RESEARCH ARTICLE

Combined analysis of land cover change and NDVI trends inthe Northern Eurasian grain belt

Christopher K. WRIGHT (✉)1, Kirsten M. de BEURS2, Geoffrey M. HENEBRY1

1 South Dakota State University, Geographic Information Science Center of Excellence (GIScCE), Brookings, South Dakota 57007, USA2 The University of Oklahoma, Department of Geography and Environmental Sustainability, Norman, OK 73019, USA

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012

Abstract We present an approach to regional environ-mental monitoring in the Northern Eurasian grain beltcombining time series analysis of MODIS normalizeddifference vegetation index (NDVI) data over the period2001–2008 and land cover change (LCC) analysis of the2001 and 2008 MODIS Global Land Cover product(MCD12Q1). NDVI trends were overwhelmingly negativeacross the grain belt with statistically significant (p£0.05)positive trends covering only 1% of the land surface. LCCwas dominated by transitions between three classes;cropland, grassland, and a mixed cropland/natural vegeta-tion mosaic. Combining our analyses of NDVI trends andLCC, we found a pattern of agricultural abandonment(cropland to grassland) in the southern range of the grainbelt coinciding with statistically significant (p£0.05)negative NDVI trends and likely driven by regionaldrought. In the northern range of the grain belt we foundan opposite tendency toward agricultural intensification; inthis case, represented by LCC from cropland mosaic topure cropland, and also associated with statisticallysignificant (p£0.05) negative NDVI trends. Relativelysmall clusters of statistically significant (p£0.05) positiveNDVI trends corresponding with both localized landabandonment and localized agricultural intensificationshow that land use decision making is not uniform acrossthe region. Land surface change in the Northern Eurasiangrain belt is part of a larger pattern of land cover land usechange (LCLUC) in Eastern Europe, Russia, and formerterritories of the Soviet Union following realignment ofsocialist land tenure and agricultural markets. Here,we show that a combined analysis of LCC and NDVItrends provides a more complete picture of the complex-ities of LCLUC in the Northern Eurasian grain belt,

involving both broader climatic forcing, and narroweranthropogenic impacts, than might be obtained from eitheranalysis alone.

Keywords land cover change, MODIS, NDVI, NorthernEurasian grain belt, Kazakhstan, Russia, time seriesanalysis, Ukraine

1 Introduction

Satellite remote sensing has typically been used to assessregional environmental change in two ways: 1) by post-classification analysis of land cover change (LCC); and 2)through image time series analysis with respect to spectralindices like the normalized difference vegetation index(NDVI). The former approach is effective for documentingdistinct, abrupt anthropogenic impacts on the land surfacelike deforestation and urbanization (e.g., Viña et al., 2004;Yuan et al., 2005). The latter approach has been used tolook for effects of climate change on land surfacephenology, a seminal example being earlier spring green-up detected in Northern Eurasia (Myneni et al., 1997).However, no attempt has been made to merge these verydifferent but complementary ways of monitoring regionalenvironmental change, despite the fact that both LCC andNDVI trend analyses can be derived from the sameunderlying remote sensing data. Here, we demonstrate theefficacy of a combined analysis of LCC and NDVI trendsusing the Northern Eurasian grain belt as an example.The Northern Eurasian grain belt is centered on Russia’s

southern border with Kazakhstan, extending westward intoUkraine. This semi-arid region is ecotonal, ranging fromforest-steppe and steppe into desert; thus, it is especiallyvulnerable to anthropogenic disturbance and climatechange (Lioubimtseva and Henebry, 2009). From the late19th century to 1990, Kazakhstan experienced a 1°Cincrease in mean annual temperature, approximately twice

