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

Tamstorf MP, Illeris L, Hansen BU, Wisz M - BMC Ecol. (2007)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Aarhus, National Environmental Research Institute, Dep, for Arctic Environment, Frederiksborgvej 399, PO Box 358, DK-4000 Roskilde, Denmark. mpt@dmu.dk

ABSTRACT

Background: 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).

Results: 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.

Conclusion: 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.

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Growing season and main response variables defined by snow melt and NDVI-FR. ESM: End of Snow Melt, SGS: Start of Growing Season, DOYmax: Day of maximum NDVI-FR, EGS: End of Growing Season, SINDVI: Seasonal integrated NDVI-FR. Brackets indicate measurements that have been removed (see text for further details).
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Figure 7: Growing season and main response variables defined by snow melt and NDVI-FR. ESM: End of Snow Melt, SGS: Start of Growing Season, DOYmax: Day of maximum NDVI-FR, EGS: End of Growing Season, SINDVI: Seasonal integrated NDVI-FR. Brackets indicate measurements that have been removed (see text for further details).

Mentions: An example of field data from the NDVI-FR plots and how the main response variables were extracted is shown in Figure 7. Percent snow cover was monitored at each plot until all snow was gone. The last day where snow was monitored was defined as End of Snow Melt (ESM). Since the actual day where all snow had melted was not known and that plants may utilise radiation and start growth under very shallow snow packs [23], the day after last observed snow cover (ESM + 1) was defined as the Start of Growing Season (SGS). Plots were only monitored once a week and to avoid different bias between plots we used the described method. For plots where no snow was present in the beginning of the field season (14 out of 141 plots) ESM was estimated by linear regression with ESM from plots that had snow. The correlations where all significant (p < 0.05) with R2 values higher than 0.9. For one plot (Sil3) there were to few points to do it directly from DOY with significant relation and summed temperatures (base: 0°C) was used instead with a significant result and a R2 of 0.82.


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

Tamstorf MP, Illeris L, Hansen BU, Wisz M - BMC Ecol. (2007)

Growing season and main response variables defined by snow melt and NDVI-FR. ESM: End of Snow Melt, SGS: Start of Growing Season, DOYmax: Day of maximum NDVI-FR, EGS: End of Growing Season, SINDVI: Seasonal integrated NDVI-FR. Brackets indicate measurements that have been removed (see text for further details).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2164935&req=5

Figure 7: Growing season and main response variables defined by snow melt and NDVI-FR. ESM: End of Snow Melt, SGS: Start of Growing Season, DOYmax: Day of maximum NDVI-FR, EGS: End of Growing Season, SINDVI: Seasonal integrated NDVI-FR. Brackets indicate measurements that have been removed (see text for further details).
Mentions: An example of field data from the NDVI-FR plots and how the main response variables were extracted is shown in Figure 7. Percent snow cover was monitored at each plot until all snow was gone. The last day where snow was monitored was defined as End of Snow Melt (ESM). Since the actual day where all snow had melted was not known and that plants may utilise radiation and start growth under very shallow snow packs [23], the day after last observed snow cover (ESM + 1) was defined as the Start of Growing Season (SGS). Plots were only monitored once a week and to avoid different bias between plots we used the described method. For plots where no snow was present in the beginning of the field season (14 out of 141 plots) ESM was estimated by linear regression with ESM from plots that had snow. The correlations where all significant (p < 0.05) with R2 values higher than 0.9. For one plot (Sil3) there were to few points to do it directly from DOY with significant relation and summed temperatures (base: 0°C) was used instead with a significant result and a R2 of 0.82.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Aarhus, National Environmental Research Institute, Dep, for Arctic Environment, Frederiksborgvej 399, PO Box 358, DK-4000 Roskilde, Denmark. mpt@dmu.dk

ABSTRACT

Background: 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).

Results: 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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus