area, Mha % Area, Mha % 110 PA – producer’s accuracy. 2 Confusion matrix of West Siberian wetland map validation (additional 11 floodplain Latitudinal distribution of wetland ecosystem types. , % 80 93 79 92 87 75 62 99 96 62 2 Patterned FensSwampsPalsa complexes 1 13 5 4 1 1 2 1 18 4 9 Real classesEstimated classes Non-wetland Non-wetland Lakes and rivers RHLC RyamsOpen RHC Fens Open Fens Patterned Fens SwampsOpen bogs Palsa complexesTotal Open bogsPA Total UA 137 3 101 1 87 3 117 1 172 7 114 1 110 83 56 61 RyamsRHC 3 1 1 6 2 Lakes and rivers RHLC 4 7 UA – user’s accuracy; 1 Wetland ecosystem types South taigaWaterWaterlogged hollowsOligotrophic hollows Middle taigaRidgesRyamsFens North taigaPalsa 0.50 hillocks 1.87Total wetland 4 area 16Total Total zonal area areaPaludification, % 0.37 1.32 5.78 3 7 30 1.70 12.04 0.00 14 3.37 28 1.66 42.96 0 3.40 4.22 5.60 28.0 16 9 35 27 3.61 19.27 19 5.14 0.00 27 13.25 5.22 3.91 56.56 0 1.77 25.3 10.0 19 34.1 9 3.37 16 1.60 21.13 1.71 8 5.94 58.46 8 1.53 11.3 8.69 7 36.1 16.6 10.11 19.3 52.44 1.71 3.3 7.52 157.97 14.3 33.2 and 33 mixed classoverlap of polygons real classified and as estimated classes. wetlands are not presented). Bold values denote Table 3. Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ; yellow – WS, green – Taiga zone). b 20176 20175 of the WS taiga zone ( (a) Wetland map Wetland complexes (I – Ryam, II – Ridge-hollow complex or RHC, III – Ridge-hollow- Figure 2. lake complex or RHLC, IV –VIII Lakes and rivers, – V – Palsa OpenOligotrophic fens, complexes) VI hollows, and – 4 Patterned – fens, ecosystems VII Ridges, – in 5 Swamps, – WS Ryams). (1 – Water, 2 – Waterlogged hollows, 3 – Figure 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (b) E. ◦ – water, (a) – palsa hillocks; (f) N, 66–66.5 ◦ – fens, (e) – SHI map (1 – Sphagnum-dominated – ryams, (% from the total cell area): (a) (d) ◦ 0.1 × 20178 20177 ◦ – this study (legend is on Fig. 2); 59–59.5 – oligotrophic hollows, (b) (c) Comparison of wetland classifications: Wetland ecosystem areas for 0.1 – waterlogged hollows, the distribution of ridges isdistribution; not the represented black because outlines divide it the is taiga quite into similar the to north, the middle oligotrophic and hollow south Taiga subzones. bogs with pools andpatterned open bogs, 3 stand – ofmires, forested 5 trees, – shrubs- 2 water and bodies), – moss-dominated ridge-hollow, mires, ridge-hollow-pool 4 and – ridge-pool moss-dominated treed Figure 4. Figure 3.