Received November 22, 2011; accepted May 4, 2012

E-mail: Christopher.Wright@sdstate.edu

Front. Earth Sci. 2012, 6(2): 177–187DOI 10.1007/s11707-012-0327-x

the global rate of increase (Pilifosova et al., 1997). Theregion has also been subject to some of the more extremeland use change events of the 20th century, includingKhrushchev’s “Virgin Lands” program where more than13 million hectares of native steppe were converted tosmall-grain cultivation (McCauley, 1976). Following theSoviet Union’s formal collapse in 1991, widespreadagricultural abandonment occurred in the grain belt (deBeurs and Henebry, 2004a; Ioffe et al., 2004), part of alarger pattern of agricultural de-intensification in theformer Soviet Union where, for instance, approximately23 million hectares of arable land have been taken out ofproduction in Ukraine, Russia, and Kazakhstan since 1991(Henebry, 2009; Lioubimtseva and Henebry, 2012).A number of studies have used the “natural experiment”

afforded by the Soviet Union’s collapse to look at effects ofbroad-scale political and socioeconomic factors on landcover/land use change (LCLUC). In the Carpathian region(including parts of Slovakia, Poland, and Ukraine) cross-border comparisons revealed substantial differences in therate of agricultural abandonment, and the spatial pattern ofsuch change, which were attributable to national differ-ences in the level of agricultural collectivization pre-collapse and different patterns of land reform/privatizationpost-collapse (Kuemmerle et al., 2006; 2008). Geographicisolation and a lack of access to markets have been linkedto agricultural abandonment in Romania (Müller et al.,2009) and European Russia (Ioffe et al., 2004).One factor largely ignored in the study of post-Soviet

LCLUC is the potential interplay of environmental forcing,say drought or climate change, with political and socio-economic factors driving land use change. In two adjoiningriver basins; the Dneiper Basin, located largely withinUkraine, and the Don Basin, located largely within Russia,Kovalskyy and Henebry (2009) found differences in meanNDVI (higher in the Dneiper Basin) and interannual NDVIvariability (greater in the Don Basin) which they attributedto impacts of drought in both basins and differentagricultural policies in Ukraine (major agricultural reform)versus Russia (extensive cropland abandonment). Werecently found that positive NDVI trends in southernRussia appeared to be concentrated in cropland, suggestiveof LCC, while less than 800 km to the south in Kazakhstan,broad-scale negative NDVI trends occurred across bothgrassland and cropland, implicating a climatic cause (deBeurs et al., 2009).Here, we expand on these results by looking at the

record of LCC, as indicated by the Moderate ResolutionImaging Spectrometer (MODIS) Global Land Coverproduct (Friedl et al., 2010) from 2001 to 2008 in theNorthern Eurasian grain belt, and how that LCCcorresponds with NDVI trends over the interveningyears. The significance of our contribution is twofold.First, post-Soviet LCC in the Northern Eurasian grain beltremains little studied (de Beurs and Henebry, 2004a)relative to recent findings from Eastern Europe (Kuem-

merle et al., 2006; 2008; Müller et al., 2009). Second, themethodology we introduce facilitates an improved inter-pretation of land surface variability as it is influenced byboth anthropogenic and climatic factors.

2 Methods

NDVI image time series from 2001 to 2008 were compiledfrom 500 m resolution MODIS Nadir Bi-directionalReflectance Distribution Function Adjusted Reflectance(NBAR) data consisting of 16-day composites reportedevery 8 days (MCD43A4; NASA, 2010). To span thegrowing season, we focused on the period from 1 March to1 October of each year. Resulting NDVI time series at eachMODIS pixel were thus comprised of 27 NDVI compo-sites per growing season (or 216 values in total). Eachspatially referenced time series was analyzed using theSeasonal Kendall (SK) trend test corrected for first-orderautocorrelation. The SK test is a nonparametric methodwell-suited to identifying monotonic trends in time seriescontaining a strong seasonal component. The sign of theSK test statistic indicates trend direction while the magni-tude of the test statistic indicates the strength of the trend;however, it cannot be interpreted as an estimate of a linearslope. Given some critical p-value, SK statistics are eval-uated as significantly positive or negative, or found sta-tistically insignificant. For additional details on the SK testand its use in analysis of geospatial data, see Hirsch andSlack (1984) and de Beurs and Henebry (2004b; 2005).Our LCC analysis was based on the 500 m resolution

