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Forested land-cover patterns and trends over changing forest managementeras in the Siberian Baikal region

L.K. Peterson a,1, K.M. Bergen a,*, D.G. Brown a, L. Vashchuk b, Y. Blam c

a School of Natural Resources and Environment, University of Michigan, 440 Church Street Ann Arbor, MI 48109, USAb Forest Service of Irkutsk, Ministry of Agriculture of the Russian Federation 31 Gorky St, Irkutsk, Russiac Department of Economic Informatics, Institute of Economics and Industrial Engineering, Siberian Branch of the Russian Academy of Sciences,

17 Prosp. Akademika Lavrentieva, Novosibirsk, Russia

1. Introduction

In the regions of the former Soviet Union and Eastern Bloc, thepast century saw widespread political and socio-economic changesassociated with the gradual implementation of communismstarting in the early twentieth century, followed by fairly abrupt

changes to post-Soviet transitioning market economies near theend of the same century. The latter events marked a distinctturning point in both general economic conditions (World Bank,1997) and specific institutional factors influencing forest manage-ment (Korovin, 1995). Recent studies have used remotely sensedobservations to compare Soviet and post-Soviet era forested land-cover and provide evidence of divergent patterns and trendsbetween these eras (Krankina et al., 2005; Kuemmerle et al., 2006,2007; Bergen et al., 2008). The results of these observationalstudies also suggest that there may be important ongoinginfluences associated with the different Soviet and post-Sovietsocio-economic and forest management eras on landscapes. This is

Forest Ecology and Management 257 (2009) 911–922

A R T I C L E I N F O

Article history:

Received 24 July 2008

Received in revised form 22 October 2008

Accepted 24 October 2008

Keywords:

Boreal forest

Forest management

Forest modeling

Logging

Russia

A B S T R A C T

Remote sensing observations over areas of the former Soviet Union suggest that there may be important

ongoing influences on forested landscapes resulting from divergent land use and forest management

associated with the Soviet versus post-Soviet eras. As the Russian Federation implements its new Forest

Code and associated regulations, knowledge of existing forest patterns and trends, plus the development

of methods with which to understand the landscape-level influence of different forest management

strategies is increasingly important. We developed spatial–temporal models and projections of forest

patterns and trends over Soviet and early post-Soviet forest management eras for a study site in the Lake

Baikal region in southern Siberia. We used Landsat-derived land-cover data, logistic regressions, and

Markov and cellular automata methods (CA–Markov) to characterize patterns and trends 1975–1989 and

1990–2001, and to develop predictive scenarios through 2013. Relationships of forest types (Conifer,

Mixed, Deciduous) and Agriculture to other explanatory environmental variables indicated mostly

consistent forest–environment relationships, but some different spatial relationships between eras were

found for Cut and Regeneration disturbance types. Landscape proportional trends showed greater

differences between eras. Cut proportions observed via Landsat in 2001 were approximately 74% lower,

and the area of Conifer observed was approximately 14% higher, than modeled proportions predicted for

2001 using 1975–1989 Soviet era transition rates. The proportion of Cut projected for 2013 was about

80% lower when based on early post-Soviet era probabilities. Overall, modeled results indicate that

should early post-Soviet trends continue, low rates of logging, some agricultural abandonment, re-

growing forests especially near access routes, increases in deciduous cover, along with continued or

increased fire events in mixed and conifer forests will define the landscape. Should forest management

change, for example to Soviet era rates and patterns of harvest, different outcomes are projected. More

broadly, results highlight the real and prospective effects that divergent management strategies can have

on forested landscapes, and demonstrate that land-cover data combined with emerging spatial–temporal

modeling methods provide an approach to understand and project the complex and ongoing influences

associated with changing forest management at landscape scales.

� 2008 Elsevier B.V. All rights reserved.

* Corresponding author. Tel.: +1 734 615 8834; fax: +1 734 936 2195.

E-mail address: [email protected] (K.M. Bergen).1 Current address: U.S. Forest Service International Programs, 1099 14th Street,

NW, Suite 5500W Washington, DC 20005, USA.

Contents lists available at ScienceDirect

Forest Ecology and Management

journal homepage: www.e lsev ier .com/ locate / foreco

0378-1127/$ – see front matter � 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.foreco.2008.10.037


due to the intrinsic nature of disturbance, regeneration, andsuccession such that forest land-use histories may continue toinfluence the longer-term vegetation compositions, spatial pat-terns and other processes of a site for decades, even centuries(Foster et al., 1998).

In addition to observation of forested landscapes using remotesensing, several spatial and temporal simulation methods havebeen developed that can be used to devise management strategiesthat consider a more complete understanding of the historical andcurrent factors driving forest dynamics and the trajectories ofthose changes (Brown et al., 2004; Sturtevant et al., 2007).Whereas logistic regression and other static modeling approacheshave found widespread use revealing the relationships of forestedand other vegetation types with environmental and spatialvariables (Guisan and Zimmermann, 2000), dynamic modelingapproaches, like Markov chains (Brown et al., 2000) and cellularautomata (Balzter et al., 1998), provide information about the ratesand patterns of change, and can be more useful for understandingthe implications of forest landscape processes. When combinedand used with land-cover data derived from moderate spatialresolution remote sensing, these methods are appropriate formodeling forested ecosystems and the influence of management atlandscape to regional scales (Zhou and Buongiorno, 2006). As suchthey fill the niche between synoptic broad area observation byremote sensing (Achard et al., 2006), and local stand level modelingof forest dynamics (Sizykh, 2007) or specific managementobjectives (Zhou et al., 2008).

