Remote Sensing of Environment 126 (2012) 195–209

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Remote Sensing of Environment

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The effect of Landsat ETM/ETM+ image acquisition dates on the detection ofagricultural land abandonment in Eastern Europe

Alexander V. Prishchepov a,b,⁎, Volker C. Radeloff a, Maxim Dubinin a,c, Camilo Alcantara a,d

a Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706‐1598, USAb Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO), Theodor-Lieser Strasse 2, 06120 Halle (Saale), Germanyc Biodiversity Conservation and Bioresources Use Laboratory, Institute of Ecology and Evolution of Russian Academy of Sciences, 33 Leninsky Prospect, Moscow 117071, Russiad Facultad de Ciencias Naturales, Universidad Autónoma de Querétaro, Antiguo Aeropuerto, Carr. Chichimequillas s/n. Terreno Ejidal Bolaños, C.P. 76140, Querétaro, Mexico

⁎ Corresponding author at: Leibniz Institute of Agr+49 345 2928 326; fax: +49 345 2928 399.

E-mail address: prishchepov@iamo.de (A.V. Prish

0034-4257/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.rse.2012.08.017

a b s t r a c t

a r t i c l e i n f o

Article history:Received 29 December 2009Received in revised form 3 August 2012Accepted 11 August 2012Available online xxxx

Keywords:Agricultural land abandonmentAccuracy assessmentChange detectionEastern EuropeLand useLand use modelLand use patternLand cover changeLandsatMulti-dateMulti-seasonalMaximum likelihood classifierSupport vector machinesSVM

Many terrestrial biomes are experiencing intensifying human land use. However, reductions in the intensity of ag-ricultural land use are also common and can lead to agricultural land abandonment. Agricultural land abandonmenthas strong environmental and socio-economic consequences, but fine-scale and spatially explicit data on agricultur-al land abandonment are sparse, particularly in developing countries and countries with transition economies, suchas the post-Soviet countries of Eastern Europe. Remote sensing can potentially fill this gap, but the satellite-baseddetection of fallow fields and shrub encroachment is difficult and requires the collection of multiple images duringthe growing season. The availability of such multi-seasonal cloud-free image dates is often limited. The goal of ourstudy was to determine howmuch “missing” Landsat TM/ETM+ images at key times in the growing season affectthe accuracy of agricultural land abandonment classification.We selected a study area in temperate Eastern Europewhere post-socialist agricultural land abandonment had become widespread and analyzed six near-anniversarycloud-free Landsat images from “Spring”, “Summer” and “Fall” agriculturally defined seasons for a pre-abandonment-time I (1989) and post-abandonment-time II (1999/2000). Using a factorial experiment,we tested how the classification accuracy and spatial patterns of classified abandonment changed over allpossible 49 image-date combinations when mapping both “abandoned arable land” and “abandoned managedgrassland”. The conditional Kappa of our best overall classification with support vector machines (SVM) was 90%for “abandoned arable land” and 72% for “abandoned managed grassland” when all six images were used for theclassification. Classifications with fewer image dates resulted in a substantial decrease of the conditional Kappa(from 93 to 54% for “abandoned arable land” and from to 75 to 50% for “abandoned managed grassland”). Wealso observed substantial decrease in accurate detection of land abandonment patterns when we compared ourbest overall classificationwith the other 48 image date combinations (the Fuzzy Kappa, ameasure of spatial similar-ity, ranged from 25.8 to 76.3% for “abandoned arable land” and from 30.4 to 79.5% for “abandoned managed grass-land”). While the accuracy of the different abandonment classes was most sensitive to the number of image datesused for the classification, the seasons captured also mattered, and the importance of specific seasonal imagedates varied between the pre- and post-abandonment dates. For “abandoned arable land” it was important tohave at least one “Spring” or “Summer” image for pre-abandonment and as many images as possible for post-abandonment, with a “Spring” image again being most important. For “abandoned managed grassland” no specificseasonal image dates yielded statistically significantly more accurate classifications. The factor that influenced theaccurate detection of “abandoned managed grassland” was the number of multi-seasonal image dates (the morethe better), rather than their exact dates. We also tested whether SVM performed better than the maximum likeli-hood classifier. SVM outperformed the maximum likelihood classifier only for “abandoned arable land” and only inimage-date-rich cases. Our results showed that limited image-date availability in the Landsat record placed substan-tial limits on the accuracy of agricultural abandonment classifications and accurately detected agricultural landabandonment patterns. Thus, we warn to use agricultural land abandonment maps produced with the sub-optimal image dateswith caution, especially when the accurate rates and the patterns of agricultural land abandon-ment are crucial (e.g., for LULCC models). The abundance of agricultural abandonment in many parts of the worldand its strong ecological and socio-economic consequences suggest that better monitoring of abandonment is nec-essary, and our results illustrated the image dates that were most important to accomplishing this task.

© 2012 Elsevier Inc. All rights reserved.

icultural Development in Central and Eastern Europe (IAMO), Theodor-Lieser Strasse 2, 06120 Halle (Saale), Germany. Tel.:

chepov).

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196 A.V. Prishchepov et al. / Remote Sensing of Environment 126 (2012) 195–209

1. Introduction

Many terrestrial biomes are experiencing intensifying human landuse (Vitousek et al., 1997), but reductions in the intensity of agriculturalland use are also common andmay result in agricultural land abandon-ment (Baldock et al., 1996). Agricultural land abandonment has oc-curred throughout history (Hart, 1968; Yeloff & van Geel, 2007) andin many parts of the world (de Beurs & Henebry, 2004; Meyfroidt &Lambin, 2008; Perz & Skole, 2003). In some regions, such in EasternEurope, agricultural land abandonment often represents the highestland-use change class, partly in response to rapid socio-economicchanges after the breakdown of the Soviet Union (Baumann et al.,2011; Bergen et al., 2008; Kozak et al., 2004; Kuemmerle et al., 2008;Prishchepov et al., 2012, 2013).

Agricultural land abandonment has strong environmental andsocio-economic consequences. Reforestation on abandoned agriculturallands can reconnect previously separated forests fragments, sequestercarbon (Smith et al., 2007), and improve hydrological regimes andwater quality (Sileika et al., 2006; Smith et al., 2007). Early successionon abandoned farm fields can increase biodiversity, but biodiversitymay decline in late-succession stages (Baur et al., 2006; DLG, 2004).Abandoned agricultural fields increase the propagule pressure of weedson remaining agricultural fields (Smelansky, 2003) and provide fuel forwildfires (Dubinin et al., 2010; Lloret et al., 2002). Agricultural land aban-donment threatens traditional land-use practices (Angelstam et al.,2003), causes spillover effects that lead to themarginalization of historicagricultural landscapes (Elbakidze & Angelstam, 2007). Agricultural landabandonment processes are partially connected to gradual social declinein recent decades in Eastern Europe (Baumann et al., 2011; Müller et al.,2009; Prishchepov et al., 2013). Widespread agricultural land abandon-ment in one area may also shift agricultural production elsewhere andthreaten pristine ecological systems (Lambin & Meyfroidt, 2011;Prishchepov et al., 2013). Averting agricultural land abandonment andits environmental and socio-economic implications is an impetus ofmany environmental, agricultural and land-use policies (IEEP, 2006).Therefore, better monitoring of agricultural land abandonment is essen-tial to understanding the trajectories and determinants of agriculturalland-use change and guiding land-use policies.

