114 research outputs found
Utilization of UAV-Lidar to infer surface roughness and leaf area index of a heterogeneous wooded area
Methane oxidation in contrasting soil types:responses to experimental warming with implication for landscape-integrated CH<sub>4</sub> budget
Larval outbreaks in West Greenland:instant and subsequent effects on tundra ecosystem productivity and CO<sub>2</sub> exchange
Insect outbreaks can have important consequences for tundra ecosystems. In this study, we synthesise available information on outbreaks of larvae of the noctuid moth Eurois occulta in Greenland. Based on an extensive dataset from a monitoring programme in Kobbefjord, West Greenland, we demonstrate effects of a larval outbreak in 2011 on vegetation productivity and CO(2) exchange. We estimate a decreased carbon (C) sink strength in the order of 118–143 g C m(−2), corresponding to 1210–1470 tonnes C at the Kobbefjord catchment scale. The decreased C sink was, however, counteracted the following years by increased primary production, probably facilitated by the larval outbreak increasing nutrient turnover rates. Furthermore, we demonstrate for the first time in tundra ecosystems, the potential for using remote sensing to detect and map insect outbreak events. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13280-016-0863-9) contains supplementary material, which is available to authorized users
Vegetation phenology gradients along the west and east coasts of Greenland from 2001 to 2015
The objective of this paper is to characterize the spatiotemporal variations of vegetation phenology along latitudinal and altitudinal gradients in Greenland, and to examine local and regional climatic drivers. Time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) were analyzed to obtain various phenological metrics for the period 2001–2015. MODIS-derived land surface temperatures were corrected for the sampling biases caused by cloud cover. Results indicate significant differences between West and East Greenland, in terms of both observed phenology during the study period, as well as the climatic response. The date of the start of season (SOS) was significantly earlier (24 days), length of season longer (25 days), and time-integrated NDVI higher in West Greenland. The sea ice concentration during May was found to have a significant effect on the date of the SOS only in West Greenland, with the strongest linkage detected in mid-western parts of Greenland
Spatial Data on different scales in GEM:Allows for Quatification of Geomorphological changes following Extreme Events
Exchange of CO<sub>2 </sub>in Arctic tundra:impacts of meteorological variations and biological disturbance
Validating the effect of Ondansetron and Mirtazapine In Treating hyperemesis gravidarum (VOMIT):protocol for a randomised placebo-controlled trial
Northern Hemisphere permafrost map based on TTOP modelling for 2000-2016 at 1 km<sup>2 </sup>scale
Permafrost is a key element of the cryosphere and an essential climate variable in the Global Climate Observing System. There is no remote-sensing method available to reliably monitor the permafrost thermal state. To estimate permafrost distribution at a hemispheric scale, we employ an equilibrium state model for the temperature at the top of the permafrost (TTOP model) for the 2000–2016 period, driven by remotely-sensed land surface temperatures, down-scaled ERA-Interim climate reanalysis data, tundra wetness classes and landcover map from the ESA Landcover Climate Change Initiative (CCI) project. Subgrid variability of ground temperatures due to snow and landcover variability is represented in the model using subpixel statistics. The results are validated against borehole measurements and reviewed regionally. The accuracy of the modelled mean annual ground temperature (MAGT) at the top of the permafrost is ±2 °C when compared to permafrost borehole data. The modelled permafrost area (MAGT 0) is around 21 × 106 km2 (22% of exposed land area), which is approximately 2 × 106 km2 less than estimated previously. Detailed comparisons at a regional scale show that the model performs well in sparsely vegetated tundra regions and mountains, but is less accurate in densely vegetated boreal spruce and larch forests
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