The ongoing influences of forested landscape patterns andtrends associated with the Soviet and post-Soviet eras may beparticularly evident in certain regions such as Siberia which havelong been heavily forest-resource dependent (Krankina et al.,2005). Development of spatial–temporal models and projectionsfor Siberian forests over late Soviet and early Russian Federationeras could provide insight into the real past and prospective futureeffects that these divergent forest management eras have had andcontinue to have on the landscape. This type of baseline knowledgecoupled with projections may become especially important inorder to understand the ongoing influence of past legacies withinthe emerging forest policy framework associated with Russia’snew Forest Code (Russian Federation, 2006), and how that code canbe effectively translated into on-the-ground management strate-gies for sustainable forest use.

1.1. Research goal and objectives

The goal of our research was to characterize patterns and trendsof forested land cover in a study site representative of southernSiberian forests, and to develop simulated predictive (through2013) scenarios to investigate possible differences at the land-scape-level resulting from forest dynamics associated with Sovietera management strategies versus those from the early RussianFederation era. We selected a study site in Irkutsk Oblast in thevicinity of Lake Baikal for a combination of practical and scientificreasons, including the availability of moderate spatial resolutiontime series land-cover data (otherwise generally scarce for Siberia)for a suitable span of years; its location in a region of the Siberianforest that is important to forest management; and its representa-tiveness of forest- and land-cover dynamics typical of the Sovietand early post-Soviet eras. Our specific objectives were to (1) usetime series land-cover data derived from Landsat remote sensingwith other environmental spatial data in logistic regressions tocharacterize relationships between forested land-cover spatialpatterns and other explanatory environmental variables in botheras, (2) couple the logistic models with Markov land-covertransition probabilities derived from the time series data and a

cellular automata (CA) method to create spatial–temporalpredictive scenarios of land-cover trends based on both eras,and (3) to analyze modeled results and compare future forestedlandscape scenarios simulating those based on Soviet and earlyRussian Federation eras. In addition to revealing landscapeinfluences over divergent forest management eras, this workdemonstrates a combined spatial–temporal modeling methodol-ogy using land-cover data and GIS that is increasingly available anduseful to forest scientists and managers at landscape to regionallevels.

2. Study site

2.1. Geography and ecology

The Baikal study site occupies a territory of approximately30,000 km2 (Fig. 1). Topography includes low mountains andplateaus dissected by streams, plus broad valleys. Lake Baikaldominates the eastern portion of the site. Elevation ranges from455 m above sea level (asl) at the lake surface to 2055 m asl withinthe Baikal Range (USSR, 1991). Soils are mainly Mollisols in broadvalleys, and Spodosols or Gelisol Turbels in mountainous areas.Areas of permafrost occur throughout the site (USSR, 1991). Theclimate is continental with long, cold and dry winters (Januarymean of�16 to�30 8C), warm summers (June mean of 15 to 25 8C),intermittent precipitation and a short growing season (Kozhovaand Izmesteva, 1998).

The study site is located in the boreal forest ecoregion (Farber,2000; Olson et al., 2001). Dominant tree species include Scots pine(Pinus sylvestris L.), Siberian larch (Larix sibirica Ledeb.), Dahurianlarch (Larix gmelinii), Siberian pine (referred to as ‘cedar’; Pinus

sibirica du Tour), European White birch (Betula pendula Roth),Upright European aspen (Populus tremula L.), Siberian spruce (Picea

obovata Ledeb.), and Siberian fir (Abies sibirica Ledeb.; Nikolov andHellmisaari, 1992; Vashchuk, 1997). ‘‘Light-coniferous’’ forests(dominated by pine and larch) are naturally more prevalent in theBaikal region than are ‘‘dark coniferous’’ forests (comprised ofcedar, spruce, and fir; Farber, 2000). Human and natural-drivenforest disturbances in the past century have included logging,agriculture, fire and insect pests (Krankina et al., 2005). In naturalsuccession, deciduous birch (with aspen in association) is thetypical pioneer species in Siberia; although in the higher andsomewhat drier Baikal region, disturbed areas may also regeneratedirectly to pine or larch. Relatively short-lived birch–aspen foreststypically succeed to mixed conifer–deciduous, and eventually tomature conifer forests.

2.2. Forest management over changing eras

Since the nationalization of forests after the 1917 communistrevolution, and throughout the Soviet era, Russian forests werestate-owned and managed in terms of both silviculture andindustrial output. High levels of timber production were main-tained, reaching a recent high in 1989 (Pappila, 1999), but forestresources suffered from over-harvesting in economically acces-sible areas (Korovin, 1995) and from inefficient managementmethods (Blam et al., 2000). The dissolution of the Soviet Union in1990 brought an abrupt reduction in state support for forestry, andlogging rates dropped sharply and remained low in the ensuingyears (Krankina and Dixon, 1992; Bergen et al., 2008). Since theestablishment of the Russian Federation in 1991, new legislationhas been enacted. National-level legislation adopted in 1993–1994(Zaslavskaja, 1994) succeeded in specifying concrete ecologicallysustainable management practices including restrictions onpermissible logging areas and discontinuation of the prevalent

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922912


Soviet era practice of logging in large landscape-sized clear-cuts byestablishing a maximum cut size of 50 ha and in non-contiguouspatches for industrial forests (Korovin, 1995). However, at thesame time, regional authorities were given increasing control overforest management and industry (Kortelainen and Kotilainen,2003; Williams and Kinard, 2003), and subsequent challenges inclarifying national versus regional roles resulted in delay anddifficulties in implementing new forest management strategies. Anew Forest Code implemented in January 2007 (Russian Federa-tion, 2006) attempts to address the latter by transferringmanagement authority to regions, though this relationshipremains operationally complex.