Despite the environmental and socio-economic importance of agri-cultural land abandonment, spatially explicit information on the ratesand geographic distribution (hereinafter, “spatial pattern”) of abandon-ment is sparse, particularly in the post-Soviet countries of EasternEurope. Agricultural statistical surveys in Eastern Europe measure dy-namics of agricultural land use (e.g., how much agricultural land wasused for sowing crops), but they are often out of date and the samplingtechniques employed are questionable (Ioffe et al., 2004). Moreover, sta-tistical reports are spatially coarse and usually aggregated by administra-tive districts (Ioffe et al., 2004). Remote sensing can be a reliable sourceof information on agricultural land abandonment (Kuemmerle et al.,2008; Peterson&Aunap, 1998; Prishchepov et al., 2012). However, aban-doned agricultural land, which we define here as both formerly plowedfields and formerly managed grasslands that are now non-managedgrasslands with early-successional shrubs and forest regrowth, are noteasily distinguishable from managed grasslands and arable fields dueto subtle changes in the reflectances recorded by satellites, ifmulti-seasonal image dates are not used to detect cropland cycles andmanagement of grasslands (Kuemmerle et al., 2008; Oetter et al., 2001;Peterson & Aunap, 1998; Prishchepov et al., 2012).

The best classification accuracies in any land cover classification areusually obtained with multiple imagery that captures different periodsof the growing season (multi-seasonal imagery) (Civco, 1989; Oetteret al., 2001;Wagner et al., 1993;Wolter et al., 1995).Multi-seasonal im-agery has proven particularly important when classifying agriculturalland use due to the different sowing and harvesting times amongcrops (Guerschman et al., 2003; Homer et al., 2004; Kalensky, 1974;PaxLenney & Woodcock, 1997). Coarse-resolution (250 m–1 km)

MODIS remote sensing time series (e.g., reflectance bands and thecalculated vegetation indices NDVI and EVI) can be used to monitoragricultural land use and land-cover dynamics, especially where theagricultural sector is dominated by large-scale farming (e.g., Russia,Ukraine, USA) (Alcantara et al., 2012). However, in regions where agri-cultural land is highly fragmented (e.g., Lithuania, Poland, China), usingcoarse-resolution products may result in false estimates of agriculturalland use (Ozdogan & Woodcock, 2006). Moreover, only the MODISdata since 1999 are available,whichprecludes themapping of abandon-ment as a land-use change process immediately after the collapse of so-cialism in the early 1990s (Friedl et al., 2002).

Thirty-meter resolution Landsat TM/ETM+ images can be a reliablesource of remote sensing information, allowing the monitoring of agri-cultural land-use land-cover change (LULCC). Unfortunately, LandsatTM/ETM+ images that capture different phenological and land-usestages (i.e., the beginning, middle and end of the growing season) arenot always available due to issues such as clouds and haze. We exam-ined all major Landsat TM/ETM+ data archives for Eastern Europe(University of Maryland Global Land Cover Facility [www.landcover.org], USGS [glovis.usgs.gov], Eurimage Inc. [www.eurimage.com], andR&D Scanex [www.scanex.com]). We found that out of 995 LandsatTM/ETM+ footprints in Eastern Europe, there existed not a single foot-print for which cloud-free images recorded in spring, mid-summer andfall were available for both a single year prior to the collapse of theSoviet Union (1988 to 1990) and a single year approximately 10 yearsafter the collapse (1998 to 2000) (Fig. 1). We should acknowledgethat image-date composites can partially overcome data availabilitylimitations by making better use of both partially clouded imageryand the overlap area among neighboring Landsat paths (Hansen et al.,2008; Knorn et al., 2009; Roy et al., 2010; Potapov et al., 2011). Never-theless, the questions remain of how multi-seasonal image dates affectthe classification accuracies of agricultural abandonmentmaps, agricul-tural land abandonment pattern and which image dates are the best touse for such composites. Furthermore, it was unclear how the use ofmore robust classifiers to identify subtle signal changes could overcomeimage date dependence. More specifically, it was unclear if anon-parametric machine learning classification algorithms (e.g., sup-port vector machines or SVM) would be less sensitive to the effects ofsub-optimal image data (data with fewer image dates) and outperformparametric classifiers such as the maximum likelihood classifier (Foody& Mathur, 2004; Homer et al., 2004; Huang et al., 2002).

Our overarching goal was to identify approaches to accuratelymapping agricultural land abandonment using 30-m resolution LandsatTM/ETM+ images. To accomplish this goal, ourfirst objectivewas to as-sess the effects of multi-seasonal image dates on classification accuracyand the resulting spatial patterns of abandoned agricultural land. Oursecond objective was to test if SVMwould result in higher classificationaccuracy than themaximum likelihood classifier, thus potentially over-coming image date dependence.

2. Materials and methods

2.1. Image selection

We defined agricultural land abandonment from a remote-sensingperspective as agricultural land used before 1989 for crops, hay cutting,and livestock grazing that was no longer used by 1999–2000 and wascovered by non-managed grasslands, often with early successionalshrubs in temperate Eastern Europe. Shrub encroachment in the studyarea usually takes place three to five years after abandonment, withfaster shrub advancement on well-drained and formerly plowed fields(Karlsson et al., 1998; Lyuri et al., 2010; Utkin et al., 2005). We focusedour classifications on areaswith at least some shrubs to be conservativebecause fields were sometimes merely fallow for one or two yearsbefore agricultural land use resumed.

Fig. 1. Cloud free image dates availability for spring, summer and fall across EasternEurope (University of Maryland Global Land Cover Facility [www.landcover.org],USGS [glovis.usgs.gov], Eurimage Inc. [www.eurimage.com], and R&D Scanex[www.scanex.com]). A: Landsat footprints for which three images are available inthree pre-abandonment years (1988–1990). B: Landsat footprints for which threeimages are available in three post-abandonment years (1998–2000).

197A.V. Prishchepov et al. / Remote Sensing of Environment 126 (2012) 195–209

To examine the effect of Landsat TM/ETM+ image dates, we se-lected one Landsat footprint in temperate Eastern Europe with bothideal image date availability and widespread agricultural land aban-donment. Based on crop management cycles and vegetation phenolo-gy in the temperate area of Eastern Europe where we conducted ourwork, we assumed that three multi-seasonal image dates would benecessary to capture agricultural land abandonment.

The first image represented the “Spring” season from an agricul-tural land use perspective (April 20th to May 20th), or the periodwhen mean daily temperatures rose above 5 °C. At this point, soilsfor summer crops were still bare, but both winter crops and managedgrasslands were vegetatively active. The second image represented“Summer” (June 20th to July 20th), or the end of the first phase ofhay harvesting and the maturing of winter crops. The third imagecaptured “Fall” (August 20th to October 10th), or when vegetationwas not yet dormant; winter crops and major summer crops werealready harvested and soil tilling began, but some summer crops(e.g., corn, rape, beets, and potatoes) remained unharvested. We lim-ited cloud contamination to less than 5% because cloud cover andcloud shadows in multiple images are typically additive, resulting inthe removal of a large portion of a footprint when classifying six im-ages jointly, and searched for near-anniversary images from 1989 and1999 to capture land use at the end of socialism and the first decadeafter the transition to a market economy. No Landsat footprint metall these requirements when querying major Landsat archives, sowe relaxed our requirements for single-year imagery and used a“Spring” image from 2000 instead of 1999. We selected Landsat foot-print World Reference System 2 (WRS 2) path 186 row 22 from sev-eral candidates because it had the best image availability and theabundant agricultural land abandonment.

2.2. Study area

The selected Landsat footprint included parts of two former SovietUnion republics (Belarus and Lithuania) and former socialist Poland(33.5%, 65.1% and 1.4% of the Landsat footprint area, respectively)(Fig. 2A, B). We acquired TM and ETM+ images for this footprint atpre-abandonment (time I; “Spring” image date—May 3rd 1989, “Sum-mer” image date—July 6th 1989, and “Fall” image date—September24th 1989) and post-abandonment (time II; “Spring” image date—May 5th 2000, “Summer” image date—July 10th 1999, and “Fall” imagedate - September 20th 1999).

The climate in the region is transitional from maritime to continen-tal and the annual precipitation ranges from 585 to 664 mm. The meandaily temperature is +16.9 °C in July and −6.1 °C in January. Thegrowing period (temperatures above 5 °C) ranges from 120 days inthe north of the image to 179 days in the south (IIASA, 2000; Stuikys& Ladyga, 1995). The topography is relatively flat (0 to 298 m).

The soil types in this area are predominantly acid soddy podzolicsandy loams with sands and drained soddy podzolic gleys. Differentsoils reflect different agricultural practices. The most productive soilsare soddy calcareous soils, predominantly the loams in Central Lithua-nia (western part of the study site), where both row crops and cattlebreeding are important (Stuikys & Ladyga, 1995). Cattle breeding anddairy farming play important roles in the agricultural sector of EasternLithuania (central part of the study area), where acid podzolic soilsare common. Podzolic soils dominate in the Grodno province ofwesternBelarus (eastern part of the study area), where large-scale livestock in-dustry and row crops are common.

While the cropland acreage declined after the collapse of theUSSR particularly in Lithuania (Lithstat, 2001), the summer cropsin 1999/2000 represented approximately the same share equalingto 66% of the total cropland as in 1989 (Belstat, 2002; Lithstat, 2001)and consisted of barley, rye, oats, sugar beets, fodder maize, potatoes,peas, summer rapeseed, and flax. The winter crops consisted of winterwheat, winter barley and winter rapeseed (Stuikys & Ladyga, 1995).Crop planting, harvesting and hay cutting followed a distinct schedule,whichwe used in addition to the vegetation phenology to identify opti-mal image dates (Fig. 3A, B).

Forests are the secondmost important land-cover type after agricul-tural lands. According to official statistics from 1988 to 2001 the forestcoverage increased from 28.1% to 30.9% of total area in Lithuania, andfrom 31.2% to 35% of total area in Grodno province of Belarus (Belstat,2002; GrodnoStat, 2001; Lithstat, 2001; State Forestry Committee ofthe USSR, 1990). The dominant tree species are northern spruce (Piceaabies), scots pine (Pinus sylvestris), silver birch (Betula pendula), andpedunculate oak (Quercus robur) (Folch, 2000; Kashtanov, 1983).

Agricultural statistics surveys showed declines in both in the num-ber of livestock (e.g., from 7.3 million heads in 1989 to just 4.7 mil-lion heads in 1999 in Belarus and from 2.4 million heads to 923,000heads in Lithuania) and crop production (e.g., from 7.4 million tonsin 1989 to 3.6 million tons in 1999 in Belarus and from 3.2 to just2.1 million tons in Lithuania). Both decrease of agricultural land useand increase of forest coverage suggested that agricultural abandon-ment was widespread in that period.

2.3. Image preprocessing

The images were co-registered using automatic tie points (LeicaGeosystems, 2006). No atmospheric correction was performed be-cause it doesn't not significantly improve classification accuracywhen multi-seasonal image dates are grouped into a single compos-ite (layerstack) and classified simultaneously for each footprint(Song et al., 2001), as this approach we used to group image datesfor time I and time II into 49 possible composites to classify satelliteimages. We used 30-m resolution Landsat TM/ETM+ bands 1–5 and7. Clouds and cloud shadows were masked out using iterative

Fig. 2. A: Location of the study area in Eastern Europe. B: Footprints of high-resolution satellite images available at Google Earth™mapping service. C: Reference points sample usingthe three step-stratification approach.

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automatic clustering (ISODATA) with ERDAS Imagine™ software[www.erdas.com] andmanual digitizing. The total cloud contaminationwas b5% of the study area and primarily covered forests.

2.4. Classification scheme and reference data collection approach

The classification scheme employed in this study focused primar-ily on agricultural transition classes (Table 1). We mapped agricultur-al transition classes from time I (pre-abandonment, 1989) to time II(post-abandonment, 1999/2000), including the following:

• “Change from arable land in time I to managed grassland in time II”;• “Change frommanaged grassland in time I to arable land in time II”;• “Change from arable land in time I to abandoned in time II” (here-inafter, “abandoned arable land”);

Fig. 3. A: Crop planting and harvesting calendar in the study area and corresponding Landsatime-series profiles for different land cover classes and selected Landsat TM/ETM+ image

• “Change from managed grassland in time II to abandoned in timeII”, (hereinafter, “abandoned managed grassland”).

We mapped “abandoned arable land” and “abandoned managedgrassland” separately because these two classes may have had differentenvironmental and socio-economic implications and thusmay have re-quired different multi-seasonal image dates for accurate detection. Wealso mapped stable managed agricultural land classes, namely:

• “Arable land in time I and time II”, (hereinafter, “stable arable land”);• “Managed grassland in time I and time II”, (hereinafter, “stable man-aged grassland”).

