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  • 1
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 349 (1991), S. 149-151 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] Non-atmospheric Ne isotope ratios and coexisting primordial 3He have previously been identified in basaltic glasses and volcanic gases from mid-ocean ridges4'6, from the Hawaiian5'7 and Yellowstone8 hotspots, and from diamonds9'10. In particular, the Ne results5 from mid-ocean-ridge basalts ...
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2020-01-13
    Description: We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a sequence of binary problems. Each branch of the tree separates one single class from all other remaining classes, using a class-specific selected feature set. We optimize the order of classification steps and the feature sets by combining classification accuracy and sequential search algorithms, looping over all remaining features in each branch. The proposed strategy can be adapted to different types of classifiers and measures for the class separability. In this study, we use a Bayesian classifier with non-parametric kernel density estimation of the probability density functions. We test our algorithm on simulated data as well as airborne and spaceborne SAR data over sea ice. For the simulated cases, average per-class classification accuracy is improved between 0.5% and 4% compared to traditional all-at-once classification. Classification accuracy for the airborne and spaceborne SAR datasets was improved by 2.5% and 1%, respectively. In all cases, individual classes can show larger improvements up to 8%. Furthermore, the selection of individual feature sets for each single class can provide additional insights into physical interpretation of different features. The improvement in classification results comes at the cost of longer computation time, in particular during the design and training stage. The final choice of the optimal algorithm therefore depends on time constraints and application purpose.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 3
    Publication Date: 2020-10-05
    Description: Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is compli- cated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared devi- ation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classifi- cation algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 4
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    In:  EPIC3International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, Texas, USA, 2017-07-23-2017-07-28
    Publication Date: 2019-07-17
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 5
    Publication Date: 2017-01-24
    Description: The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m−2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 6
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    Unknown
    ESA Communications
    In:  EPIC3SeaSAR 2012, Proceedings of the conference held 18-22 June 2012 at Tromsø, Norway, ESA Communications, ESA SP, ISSN: 1609 - 042X
    Publication Date: 2016-02-02
    Description: This paper summarizes the knowledge status in some areas of SAR monitoring of sea ice. It starts with a brief summary of the whitepaper by Breivik et al. from OceanObs’09 [3], and then focuses on segmentation and classification, drift estimation, and assimilation strategies, which are considered as key areas in the development of more mature sea ice products from SAR and polarimetric SAR (PoLSAR) data.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , notRev
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  • 7
    Publication Date: 2022-05-26
    Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 7 (2017): 40850, doi:10.1038/srep40850.
    Description: The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m−2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.
    Description: This study was supported by the Centre for Ice, Climate and Ecosystems (ICE) at the Norwegian Polar Institute, the Ministry of Climate and Environment, Norway, the Research Council of Norway (projects Boom or Bust no. 244646, STASIS no. 221961, CORESAT no. 222681, CIRFA no. 237906 and AMOS CeO no. 223254), and the Ministry of Foreign Affairs, Norway (project ID Arctic), the ICE-ARC program of the European Union 7th Framework Program (grant number 603887), the Polish-Norwegian Research Program operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009–2014 in the frame of Project Contract Pol-Nor/197511/40/2013, CDOM-HEAT, and the Ocean Acidification Flagship program within the FRAM- High North Research Centre for Climate and the Environment, Norway.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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