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Satellite-aided studies of vegetation cover, biomass and productivity are becoming increasingly important for monitoring the effects of a changing climate on the biosphere. With their large spatial coverage and good temporal resolution, space-borne instruments are ideal to observe remote areas over extended time periods. However, long time series datasets with global coverage have in many cases too low spatial resolution for sparsely vegetated high latitude areas. This study has made use of a newly developed 30 year 1 km spatial resolution dataset from 1986 to 2015, provided by the NOAA AVHRR series of satellites, in order to calculate the annual maximum NDVI over parts of Svalbard (78°N). This parameter is indicative of vegetation productivity and has therefore enabled us to study long-term changes in greening within the Inner Fjord Zone on Svalbard. In addition, local meteorological data are available to link maximum NDVI values to the temporal behavior of the mean growing season (summer) temperature for the study area. Over the 30 year period, we find positive trends in both maximum NDVI (average increase of 29%) and mean summer temperature (59%), which were significantly positively correlated with each other. This suggests a temporal greening trend mediated by summer warming. However, as also recently reported for lower latitudes, the strength of the year-to-year correlation between maximum NDVI and mean summer temperature decreased, suggesting that the response of vegetation to summer warming has not remained the same over the entire study period.

Abstract:We report a methane sensor based on an integrated MachZehnder interferometer, which is cladded by a styrene-acrylonitrile film incorporating cryptophane-A. Cryptophane-A is a supramolecular compound able to selectively trap methane, and its presence in the cladding leads to a 17-fold sensitivity enhancement. Our approach, based on 3 cmlong low-loss Si 3 N 4 rib waveguides, results in a detection limit as low as 17 ppm. This is 1-2 orders of magnitude lower than typically achieved with chip-scale low-cost sensors.