We also included “forest in time I and time II”, “change from forestin time I to forest clearcut in time II” and “change from forest clearcutin time I to forest regrowth in time II” because these classes could

t image date selection. Adapted from Bujauskas and Paršeliunas (2006). B: MODIS NDVIdates.

Table 1Classification scheme, training pixels for SVM and maximum likelihood classifier and reference pixels.

Class name Acronym Number ofreference pixels

Number of training pixelsused for maximumlikelihood classifier withinoutlined training polygons

Number of trainingpixels used for SVM

Forest in time I and time II F 380 7311 731Change from forest in time Ito forest clearcut in time II

Cl 92 1527 229

Change from forest clearcut intime I to forest regrowth in time II

Rg 42 660 198

Arable land in time I and time II(stable arable land)

Ar 154 10,494 840

Change from arable land in time Ito managed grassland in time II

ArMGr 70 1637 327

Change from arable land in time Ito abandoned in time II(abandoned arable land)

ArAb 102 1241 434

Managed grassland in time Iand time II(stable managed grassland)

MGr 133 893 402

Change from managed grasslandin time II to abandoned in time II(abandoned managed grassland)

MGrAb 42 1051 399

Change from managed grasslandin time I to arable land in time II

MGrAr 44 1656 331

Non-managed grassland and shrubsin time I and time II (shrubs)

NGrShr 32 1145 401

Wetland Wt 33 3969 397Other (impervious surface, bare soil,open peat quarries, water)

Other 54 15,289 438

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potentially be confused with agricultural land abandonment, as wellas “non-managed grassland and shrubs in time I and time II” (e.g., ri-parian vegetation), hereafter classified as “shrubs”, “wetland” and an“other” class comprised of impervious surfaces, bare soil, open peatquarries, and water.

Validation (reference) datawere collected independently of trainingdata using a three-step stratified random sampling approach modifiedfrom Edwards et al. (1998). The validation data were collected withinthe footprints for cloud-free 1.28-meter resolution QuickBird andIKONOS images (Fig. 2B, Table 2) available via the Google Earth™map-ping service as we intended to use for the validation high-resolution

Table 2High resolution satellite images used to support ground based training and referencedata collection.

ID Year Month Day Digital globe image ID Cloud cover (%)

1 2002 9 25 101001000147BF01 02 2002 7 28 1010010000E18301 13 2002 7 10 1010010000C2DE02 34 2002 7 10 1010010000C2DE03 95 2003 5 25 1010010001ED8B01 46 2003 6 4 1010010001F4A302 07 2003 9 23 10100100024FCB01 08 2004 8 4 1010010003251301 49 2004 7 30 1010010003232C02 410 2004 7 30 1010010003232C03 111 2004 7 30 1010010003232C03 112 2004 7 30 1010010003232C20 013 2004 9 7 10100100033BA501 814 2005 7 10 10100100045C8B01 215 2005 7 10 10100100045C8B02 716 2005 7 15 10100100045F6701 717 2006 6 30 10100100050E3D0E 018 2007 4 27 1010010005986700 819 2007 10 14 101010010007434E 020 2007 4 17 1010010005942903 121 2007 4 17 1010010005942904 022 2007 4 17 1010010005942905 0

images, field observations and ancillary data (e.g., multi-date LandsatTM/ETM+ satellite imageries and topographic maps). The footprintsof the high-resolution QuickBird and IKONOS images covered 33% ofthe studied Landsat TM/ETM+ footprint (Fig. 2B), and the distributionof major soil types that reflected the different agricultural land uses inthe areas captured by high-resolution imagery was similar to that ofthe entire Landsat footprint (Table 3).

Next, to concentrate field-based reference data collection for the ag-ricultural and transitional (agricultural land abandonment) classes, wederived a forest versus non-forest mask for the QuickBird and IKONOSimages. For the areawithin Lithuania, we used the expert-basedmanualland cover classification with Landsat TM/ETM+ and SPOT data at100-m resolution for the year 2000 under Coordination of Informationon theEnvironment-CORINEproject (EEA, 2006). Different forest classesin the CORINE project were classified with at least 86% of the producer'sand user's accuracies. Due to the absence of CORINE land cover productfor Belarus, we used a 1:500,000 GIS product based on digitized de-classified Soviet topographic maps from circa 1989 for the studied partof Belarus (East View Cartographic, 2005; VTU GSh, 1989a). This GISproduct was of good quality and captured forest edges with a horizontalaccuracy of 20 m.

Finally, we randomly placed reference points within the forestedpart of the forest/non-forest mask and within the non-forested part ofthe forest/non-forest mask that were within 300 m of roads, whichwe had digitized from topographic maps, the QuickBird and theIKONOS images, to facilitate field visits (Fig. 2C).We reduced the spatialautocorrelation by 0.15 (Moran's I) across our footprint by placing a dis-tance lag of at least 500 m between validation points based on the as-sessment of variograms constructed with the GS+ geostatisticalpackage (Gammadesign, 2010) for six randomly placed 30-m resolu-tion 10×10 kmblocks for our studied Landsat footprint (three for Bela-rus and three for Lithuania).

Of a total 1178 randomly placed points, 502 were labeled as agricul-ture and abandoned classes and 250 of these were visited in the field in2007 and 2008. The points were geolocated using a non-differential GPS.We used spectral similarity and textural information for the remaining252 agricultural points to assign their land-cover types from the imagery.

Table 3Soil type distribution within and outside Quickbird and IKONOS footprints.

Class Inside high resolutionfootprints (%)

Outside high-resolutionfootprints (%)

Histosols 6 10Podzoluvisols 10 7Luvisols 28 44Arenosols 56 38

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Points in other land-cover types (e.g., forest in time I and time II) werenot visited in the field but were verified based on high-resolution(Quickbird and IKONOS) and Landsat TM/ETM+ imagery and detailed1:100,000 topographic maps (VTU GSh, 1989b). We used spectral simi-larity and textural information for points within the forested part ofthe forest/non-forestmask that corresponded to stablemanaged agricul-ture and agricultural transition classes to assign land cover types fromthe imagery. Field observations were also used if the points close to theforest edge were accessible.

To avoid potential errors due to the time differences between ourfieldwork (2007 and 2008), the high-resolution imagery (2002 to2007), and the Landsat images used for the classification (2000 and ear-lier), we used a Landsat 5 image from May 20, 2007, to verify that noland-use changes had taken place between 2000 and 2007.

2.5. Classification training data collection approach

The data used to train our classifiers represented an expert-basedassignment of training sites (polygons) to land-cover classes. Thetraining data were selected uniformly across the Landsat footprintand the dataset was collected independently of the reference datacollection; we ensured that the training and reference data pointswere at least 500 m apart.