MODIS Global Land Cover (GLC) Collection 5(MCD12Q1; Friedl et al., 2010) for the years 2001 and2008. To simplify this analysis, we consolidated the 17-class International Geosphere-Biosphere Programme(IGBP) classification scheme available in MCD12Q1into 11 land cover (LC) classes by generalizing forest,shrub, and savanna types (Table 1). We used the IGBPclassification, instead of other schemes available inMCD12Q1, because it contains a cropland/mixed vegeta-tion mosaic class. Previous results in the Russian portion ofthe grain belt using 30 m resolution Landsat imagerysuggested that agricultural LCC was occurring at a finerspatial scale than 500 m MODIS pixels (de Beurs et al.,2009). LCC rates were determined by post-classificationcomparison of 2001 and 2008 LC maps. The NDVI trendanalysis and LCC analysis were combined by calculatingLCC rates conditional on NDVI trends, i.e., by intersectingthe NDVI trend map developed using a critical p-value of0.05 with the 2001 and 2008 LC maps. This contingencyanalysis was made possible by the fact that both MODISNBAR and global LC products are recorded on the same500 m grid (and, in fact, are derived from the same 500 mresolution multispectral data). Chi-square tests of inde-pendence were used to evaluate the dependence of LCCrates on NDVI trends.

178 Front. Earth Sci. 2012, 6(2): 177–187

3 Results

Over the period from 2001 to 2008, NDVI trends weregenerally negative across the grain belt, with the bulk ofhighly significant (p£0.01) negative trends occurring inKazakhstan (Fig. 1). Region-wide, 57% of the land surfaceexhibited statistically significant (p£0.05) negative NDVItrends. In the north central portion of the grain belt, largelyin Russia, a spatially heterogeneous mixture of positive

and negative NDVI trends was found (see Inset A inFig. 1). However, statistically significant (p£0.05)positive NDVI trends covered only 1% of the land surface.In the Northern Eurasian grain belt, the dominant land

cover types identified in the MODIS GLC product arecropland, grassland, and a mixed cropland/natural vegeta-tion (crop/veg) class. Together they comprise over 90% ofthe total land area. Increases in grassland cover between2001 and 2008 were nearly matched by decreases in the

Table 1 Simplification of the IGBP LC classification scheme and net LCC from 2001 to 2008

IGBP LC classification Simplified LC classification 2001 LC(% of area) 2008 LC(% of area) Net change

Evergreen needleleaf Forest 1.79 2.43 + 0.64

Evergreen broadleaf

Deciduous needleleaf

Deciduous broadleaf

Mixed forest

Closed shrub Shrub 1.24 0.16 – 1.08

Open shrub

Woody savanna Savanna 1.03 0.66 – 0.36

Open savanna

Grassland Grassland 19.41 23.89 + 4.48

Cropland Cropland 66.40 63.53 – 2.86

Cropland/Natural vegetation Cropland/Natural vegetation 8.16 7.11 – 1.04

Barren Barren 0.11 0.04 – 0.07

Snow/Ice Snow/Ice < 0.01 0.02 + 0.02

Water Water 1.07 1.10 + 0.03

Wetland Wetland 0.29 0.55 + 0.26

Urban Urban 0.51 0.51 0.00

Fig. 1 NDVI trends over the period 2001 to 2008 across the Northern Eurasian grain belt. Non-significant (p> 0.05) SK tests appearwhite. Negative trends appear blue; positive trends appear red. Statistically significant (p£0.05) trends appear in lighter shades of blueand red; highly significant (p£0.01) trends appear in darker shades of blue and red. Inset A illustrates an area in the north-central grainbelt with a spatially heterogeneous mixture of positive and negative NDVI trends. National boundaries appear as black lines

Christopher K. WRIGHT et al. Combined analysis of LCC and NDVI trends 179

cropland and crop/veg classes (Table 1). Given thatchanges in other minor land cover classes were typicallyvery small, we focused on the cropland, grassland, andcrop/veg classes in subsequent analyses.Among pixels classified as cropland or grassland in

2001, the LCC rate from 2001 to 2008 was approximately13%, and much higher in the crop/veg class, over 42%(Fig. 2). Chi-square tests showed that LCC rates were notindependent of NDVI trends (p< 0.01). Differences inLCC rates conditional on statistically significant (p£0.05)NDVI trends relative to overall, undifferentiated ratesvaried as a function of LC class in 2001 (Fig. 2). Forexample, the LCC rate among positively trending pixelsclassified as grassland in 2001 was dramatically higherthan the combined rate of change in grassland, regardlessof NDVI trend.