The Baikal region forests are representative of the above forestmanagement transitions. The location of Lake Baikal on the Trans-Siberian Railway has allowed access to the region for industrialtimber management and harvesting since 1896 (Matthiessen andNorton, 1992). Since that time, the greater Irkutsk Oblast, with aforest density (defined as forest cover as a percent of the Oblasttotal area) of 1.7 times the Russian Federation average, has beenone of Russia’s most important forest management and timberproducing regions (Blam et al., 2000). Immediately after 1989, theforest industry in Irkutsk Oblast dropped sharply to about one-fourth of late Soviet era output (All Union Scientific ResearchInstitute of Economics, 1991; Obersteiner, 1999; Fig. 2), and

through the early 2000s post-Soviet era had not recovered. As aresult of new legislation, forests in the near coastal zone of LakeBaikal received protective status preventing final felling, andbecause of the globally important presence of Lake Baikal itself, theBaikal region of Irkutsk Oblast is attracting the interest of Russianand international sustainable forest management and develop-ment projects. As focus shifts back to forest sector development,sustainable forest management in the region will need stronginformation databases, including information on spatial distribu-tions as well as past and continuing responses of regional forests tohuman management practices and natural disturbances (Korovin,1995).

3. Methods

3.1. Spatial data

A set of forest- and land-cover data of the Baikal study sitecreated using Landsat Multi-Spectral Scanner (MSS), ThematicMapper (TM), and Enhanced Thematic Mapper (ETM+) imagesfrom 1975, 1989, and 2001 respectively were available for thisstudy. The dates of these data corresponded to (1) the Soviet era(1975), (2) the approximate peak of Soviet era forestry (1989) justprior to transition, and (3) a post-Soviet era date at a similar time

Fig. 1. The location of the study site within Siberia and regional ecosystem types (ESRI, 2004; Federal Geodesy and Cartography Service of Russia, 1991; Olson et al., 2001).

L.K. Peterson et al. / Forest Ecology and Management 257 (2009) 911–922 913


interval (Table 1). Land-cover data had been compiled at a 30-mspatial resolution, and included the following classes representa-tive of regional forest ecology and land-use: Conifer, Mixed,Deciduous, Bog/Sparse Conifer (Forest group); Cut, Burn andRegeneration (Disturbance and Recovery group); Floodland, Wet-land, Agriculture, Bare, Urban, and Water (Other Land Coversgroup) (Fig. 3; Bergen et al., 2008). Hereafter we use the term covertype to refer to individual forest, disturbance, regeneration, andother land-cover types in the study region.

Cover type proportions for each date were extracted from theabove dataset (Fig. 4) as was associated classification accuracy data(Table 2). Modifications to the dataset for the present study

included combining Floodland (riparian) and Wetland cover typesand masking Water and the small classes of Urban and Bare out ofthe dataset.

The following environmental features were digitized in ArcGIS(ESRI, 2004) for this study from 1:200,000 scale Russiantopographical map quadrangles (USSR, 1991): all hydrologicfeatures (rivers and lakes), roads (paved, dirt, and forest roads),and urban areas (towns and villages). Major infrastructurefeatures, such as urban areas, rivers, lakes, and larger roads (bothpaved and dirt), were observed on the Landsat imagery to remainmostly constant throughout the study period in this relativelyunder-developed region; however it is possible that some forestroads were transient. A higher resolution (90 m) hydrologicallycorrected DEM was interpolated from a 1-km elevation DEM(USGS, 1996) using the digitized river data to enforce valleys anddrainage direction. The above land-cover and environmental datacomprised the data upon which the modeling process was based(Table 1). Additional variables were created from these dataspecifically for the logistic regression step and are described in thatsection.

3.2. Logistic regression

The relationships between the existing spatial patterns of covertypes and environmental variables were tested for each of thethree years using logistic regression analysis. Logistic regressionwas used to estimate parameters on the independent variables inmodels of the categorical dependent variables that included:Conifer, Mixed, Deciduous, Bog/Sparse Conifer, Regeneration,Floodland/Wetland, Burn, Cut, and Agriculture. Dichotomous 30-m grids representing the presence and absence of the cover typeswere created from the land-cover data in ArcGIS. Using the DEMand environmental data (Table 1), additional independent vari-ables and grids were derived (Table 3). Distance to rivers (D2River),distance to roads (D2Road), and distance to urban (D2Urban) werehypothesized to influence cover type patterns and trends becauseof the access they provide to processing facilities and markets.Slope and aspect were derived and topographical wetness index(TWI) was calculated by combining specific catchment area withslope steepness and used as a proxy for soil moisture (Beven andKirkby, 1979).

A data sample was selected from the land-cover and environ-mental grids for use in logistic regression analysis and importedinto SPSS statistical software (SPSS, 2004). A systematic samplewith a spacing of 1 km was selected to maximize space between

Fig. 2. Forest industry metrics of (a) total wood removal and (b) total sawnwood

production in Irkutsk Oblast and in the Russian Federation (All Union Scientific

Research Institute of Economics, 1991; State Committee of Statistics of the Russian

Federation (GOSKOMSTAT), annual).

Table 1Data sources on which the available Landsat-derived land-cover classifications were based (A) and from which environmental data and variables were created during this

study (B).

