Text of Changing Regimes: Forested Land-cover Dynamics in Central...
AbstractThe Twentieth century saw fundamental shifts in northernEurasian political and land-management paradigms, in Russiaculminating in the political transition of 1991. We used the1972 to 2001 Landsat archive bracketing this transition toobserve change trends in southern central Siberian Russia inprimarily forested study sites. Landsat resolved conifer, mixed,deciduous and young forest; cuts, burns, and insect distur-bance; and wetland, agriculture, bare, urban, and water landcovers. Over 70 percent of forest area in the three study siteswas likely disturbed prior to 1974. Conifer forest decreasedover the 1974 to 2001 study period, with the greatest decrease1974 to 1990. Logging activity (primarily in conifers) declinedmore during the 1991 to 2001 post-Soviet period. The area ofYoung forest increased more during the 1974 to 1990 timeperiod. Deciduous forest increased over both time periods.Agriculture declined over both time periods contributing toforest regrowth in this region.
IntroductionFundamental shifts in human-driven land-cover changeregimes can significantly change the amount, type, andsuccessional state of forests on a landscape, and over largespatial and temporal scales, this may result in long-term impactson regional forest ecology and carbon dynamics (Foster et al.,1998; Houghton and Goodale, 2004). As a monitoring method,satellite remote sensing data, which became broadly avail-able starting in the 1970s, has contributed to more accuratecharacterization of the legacy of such trends to forest ecologyand forest carbon (Cohen and Goward, 2004)
The Twentieth century was an era of fundamental shiftsfor northern Eurasia, where large-scale political and economictransformations have occurred and where forest and land-management paradigms have changed (Korovin, 1995; World
Changing Regimes: Forested Land-coverDynamics in Central Siberia 1974 to 2001
K.M. Bergen, T. Zhao, V. Kharuk, Y. Blam, D.G. Brown, L.K. Peterson, and N. Miller
Bank, 1997). In the vast and largely remote forested region ofSiberian Russia, the end of the former Soviet Union in 1991also ended decades of centralized state-supported economy.While the middle Twentieth century saw the height ofSoviet-era agricultural collectivization and logging shortlyafter 1991, timber harvest and lumber production haddropped to approximately one-quarter of former annual rateand had not substantially recovered by 2001 (Krankina andDixon, 1992; Blam, 2000). The abandonment of collectivefarmlands began before the 1991 transition.
In the absence of consistent and available statistics, remotesensing data are advantageous for directly observing andcharacterizing long-term changes on the Siberian forestedlandscape. To be most useful, remotely sensed data collectedover a span of years, at spectral and spatial scales capturingboth natural and human-driven change are required. Landsat isa strong candidate for this given its now over 30-year period ofoperation and characteristics suitable for vegetation analysis(Goward and Masek, 2001). The goal of our project was toobserve and characterize trends in forest- and land-coverchange before and after the 1991 political transition in casestudy sites representative of regional forest- and land-coverchanges in Tomsk Oblast, Krasnoyarsk Krai, and Irkutsk Oblastusing the 1972 to 2001 Landsat archive. Our specific objectiveswere to (a) use Landsat to map and quantify forest- and land-cover for three dates bracketing the political transition withone of the scenes at the transition date, (b) quantify 1972 to2001 forest- and land-cover change trends and compare thosefrom the Soviet-era with those from the post-Soviet era, and (c)analyze how these trends are changing the landscape in termsof forest amount, type, and successional states.
BackgroundThe climate in the central Siberian study region (Figure 1) iscontinental with January and July mean temperatures of�22°C, and �18°C, respectively. Fifty to sixty percent ofprecipitation as rainfall occurs between July and September,and snow cover begins in October and remains for six toseven months. Physiography includes the eastern reaches ofthe west Siberian lowlands, the central Siberian plateaudissected by major rivers, and low mountain areas near LakeBaikal. Soils consist of sands, clays, and podzols. In winter-time, soils may be frozen to a depth of 2 to 3 meters, withlocal cases of permafrost.
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K.M. Bergen, T. Zhao, and D.G. Brown are with the Schoolof Natural Resources and Environment, University ofMichigan, Ann Arbor, MI 48109 ([email protected]).
V. Kharuk is with the V.N. Sukachev Institute of Forest,Krasnoyarsk Akademgorodok 660036, Russia.
Y. Blam is with the Department of Economical Infor-matics, Institute of Economics and Industrial Engineering,Novosibirsk, Russia.
L.K. Peterson is with the U.S. Forest Service Interna-tional Programs Outreach and Partnerships Unit,Washington, D.C., 20005.
N. Miller is with Radiance Technologies, Inc., StennisSpace Center, MS 39529, and formerly at ERIM Interna-tional, Ann Arbor, MI
Photogrammetric Engineering & Remote Sensing Vol. 74, No. 6, June 2008, pp. 000–000.
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Ecoregions in the study area are dominated by borealforests (taiga). The forest is a mosaic of several types atvarious successional states and with characteristic distur-bances (Farber, 2000; Hytteborn et al., 2005; Nikolov andHelmisaari, 2005). The “light-needle” coniferous forestincludes Scots pine (Pinus sylvestris L.) and larch (Larixsibirica Ledeb.). The “dark needle” forests of spruce (Piceaobovata Ledeb.), fir (Abies sibirica Ledeb.), and Siberianpine (Pinus sibirica Du Tour) exist typically as latesuccessional forests or as accompanying species in light-needle dominated communities. Deciduous birch andaspen (Betula pendula Roth and Populus tremula L.) occurin pure stands as young forests on post-disturbance sitesor as accompanying species in mid-successional mixedforests.