With respect to specific land cover transitions, wefocused on LCC originating from pixels classified ascropland, grassland, or crop/veg in 2001 (Fig. 3). Other LCclasses included as endpoints of LCC in Fig. 3 are limitedto forest, shrub, and savanna as transitions to other classeswere negligible or non-existent. Again, we find strongdependence of LCC rates on the directionality ofstatistically significant (p£0.05) trends in NDVI. Forexample, negatively trending cropland pixels were muchmore likely to convert to grassland than their counterpartsexhibiting nonsignificant (p> 0.05) or positive NDVItrends (Fig. 3(a)). Also, even if the LCC rate for a specificNDVI trend/LC class combination is high, that transitionmay be rare in frequency. For example, in positively

trending grassland the rate of LCC to cropland is greaterthan 30% (Fig. 3(b)) but at a frequency of just over 4000pixels from 2001 to 2008 (Fig. 3(e)). Meanwhile, thefrequency of grassland to cropland conversion is muchhigher among nontrending and negatively trending pixels(Fig. 3(e)).Mapping selected combinations of LCC and NDVI

trends, we found a broad band of cropland to grasslandconversion coinciding with negative, statistically signifi-cant (p£0.05) NDVI trends across the southern range ofthe grain belt, primarily in Kazakhstan, but also in southernRussia (Fig. 4). LCC in the opposite direction, fromgrassland to cropland, occurs across much of the samearea, also associated with negative NDVI trends; however,grassland to cropland conversion among pixels exhibitingnonsignificant (p> 0.05) NDVI trends occurs with com-parable frequency (Fig. 3(e)). In the central portion of thegrain belt (in the vicinity of Inset C in Fig. 4) we find small,isolated clusters of grassland to cropland conversionassociated with positive NDVI trends (Fig. 5(C)). Overall,the land area covered by LCC change from cropland tograssland, regardless of NDVI trends, is 180% greater thanthe area where grassland to cropland conversion is found.In the northern grain belt, negative NDVI trends are

largely associated with LCC from the crop/veg class tocropland, mostly in Russia (Fig. 4). LCC in the oppositedirection, from cropland to crop/veg, occurs among bothpositive and negatively trending pixels, albeit it withgreater frequency in negatively trending pixels (Fig. 3(d)).Spatial aggregations of LCC from cropland to crop/veg arealso relatively small and mixed with other types of change(Figs. 5(A) and (B)). Overall, apparent agriculturalintensification associated with LCC from crop/veg topure cropland is approximately 34% more frequent thanapparent de-intensification signified by LCC from crop-land to crop/veg (Fig. 3).

4 Discussion

The dominant pattern emerging from our combinedanalysis of LCC and NDVI trends in the Northern Eurasiangrain belt is one of agricultural de-intensification across thesouthern range of the grain belt and agricultural intensi-fication in the north. These opposing transitions— fromcropland to grassland in the south and from cropland/mixed vegetation mosaic to pure cropland in the north—are both concentrated among pixels exhibiting statisticallysignificant (p£0.05) negative trends in NDVI (Figs. 3 and4). In total, cropland to grassland LCC coincides withnegative NDVI trends across 4.7% of the grain belt’s landarea, while LCC from crop/veg to cropland coincides withnegative NDVI trends within 1.7% of the grain belt.The value of combining NDVI trend analysis with LCC

analysis is in the partitioning of change events identifiedfrom the MODIS GLC product. Where LCC rates

Fig. 2 LCC rates from 2001 to 2008 for pixels identified ascropland, grassland, or mixed cropland/natural vegetation (crop/veg) in 2001 and conditional on NDVI trends. Black bars are LCCrates for all pixels in each LC class. White bars are LCC rates forpixels where NDVI trend tests were not significant (p> 0.05). Bluebars are LCC rates for pixels exhibiting negative, statisticallysignificant (p£0.05) NDVI trends. Red bars are LCC rates forpixels exhibiting positive, statistically significant (p£0.05) NDVItrends. Chi-square tests of independence were highly significant(p< 0.01) for each of the three LC classes in three separateanalyses