(A) Landsat data

Name WRS Path Row Date Platform Sensor Resolution

(m)

ETM+ 2 133 23 8-13-2001 Landsat 7 ETM+ 30

TM West* 2 134 23 8-19-1989 Landsat 4 TM 30

TM Center 2 133 23 8-28-1989 Landsat 4 TM 30

TM East* 2 132 23 8-21-1989 Landsat 4 TM 30

MSS West 1 144 23 7-28-1975 Landsat 2 MSS 60

MSS East* 1 143 23 6-21-1975 Landsat 2 MSS 60

(B) Environmental data sources

Feature Source Date

Transportation, Hydrology, Urban Topographic map. 1:200,000. Federal Geodesy and Cartography Service of Russia, USSR. 1991

Elevation Digital Elevation Model, USGS. 1996

* Indicates the primary scene used.



sample points and minimize the size of the dataset, whilemaintaining an adequate sample size for modeling less abundantcover types. For each cover type, a file containing all sample pointsat which the type was present was combined with an equalnumber of random sample points at which it was absent.

A forward stepwise logistic regression procedure was used inSPSS to first create exploratory models (Miller and Franklin, 2002).To deal with the spatial dependence inherent in data extractedfrom land-cover maps (Bailey and Gatrell, 1995; Miller et al., 2007),

the sampling scheme described above was intended to reducespatial autocorrelation. Remaining spatial dependence was esti-mated by calculating Moran’s I, a measure of spatial autocorrela-tion (Anselin, 2003), on model residuals, and was addressedthrough the inclusion of a lag variable in subsequent regression

Fig. 3. Landsat-derived land cover of the study site: (a) 1975 (MSS), (b) 1989 (TM), (c) 2001 (ETM+), (d) cover type names.

Fig. 4. Landsat-derived cover type proportions for the study site for the three

imaged dates.

Table 2Classification accuracy of cover types for each year. Shown is producer’s accuracy in

percent for each category plus overall accuracy. Also given are cover type group

affiliations used to interpret selected results in this study.

Classification accuracy (%)

Group Type 1975 1989 2001

Forest Conifer 94.9 97.0 98.5

Mixed 81.1 90.3 81.3

Deciduous 86.4 80.3 85.0

Bog/Sparse Conifer 67.9 93.4 98.7

Disturbance and Recovery Cut 91.6 94.6 82.5

Burn 59.5 92.0 99.9

Regeneration 51.0 71.0 77.6

Other Land Covers Floodland/Wetland 76.3 92.5 72.8

Agriculture 94.9 92.4 96.3

Urban 80.6 98.7 98.8

Bare 99.3 100.0 98.5

Water 100.0 100.0 99.9

Overall 84.8 92.5 89.9



analysis (Miller and Franklin, 2002). Exploratory model results,both including and excluding the lag variable, were compared toevaluate the strength and significance of the variables included inthe models. Variables were also interpreted to assess if they madesense ecologically. After determining which variables should beincluded to produce the best model for each cover type, logisticregression analysis was again performed, this time entering thesignificant variables into the model. Spatial autocorrelation in theresiduals was again tested and a new lag variable was created andincluded. Two final models, including and excluding the spatial lagvariable, were created for each cover type at each date. Statisticaloutput included several indicators of model fit and variablesignificance. A Nagelkerke R2 (Nagelkerke, 1991) greater than 0.2indicates a relatively fit model; classification results also indicatethe relative model fitness, with 100% indicating a perfect model.

Once the best-fit models were determined through logisticregression analysis, probability maps for input into the CA–Markovsimulation were created for each of the nine cover types in 1975,1989, and 2001. Probability maps were created at the 30-mresolution using the following Eq. (1):

Cover type probability ¼ elp

1þ elp(1)

where lp is the linear predictor equation resulting from logisticregression analysis (Miller and Franklin, 2002). Probability mapsfor all cover types were grouped by date for use in the CA–Markov

procedure. For input to CA–Markov analysis, all variable andprobability grids were resampled to a 60-m resolution to improvecomputational efficiency and imported into IDRISI (Clark Labs,2003).

3.3. CA–Markov

With Markov chain analysis, future land cover can be modeledon the basis of the preceding state; that is, a matrix of observedtransition probabilities between states can be used to projectfuture changes in the landscape from current patterns (Brownet al., 2000). Because spatially proximate objects are often morelikely to exhibit similar attributes (Miller and Franklin, 2002), theincorporation of neighboring states through combination ofMarkov and cellular automata (CA–Markov) approaches has beenshown to improve models that describe complex natural patterns(Baker, 1989; Deadman and Brown, 1993). Transition matrices,representing probability of change between individual cover types,were calculated for the two periods 1975–1989 and 1989–2001using the corresponding grids.

Future cover types were then predicted using a CA–Markovmodel based on the 1989 or 2001 land-cover classifications, plusthe transition matrices, probability maps, and contiguity. Calcula-tions were made in yearly increments, incorporating a classifica-tion error value based on the input land-cover data (0.15 for theperiod 1975–1989 and 0.10 for the period 1989–2001). Astransition rates were calculated for change occurring over a period

Table 3Results and model comparison from logistic regression analysis. Models are grouped by cover type for comparison across dates. Positive and negative coefficients for

significant variables are indicated by + or�, respectively. Model fitness is indicated by Nagelkerke R2 and overall classification accuracy, both excluding (1) and including (2) a

lag variable. Abbreviations: Regen = Regeneration, Elev = Elevation, TWI = Topographical Wetness Index, D2River = Distance to rivers, D2Road = Distance to roads,

D2Urban = Distance to urban, Con = Conifer, Mix = Mixed, Dec = Deciduous.