Human settlement in the region started in the Seven-teenth century, but did not have significant impact until thelatter half of the Nineteenth century. A few large urbancenters are present (Irkutsk, Krasnoyarsk, Tomsk, andNovosibirsk), but much of Central Siberia is removed fromsignificant urban infrastructure, with settlements consistingprimarily of small towns and villages. A number of ethnicgroups are represented in the region although, as groups,indigenous peoples are found to the north of the studyregion. Logging started intensively in the 1940s and 1950s,staffed in early decades by prison labor, and peaked in the1960s to 1980s (Kortelainen and Kotilainen, 2003). Between1930 and 1957 the Siberian silkmoth (Dendrolimus super-ans sibiricus Tschetw.) damaged and killed about 7 millionha of forests. In the 1994 to 1996 outbreak in KrasnoyarskKrai 0.7 million ha of forest were affected, and about0.3 million ha experienced complete mortality (Kharuket al., 2001; Kharuk et al., 2003). Fires are a natural part ofboreal ecology (Conard and Ivanova, 1997) and beforehumans came to dominate disturbance regimes, they werecaused primarily by lightning. Most are now thought tobe human-caused (Goldammer and Furyaev, 1996), often
fuelled by the debris left from the clear-cutting of trees orinsect mortality (Korovin, 1996).
Landsat Remote Sensing in SiberiaThe Landsat program began in 1972 with the Multi-SpectralScanner (MSS; 1972 to 1983). This sensor was followed bythe Thematic Mapper (TM4 and TM5) beginning in 1982 andby the Enhanced Thematic Mapper (ETM�) in 1999. TheLandsat-5 TM and Landsat-7 ETM� continue to acquireimagery, though each has experienced technical failures thathave degraded their quality (NASA Landsat Project ScienceOffice, 2006).
A general challenge in using Landsat data for long-termstudies is that the information archived from the succes-sion of satellites (MSS, TM, and ETM�) differs to varyingdegrees in its spatial, spectral, and radiometric resolutionsplus noise content, complicating attempts to create consis-tent classifications over time or apply change-detectionmethods. More specific to Siberia, the availability ofhistorical Landsat data for the study region was not well-documented, boreal regions are frequently cloud-covered,and Russian and Western scientists have only recentlybegun to collaborate on use of environmental remotesensing (Bergen et al., 2003), so data archives and networksremain scarce.
Recent work, however, has demonstrated application ofLandsat data to land-cover studies in Central Siberia, andalso in western Russia and Far East Siberia. In CentralSiberia research has focused on combining Landsat ETM�and radar sensor data for mapping boreal forest types anddisturbance (Kharuk et al., 2003; Ranson et al., 2003). In fareastern Siberia, rates and patterns of forested land-coverchange between 1972 and 1992 were analyzed using Landsat(Cushman and Wallin, 2000). In western Russia, Landsat hasbeen compared with other sensors (Bartalev et al., 1995) andused to document forest disturbance and carbon content(Krankina et al., 2004).
Figure 1. (a) Study regions, and (b) Case study sites and location within the RussianFederation (Olson et al., 2001; Environmental Systems Research Institute (ESRI), 2006).
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TABLE 1. LANDSAT TIME SERIES SCENES USED IN THE STUDY. THE PRIMARY SCENES FOR ALL SITE/DATE COMBINATIONS ARE INDICATED WITH AN “*”.USEABLE AREA REFERS TO THE PERCENT AREA AVAILABLE FOR LAND-COVER CLASSIFICATION AFTER CLOUDS AND HEAVY HAZE WERE REMOVED FROM
ALL THREE DATES IN A STUDY SITE COMPARED TO THE TOTAL AREA OF THE WRS 2 PATH/ROW
Images ETM� 09 July 1999* 18 August 2000* 13 August 2001*
TM 07 September 1989* (1) 14 May 1991* (1) 21 August 1989 e (p132r23)*(2) 07 July 1990 (e) (p140r20) (2) 19 August 1989 w (p134r23)(3) 02 July 1989 (w) (p142r20)
MSS (1) 30 August 1975 (p159r20)* 26 June 1974* (p152r20) (1) 21 June 1975 (e) (p143r23)*(2) 08 July 1975 (w) (p160r19) (2) 28 July 1975 (w) (p144r23)
Useable Area 100% 99.5% 91.7%
MethodsCase Study Sites SelectionCase study sites were selected from Tomsk Oblast, KrasnoyarskKrai, and Irkutsk Oblast. These three administrative unitsare the three most important in central Siberian forest sectorin terms of forest reserves, timber removal, and lumberproduction (Blam et al., 2000). Criteria for site selectionwere: (a) availability of cloud-minimized Landsat images foreach of three dates between the 1972 and 2001 endpoints,with a middle date at or near 1990, (b) geographic distribu-tion, with a case study site located in each of the threeadministrative units, (c) representative land-cover andchange as understood from the literature and prior studies(Bergen et al., 2000; Farber, 2000; Kharuk et al., 2001), and(d) interest to Russian study team members and availabilityof reference data sources.