180 Front. Earth Sci. 2012, 6(2): 177–187

Fig. 4 Selected LCC from 2001 to 2008 among pixels exhibiting statistically significant (p£0.05) NDVI trends. See legend for LCC/NDVI trend combinations represented by different colors. Lettered boxes are boundaries of insets displayed at higher resolution in Fig. 5

Fig. 3 LC transitions from 2001 to 2008 starting from pixels classified as cropland, grassland, or mixed crop/natural vegetation (crop/veg) in 2001, conditional on NDVI trends. (a)–(c): relative LCC rates calculated with respect to marginal sums of 2001 land cover/NDVItrend combinations. (d)–(f): frequencies, in number of pixels, of specific land cover transitions. Grey bars are LCC rates (frequencies) forpixels where NDVI trend tests were not significant (p> 0.05). White bars are LCC rates (frequencies) for pixels exhibiting negative,statistically significant (p£0.05) NDVI trends. Black bars are LCC rates (frequencies) for pixels exhibiting positive, statisticallysignificant (p£0.05) NDVI trends

Christopher K. WRIGHT et al. Combined analysis of LCC and NDVI trends 181

conditional on statistically significant (p£0.05) NDVItrends are substantially higher than LCC rates amongpixels where NDVI trends are nonsignificant (p> 0.05) wefind confirmation that some type of change has actuallyoccurred. That change may be true LCC, or it could reflectcompromised performance of the classification algorithmwith respect to an identifiable subset of pixels. Forexample, perhaps the GLC algorithm is prone tomisclassifying cropland as grassland during drought. Ineither case, identification of pixels exhibiting elevated ratesof change via NDVI trend analysis should allow users ofthe MODIS GLC product to draw more informed

inferences with respect to land surface variability.Recent climate trends in northern Kazakhstan, including

decreased annual precipitation and increased growing-season air temperatures (Akhmadieva and Groisman,2008; Wright et al., 2009a) suggest that drought is drivingapparent agricultural de-intensification in the southerngrain belt. In the Russian portion of the grain belt,contemporary precipitation trends determined from theGlobal Precipitation Climatology Centre Full Data Rea-nalysis v4 (Schneider et al., 2008) are generally positive,although areas of drought are found in the vicinity of InsetD in Fig. 4, and in the western grain belt between

Fig. 5 Higher resolution examples of different LCC/NDVI trend combinations. Letters A-D reference the spatial location of each insetin Fig. 4. See legend for LCC/NDVI trend combinations represented by different colors

182 Front. Earth Sci. 2012, 6(2): 177–187

Kazakhstan and Ukraine (Wright et al., 2009b). None-theless, LCC from crop mosaic to pure cropland in thenorthern grain belt, i.e., apparent agricultural intensifica-tion, is not consistent with weather-related impacts on landuse. While we might expect LCC from crop/veg to purecropland to generate a signal of declining NDVI due tocontraction of the growing period following conversion ofthe natural vegetation component to agriculture, it is notclear why climate trends would prompt such a change inland use. This pattern of agricultural intensification in theRussian portion of the grain belt also contrasts with theprevailing trend of agricultural decline in post-SovietRussia (Ioffe et al., 2004).In the northern grain belt, particularly in Russia, it

appears that socioeconomic forces may be more importantin driving LCC. For example, the cross-border contrastbetween apparent agricultural intensification in Russia andapparent agricultural abandonment in Kazakhstan isstriking (Fig. 5(D)), analogous to cross-border differencesin agricultural LCLUC recently found in Eastern Europe(Kuemmerle et al., 2006; 2008; Müller et al., 2009). Thelink between positive NDVI trends and LCC fromcropland to crop/veg (Fig. 3(a)) is consistent withpreviously documented increases in NDVI found afteragricultural abandonment in Kazakhstan post Sovietcollapse (de Beurs and Henebry 2004a; 2005). Spatiallyheterogeneous mixtures of positive and negative NDVItrends (Fig. 1 Inset A), and the different types of LCCassociated with these trends within relatively small clustersof localized change (Figs. 5(A) and (C), further illustratethat land use decision making is not uniform across theregion. In Poland, Angelstam et al. (2003) found a similarmixture of agricultural abandonment and agriculturalintensification at the village scale in the post socialistperiod. In the southern grain belt, conversion of grasslandto cropland among negatively trending pixels within alarger pattern of agricultural abandonment (Fig. 4) alsodemonstrates heterogeneity of land use decisions in theface of climatic forcing. These smaller clusters of contraryLCLUC (e.g., on the Kazakhstan side of the border in Fig.5(D)) may reflect governmental efforts to stabilize theagricultural sector in Kazakhstan, mainly by increasingagricultural subsidies, which have led to increasedfertilizer use and wider adaptation of high quality seedstocks since 1999 (Lioubimtseva and Henebry, 2012).In western Ukraine, previous studies have found high