We usedfield observations, the high-resolution images, and informa-tion gained from dense multispectral Landsat images and ancillary datato select the training data (CORINE land cover, EEA, 2006; 1:100,000 to-pographic maps, VTU GSh, 1989b).

2.6. Classification methods

We used both a non-parametric classifier (SVM) and a parametricclassifier (maximum likelihood) for classification. SVM often out-perform other classifiers (Foody & Mathur, 2006; Huang et al.,2002). Based on the training data (support vectors), for those classesthat are difficult to separate using linear model SVM achieve optimalseparation of each of two classes in iterative way by transforming dataand fitting a hyperplane to separate these classes in n-dimensionalspace using Lagrangemultipliers and a kernel function (e.g., polynomialand Gaussian radial basis function-RBF) (Alcantara et al., 2012; Huanget al., 2002). Thus, SVM are well suited to separate multimodal classes,which are difficult for parametric-based classifiers to classify accuratelydue to the violation of the assumption of a normal distribution of reflec-tance values within one class. This difference is highly relevant to agri-culture classes, where tilled soils, managed grasslands and maturedcrops exhibit multimodal reflectances. Prior studies have showed thatSVM can capture agricultural land abandonment well, especially whenoptimal image dates are available (Baumann et al., 2011; Kuemmerleet al., 2008; Prishchepov et al., 2012). However, it is unclear if SVMcan overcome the limitations imposed by sub-optimal image dates.We used the SVM implemented in ImageSVM (Rabe et al., 2009)based on LIBSVM (Chang & Lin, 2011), which can be run as a stand-alone application with IDL Virtual Machine™ or as an add-on to ENVI™[www.ittvis.com]. To select distinctive support vectors among the initialtraining data, ImageSVM automatically selects two required parametersfor SVM and that are critical to define a radial basis function imple-mented in ImageSVM, the optimum Gaussian radial basis function

parameter (γ) that controls the kernel width and the regularization pa-rameter (C) that controls the penalty given to misclassified trainingpixels (Kuemmerle et al., 2008) within a range of 0.1 to 1000 (Rabe etal., 2009). We used a “one-against-one” approach, in which each classwas separated from every other class individually for multi-class SVMclassifications, to avoid the unbalanced classifications that have beenreported with the “one-against-all” approach (Melgani & Bruzzone,2004).

The maximum likelihood classifier is a parametric classifier that as-signs each pixel of multispectral satellite images to a land-cover classbased on a priori calculated probabilities from the training data. Themaximum likelihood classification assumes that the training data arenormally distributed. Merging all training data for one class can leadto non-normal distributions. We compared classifications in which allthe training polygons for one land-cover class were merged into onetraining signature to classifications in which the training polygonswere clustered (Ward Euclidian distance hierarchical clustering) into124 different spectral types. The tests employed showed no statisticallysignificant difference between the classification accuracies (α=0.05),and we used only one averaged signature for each land-cover class inthe classifications to save processing time.

SVM are computationally demanding (Huang et al., 2002), and thatdemand precluded us using all of the training data collected initially forboth SVM and themaximum likelihood classifier to classify all 49 imagedate composites. We tested several sets of sampled training pixels frominitial training polygons and performed the classifications using SVMwith all multi-seasonal image dates to ensure that the selected finaltraining sample led to stability of the classification in terms of its accu-racy. We randomly sampled 300 to 1000 training pixels per class fromthe training polygons that we used with the maximum likelihood clas-sifier based on several sampling experiments and accuracy assessments(Table 1). To ensure that the differences between the SVM and maxi-mum likelihood classificationswere not caused by the different trainingsets, we also trained SVM for several selected multi-seasonal image-date composites with the full training set used for the maximum likeli-hood classifier. The classification that resulted from this test was notsignificantly different (at α=0.05) from its classification with the fulltraining set. However, whenwe trained themaximum likelihood classi-fierwith a reduced SVM training set, the resulting classificationwas lessaccurate (at α=0.05) than the classification produced with the fulltraining set.

SVM and maximum likelihood classifications were performed foreach of 49 image date composites. The training sets used to trainboth the SVM and maximum likelihood classifiers were derived foreach of the 49 image multi-date composites. Each training supportvector for SVM and training polygon for the maximum likelihood rep-resented one of 12 thematic LULCC class trajectories from time I totime II.

2.7. Accuracy assessment and analysis of the detected spatial pattern ofabandonment

The classification accuracy was estimated with contingency matri-ces. We calculated the Kappa coefficient (KHAT) for the overall classifi-cation accuracy and conditional Kappa coefficients for each class, user'sand producer's accuracies. We used a non-parametric McNemar's testwith continuity correction to determine if the SVM classificationswere statisticallymore accurate than the correspondingmaximum like-lihood classifications (Foody & Mathur, 2004). For this test, we sepa-rately recoded “abandoned arable land” and “abandoned managedgrassland” as “1” and all other classes as “0”. We adjusted the statisticaltests for false discovery rate (FDR) to avoid incorrectly rejected null hy-potheses (Benjamini & Yekutieli, 2001). The classification results weregrouped according to the number of Landsat images used in the pre-and post-abandonment periods (i.e., “one and one,” “one and two”and “two and one”, “one and three” and “three and one”, “two and

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two”, “two and three”, and “three and two”, and “three and three”image date combinations). There were a total of 49 possible combina-tions - composites for each agricultural land abandonment class, andall were tested. We also used McNemar's test (α=0.05) to check ifone classification was statistically more accurate than any other classi-fication within the identified groups.

We used Geonamica's Map Comparison Kit software to estimatethe effects of image date selection on the classification of the spatialpattern of abandonment and calculated several measures of agree-ment between the classifications (i.e., the number of correctly classi-fied pixels in both maps being compared, Kappa, and Fuzzy Kappa)(Visser & de Nijs, 2006). A preliminary check showed that the numberof pixels, Kappa, and Fuzzy Kappa measures of the accuracy werehighly correlated (R≥0.80), which is why we have reported onlythe Fuzzy Kappa results (Hagen & Uljee, 2003; Hagen-Zanker et al.,2005). Fuzzy Kappa is a measure of the similarity between twomaps that accounts for the neighborhood of a cell to reduce impactof errors due to misregistration. We set the radius of the neighbor-hood to four cells using an exponential decay function with a halvingdistance of two units (Hagen & Uljee, 2003).

For the separate map comparison of “abandoned arable land” and“abandoned managed grassland”, we recoded the abandoned class as“1” and stable managed agricultural class as “0”, and all other classeswere masked out. First, we compared the best overall classified map(using three image dates for pre- and post-abandonment) with theother 48 image date combinations for the “abandoned arable land”and “abandoned managed grassland” classes separately. We then com-pared the maps within each group (e.g., “one and one”, “one and two”and “two and one”, etc.) to each other to estimate the effect of accuracyon the detected spatial pattern of agricultural land abandonment.