The Landsat United States Geological Survey (USGS),Beijing, China and Kiruna, and Sweden archives weresearched to locate all useable scenes in each World Refer-ence System (WRS) path/row location that fell within theentire extents of the three administrative units, and for allyears 1972 to 2001. All potentially usable scenes wererecorded into an Access® database; the database searched forall potential locations with three or more time-series scenesthat met the study objectives, and three case study siteswere selected (Figure 1).
Case Study SitesTomsk SiteLandsat path/row 147/20 (WRS-2) is in eastern Tomsk Oblast(Figure 1). This westernmost site is centered on 57° 17' 57" N,86° 33' 18" E. The Tomsk site is comprised of boreal taigaforest (72 percent), northern temperate forest (27 percent, nowprimarily agricultural lands), and forest-steppe (�1 percent)ecoregions (Olson et al., 2001). Eastern Tomsk Oblast lies atthe transition between the west Siberian lowlands and centralSiberian plateau and has minor topographic relief withelevation ranging from approximately 100 to 200 m above sealevel (ASL) (USGS, 1996). Tomsk Oblast is approximately 90percent forested (Shumilova, 1962).
Krasnoyarsk SiteLandsat path/row 141/20 is in Krasnoyarsk Krai and iscentered on 57° 19' 27" N, 96° 05' 01" E. The site is com-prised of boreal taiga forest (91 percent) and forest-steppe(9 percent) ecoregions. The site lies on the central Siberianplateau with relatively minor topography ranging from approx-imately 150 m to 500 m ASL, but largely between approxi-mately 200 to 400 m ASL (USGS, 1996). Krasnoyarsk Krai
was first among all regions of the Russian Federation intimber reserves as of 1995, and in the top three in loggingand lumber production (Blam et al., 2000). Krasnoyarskforests were where some of the most extensive logging byprison labor in the mid-Twentieth century occurred.
Irkutsk SiteLandsat path/row 133/23 is in Irkutsk Oblast. This eastern-most study site is centered on 53° 06' 47" N, 106° 06' 25"E. Part of the autonomous region of Ust-Orda Buryatsky isin the site southwest. The site is comprised of boreal taigaforest (88 percent), northern temperate forest (2 percent) andforest-steppe (10 percent) ecoregions. Topographic relief isgreatest in this site, ranging from approximately 450 m ASLat the Lake Baikal surface to approximately 2,055 m ASL inthe Baikal range (Peterson, 2004) north and west of LakeBaikal. Irkutsk Oblast was second after Krasnoyarsk Kraiin timber reserves in 1995 (Nilsson and Shvidenko, 1999).
Landsat DataWhile it was possible to find time series for each adminis-trative unit with scene dates of approximately 1972, 1990,and 2001 (Table 1), even to meet study minimum require-ments it was necessary to combine two or more scenes for atleast one of the dates in each of the case study sites (i.e., if alarge area of clouds was present in a corner of the otherwisemost cloud-minimized primary scene).
Other Spatial DataIn addition to Landsat data, a digital elevation model (DEM),and ancillary GIS datasets (e.g., water, transportation, andurban) were needed. At the time the Landsat data wereprocessed, high-resolution DEM (i.e., Shuttle Radar Topogra-phy Mission (SRTM) data were not available for this region,and therefore a 1 km DEM was extracted from the USGSGTOPO30 data (USGS, 1996). High-resolution GIS data werenot available, and so hydrology, transportation, and urbanfeatures were digitized by project staff from Russian 1:200 000topographic maps held at the University of Michigan(Federal Geodesy and Cartography Service of Russia, 1991).
Landsat Data PreparationGeorectificationDefinitive ephemeris processing had been used to obtaincoordinates of the study ETM� images, and these data weregeometrically corrected with the coordinates available in theheader files (Masek et al., 2001; NASA Landsat ProjectScience Office, 2002). The radiometrically and systematicallycorrected ETM� data in Space Oblique Mercator wereimported into ERDAS Imagine® and orthorectified (Leica
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TABLE 2. CATEGORIES USED IN LANDSAT CLASSIFICATIONS (COLUMN 1) AND
FOREST AND LAND-COVER/LAND-USE TYPES OF THE STUDY SITE (COLUMN 2)
Category Landsat Classification Forest and Land-cover/Number Category Land-use
1 Conifer Spruce/fir forest, uplandSpruce/fir/Scots pine forest,
4 Young Post-cut or post-fire with deciduous (birch-aspen) or conifer regeneration
Regeneration from agriculture5 Cut Fresh cuts6 Burn Fresh fire scars7 Insect Insect infestation8 Wetland Wetlands, flood-lands9 Agriculture Agriculture (crops, hay,
pasture)10 Urban Built-up areas11 Bare Bare ground12 Water Water
Geosystems, 2005) using the Landsat-7 geometric model andthe 1 km DEM, and resampled into the project coordinatesystem (Krasovsky Spheroid, Pulkovo 1942 datum, AlbersConical Equal Area projection). This procedure was appliedto the MSS and TM images which were then registered to theETM� images (RMS error between 0.5 and 0.85 pixels). Imageswere resampled, ETM�/TM images to 30- and 60-meters, andMSS images to a 60-meter resolution. Each time-series wassubset to the common image area of the three dates.
