2011
DOI: 10.1111/j.1365-2486.2011.02419.x
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Changes in satellite‐derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006

Abstract: Monitoring changes in vegetation growth has been the subject of considerable research during the past several decades, because of the important role of vegetation in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio-temporal patterns of changes in vegetation growth and their linkage with changes in temperature and precipitation in temperate and boreal regio… Show more

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Cited by 618 publications
(460 citation statements)
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“…Most of the Eurasian boreal forests and Arctic regions have greened, whereas in North America only the Arctic region underlain by continuous permafrost north of the treeline has greened and most of the boreal forests have undergone browning trends. Although these contrasting continental and biome trends have been documented in earlier analyses of the GIMMS NDVI record [9,[15][16][17]19,22], our empirical results highlight the significant role of factors other than temperature and moisture on these vegetation patterns. A similar regional heterogeneity in the drivers of the greening trends in northern ecosystems has been found in attribution studies based on ecosystem models [85].…”
Section: Drivers Of Vegetation Greening and Browning Trendssupporting
confidence: 43%
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“…Most of the Eurasian boreal forests and Arctic regions have greened, whereas in North America only the Arctic region underlain by continuous permafrost north of the treeline has greened and most of the boreal forests have undergone browning trends. Although these contrasting continental and biome trends have been documented in earlier analyses of the GIMMS NDVI record [9,[15][16][17]19,22], our empirical results highlight the significant role of factors other than temperature and moisture on these vegetation patterns. A similar regional heterogeneity in the drivers of the greening trends in northern ecosystems has been found in attribution studies based on ecosystem models [85].…”
Section: Drivers Of Vegetation Greening and Browning Trendssupporting
confidence: 43%
“…The analysis of the global satellite record of Normalized Difference Vegetation Index (NDVI) imagery since 1981 (e.g., [14]) has shown that increases in vegetation photosynthetic activity have been stronger in Eurasia than in North America and more sustained in the colder tundra biome than over the boreal forests [15][16][17][18][19]. The most recent studies have found that after the initial increasing trend in NDVI (i.e., greening) observed in the first decade of the record [5,20], longer and hotter growing seasons appear to have resulted in reductions of summer NDVI (i.e., browning) in the drier and more continental regions of the boreal forest since the 1990s [15][16][17]21].…”
Section: Introductionmentioning
confidence: 99%
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“…This period is the same as that covered by recent studies documenting productivity and biomass changes in Arctic environments as derived from satellite measurements, for example, Bhatt et al (2010), Beck andGoetz (2011), Goetz et al (2011) and Piao et al (2011). It also encompasses the 1990-2006 period studied by McGuire et al (2012) for the purposes of constraining a carbon balance for the Arctic.…”
Section: Model Domain and Study Periodmentioning
confidence: 97%
“…The monthly EVI, temperature, and precipitation maps were spatially averaged over the six shrubland types and then averaged seasonally and growing seasonally to obtain the times series of the EVI and the climate variables. Our study focused on the growing season (from April to October), which can be divided into three seasons: spring (April and May), summer (June, July and August) and fall (September and October) [50,52]. To explore the effects of winter climate change on the starting days of the growing season, we defined winter as November of the prior year to March of the following year [53].…”
Section: Data Acquisition and Preprocessingmentioning
confidence: 99%