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Open Access Research article

Spectral measures and mixed models as valuable tools for investigating controls on land surface phenology in high arctic Greenland

Mikkel P Tamstorf1*, Lotte Illeris2, Birger U Hansen3 and Mary Wisz1

  • * Corresponding author: Mikkel P Tamstorf

Author Affiliations

1 University of Aarhus, National Environmental Research Institute, Dep. for Arctic Environment, Frederiksborgvej 399, PO Box 358, DK-4000 Roskilde, Denmark

2 University of Copenhagen, Institute of Biology, Øster Farimagsgade 2D, DK-1353 Copenhagen K, Denmark

3 University of Copenhagen, Department of Geography and Geology, Øster Voldgade 10, DK-1354 Copenhagen C, Denmark

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BMC Ecology 2007, 7:9  doi:10.1186/1472-6785-7-9

Published: 19 September 2007



Changes in land surface phenology are of major importance to the understanding of the impact of recent and future climate changes in the Arctic. This paper presents an extensive study from Zackenberg Ecological Research Operations (ZERO) where snow melt, climate and growing season characteristics of six major high arctic vegetation types has been monitored during 1999 to 2005. We investigate the growth dynamics for dry, mesic and wet types using hand held measurements of far red normalised difference vegetation index (NDVI-FR) and generalized additive mixed models (GAMM).


Snow melt and temperature are of major importance for the timing of the maximum growth as well as for the seasonal growth. More than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types. We find several non-linear growth responses to the environmental variables.


We conclude that the uses of GAMMs are valuable for investigating growth dynamics in the Arctic. Contrary to several other studies in the Arctic we found a significant decreasing trend of the seasonally integrated NDVI-FR (SINDVI) in some vegetation types. This indicates that although greening might occur wide-spread in the Arctic there are variations on the local scale that might influence the regional trends on the longer term.