Clouds, Haze, and Sensor AnomaliesNoise occurred in the some MSS and TM data (primarilyblue bands) in the form of line dropout. As much noise aspossible was removed from classification features by eithernot using the blue bands with noise, or identifying areaswith line dropout and replacing them with values based ona neighborhood majority. Clouds and cloud shadows weresegmented into spectrally similar objects using eCognition®
(Definiens, 2002), and then masked from the datasets. Ifa cloud pixel was present at one of the image dates, itwas removed from all image dates for that site (Table 1).Standard de-hazing algorithms proved unsatisfactory withspatially-varying haze patterns. Heavy haze was removedalong with clouds during the cloud-masking stage, and areasof light haze containing potentially usable data wereaddressed later during the classification process.
Forest- and Land-cover ClassificationA test using the Tomsk imagery of the potential for use ofradiometric-based change detection for this study resultedin limited success, primarily due to spatially-varying haze,although the lack of exact anniversary dates and the differ-ent MSS, TM, and ETM� data also reduced radiometric compat-ibility. Given similar characteristics in other sites andscenes, individual image classification and post-classificationchange detection were selected as the overall methods.
Forest- and Land-cover ClassesA set of classes applicable to each of the nine Landsatscenes to be classified was arrived at by (a) assessment ofthe ecology of the region, (b) examination of spectral plotsand separability in MSS/TM/ETM� data during initial classifi-cations, and (c) testing and refinement of classifications.Forests compositions (including community mixes) areprovided on text accompanying the Russian topographicquadrangles. Prior studies provided information on theInsect disturbance class (Eastern Siberian State ForestryEnterprise, 1996). Floodlands and wetlands were notseparable and were combined and assigned to the Wetlandsclass; pure larch (rare, with exceptions in the Irkutsk site)and bogs were assigned to the Conifer class. Conifer andDeciduous are maturing or mature mostly closed forestcanopies. Several different compositions of mixed forestwere assigned to one Mixed class, including those withand without larch and those representing mid-successionalstages. The final classes included: Conifer, Mixed, Deciduous,and Young forest; Burn, Cut, and Insect disturbances; andWetland, Agriculture, Bare, Urban, and Water (Table 2).
Training and Testing DataBecause of restrictions on use of global positioning system(GPS) and remoteness and size of the study area, we could notrely primarily on field data collection for reference data.Reference data were assembled independently by Russianstudy team members based on map and image datasets andlimited field data (Table 3). Because these sources were notnecessarily temporally coincident with all three Landsat imagedates, all potential sites were compared a priori with theLandsat composites to provide the correct interpretation at the
time of the image date. Multiple reference sites per class wereselected over the geographic extent of each test site at eachdate to capture within-class variability; during the classifica-tion process, some additional sites were added as needed byclassification analysts, i.e., �400 total were selected per scene.The selected reference locations were assembled as annotationlayers in ERDAS Imagine® where annotations were pointsrepresenting the central location of homogeneous areas. Thereference sites were divided, with one half for use in classifi-cation training and the other for accuracy assessment.
ClassificationIndividual images of each date for each site were classified at60-meter resolution, consistent with the MSS base resolution.The first step for each scene was an unsupervised classifica-tion using the ISODATA algorithm in ERDAS Imagine® (Tou andGonzalez, 1974). Clusters were grouped into coarse categories(water, forest, disturbance, bare, wetland, agriculture, andurban) and labeled by referencing available ancillary data.Water, Bare, and Urban, were identified in this step alsoaided by GIS data and masked out of the imagery.
Remaining data were then classified using a supervisedclassification procedure. Prior to use for supervised classifi-cation training, polygons of multiple pixels exhibitingsimilar spectral properties were grown from seed pixelslocated at the training data annotation label, and thesepixels used to develop signatures. At each step of thesupervised classification, signature separability was evalu-ated through visual inspection of histograms, ellipse plots,and by calculating matrices of Jeffries-Matusita distance(Leica Geosystems, 2002). Where separability was notsuccessful, classes were combined as discussed above. Imagepixels were classified into categories using a maximum-likelihood decision rule.
The supervised classification procedure was iterative inthat forest (Conifer, Deciduous, Mixed, and Young) andInsect categories were first classified, and masked out. Theremaining area was then re-classified to Burn, Cut, Wetland,and Agriculture. The separate classified image layers were
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TABLE 3. PRIMARY SOURCES CONSULTED FOR REFERENCE SITES SELECTION
Classes Source Date Sites
All Topographic Map Series 1:200 000 1991 AllFederal Geodesy and Cartography Service of Russia
All Forest map of USSR. State Forest Committee 1990 AllForest Forest vegetation of South Predbaikalye 1:200 000 1982 Irkutsk
Academy of SciencesInsect Map 1:200 000. East Siberian State Forestry Enterprise 1996 KrasnoyarskInsect Airborne false color infrared 1979 KrasnoyarskAll Vegetation type map, 1:2,500,000. Atlas of the USSR 1992 AllForest Forest type map, 1:2 500 000. Atlas of the USSR 1992 AllForest, Selected field reconnaissance 2002 IrkutskDisturbanceAll Selected field reconnaissance 1999, 2000 Krasnoyarsk
TABLE 4. THE NINE CHANGE CATEGORIES AND THEIR CHANGE COMPONENTS
From Class To Class Change Category (Time 1) (Time 2)
1 No Change 1,2,3,4,5,7,8,9,11 same as time 12 Logged 1,2,3,4 53 Burned 1,2,3,4,5,6,7,8,9 64 Insect Damage 1,2,3,4 75 Regenerated Type 1 1,2,3 4
1 36 Regenerated Type 2 5,6,7 4,3,2
9 4,37 Forest Succession 4 3,2,1
3 1,28 Development 1,2,3,4,5 9,109 Other/Noise Various Various
spatially recombined using the ERDAS Imagine® ModelMaker. In the Irkutsk site where spatially-varying hazeaffected parts of images, TM/ETM� classification was alsodone at 30 m to attempt to improve results. Poorly classifiedhazy areas were identified using a distance image (LeicaGeosystems, 2002) produced during preliminary classifica-tions and classified separately before being recombined withthe main portions of the imagery.