rates of agricultural abandonment during the 1990s(Kuemmerle et al., 2006; 2008). In eastern Ukraine,however, the MODIS GLC shows little LCC within thegrain belt. And where LCC is indicated, it is indicative ofagricultural intensification, i.e., crop/veg to crop in Fig. 4or grassland to cropland conversion associated withnonsignificant NDVI trends (result not shown). Thisapparent LC resilience, in spite of widespread negativetrends in NDVI across eastern Ukraine (Fig. 1), is furtherevidence of relative stability within the agricultural sector

of Ukraine relative to the rest of the grain belt (Kovalskyyand Henebry, 2009).Post-classification analysis is susceptible to compound

errors leading to misidentification of LCC, i.e., errors in theinitial classification or the secondary classification, or both.For the MODIS GLC, the reported map user’s accuracy inthe cropland class (estimated globally by cross-validation)is nearly 93%, although estimated user’s accuracies for thecrop/veg and grassland classes are much lower; 28% and56%, respectively (Friedl et al., 2010). Not surprisingly,the generators of the GLC have reported difficulty inseparating the cropland and crop/veg classes spectrally;they also make a number of adjustments to theirclassification algorithm in order to reduce global over-prediction of cropland (Friedl et al., 2010).To assess whether apparent LCC in the Northern

Eurasian grain belt could be the result of systematic errorsacross the region, we used LC confidence data includedwith the GLC in order to isolate LCC events whereconfidence in the LC classification was low at either theearlier date (2001), the later date (2008), or both. LCconfidence is a pixel-scale estimate of the probability that apixel has been correctly classified (McIver and Friedl,2001; Friedl et al., 2010). In the grain belt, mean LCconfidence was 0.77 in 2001 and 0.79 in 2008; however,the spread between the 1st quartile of LC confidencevalues in 2001 (0.61) and the 1st quartile in 2008 (0.66)was wider. This difference complicates our definition ofwhat we mean by “low” confidence. For example, supposewe set aside all LCC events involving pixels falling withinthe 1st quartile of LC confidence in 2001 or within the 1stquartile of LC confidence in 2008. Thus, a pixel with aconfidence value of 0.65 in both 2001 and 2008 would beisolated, despite passing the 2001 threshold where LCconfidence is lower overall; such a criterion would alsopenalize the 2008 LC classification for having a highermean confidence. Therefore, we decided to use the 20011st quartile as a LC confidence threshold, isolating allpixels with values£0.61 in 2001 or 2008. Here we see thatareas of lower LC confidence roughly correspond withareas where LCC is associated with statistically significant(p£0.05) trends in NDVI (Fig. 6). However, the spatialpattern of LC confidence is locally complicated, withvarying degrees of overlap between LCC associated withsignificant trends in NDVI and low LC confidence (seeInset A in Fig. 6). When we partition our combinedanalysis of LCC and NDVI trends using the sameconfidence threshold, we find that LCC is concentratedamong pixels where LC confidence is lower (Table 2).However, almost one-third of the pixels exhibiting anyLCC have confidence scores greater than 0.61 in both 2001and 2008. Additionally, relationships between NDVItrends and LCC involving the cropland, grassland, andcrop/veg classes at higher levels of LC confidence (Fig. 7)are comparable to those seen in the full data set (Fig. 3).Thus, restricting our combined analysis to only those

Christopher K. WRIGHT et al. Combined analysis of LCC and NDVI trends 183

pixels with higher LC confidence scores does not alter ourinterpretation of LCC in the Northern Eurasian grain belt.Among pixels with LC confidence scores greater than 0.61in both 2001 and 2008, for example, the land area coveredby reversion of cropland to grassland (56806 km2) is 345%greater than the area of grassland to cropland conversion(12760 km2); as compared to 180% in the full data set(120175 km2 cropland to grassland; 42952 km2 grasslandto cropland).