Logistic regression analysis: model comparison and selected results

Year N of model

pixels

Dependent

variable

Independent variables Nagelkerke R2 Classification (%)

Elev Slope TWI D2River D2Road D2Urban Previous forest type 1 2 1 2

Con Mix Dec

1975 9454 Conifer + + � + 0.323 0.574 71.0 81.5

1989 7468 + + � + N/A 0.271 0.494 68.8 78.1

2001 7680 + + � + 0.261 0.491 67.8 78.2

1975 14298 Mixed + � � � 0.219 0.453 67.5 75.7

1989 14638 + � � � N/A 0.239 0.415 67.5 73.8

2001 12844 + � � � 0.208 0.403 65.7 73.3

1975 4058 Deciduous + � � 0.118 0.398 58.6 73.1

1989 3948 + � — N/A 0.158 0.367 62.2 72.4

2001 5800 + � � 0.123 0.361 59.7 72.1

1975 4092 Bog/Sparse Conifer + � � + 0.224 0.367 67.3 71.9

1989 4398 + � � + N/A 0.245 0.421 69.6 75.5

2001 4510 + � + 0.176 0.338 65.3 71.8

1975 368 Cut � 0.330 0.763 70.1 91.0

1989 1002 � � � � 0.289 0.638 67.4 85.0

2001 306 + 0.119 0.484 65.4 78.8

1975 80 Burn + 0.583 0.666 78.8 81.3

1989 172 + 0.152 0.563 59.9 80.8

2001 1188 + � + � 0.359 0.759 71.0 89.3

1975 716 Regen � � � 0.284 0.442 70.3 77.7

1989 624 + + � � N/A 0.179 0.396 63.6 73.1

2001 794 + + � � 0.183 0.336 61.6 71.8

1975 5950 Floodland/Wetland � + � � 0.173 0.370 62.7 72.6

1989 4278 � + � � N/A 0.223 0.391 66.6 73.5

2001 7212 � + � � 0.235 0.372 65.5 72.0

1975 7578 Agriculture � � � � � 0.650 0.805 85.6 91.4

1989 7044 � � � � � N/A 0.655 0.806 85.8 91.6

2001 6590 � � � � � 0.662 0.806 85.8 91.5



slightly longer than a decade, predictions were made using one 12-year time step. Contiguity filters of several sizes were tested andthe 5 � 5 contiguity filter was applied to define the neighborhoodof each cell and used to weight the suitability of areas near eachexisting cover type higher for the establishment of that cover type.

Three maps were generated: (1) as a test of the temporalstationarity in the forest- and land-cover trends, 2001 cover waspredicted from 1989 cover and the 1975–1989 transition rates andcompared with the observed 2001 data; (2) future (2013) coverwas predicted from the 2001 data using 1989–2001 transitionrates, i.e., assuming post-Soviet forest- and land-change patternsand trends continue; and (3) future cover was again predicted from2001 data based on 1975–1989 transition rates adjusted to 2001cover proportions, i.e., assuming a return to Soviet era patterns andtrends.

4. Results and discussion

4.1. Logistic regression models

Twenty-seven models, both including and excluding the lagvariable, were created that described the relationship betweenseveral explanatory environmental variables and study site covertypes existing in 1975, 1989, and 2001 (Table 3). Randompermutation tests for each model (excluding the lag variable)had indicated that Moran’s I for the residuals departed significantlyfrom zero in all models (p < 0.001), providing evidence that spatialautocorrelation was probably an important factor that should beconsidered in analyses. All models improved significantly with theinclusion of the lag variable, indicated by higher Nagelkerke R2 andhigher classification accuracy in the autoregressive models.

4.2. Forest- and land-cover patterns by era

Among the cover types in the Forest group (see Table 2), Coniferwas fit well by the models (Table 3) and relationships did not varyby era. At all dates, Conifer forests, in the form of pine and/or larch-dominant stands, are generally concentrated in the higherelevation and steeper slope regions of the study site. Therelationship with D2Urban (+) (Table 3) is also logical becauseareas near cities are more likely to be used for agriculture or othermore intensive uses. Intuitively, it seems that coniferous forestsshould similarly be associated with an absence of roads; however,the minor and forest road networks are, by nature, built into forestmanagement areas used for harvesting and the D2Road relation-ship (�) is likely indicative of the importance of Conifer to theforest industry of the region. Mixed forest models also fit well.Mixed forests are similarly concentrated at higher elevations in thestudy site at all dates; these areas have a lower TWI (�). BecauseMixed can include secondary succession, its closeness to accessroutes of roads (�) and rivers (�) may represent re-growingaccessible forests logged earlier in the Soviet era. In addition toroad transportation, much Soviet era logging was floated on rivers,a management practice no longer widely allowed (Shvidenko andNilsson, 1996). Models for Deciduous fit slightly less well but wereconsistent over all dates. Correlation with Elevation (+) may beexplained by the fact that other land-cover types are more suited toconditions at lower elevations (i.e., Floodland/Wetland); addition-ally, deciduous forests often occupy previously disturbed areasthat preferentially occur at higher elevations where fire- andlogging-prone conifers are more prevalent. Correlation withD2Urban (�) and D2Road (�) is also likely due to the fact thatin this region deciduous forests tend to occupy previouslydisturbed areas; regions with developed infrastructure are alsomore likely to have been disturbed. More remote, less developed

areas in this region should tend towards non-harvested matureforests or forest types not suited for harvest.