Accuracy AssessmentPrior to accuracy assessment, polygons of multiple pixelsexhibiting similar spectral properties were grown from seedpixels located at the accuracy data annotation label. Asubset of single non-contiguous pixels were then chosenrandomly from the generated testing polygons, in order toensure an adequate sample but to minimize spatial autocor-relation (Leica Geosystems, 2005). Those classes expecteda priori to be smaller by area were represented by testingfewer pixels than the expected larger classes, with classesvarying somewhat by scene composition. Contingencymatrices comparing testing data to classification results werecreated and producer’s, user’s, overall accuracy and Kappastatistics of agreement (KHAT) were generated.
Forest- and Land-cover ChangeIn addition to land-cover proportions provided for each of thenine site-date combinations, the classifications were used toaggregate meaningful types of land-cover changes categoriesfor the two Landsat time periods. Matrices and change imageswere created by comparing each temporal pair of images.Although most combinations did not occur or occurredminimally with twelve land-cover categories, there were144 changes possible. Based on the matrix output, forest-and land-cover change directions were grouped into: NoChange, Logged, Burned, Insect Damage, Regenerated, ForestSuccession, Development, and Noise/Error (Table 4). Regener-ated Type I refers to Conifer or Mixed forest areas that werecut or burned at some time between the change pair datesand were forest at the earlier date; Regenerated Type II refersto areas that were labeled Cut, Burn, Insect, or Agriculture atthe first date and were forest at the later date.
Change AccuracyAssuming independence of errors in time series of land-cover maps, the estimated overall accuracies of subsequentlycalculated data on land-cover change for the two changeperiods was calculated by multiplying overall accuracies foreach pair of dates, (Yuan et al., 1998). The change matricesincluded a category of Error/Noise, pixels that transitionedto non-logical categories between the two image dates, andthese were recorded for potential accuracy/error information.
ResultsForest- and Land-Cover ClassificationsResults of classification included the nine maps andaccuracy statistics (Table 5). Accuracy figures for individ-ual classes in the text below are producer’s accuracy.
Land-Cover Accuracy AssessmentsAll sites had overall accuracies 81 percent or greater (Table 5),but Tomsk and Krasnoyarsk were on average classified betterthan the Irkutsk site. Among the image dates, the MSS imageshad the lowest overall classification accuracies for all sites.
The three forest types Conifer, Mixed, and Deciduous,were generally classified well, with all (producer’s) accura-cies �80 percent for Tomsk and Krasnoyarsk but some loweraccuracies for Irkutsk at the MSS date. Young forest wasalso well classified in the Krasnoyarsk and Tomsk sites(�82 percent), with most confusion occurring betweenDeciduous and Cut. Young forest at the Irkutsk site was themost difficult of all categories to classify, especially at theMSS date and was confused with Wetland. Wetland was wellclassed in Tomsk and Krasnoyarsk but posed problems at theIrkutsk site where it was confused with the Conifer (bogs),Agriculture, and Young. Cut was somewhat variable butmoderately to well classified and where confused, generallywith Young forest class. Burn was not present at all site-datecombinations and was sometimes confused with bare, cut oragriculture and with accuracy higher for dates with largerburn area and patch sizes. Insect was present only at theKrasnoyarsk site and was classed less well at the TM date
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TABLE 5. ACCURACY OF THE CLASSIFICATIONS FOR EACH OF THE NINE LANDSAT-DERIVED LAND COVER MAPS. SHOWN IS PRODUCER’SACCURACY AND N of Testing Pixels for Each Class Plus Overall Accuracy and the KHAT Statistic
Total 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
where insect outbreak was very minor, occurring in isolatedsmall patches (�0.5 percent of the site area); it was very wellclassified in the ETM� image where it represented a majorregional outbreak (almost 7 percent of the study site).
Land-coverLand-cover results describe the present landscape (Table 6;Figures 2 and 3). The Tomsk image is bisected by theChulym and Kiya Rivers (tributaries of the Ob River) andassociated floodland Wetland. Agriculture is interspersedwith small settlements and located in the southern part ofthe image coinciding with incursions of northern temperateforest and forest-steppe ecoregions. The remainder of theimage is primarily taiga forest. Several patches of matureConifer still exist within a mosaic of secondary succes-sional forests and disturbance patches. The full resolutionimagery shows continued logging, including small rectan-gular Cut patches (clear-cuts) following new prescribed size
limits in the ETM� image (Yaroshenko et al., 2001). Fire inthe first two image dates occurred in close proximity tothese areas of Cut. A similar forest landscape is present inthe Krasnoyarsk site. The image is bisected by the Chunaand Biryusa Rivers (tributaries of the Angara River whichfeeds the Yenisei River) with associated wetland areas.Agriculture is located in the southern third of the image. Inthe northern two-thirds of the image, patches of matureConifer remain within a mosaic of secondary successionalforest and disturbance patches. As in the Tomsk image, theKrasnoyarsk full resolution imagery reveals some activelogging. Fire occurred in forested areas near the second(TM) date. Insect infestation occurred in Conifer and Mixedforest in the Krasnoyarsk images observed at the especiallyat the ETM� date. In both the Tomsk and Krasnoyarskimages and over all dates Mixed is the largest forestcategory on the landscape. In the Irkutsk image this is alsothe case, however, known presence of significantly more
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Figure 2. Landsat ETM� land-cover of the three case study sites: (a) Tomsk, (b) Krasnoyarsk, and(c) Irkutsk. White space in the Irkutsk data is from necessity to use combined non-contiguousimages and removal of some areas due to clouds/haze. A color version of this figure is available atthe ASPRS website: www.asprs.org.