5 Conclusions

Post-classification change analysis is focused on discretechanges on the land surface, principally as those changesreflect distinct, abrupt anthropogenic impacts. By contrast,NDVI trend analysis is better suited to identifyingcontinuous, gradual change on the land surface, primarilyas changes in aboveground net primary production aredriven by climatic trends. Land surface changes are, in fact,both discrete and continuous depending on perspective,

and they can be mutually reinforcing. Land use decisionsinfluence NDVI trends; climatic drivers effecting gradualchanges in primary productivity may prompt specific typesof land use change. Here we have shown the complemen-tarity of post-classification change analysis and NDVItrend analysis as tools for regional environmental monitor-ing. LCC results inform our interpretation of NDVI trends,for example potential anthropogenic causes of positiveNDVI trends within a “sea” of negative values (Fig. 1).Where LCC is inconsistent with the effects of regionaldrought, e.g., agricultural intensification in the northerngrain belt or grassland to cropland conversion in thesouthern grain belt, we find evidence of socioeconomicforces driving land use decisions (Figs. 4 and 5). Eachanalysis, alone, provides an incomplete picture of thecomplexities of LCLUC in the Northern Eurasian grainbelt.Friedl et al. (2010) warn against inferring LCC by

differencing the MODIS GLC product across years, giventhat the rate of interannual variability in class labels isabout 10% globally, well above the rate of actual global

Table 2 Partition of LCC events by LC confidence. Values are the number of LCC pixels exhibiting nonsignificant or significant (p£0.05) negative

or positive NDVI trends conditional on LC confidence. For each NDVI trend category, the percentage of LCC within each partition appears in the

parentheses

NDVI trend LC confidence£0.61 in 2001 or 2008* LC confidence> 0.61 in 2001 and 2008* All LCC

ns 448530 (72.43) 170750 (27.57) 619280

Negative 617638 (66.61) 309675 (33.39) 927313

Positive 24624 (76.80) 7440 (23.20) 32064

SUM 1090792 (69.10) 487865 (30.90) 1578657

Note: *Threshold = 1st quartile of 2001 LC confidence

Fig. 6 Partition of LC confidence values in the Northern Eurasian grain belt. Pixels with LC confidence> 0.61 in 2001 and 2008 aregray; pixels with LC confidence£0.61 in 2001 or 2008 are white. Polygons outline areas where LCC is indicated by the 2001 and 2008MODIS GLC (irrespective of LC confidence) and NDVI trends are statistically significant (p£0.05)

184 Front. Earth Sci. 2012, 6(2): 177–187

LCC. We contend that combining an analysis of NDVItrends with post-classification change analysis mayprovide “quality weighting” to specific LC transitions byconfirming that significant changes have indeed occurredat the land surface. Certainly our consideration of LCconfidence information distributed with the MODIS GLCserves as a caveat against uninformed differencing of the2001 and 2008 LC classifications in the Northern Eurasiangrain belt. However, that analysis did not fundamentallychange our interpretation of the interplay between NDVItrends and LCC in the region. The methodology we haveproposed is new, and much of what we have discussed withrespect to the linkage between the NDVI trends andimputed LCC is speculative. Yet, the types of LCC that weobserved, particularly in the Russian portion of the grainbelt, are consistent with other studies of LCLUC postSoviet collapse (Angelstam et al., 2003; de Beurs andHenebry, 2004a; 2005; Kuemmerle et al., 2006; 2008; deBeurs et al., 2009; Müller et al., 2009).Global climate models predict that temperatures in the

