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  • 1
    Online Resource
    Online Resource
    Cham :Springer International Publishing AG,
    Keywords: Aquatic biology. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (244 pages)
    Edition: 1st ed.
    ISBN: 9783319300757
    Series Statement: Springer Remote Sensing/Photogrammetry Series
    DDC: 577.70285
    Language: English
    Note: Intro -- Preface -- References -- Acknowledgments -- Contents -- About the Authors -- Abbreviation and Symbols -- List of Figures -- List of Tables -- 1 Methodological Approaches -- 1.1 Specificity of Marine Waters as Optical Media -- 1.2 Algorithms and Hydro-Optical Models for the Retrieval of Water Quality Parameters -- 1.2.1 Case II Waters -- 1.2.1.1 The BOREALI Algorithm -- 1.2.1.2 The Neural Network (NN) Algorithm -- 1.2.2 Case I Waters -- 1.3 Practicalities of Hydro-Optical Model Development -- 1.4 Algorithms and Hydro-Optical Models for the Retrieval of Primary Production -- 1.5 Algorithms and Hydro-Optical Models for the Identification of Harmful/Nuisance Algal Blooms -- 1.5.1 Emiliania huxleyi Blooms -- 1.5.2 Lepidodinium chlorophorum Blooms -- 1.5.2.1 Modified NN Algorithm -- 1.6 Methodology for Objective Zonation of Aquatic Environments -- 1.6.1 Water Quality Retrieval Algorithm -- 1.6.2 Algorithm for the Estimation of Light Availability at the Bottom -- 1.6.3 Gap Filling Using Interpolation -- 1.6.4 Principal Component Analysis (PCA) -- 1.6.5 Cluster Analysis of Principal Components -- 1.6.6 Vectorization of the Raster Zone Map -- 1.7 Algorithms and Hydro-Optical Models for the Retrieval of Complementary Data: Wind Speed and Direction, SST, ICE and Cloudiness -- 1.7.1 SST Retrieval Technique -- 1.7.2 Cloudiness Screening and Filtering -- References -- 2 Investigations of the Water Body Biogeochemistry and Phytoplankton Biomass Variability in Time and Space -- 2.1 Atlantic Ocean -- 2.1.1 Bay of Biscay -- 2.1.1.1 General Characteristics -- 2.1.1.2 Remote Sensing Observations -- 2.1.2 Adriatic Sea -- 2.1.2.1 General Characteristics -- 2.1.2.2 Adriatic Sea: Ecological Challenges -- 2.1.2.3 Satellite Data -- 2.1.2.4 Unsupervised Classification of the First 5 Principal Components. , 2.1.2.5 Dynamics of Water Quality Parameters in the Identified Zones -- Western Coast -- Zones of Group I on the Western Coast -- Zones of Group II on the Western Coast -- Eastern Coast -- Zones of Group I on the Eastern Coast -- Zones of Group II on the Eastern Coast -- Coccolithophores -- 2.2 Arctic Ocean -- 2.2.1 White Sea -- 2.2.1.1 General Characteristics -- 2.2.1.2 Remote Sensing Observations -- 2.2.2 Kara Sea -- 2.2.2.1 General Characteristics -- 2.2.2.2 Remote Sensing Observations of the Biogeochemical Features in the Kara Sea -- Quantitative Assessment of doc Fluxes -- Assessment of the Allochthonous and Autochthonous Components of the Total doc Flux -- 2.2.3 Barents Sea -- 2.2.3.1 General Characteristics -- 2.2.3.2 Cyclones and Phytoplankton and SST Variability in Time and Space -- 2.2.3.3 A Concise Overview of Previous Studies -- 2.2.3.4 Remote Sensing Observations -- References -- 3 Investigation of Harmful/Nuisance Algae Blooms in Marine Environments -- 3.1 Green Dinoflagellates -- 3.1.1 Atlantic Ocean -- 3.1.1.1 English Channel and Bay of Biscay -- 3.2 Coccolithophores -- 3.2.1 Atlantic Ocean -- 3.2.1.1 Bay of Biscay and English Channel -- 3.2.1.2 The North Sea (Co-authored by D. Kondrik) -- General Characteristics -- Remote Sensing Observations -- 3.2.1.3 Black Sea -- General Characteristics -- Remote Sensing Observations -- 3.2.2 Arctic Ocean -- 3.2.2.1 Barents, Greenland and North Norwegian Seas (Co-authored by D. Kondrik) -- General Characteristics -- Remote Sensing Observations -- 3.2.3 Pacific Ocean -- 3.2.3.1 Bering Sea (Co-authored by D. Kondrik) -- General Characteristics -- Remote Sensing Observations of E. huxleyi Blooms Over the Time Period 1998-2013 -- 3.3 Raphidophytes -- 3.3.1 Atlantic Ocean -- 3.3.1.1 Chattonella/Pseudochattonella spp. -- 3.4 Haptophytes -- 3.4.1 Atlantic Ocean -- 3.4.1.1 Chrysochromulina polylepis. , References -- 4 Investigations of the Primary Production Dynamics in the Atlantic and Arctic Oceans -- 4.1 Arctic Ocean -- 4.1.1 Basin and Peripheral Seas: Baffin Bay, and Greenland, Barents, Kara, Laptev, and East-Siberian Seas -- 4.1.1.1 