larch means a somewhat different Mixed category composi-tion (Peterson, 2004). Drier forested uplands occur to thewest of Lake Baikal and Cut and active Fire classes wereconcentrated in this area in the ETM� image. In the north-west, finely dissected physiography supports primarilyConifer or Mixed (with larch) forests on drier slopes andbogs and other wetlands in valley bottoms. The southcentral area is comprised of Agriculture and small Urbansettlements. Bare ground occurs only in the Irkutsk imagealong Lake Baikal.
Forest- and Land-cover ChangeChange Accuracy and EvaluationEstimates of the accuracies of land-cover changes were allbetween 73 percent-83 percent (Table 7). These values arelower than the individual land-cover overall classificationaccuracies and represent the probability that both classes arecorrect, and by extension that the change class is correct.
Logical change directions potentially contain some error,but non-logical directions are assumed to be error. Changedirections categorized as Error/Noise were less than or equalto 5 percent of area in the Tomsk and Krasnoyarsk sites, butwere higher for Irkutsk, possibly resulting from Irkutsklower accuracies and its lower accuracies generally occur-ring in classes with greater numbers of pixels than those inTomsk and Krasnoyarsk.
Forest- and Land-Cover ChangeInterpretation of the land-cover proportions (Table 6) showstwo major trends consistent across all three sites over alldates. These are a decrease in the proportion of Conifer foreston the landscape, and an increase in the proportion ofDeciduous forest. Agriculture also decreased over all threesites and over all dates except for the Irkutsk site, where thefirst two dates were nearly equal. Cut and Mixed forest alsodecreased over the three dates for the Tomsk and Krasnoyarsk
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TABLE 7. LAND-COVER CHANGE ESTIMATED OVERALL ACCURACIES AND ERROR/NOISE PROPORTIONS
Figure 3. Forest- and land-cover compositions for eachcase study site for each date (water is excluded): (a) Tomsk, (b) Krasnoyarsk, and (c) Irkutsk.
sites and between the second and third date at the Irkutsksite. Young forest (regrowth from Cut, Burn, and Agriculture)increased over the three dates in the Tomsk and Irkutsk siteand between the first and second date in the Krasnoyarsk site.Wetland was stable over the three dates in Tomsk andKrasnoyarsk, but problematic in Irkutsk probably due toclassification error where wetlands were confused with forestclasses and Agriculture. Other land covers were either stable(Urban, Bare) or more stochastic by nature (Fire, Insect).
Interpretation of the aggregated change categories showstwo trends that are consistent across all three sites (Table 8).These trends are the decrease in the amount of the Loggedcategory between the first and second time periods and thedominance of Forest Succession as the largest changecategory in the second time period. Forest Succession, was agreater proportion in the second time period than in the firstin the Tomsk and Krasnoyarsk site and nearly the same inthe Irkutsk site. In the Tomsk and Krasnoyarsk sites, therewas a somewhat greater amount of total change in thesecond period (1990 to 2001) than in the first period (1975to 1989), but primarily as Succession from earlier distur-bance. In Irkutsk total change was slightly greater in thesecond period, but this is inconclusive due to classificationerror in its more problematic scenes. Given the high amountof non-logical change at the Irkutsk site, it is expected thattrue No Change is underestimated.
DiscussionForest Change in Central SiberianForest Type and AmountAlthough the boreal forests of central Siberia have largelynot been converted to other land uses, and many are still onthe frontiers of human habitation, they are not by any meansquiescent landscapes. Human and natural disturbance havelong been changing the amount, type, and successional stateof this forested landscape. The primary forest of this regionhad a large proportion of dark coniferous forest up throughthe Nineteenth century, whereas the late-Twentieth centuryforest is dominated by light-coniferous mixed or deciduousforest types and the deciduous type appears to be increasing(Hytteborn et al., 2005). In the southern part of KrasnoyarskKrai (including the vicinity of our case study site) forexample, disturbance affected 62 to 85 percent of forestsover the previous (Twentieth) century (Sokolova, 2000).
The remote sensing analyses in the case study sitesappear to corroborate and further illuminate these trends(Figure 3). The ongoing effects of disturbance prior to andinto the early Twentieth century are evidenced by typicallymid-successional Mixed forest (light coniferous-larch-decidu-ous) as the largest category on the landscape. Mid-Twentiethcentury change is evident from the relatively large amountsof forest in early secondary succession in the 1970s images(23 percent, 17 percent, and 9 percent in Young or Decidu-ous category in the Tomsk, Krasnoyarsk, and Irkutsk sites,respectively). These early- to mid-successional forest patchessurround “islands” of remaining mature Conifer forests onsimilar underlying physiography.