Northern Eurasian grain belt will increase by 1.5°C–3.5°C

by 2030–2050, with the greatest increase in winter (IPCC,2007). Precipitation is predicted to increase slightly inwinter, with decreases in spring and summer (Lioubimt-seva and Henebry, 2009). While some evidence indicatesthat the agricultural sector in the Northern Eurasian grainbelt has been in a recovery phase beginning around 1999(Lioubimtseva and Henebry, 2012), climate change in theregion may very well have effects similar to the extensivedeclines in NDVI that we found from 2001 to 2008(Lioubimtseva and Henebry, 2009). In a region wherecomprehensive ground level environmental data are sparse(Akhmadieva and Groisman, 2008; Wright et al., 2009a), aremote sensing approach like the one we have describedhere may serve as a critical tool for regional environmentalmonitoring.

Acknowledgements This research was supported in part by the NEESPIand NASA LCLUC projects entitled Evaluating the effects of institutionalchanges on regional hydrometeorology: Assessing the vulnerability of theEurasian semi-arid grain belt to GMH and Land Abandonment in Russia:Understanding Recent Trends and Assessing Future Vulnerability andAdaptation to Changing Climate and Population Dynamics to KMdB. We

Fig. 7 LC transitions from 2001 to 2008 starting from pixels classified as cropland, grassland, or mixed crop/natural vegetation (crop/veg) in 2001, conditional on NDVI trends. Here LCC is restricted to pixels with LC confidence> 0.61 in 2001 and 2008. For purposes ofcomparison to the full data set, y-axes are scaled the same as in Fig. 3

Christopher K. WRIGHT et al. Combined analysis of LCC and NDVI trends 185

would like to thank P. de Beurs for the application development that allowedus to estimate the trend statistics efficiently.

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AUTHOR BIOGRAPHY

Christopher K. Wright earned both an M.S. in agronomy (1993)and a Ph.D. in biological sciences (2003) from Montana StateUniversity in Bozeman, Montana, USA. He is a Postdoctoral Fellowat the Geographic Information Science Center of Excellence inBrookings, South Dakota, USA. Previously, he was a NationalResearch Council Postdoctoral Associate at the U.S. GeologicalSurvey Center for Earth Resources Observation and Science inSioux Falls, South Dakota. His research interests include landscapeecology, macrosystems biology, and ecological remote sensing. Dr.Wright is a member of the American Geophysical Union (AGU) andthe US Chapter of the International Association for LandscapeEcology (US-IALE). Email: Christopher.Wright@sdstate.edu.

186 Front. Earth Sci. 2012, 6(2): 177–187

Kirsten M. de Beurs earned a MSc. (2000) in agricultural systemscience from Wageningen University in the Netherlands and a Ph.D.(2005) in natural resource sciences from the University of Nebraska-Lincoln. She is an assistant professor in the department ofGeography and Environmental Sustainability at The University ofOklahoma in Norman, Oklahoma, USA. She previously was anassistant professor at Virginia Polytechnic Institute and StateUniversity (2007–2010) and a Postdoc at the University ofWisconsin and South Dakota State University. She is an author onover twenty five peer-reviewed publications involving the analysisof long image time series. She is interested in the effect of bothhuman and climate on the global vegetated land surface. Dr. de Beursis a member of the American Geophysical Union (AGU) and the USChapter of the International Association for Landscape Ecology(US-IALE). Email: kdebeurs@ou.edu.

Geoffrey M. Henebry earned his M.S. and Ph.D. in environmentalsciences from The University of Texas at Dallas in 1986 and 1989,respectively. He is a Senior Scientist and Interim Co-Director at theGeographic Information Science Center of Excellence and Professorin the Department of Natural Resource Management at South DakotaState University in Brookings, South Dakota, USA. He previouslyheld positions at University of Nebraska-Lincoln, Rutgers-Newark,and Kansas State University. His research interests include landdynamics, land surface phenology, ecological remote sensing, andgrasslands ecology. Prof. Henebry is an active member of theAmerican Geophysical Union (AGU), the US Chapter of theInternational Association for Landscape Ecology (US-IALE), andthe Ecological Society of America (ESA). He currently serves on theeditorial boards of BioScience and Landscape Ecology. Email:geoffrey.henebry@sdstate.edu.

Christopher K. WRIGHT et al. Combined analysis of LCC and NDVI trends 187