In the Disturbance and Recovery group, variables significant inpredicting Cut differed by date. In 1975, only D2River (�) wassignificant. Historically, logging would preferentially occur in themore easily accessible river valleys where access routes existed;however, D2Road did not show correlation to Cut areas in the 1975model. The 1989 model again included significant correlation withD2River (�) and also D2Road (�). Data from 2001 produced theweakest model describing cut areas; Cut was associated only withElevation (+). Poorer model fit was likely associated with thelimited territory affected by Cut in 2001 due to decreases in loggingactivity. Analysis of Burn produced relatively good models. Modelsof Burn for both 1975 and 1989 included Elevation (+) as the onlyexplanatory variable. There were few recent Burn pixels in theland-cover data for these dates, and therefore presence pointsavailable for the analysis were concentrated in a few small areas. Alarger area was affected by Burn in 2001 and this date produced thebest fitting model. Similar to 1975 and 1989 models, Elevation wassignificant (+). In addition, 2001 Burn occurred closer totransportation routes, where logging and other human-drivenfactors are more likely to affect the landscape. Burn showed apositive relationship with D2Urban; in the study region and dates,fires seem to be more associated with forest use than humansettlements. Models describing the probability of occurrence ofRegeneration also differed by date. This was expected for a covertype dependent on various disturbance types. Correlation withD2River (�) and D2Road (�) in all models is consistent with thehypothesis that disturbance (whether fire, logging, agriculture),and thus regeneration, occurs more frequently near access routes.Positive correlation of Regeneration to Elevation (+) and Slope (+)at 1989 and 2001 dates indicates that disturbance preceding forestregeneration occurred in higher elevation, dissected areas.

In the Other Land Covers group, Agriculture produced strongmodels for all three dates. The most suitable land for agricultureduring both eras is found in lower elevation flat-lands near rivernetworks and in close association with infrastructure, indicated byroads and urban. Models for Bog/Sparse Conifer fit somewhat wellin 1975 and 1989 and less well in 2001. Models predicting Bog/Sparse Conifer in 1975 and 1989 showed a positive correlationwith Elevation and D2Urban and a negative correlation with bothD2River and D2Road. Based on these results, positive correlationwith high elevation is likely due to inclusion of sparse coniferforests in this mixed cover type category. This cover type is alsogenerally located in the more remote regions of the study site,farther from urban areas. Analysis of Floodland/Wetland resultedin logical albeit relatively weak models. Floodland/Wetland wasassociated with Elevation (�) in 1975 and 1989 and with TWI (+),D2Road (�) and D2River (�) in all dates, logically occurring inmoist, accessible valleys. In 2001, Elevation was not significant,though Slope was associated with Floodland/Wetland (�), whichtypically occurs in flatter areas.

4.3. Projections of forest- and land-cover trends

Three maps and associated statistics of forest- and land-coverproportions resulted from the CA–Markov analysis: (1) 2001 coverprojected from 1989 data using 1975–1989 transition rates; (2)2013 cover projected from observed 2001 data using 1989–2001transition rates; (3) 2013 cover projected from 2001 data using1975–1989 transition rates adjusted to 2001 land-cover propor-tions (Figs. 5 and 6).

When predictions for 2001 using 1975–1989 Soviet eratransition rates were compared with observed 2001 land cover,results revealed some potential differences that might have



occurred had pre-transition forest management and other condi-tions continued to shape the landscape. The actual Cut proportionobserved in 2001 was approximately 74% lower than thatpredicted by 1975–1989 transition rates. This reflects thedecreases in timber harvest and forest industry production.Conifer forest was a somewhat lower proportion (by about 14%)in the modeled 2001 scenario compared to the observed, likelyindicative of continued higher rates logging in Conifer shouldSoviet era trends have continued. Mixed was predicted as a greaterproportion in the modeled 2001 scenario. This was likelyinfluenced by a more severe fire year in 2001 observed by theland-cover data, and where fires may have actually burned in thesemixed forests; some may also be due to continued growth ofsecondary forests. Presence of Agriculture was predicted at aslightly higher proportion based on the 1975–1989 era data,

possibly indicative of less abandonment of collective agriculture(or less visible forest regrowth on these sites) compared to thepost-Soviet era.

Forest- and land cover predicted for 2013 based on 1975–1989transition rates and the observed 2001 data proportions, indicatesthe potential state of the landscape if dynamics were to revert topre-1990 conditions. In this model, Conifer proportion decreasedby about 16% from the 2001 observed proportion while theproportion of Mixed increased (again, in part likely due to fires in2001 observed). The proportion of Cut was more than three timesas great in the 2013 modeled data compared to 2001 observed. Theproportion of Deciduous predicted for 2013 increased slightly fromthat predicted for 2001 using the same 1975–1989 transition data,however it did not increase as much as in observations andpredictions based on the early Russian Federation era trends.

Fig. 5. Predicted maps from CA–Markov models: (a) predicted 2001 cover based on 1975–1989 transition probabilities, (b) observed 2001 cover, (c) predicted 2013 cover

based on 1975–1989 transition probabilities, (d) predicted 2013 cover based on 1989–2001 transition probabilities.



Agriculture proportions continued to decline slightly by 2013when based on these Soviet era trends, but the decline was not asgreat as that in predictions based on the early Russian Federationera trends.