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TABLE 8. TERRESTRIAL LAND-COVER CHANGE BETWEEN EACH SET OF CHANGE PAIRS (WATER IS EXCLUDED) AS A PERCENTAGE
LoggingForest harvest has occurred primarily in either remainingdark coniferous forests, especially in areas where desirableSiberian pine is a dominant component of that type, or inlight-coniferous forest where Scots pine and larch are timberspecies. Approximately ninety percent of harvest has beenthrough clear-cutting (Hytteborn et al., 2005), most at thelarger landscape scale prior to new size restrictions. Loggingis often inefficient, leaving slash and logs susceptible to fire.
Russian forest statistics collected for Tomsk, Krasno-yarsk, and Irkutsk for this study confirmed that logging inthe study region had peaked in the 1960s to the 1980s anddropped sharply in approximately 1990 to 1991 (Figure 4).This trend also mirrored the trends for the Soviet Union andRussian Federation as a whole (Figure 4). Based on theLandsat analysis, while some logging continued in the casestudy sites 1974 to 2001, the category of Cut decreased overall three dates in the Tomsk (by 40 percent) and Krasno-yarsk (by 50 percent) case study sites, and between themiddle and last dates in the Irkutsk site, corroborating anoverall trend of decreased forest harvest (Figure 4). TheLandsat-observed reduction in the area of the Cut categoryin the Tomsk and Krasnoyarsk sites appears to have slightlypreceded the sharp drop seen at about 1990 to 1991 inofficial statistics of the overall region.
FireForest lands in the study regions, especially those inKrasnoyarsk Krai are some of the most “fire prone” inRussia (Sokolova, 2000) and in addition forest harvest oftenleaves slash fuels which may feed back into fire disturbancepatterns. On an annual basis, an average of 1.2 percent ofthe Russian far east boreal forest burns each year, withhigher fire activity being found in the southern parts of thisregion than in the north; during years with large fires in thesouthern regions (such as 1998 and 2003), fires can affect 5to 10 percent of the total land area (Sofronov et al., 1998;Sukhinin et al., 2004).
Based on the Landsat analysis, the area of new burns onthe landscape appears to be similar to these reported in theliterature. In the years that relatively severe fire occurred inTomsk (1989) and Irkutsk (2001), approximately 2.3 to 2.4percent of the case study site burned (Table 6). In Krasno-yarsk 0.70 to 0.77 percent of the landscape burned at allthree dates. Because fire is a more stochastic event, threeimage dates are not sufficient for estimating change in ratesof fire over time.
Insects and Other AnimalsSiberian pine and fir are nutrition sources for pest caterpil-lars and are particularly susceptible to the caterpillars of theSiberian silkmoth, which damage or kill their needles.
Insect-damaged areas may be susceptible to subsequent fire.In the Tomsk site, the insect damage that occurred in 1954to 1957 is now observed in the form of long-term succes-sional consequences. Severe insect infestation 1994 to 1996in the Krasnoyarsk site was observed in the 2000 Landsatimage as affecting 6.7 percent of total land cover primarily inMixed and Conifer forests (Kharuk et al., 2003). In suchsevere outbreaks where forests were defoliated or killed, thespectral signature of the Insect class was clearly distin-guished. While outside the case study sites (northern extent
Figure 4. Russian official statistics showing forestsector trends from 1965 to 2000: (a) Total WoodRemoval, and (b) Total Sawnwood Production. (AllUnion Scientific Research Institute of Economics,1991; Goskomstat Rossia, 2007).
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of approximately 58°N), areas of central Siberia north andwest of approximately 64°N are also impacted by wildreindeer (Rangifer tarandus) whose browsing influencesrecruitment dynamics of secondary forests (Webber andKlein, 1977).
Agriculture and RegrowthThe Landsat analyses indicate that conversion of agricul-tural lands to forest regrowth was a component of land-scape change in central Siberia 1974 to 2001. Beginningbefore the dissolution of the government-run economy,some areas assigned to collective farms were vacated andarea in production decreased (Figure 5). Once abandoned,agricultural areas can acquire tree regeneration, eventuallysucceeding to young deciduous or mixed forests. Studyresults show that agriculture decreased in all three casestudy sites over the time period, and that this began beforethe 1991 transition.
Forest SuccessionGiven this disturbance legacy, it is expected that forestsecondary succession is a major component of the currentcentral Siberian landscape. Fire or logging typically pro-motes young deciduous regeneration which grows intodeciduous forests that may mature and remain dominant upto 70 to 100 years. For example, where Siberian pine occursit is very susceptible to fire, and after-fire stands are typi-cally colonized by young and deciduous birch-aspen forest(Hytteborn et al., 2005). In mid-succession, mixed forestsdominate. In some very xeric sites, light coniferous forests(Scots pine-larch) may regenerate directly after fire, alsoforming mature closed stands after 80 to 100 years. Succes-sion to late-successional dark coniferous forests is a veryslow process that occurs on the centuries-scale (Nikolov andHelmisaari, 2005).