Introduction -- 4.1.1.2 Investigation of Multi-year PP Trends in the Pelagic Ice-Free Zone in the Arctic Basin -- 4.1.1.3 Investigation of Multi-year PP Trends in the Ice-Free Shelf Zone -- 4.1.1.4 Assessment of the Phytoplankton Productivity Trend in the Ice-Free Arctic Basin Prior to 1998 -- References -- 5 Numerical Modeling of the Marine Ecosystem -- 5.1 Arctic Ocean -- 5.1.1 White Sea -- 5.1.1.1 A Concise Description of the IO RAS-AARI Model -- Numerical Modeling Results -- 5.1.1.2 Kara Sea -- 5.1.1.3 Adjustment of the Model to the Kara Sea Conditions -- 5.1.1.4 Simulation Results -- 5.2 Atlantic Ocean -- 5.2.1 A Concise Description of the Numerical Model -- 5.2.2 Norwegian and North Seas -- 5.2.2.1 Harmful Algae -- Chattonella/Pseudochattonella Bloom Simulations -- References -- 6 Automatic System for a Synergistic Processing of Satellite Data -- 6.1 NANSAT + Threads Server Profile. Server's Accessibility and Its Role in the Norwegian Environmental Monitoring Service -- 6.1.1 Introduction -- 6.1.2 Nansat: Scientific Python Toolbox for Geospatial Data Analysis -- 6.1.3 Nansat Functional Structure -- 6.1.4 Nansat Package Structure -- 6.1.5 Nansat Quality Control -- 6.1.6 Reuse Potential -- 6.1.7 An Everyday Life Example -- 6.1.8 Fusion of Sea Surface Salinity and Water Leaving Reflectance Compared to Surface Current -- References -- Afterword -- About the Two Nansen Centres -- Blub -- References.
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  • 2
    Keywords: Remote sensing ; Aquatic ecology ; Climate change ; Environmental management ; Marine sciences ; Freshwater ; Geography
    Description / Table of Contents: This book provides results of spatial and temporal distributions of water quality parameters and marine primary production and its relationship with the driving atmospheric, ocean circulation and hydrobiological mechanisms established through a synergistic use of multi-spectral region spaceborne data and results of numerical model simulations of marine in-water and atmospheric processes related to the marine ecosystem. The changes in the studied marine/oceanic environments are analysed in light of recent climate change that imposes its influence through a set of forward and feedback interactions and forcing
    Type of Medium: Online Resource
    Pages: Online-Ressource (XXXVI, 215 p. 117 illus., 73 illus. in color, online resource)
    ISBN: 9783319300757
    Series Statement: Springer Remote Sensing/Photogrammetry
    Language: English
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  • 3
    Publication Date: 2021-02-08
    Description: We propose a satellite mission that uses a near-nadir Ka-band Doppler radar to measure surface currents, ice drift and ocean waves at spatial scales of 40 km and more, with snapshots at least every day for latitudes 75 to 82°, and every few days for other latitudes. The use of incidence angles of 6 and 12° allows for measurement of the directional wave spectrum, which yields accurate corrections of the wave-induced bias in the current measurements. The instrument's design, an algorithm for current vector retrieval and the expected mission performance are presented here. The instrument proposed can reveal features of tropical ocean and marginal ice zone (MIZ) dynamics that are inaccessible to other measurement systems, and providing global monitoring of the ocean mesoscale that surpasses the capability of today's nadir altimeters. Measuring ocean wave properties has many applications, including examining wave–current interactions, air–sea fluxes, the transport and convergence of marine plastic debris and assessment of marine and coastal hazards.
    Type: Article , PeerReviewed
    Format: text
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  • 4
    Publication Date: 2024-02-07
    Description: The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also call for urgently needed actions for enabling continuity, archiving, rescuing, and calibrating efforts to assure improved and long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.
    Type: Article , PeerReviewed
    Format: text
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  • 5
    Publication Date: 2019-06-17
    Description: Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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