3. Results

Our results showedwidespread agricultural land abandonment in thestudy area (Fig. 4). The best overall classificationwith SVM,which provedto be our best classifier, for 6 image date composites revealed that an av-erage of 22%of 1989 agricultural landwas abandonedby2000 (27% aban-doned in Lithuania and 13% in Belarus) (Fig. 4). A total of 18% of 1989arable land and 8% of 1989 managed grassland were abandoned by2000 in Lithuania, and the rates in Belarus were 8% and 5%, respectively.

3.1. Analysis of accuracy measures

Among the 49 image date combinations classified with SVM, theoverall Kappa using a 12-class classification scheme ranged between77.0 and 90.2%, and for the 22 image date combinations yielded anoverall Kappa of at least 80%. The conditional Kappa for “abandonedarable land” ranged between 54.0 and 93.5% (Fig. 5), the user's accu-racy between 69.3% and 94.5% (Fig. 6), and the producer's accuracybetween 56.8 and 94.1% (Fig. 7). The conditional Kappa for “aban-donedmanaged grassland” ranged between 50.2 and 75.3%, the user'saccuracy between 40.3% and 72.7%, and the producer's accuracy be-tween 52.3 and 76.1%. The classification accuracy was consistentlylower for "abandoned managed grassland" than for "abandoned ara-ble land" (Fig. 5). The overall Kappa was the highest (90.2%) whenclassifying the maximum number of image dates (three) for bothpre-abandonment and post-abandonment periods.

All six “two and three” and “three and two” image date combina-tions yielded the conditional Kappa of at least 80% for “abandonedarable land” and at least 70% for “abandoned managed grassland.”Only five image date combinations yielded conditional Kappa valuesof at least 70% for both abandonment classes. Three “two and three”image date combinations led to the highest classification accuraciesfor the “abandoned arable land” class (they were statistically signifi-cantly better than the other 46 image date combinations, with α=0.05). None of the classifications for the “abandoned managed

grassland” class was statistically significantly better than all otherclassifications within this group.

When only two images were analyzed for each year (“two andtwo” image date combinations with 9 possible image date combina-tions), the conditional Kappa ranged between 69.5 and 84.0% for“abandoned arable land” and between 62.8 and 70.4% for “abandonedmanaged grassland.” Only two “two and two” image date combina-tions yielded conditional Kappa values of at least 70% for both aban-donment classes. The “Spring” and “Summer” images for both pre-and post-abandonment periods for the “abandoned arable land” classyielded statistically significantly better classification than the otherimage date combinations (α=0.05). None of the nine possible imagedate combinations for the “abandoned managed grassland” classresulted in statistically significantly better classifications than all otherclassifications within the “two and two” group.

For the “one and three” and “three and one” image dates (6 pos-sible combinations), the conditional Kappa ranged between 55.7and 90.3% for “abandoned arable land” and between 55.8 and 67.7%for “abandoned managed grassland”. No single image-date combina-tion yielded conditional Kappa values of greater than 70% in bothabandonment classes. In the case of “abandoned arable land”, threeimage-dates of post-abandonment combined with a single “Spring”or “Summer” image of pre-abandonment resulted in statistically sig-nificantly higher results. In the case of “abandoned managed grass-land”, the accuracy was generally higher with three image datesfrom the pre-abandonment period and any single image from post-abandonment, but the results were not statistically significant.

For “one and two” and “two and one” image dates (18 possiblecombinations), the conditional Kappa ranged between 54.0 and 86.1%for "abandoned arable land" and between 50.2 and 75.2% for “aban-doned managed grassland”. Three image date combinations yieldedconditional Kappa values of at least 70% for both abandonment classessimultaneously. The classifications of “abandoned arable land” werestatistically significantly more accurate when two images were includ-ed for post-abandonment, preferably from “Spring” and “Summer”, andwhen the single pre-abandonment image was from either “Spring” or“Summer”. None of the image-date combinations for “abandonedman-aged grassland” was statistically more accurate.

When only one image was available for both pre- and post-abandonment (“one and one” image date combinations, with 9possible combinations), the conditional Kappa for "abandoned arableland" ranged between 62.1 and 73.5% and between 52.5 and 70.1% for“abandoned managed grassland”. No image date combination yieldedconditional Kappa values greater than 70% for both abandonmentclasses. No single image date combination was statistically significantlybetter than any other for both “abandoned arable land” and “abandonedmanaged grassland”.

3.2. Similarity of the spatial patterns of abandonment among ourclassifications

When comparing the spatial pattern of our best overall classifica-tion (6 multi-seasonal image dates including “Spring”, “Summer”and “Fall” images for pre- and post-abandonment) with the other48 image date combinations, the Fuzzy Kappa, a measure of spatialsimilarity, ranged from 25.8 to 76.3% for “abandoned arable land”and 30.4 to 79.5% for “abandoned managed grassland” (Fig. 8). Thecorrelation between the conditional Kappa (estimated using anindependent validation dataset) and Fuzzy Kappa for “abandonedarable land” was R=0.81 and R=0.62 for “abandoned managedgrassland”.

When we compared the range of similarities among classificationswithin each group (i.e., “one and one”, “one and two” and “two andone”, “one and three” and “three and one”, “two and two”, “two andthree” and “three and two”, and “three and three” image date combina-tions), specific image dates had notable effects on the spatial pattern of

Fig. 4. Detected agricultural LULCC pattern with one of the best classifications (three images for pre-and post-abandonment).

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agricultural land abandonment. For instance, in the case of the “one andthree” and “three and one” classification group, which we selected as acontrasting example, the Fuzzy Kappa for “abandoned arable land”ranged from 11.2 to 51.0% and that of “abandoned managed grassland”from 22.9 to 50.2%. Within this group, the highest similarity with thebest overall classification (6multi-seasonal image dates) had a FuzzyKappa of 55.0% when a “Spring” image date was available forpre-abandonment and three image dates were available for post-abandonment. The highest similarity between the pairs of imageswithin this group (Fuzzy Kappa=51.0%) was again obtained whenone image was allocated for pre-abandonment and three imagesfor post-abandonment. In the case of “abandoned managed grassland”,the highest similarity with the best overall classification comprised of“one and three” and “three and one” had a Fuzzy Kappa of 60.5% andwas obtained with three images for pre-abandonment and one fallimage for post-abandonment (Fig. 9). The highest similarity betweenthe pairs of images within this group (Fuzzy Kappa=50.2%) wasobtained when three images were located for pre-abandonment andone image for post-abandonment.