Landsat image data over the 1974 to 2001 study periodshowed that Deciduous forest increased in all three sitesover both periods. This is attributable to succession fromYoung forest during the study period, and the continuedgrowth and succession of areas logged or burned prior to theearliest Landsat MSS date (Table 6). Succession to Deciduousalso appears to have resulted from succession of Young
regrowth on earlier abandoned agricultural lands. Somesuccession to Mixed (light-coniferous-deciduous) forest alsooccurred on the study landscape. Overall observed succes-sional trends seem to follow logically for a landscape wheredisturbed forests are currently re-growing naturally atvarious stages.
Using the Landsat Archive in Siberian ForestsGeneral challenges in using the Landsat archive in thisstudy included the different spatial and spectral resolu-tions of MSS/TM/ETM�. While the use of the slightly coarser60-meter versus 30-meter spatial resolution did not appearto negatively impact classifications in this landscape ofrelatively large grain-size, the more limited spectral resolu-tion of MSS images probably influenced its somewhat lowerclassification accuracies.
Optical remote sensing issues characteristic of borealregions include clouds and haze which were present tosome degree in all case study sites, but in the Irkutsk sitespatially-varying haze especially negatively impacted changedetection. Cloud and haze issues for contemporary land-scapes should be addressed by synergy of Landsat withradar; however radar data are not available for long-termchange studies.
Classification results of the Irkutsk site were alsoimpacted by topography. Use of finer spatial resolution DEMdata for orthorectification such as 90 m SRTM (available forareas up to 60°N) may be advantageous (Farr et al., 2007).Image ratio channels were explored to possibly normalizeeffects of Irkutsk site topography but tests resulted in littleimprovement, possibly because topography occurred in hazeareas.
Separation of boreal mixed forests whose conifercomponent is dominated by larch (primarily in Irkutsk)from those dominated by Scots pine was not generallypossible. However, known larch areas coincided with hazyareas so lack of separation might be possible with betterLandsat scenes. Several mixed forest compositions weresubsumed under the Mixed class, however all representeda mix of conifer and broadleaf deciduous. Small ruralsettlements (Urban) in central Siberia are often interspersedwith Agriculture and forest cover and difficult to distin-guish. Future studies might consider exploring otherspectral features to possibly aid such now identifiedseparability issues.
Finally, challenges with Landsat availability in theregion included lack of complete geographic coverage andlack of good a priori knowledge about coverage gaps. Thesurvey of available data conducted by this study showedthat early MSS and TM data over the northern half ofKrasnoyarsk Krai and Irkutsk Oblast were not available.Selected TM data and ETM� data were available for lateryears for the entire region. Change analysis relies onavailability of good anniversary scenes at scientificallyappropriate intervals. Our research shows that whileavailability of time series Landsat data is somewhatrestricted for more than two dates, there exists selectedavailability of change pairs or series, for example as shownin Figure 6 for one and five year intervals.
ConclusionsDespite its considered remoteness, the central Siberianforest exists in a region that has experienced fundamentalshifts in human-driven land-cover change regimes in thepast century and in the most recent decades. As archives ofremote sensing data have lengthened to over 30 years,interest in using them for long-term change analysis ofsuch landscapes has increased. This study is one of the
Figure 5. Russian official statistics showing analready decreasing trend in land-use for grainagriculture prior to 1990 (All Union ScientificResearch Institute of Economics, 1991; Goskom-stat Rossia, 2007).
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Figure 6. WRS-2 grid with the number of useableLandsat 1972 to 2001 time series scenes in (a) fiveyear increments, and (b) one-year increments.
first to use the Landsat archive to investigate long-termforest- and land-cover change trends in Siberian forests.Our overall goal was to use the Landsat 1972 to 2001archive to quantify and interpret trends in forested land-cover change in central Siberia at case study sites andbracketing the 1991 transition from the Soviet Union to theRussian Federation.
Results characterized changes in the amounts, types,and successional states of forests on the study landscape.Overall, and despite some challenges with the Landsat data,the observed land-cover and land-cover change patterns arereasonably consistent with expectations based on oursummary statistical data and with published literature. TheLandsat study results describe a dynamic central Siberianforest landscape, and one that between 1974 and 2001,appears to have been dominantly re-growing from earlierprimarily Twentieth century disturbance and which iscontinuing to undergo change in forest type and succes-sional state. As expected, a decline in logging post-1991 wasobserved. Observation of regrowth of young forest fromabandoned agriculture was not a specific expectation prior
to this study, but very quickly its importance becameevident. Overall, deciduous forest is currently increasing inarea in this southern taiga region. Studies of understoryregeneration are needed to predict longer-term successionaltrends, as are studies of the influence of climate change, andfire and insect disturbance. Research in areas further northin Siberia shows some evidence of evergreen conifer expan-sion into larch-dominated communities driven by climatechange (Kharuk et al., 2005) and expansion of wetlands dueto thermokarst processes and fires (Chapin et al., 2004). Thissuggests that what remains to be more fully known is howclimate change as an agent might affect future forests of theregion or how climate change combined with disturbanceand successional forests, such as in the case study sitesobserved here, could impact the larger-scale biogeography ofthe central Siberian taiga.
AcknowledgmentsThis project was supported by NASA Land-Cover Land-UseChange (LCLUC) Program through Contract NAG5–11084. Weextend appreciation to Dr. Garik Gutman, NASA LCLUCProgram Manager; RAS Academician E.A. Vaganov of theV.N. Sukachev Institute of Forest; and Shannon Brines,Stephanie Hitztaler, Iryna Dronova, and Bryan Emmett at theUniversity of Michigan ESALab.
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