Forest- and land-cover proportions predicted for 2013 from the1989–2001 early Russian Federation era rates indicate patternsthat could result if forest management and associated conditionsobserved over the early Russian Federation era continue into thefuture. In this model, 2013 Cut proportions showed a continueddecrease (by approximately 10%) from observed already low 2001proportions. Agriculture proportions also continued to decrease(by about 13%) with respect to observed 2001 proportions, andConifer proportions decreased slightly (about 5%). Lower Mixedpredictions are, again, likely influenced by large fires on the 2001image. Deciduous occupied a greater proportion of the landscapethan its proportion based on Soviet era projections.

4.4. Comparisons with other studies in Siberia

This study is the first to undertake landscape-scale analyses ofSiberian forests focusing on both forest–environmental relation-ships and spatial–temporal projections of trends over divergentforest management eras. A comparison of the patterns and trendsresulting from this analysis with those found in other studies ofSiberian forests provides context and support for the overall resultsof the present study.

Several studies have used finer scale field data to investigateforest–environmental relationships in the broader Baikal region(Chytry et al., 2008) as well as other regions of Siberia (Cushmanand Wallin, 2002). Working over an elevation gradient in a site tothe southwest of the Baikal study site, Chytry et al. (2008) foundtopography to be the main source of variation in forest types. Theirresults indicated that light versus dark coniferous forests, or evendifferent species, may occupy different topographical aspects.Although our remote sensing derived land-cover data was ofcoarser thematic resolution than their community- and species-specific field data, logistic regression results for the Baikal site alsodemonstrated that topography played a major role in the presenceof forest cover types. Elevation was positively associated withConifer, Mixed, and Deciduous; Conifer was also associated withsteeper slopes, as was Regeneration at the second and third dates,presumably in harvested Conifer areas. Lack of species-level data inthe Baikal site forest-cover dataset, plus the overall prevalence oflight-coniferous forests in the Baikal site are likely reasons whyAspect, although it was tested as an independent variable, was notincluded in final models.

As early as the mid-1990s, studies in Siberia suggestedcorrelations between fires and closeness to roads (Korovin,1996). Recently, Kovacs et al. (2004) found a positive spatialcorrelation of fire with roads (r2

2001 ¼ 0:81, r22002 ¼ 0:90,

r22003 ¼ 0:88) in central Siberia; this was also the case with forests

near railroads, settlements and mining industry locations. Theseresults agree with our logistic models which support a strongrelationship between fires and proximity to roads, but divergesomewhat in that fires in the Baikal site tended to be either notrelated to or found away from settlements, indicating strongerassociation with forest use. Crevoisier et al. (2007) found thatwhile climate variables were the most important drivers of fire,that fire had a positive association with road density. We foundsimilar relationships between fire occurrence and D2Road (�) inthe 2001 model (i.e., the year with significant fire presence). Theformer study also found that above a given road density threshold,no burning is expected to happen. While they attribute this tobetter fire suppression, this agrees with our Baikal site findingsthat Burn in 2001 occurred at greater distances from settlements(which in turn have denser transportation networks). While firefrequency trends cannot be inferred from the three dates in ourdataset, Baikal site statistics do not disagree with studiesindicating that fire return intervals (FRI) may be decreasing (i.e.,more frequent fires) in the Siberian forest, attributed primarily toincreasingly more favorable weather conditions (Cushman andWallin, 2002; Soja et al., 2007; Kharuk et al., 2008), and tocombined human–climate inter-relationships (Achard et al., 2008),with potential implications for forest and fire management. Anadditional disturbance, not present in our dataset is infestation bythe Siberian silkmoth (Dendrolimus superans sibiricus Tschetw.).Kharuk et al. (2007) showed that tree mortality from this forestpest was related to the conifer cover type and to topographicfeatures, especially elevation and also steepness, relationships offorest management interest that lend themselves to the methodsused in this study.

With respect to forest-cover trends, in a project using 1-kmland-cover data from �1990 and �2000, Achard et al. (2006)characterized types of rapid post-Soviet era forest-cover changeover boreal Eurasia. In sample sites near Lake Baikal they foundtimber industry with moderate intensity clear-cut or selectivelogging but at small to moderate change rates; moderate levels ofchange in species composition including natural regrowth withchange in species towards deciduous; and increased firefrequency. Low post-Soviet era logging rates, trends towardgreater deciduous forest compositions and increased fire propor-tions agree with our findings during an equivalent era. In a study

Fig. 6. Projected cover type proportions resulting from CA–Markov models.



that combined official Russian Federation forestry statistics withremote sensing-derived land-cover statistics, forested landscapesin Tomsk Oblast and Krasnoyarsk Krai showed significantlyreduced logging proportions, a decrease in agriculture propor-tions, and an increase in new regrowth/deciduous forestsproportions at a post-Soviet (�2000) date compared to Sovietera (�1974) proportions (Bergen et al., 2008). Shvidenko andNilsson (1996) found declines in mature coniferous forests inRussia including in Siberian Russia, results that are also consistentwith this study.

4.5. Spatial–temporal modeling methods

The approach implemented in this study provided a way toinvestigate and project the forest- and land-cover patterns andtrends characteristic of a southern Siberian boreal forest over a�35 year period surrounding the transition from the Soviet Unionto the Russian Federation, and to infer possible influences of thedifferent forest management eras on regional ecology. Use oflogistic regression analysis revealed the relationships betweeneach cover type and explanatory environmental variables and overthe different eras. Probability maps from logistic regression andtransition rates derived from time series land-cover data were usedin a CA–Markov model to project trends and to include a spatialcomponent. This combined dynamic approach improves on staticmodels and allowed us to project landscape conditions under twodifferent trend scenarios based on Soviet and post-Soviet eraconditions.