3.3. Support vector machines versus maximum likelihood classifier

Out of 49 possible image date combinations, only 13 image datecombinations resulted in statistically significantly more accurate clas-sifications for SVM versus maximum likelihood-based classificationswhen mapping “abandoned arable land”, and none were statisticallymore accurate for SVM when “abandoned managed grassland” wasmapped (Fig. 5). However, because arable land was more widespreadin the study region (84% of the total agricultural land in 2000), it was

more important to detect “abandoned arable land” accurately, whichis why we considered SVM to be the better classifier.

4. Discussion

4.1. Analysis of the accuracy measures

Our analyses showed that abandoned agriculture could bemapped from Landsat satellite imagery with accuracies exceeding80% (Figs. 5–7). However, such high classification accuracies re-quired multi-date imagery, ideally three images (one each from“Spring”, “Summer”, and “Fall”) for a single year in both the pre-and the post-abandonment periods. When fewer images were ana-lyzed, thus reflecting the conditions representative of most of theLandsat footprints for which optimally timed images did not exist(Fig. 1), the classification accuracy dropped markedly and was aslow as 54% of the conditional Kappa for “abandoned arable land”and 50% for “abandoned managed grassland” (Fig. 5). However,some suboptimal image date combinations (data with fewer multi-seasonal image dates) can still yield agricultural land abandonmentmaps with high accuracy (Figs. 5–8).

In addition to the number of images, we found that the specificimage dates were important, but the best dates differed for the twoabandonment classes. Generally, “abandoned arable land”wasmore ac-curately mapped than “abandoned managed grassland”. We observedthat the classifications for “abandoned arable land” were statisticallysignificantly more accurate (α=0.05) when “Spring” and “Summer”or “Spring” and “Fall” images, or at least one “Spring” image, were avail-able for pre-abandonment and as many images as possible were avail-able for post-abandonment.

Fig. 5. Conditional Kappa reports for “abandoned arable land” and “abandoned managed grassland” detection for all 49 possible image combinations using SVM as the classificationalgorithm.

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Having a “Spring” image to accurately detect “abandoned arableland” was important for several reasons. The “Spring” image distin-guished between the new vegetative growth of winter crops andmanaged grasses, senescent vegetation on fallow fields, and exposedsoil after tilling for summer crops, which represented 66% of the totalarea of arable land in 1989 and 2000 for our study site. The “Summer”image allowed for the separation of agricultural land associated withadvancing crops, matured crops, exposed tilled soil, grasslands afterthe first round of hay cutting, actively used managed grasslands forlivestock grazing and non-managed grassland encroached by shrubs.However, matured croplands were sometimes misclassified as man-aged and non-managed grasslands when using “Summer” images,thus reducing the accuracy of detecting “abandoned arable land”.

The “Fall” image captured exposed soil after harvesting and summercrops and allowed for the separation of actively managed grasslandsfrom abandoned agricultural lands with abundant senescent herba-ceous vegetation and shrubs. However, grasses being regrown onagricultural fields after harvesting may have complicated the separa-bility of the arable areas from other classes.

No specific image dates yielded statistically significantly more ac-curate classifications within any groups for the “abandoned man-aged grassland” class. However, we noticed that the classificationaccuracy was slightly higher with more image dates placed for thepre-abandonment. In other words, what mattered for the accuratedetection of “abandoned managed grassland” class was the numberof images (the more the better), rather than their exact dates.

Fig. 6. User's accuracy of “abandoned arable land” and “abandoned managed grassland” detection for all 49 possible image combinations using SVM as the classification algorithm.

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4.2. Similarity of the spatial patterns of abandonment among theclassifications

The similarity of the spatial patterns between the best classification(“Spring”, “Summer” and “Fall” images at pre-and post-abandonment)and the classification of the other 48 image-date combinations substan-tially declined when the number of image-dates used for classificationwas lower (Fig. 8). The Fuzzy Kappa for “abandoned arable land” wasalso strongly positively correlated with the conditional Kappa, whichwas estimated using validation data, and the two measures showedmoderate positive correlation for “abandoned managed grassland”.

When the Fuzzy Kappa was analyzed within the groups of imagedates, the similarity of the detected patterns was always higher forthe best overall classification when key image dates and their combina-tions were included. In the case of “one and three” and “three and one”classification groups, a “Spring” image date for pre-abandonment andthree image dates for post-abandonment in the case of “abandonedarable land”, and more image dates for the pre-abandonment periodfor “abandoned managed land” yielded more accurate classification

patterns. In general, the Fuzzy Kappa results for the detected patternof agricultural land abandonment were similar to the conditionalKappa estimates when using the validation datasets.

4.3. Support vector machines versus maximum likelihood classifier

When comparing the performance of SVM and the maximum like-lihood classifier, the SVM performed particularly well when thechange in the reflectance of “abandoned arable land” was drastic(Fig. 5). However, SVM outperformed the maximum likelihood classi-fier only when many images were available (e.g., “two and two”, “twoand three”, “three and two” and “three and three” image date combi-nations). SVM also did not perform better than the maximum likeli-hood classifier in detecting “abandoned managed grassland”. Theparametric approaches were relatively appropriate for mapping“abandoned managed grassland” when the number of available dis-tinctive support vectors was limited and a complex support vectorcollection approach was required for the successful SVM training ofclasses with subtle reflectance differences (Foody & Mathur, 2006).

Fig. 7. Producer's accuracy of “abandoned arable land” and “abandoned managed grassland” detection for all 49 possible image combinations using SVM as the classificationalgorithm.

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When we examined the support vectors for the 49 combinations indetail, we found that the number of distinctive support vectors select-ed by ImageSVM increased by a factor of two to three when wemapped “abandoned managed grassland” and when multi-seasonalimage date combinations were suboptimal (i.e., fewer image-dates).Thus, while we considered SVM as a preferable classification methodfor agricultural land abandonment in general, the limited advantageof SVM's classification performance and substantially longer compu-tation time left room for parametric-based classifiers and possiblyother non-parametric classifiers (e.g., decision trees).

4.4. Other methodological considerations

“Abandoned managed grassland” was more difficult to map accu-rately than “abandoned arable land,”whichwasmost likely due to a va-riety of reasons. Managed grasslands typically occurred in our studyarea where the soils were marginal (e.g., highly acidic soils, driedpeatlands, and fens and mires converted to managed grasslands).

Succession was slower on these marginal sites, meaning that therewas less shrub encroachment than on abandoned previously fertilizedand meliorated arable land over the same period. Succession may alsohave been slower on former grasslands compared to arable land be-cause a dense sod and senescent vegetative material could haveinhibited the establishment of woody vegetation.

From a remote sensing perspective, the change in reflectance thatoccurred when arable land was abandoned was very marked, makingit easier to classify abandoned arable land compared to the more grad-ual change from managed to abandoned grasslands. Our accuracy as-sessment showed that the “stable managed grassland”, “abandonedmanaged grassland” and “shrub” classes were frequently misclassified,even with optimal image dates (Table 4), and errors increased whenkey dates were missed. Because hay cutting occurred only once ortwice a year in our study area, it was crucial to capture those areas di-rectly after they were cut. If no satellite image was available for thattime, then it became very difficult to assess if grassland managementhad taken place in a given year. We also acknowledge that because

Fig. 8. Fuzzy Kappa similarities reports for “abandoned arable land” and “abandoned managed grassland” spatial pattern for 48 possible image combinations versus for overall bestclassification (“Spring”, “Summer” and “Fall” images for pre-and post-abandonment) using SVM as the classification algorithm.

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we mapped abandonment between 1989 and 1999/2000, abandon-ment that commenced in recent years prior to image acquisition(Baumann et al., 2011; Bergen et al., 2008; Hostert et al., 2011; Kozaket al., 2004) would also have contributed to the difficulty of detectingagricultural land abandonment due to the subtle signal changes in-curred by this time frame. It is for this reason that we considered map-ping abandonment with shrub succession (allowing for more yearsbetween the start of the abandonment and the present day).

Our results indicated that SVMwere unable to overcome constraintsimposed by limited image availability, making knowledge of the effectsof suboptimal image date combinations on change detection important.Our results also highlighted the importance of multi-seasonal imageryfor the accurate classification of agricultural land abandonment that iswidespread in Eastern Europe (Baumann et al., 2011; Bergen et al.,2008; Kozak et al., 2004; Kuemmerle et al., 2008; Prishchepov et al.,2012). However, themulti-seasonal imagery required for this classifica-tion was rarely available. As stated above, no footprint among the avail-able 995 Landsat footprints in Eastern Europe provided three cloud-free

images in a single year during both pre-abandonment (1988–1990) andpost-abandonment (1998–2000) (Fig. 10A). Furthermore, only twocloud-free Landsat TM/ETM+ footprints allowed for the collection of“two and three” and “three and two” image dates, five footprints for“two and two” image dates, and three footprints for “one and three”and “three and one” image date scenarios.

Composite images (Hansen et al., 2008; Roy et al., 2010; Potapov etal., 2011) and those using overlapping footprintsmay be able to partiallycompensate for these data limitations. Such approaches can be useful toovercome image date limitations, but still may allow accuratelymappingslow LULCC processes (e.g., natural succession on abandoned agricultur-al fields in temperate Europe) (Prishchepov et al., 2012). We relaxedcloud constraints for the image dates with up to 5% cloud contaminationhaving any “Spring”, “Summer” and “Fall” image dates for 1988–1990and 1998–2000 (e.g., “Spring” image date from 1988, “Summer” imagedate from 1989 and “Fall” image date from 1990 for pre-abandonment,“Fall” image date from 1998, “Summer” image date from 1999 and“Spring” image date from 2000 for post-abandonment) (Fig. 10B). In

Fig. 9. Example of the mapped spatial pattern for “abandoned arable land” and “abandoned managed grassland” with different combinations of image dates.

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that case, only 35 Landsat TM/ETM+ footprints had three image datesfor both pre- and post-abandonment, and 75 Landsat TM/ETM+footprints provided “two and three” and “three and two” image date

Table 4Confusion matrixes representing best overall classification using SVM and “Spring”, “Summ

Classification Reference

F Cl Rg Ar ArMGr ArAb MGr

F 371 10 2Cl 78Rg 5 3 39Ar 2 136 1 3ArMGr 1 3 63 2 1ArAb 6 2 93MGr 1 110MGrAb 0 2 10MGrAr 2NGrShr 1 1 3 2 10Other 1Wt 1 1Total 380 92 42 154 70 102 133Producer's accuracy(%)

97.6 84.8 92.9 88.3 90.0 91.2 82.7

combinations. Evenwith theuse of the Landsat archives, the actual avail-able image dates suitable for change detection are very limited. Thus, abetter understanding of the effects of suboptimal image dates (i.e., data

er” and “Fall” image dates for both pre- and post-abandonment.

User's accuracy (%)

MGrAb MGrAr NGrShr Other Wt Total

1 1 386 96.11 79 98.7

2 2 2 53 73.63 145 93.8

1 71 88.7101 92.1

2 2 115 95.731 1 4 48 64.6

37 46 80.48 25 50 50.0

1 50 52 96.230 32 93.8

42 44 32 54 33 117873.8 84.1 78.1 92.6 90.9 90.2

Fig. 10. A: Image date combinations available with a 0% cloud constraints when selectingany “Spring”, “Summer” and “Fall” image dates during three pre-abandonment years(1988–1990) andduring three post-abandonment years (1998–2000). B: Imagedate com-binations available with a 5% cloud constraints when selecting any “Spring”, “Summer”and “Fall” image dates during three pre-abandonment years (1988–1990) and duringthree post-abandonment years (1998–2000). Landsat TM/ETM+ image archives (Univer-sity of Maryland Global Land Cover Facility [www.landcover.org], USGS [glovis.usgs.gov],Eurimage Inc. [www.eurimage.com], and R&D Scanex [www.scanex.com]).

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with fewer image dates) on change detection accuracies and detectedLULCC patterns, as indicated by our study, is crucial to estimating theexpected accuracies of land abandonment maps and agricultural landabandonment patterns for Eastern Europe that can be obtained from cer-tain image-date composites.

To monitor accurately agricultural areas at the regional level with30-m resolution Landsat TM/ETM+-like satellite images, we suggestcombining as many pre-abandonment satellite images as possiblewith “Spring” images for post-abandonment, especially when arablelands are the dominant land cover. When the accurate monitoringof managed grasslands is also important and their percentage of thearea's total agricultural land is high, we suggest using as many satel-lite images as possible for both periods. We suggest using SVM orother non-parametric classification approaches to map agriculturalland abandonment because these approaches facilitate the accuratemapping of abandoned agricultural land, especially when many im-ages and distinctive training sets are available. And such mappingopens up many applications for both routine agricultural monitoringand land use science. We also warn to use agricultural land abandon-ment maps produced with the sub-optimal image dates with caution,especially when the accurate rates and the patterns of agriculturalland abandonment are important (e.g., for LULCC models).

Acknowledgments

We gratefully acknowledge support from the NASA Land Coverand Land Use Change (LCLUC) program, the Division of InternationalStudies of the University of Wisconsin—Madison. We also express ourgratitude to I. Plytyn, who assisted us during field visits, and toT. Kuemmerle, for technical assistance and fruitful discussions. Wethank A. Burnicki, D. Lewis, M. Ozdogan and P. Townsend for theirvaluable comments on an earlier version of this manuscript and

three anonymous reviewers for their constructive comments thatgreatly assisted us in improving the manuscript.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.rse.2012.08.017. These data include Googlemaps of the most important areas described in this article.

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