Some limitations that introduce uncertainty into results shouldbe noted. Land-cover data were available on slightly longer than adecadal basis; therefore only large-scale, long-term change couldbe modeled for a single increment 12 years into the future. The CA–Markov contiguity filter prevented randomly dispersed changesfrom occurring; however neighborhood constraints were equallydefined for all cover types. In reality, the different cover typesmodeled occur with different degrees of spatial dependence. Interms of spatial datasets, GIS data were not widely available forSiberia and the inability to accurately map the class of transientminor forest roads for the 1975 and 2001 dates may have slightlybiased some results. While remote sensing-derived data typicallyhas error, those present in the land-cover dataset were probablyless influential in logistic regressions given that the majority ofpixels were classed correctly at each date (Table 2), but pixel-levelclassification confusion between similar forest cover typesbetween dates may underlie the sometimes non-inclusion ofprevious forest type as significant explanatory variables fordisturbance models (although where these variables wereincluded in models they were as expected ecologically). To addressthese issues and to further reduce uncertainty in future studies,logical steps to refine the models would ideally include (1) use offurther refined land-cover and GIS data, (2) additional dates ofland-cover data to create models at finer temporal resolutions; and(3) more flexibility to tailor projections to the differences in thespatial relationships inherent in the different cover types (i.e.,variable contiguity filters).

4.6. Information for forest management

Modeled results from this study indicate that should earlypost-Soviet trends continue, low rates of logging, some agricul-tural abandonment, re-growing forests especially near accessroutes but away from settlements, some increase in deciduous(although perhaps not as great as in some other Siberian regions),along with continued or increased fire events in mixed andconiferous forests will define the landscape. However, it is also

likely that new developments will influence forest trends in theregion. Russia’s new Forest Code contains two significantchanges: (1) transfer of forest management and administrativeauthority from the Federal Forestry Agency to regional autho-rities, and (2) emphasis on long-term leases that shift manage-ment responsibilities (for example reforestation, forestprotection, and other management functions) to lease-holders.Current forest sector development plans seek to increase timberproduction and economic potential of forests through investmentin road infrastructure and local value-added processing, com-bined with improved forest management practices (Vashchuk,1997; Williams and Kinard, 2003).

This study shows that an increase in production approachingthat of the Soviet era would likely have an influence on certainforested landscape patterns and trends. Infrastructure, in terms ofdistance to roads (D2Road), was one of the most frequentlysignificant study explanatory variables, in particular in predictinglogging activity locations subsequent to the decline of Soviet erapractices which also may have included river transportation. Anew focus on road-building to increase timber production wouldlikely target areas associated with coniferous forests, which arefound at higher elevations, with steeper slopes and away fromsettlements. In turn, this may necessitate increased attention torestrictions on logging on steeper slopes in order to protect fragilesoils and watersheds (Krankina and Dixon, 1992). Areas ofregeneration, deciduous and mixed forests are presently foundcloser to existing roads and settlements, but the deciduous specieswhich comprise a major component of these cover types areconsidered of low-quality for forest industry (Shvidenko andNilsson, 1996). Some newer developments may influence land-scapes over the longer-term in ways not yet well-known. Recentlegislation has changed certain harvesting practices includingrequirements for fewer and smaller clear-cuts, and therefore evenif logging rates reverted to Soviet era levels, these requirementscould result in more sustainable landscapes than those createdduring the Soviet era where large landscape-scale clear-cuts wereprevalent.

In acknowledgement of potentially complex forest manage-ment considerations such as the above and others, Russia isseeking to better assess existing forest resources through a newNational Forest Inventory system and to incorporate increasedreliance on remote sensing and GIS data. This information, alongwith methods such as those demonstrated through this study,could provide valuable information to enable sustainable forestmanagement at multiple scales, including regional landscapescales.

5. Conclusions

This analysis provides significant insight into observed andprojected landscape patterns and trends occurring in a largelyforested Baikal region study site over Soviet and post-Soviet socio-economic and forest management eras. Although inference ofspecific causality of trends for the period 1975–1989 as comparedto 1989–2001 is beyond the scope of this study, our resultssuggest that while some patterns are associated primarily withenvironmental variables, other differences in land-cover patternsand trends are likely related to the institutional changesassociated with the different forest management eras. The resultscorroborate those from other studies of remotely sensedobservations of patterns and trends in Siberian forests; they alsoextend static analyses to provide models of future landscapescenarios.

As the situation in Russia stabilizes, natural resource legislationand management is increasingly codified and resulting practices



will affect future forest patterns and dynamics in the region.Russia’s new Forest Code, which now officially includes transfer ofadministrative and management functions to regional authoritiesand emphasis on long-term leases, continues the early post-Sovietera trend of less central control and will undoubtedly furtherinfluence forest dynamics in Siberia and the Baikal region of study.Because Russia contains a large proportion of the world’s forests,the management and dynamics of forests of the study region and ofSiberia more broadly may have considerable impact on carbon,climate change, biodiversity, and forest products. Understandingthe potential legacy of the different forest management eras onforested land-cover trends, as well as continued development ofspatial–temporal methods with which to understand them, will beimportant as the Russian Federation moves into new era of forestmanagement.

Acknowledgements

This project was supported by the NASA Land-Cover Land-UseChange (LCLUC) Program through contract NAG5-11084. Weextend appreciation to Dr. Garik Gutman, NASA LCLUC Program;Vasily Olenik of the Irkutsk Ministry of Natural Resources; andShannon Brines, Stephanie Hitztaler, Tingting Zhao, and BryanEmmett at the University of Michigan ESALab.

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