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  • 1
    Call number: AWI G8-19-92587
    Description / Table of Contents: Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed: • Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases? • How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations? • How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization? To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained. Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum. Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments. Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale. Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
    Type of Medium: Dissertations
    Pages: vi, 126 Seiten , Illustrationen
    Language: English
    Note: Dissertation, Universität Potsdam, 2019 , Table of Contents Abstract Zusammenfassung Abbreviations 1 Introduction 1.1 Scientific Background and Motivation 1.1.1 Arctic Tundra Vegetation 1.1.2 Remote Sensing of Arctic Tundra Vegetation 1.1.3 Hyperspectral Remote Sensing of Arctic Vegetation 1.2 Aims and Objectives 1.3 Study Area and Data 1.3.1 Toolik Lake Research Natural Area 1.3.2 In-situ Canopy-level Spectral Data 1.3.3 True-colour Digital Photographs 1.3.4 Leaf-level Photosynthetic Pigment Data 1.3.5 Airborne AISA Imagery 1.3.6 Simulated EnMAP and Sentinel-2 Reflectance Spectra 1.3.7 Simulated EnMAP Imagery 1.4 Thesis Structure and Author Contributions 1.4.1 Chapter 2 -A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope Alaska 1.4.2 Chapter 3 -Monitoring Pigment-driven Vegetation Changes in a Low Arctic Tundra Ecosystem Using Digital Cameras 1.4.3 Implications of Litter and Non-vascular Components on Multiscale Hyperspectral Data in a low-Arctic Ecosystem 2 A Phenological Approach to Spectral Differentiation of Low Arctic Tundra Vegetation Communities, North Slope Alaska 2.1 Abstract 2.2 Introduction 2.3 Materials and Methods 2.3.1 Study Site and Low Arctic Vegetation Types 2.3.2 Ground-Based Data and Sampling Protocol 2.3.3 EnMAP and Sentinel-2 Surface Reflectance Simulation 2.3.4 Stable Wavelength Identification Using the InStability Index 2.4 Results 2.4.1 Spectral Characteristics by Phenological Phase 2.4.2 InStability Index and Wavelength Selection of Ground-based Spectra 2.4.3 InStability Index and Wavelength Selection of Simulated Satellite Reflectance Spectra 2.5 Discussion 2.5.1 Phenological Phase and Wavelength Stability of Ground-based Spectra 2.5.2 Phenological Phase and Wavelength Stability of Satellite Resampled Spectra 2.5.3 Influence of Spatial Scale 2.6 Conclusions 2.7 Acknowledgements 2.8 Supplementary Material 2.8.1 Data Publication 3 Monitoring Pigment-driven Vegetation Changes in a Low Arctic Tundra Ecosystem Using Digital Cameras 3.1 Abstract 3.2 Introduction 3.3 Methods 3.3.1 Study Site 3.3.2 Digital Photographs 3.3.3 Field-based Spectral Data 3.3.4 Vegetation Pigment Concentration 3.3.5 Data Analyses 3.4 Results 3.4.1 RGB Indices as a Surrogate for Pigment-driven Spectral Indices 3.4.2 RGB Indices as a Surrogate for Leaf-level Pigment concentration 3.5 Discussion 3.6 Conclusions 3.7 Supplementary Material 3.7.1 Data Publication 4 Implications of Litter and Non-vascular Components on Multiscale Hyperspectral Data in a Low Arctic Ecosystem 4.1 Abstract 4.2 Introduction 4.3 Materials and Methods 4.3.1 Study Site 4.4 Remote Sensing Data 4.4.1 Ground-based Image Spectroscopy Data 4.4.2 Airborne AISA Hyperspectral Data 4.4.3 EnMAP Simulation 4.4.4 Spectral Comparison by Wavelength 4.4.5 Linear Mixture Analysis 4.5 Results 4.5.1 Spatial Scaling of Spectral Signals 4.6 Discussion 4.7 Conclusions 4.8 Acknowledgements 5 Synthesis and Discussion 5.1 Phenological Phase: does phenology influence the spectral variability of dominant low Arctic vegetation communities? 5.2 Vegetation Colour: How does canopy-level vegetation colour relate to phenological changes in leaf-level photosynthetic pigment concentration? 5.3 Intrinsic Ecosystem Components: How does spatial aggregation of high spectral resolution data influence low Arctic tundra vegetation signals? 5.4 Key Innovations 5.5 Limitations and Technical Considerations 5.6 Outlook: Opportunities for Future Research 6 References Acknowledgements
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  • 2
    Call number: AWI P5-16-16323
    Description / Table of Contents: This report is organized in four parts. Chapter 1 provides an introduction. Chapter 2 assesses the state of the Arctic coast under three broad disciplinary themes - physical, ecological, and human systems. Chapter 3 considers the need for and progress toward integrative approaches to monitoring, understanding, and managing change in Arctic coastal systems. Chapter 4 provides a synthesis and identifies data gaps and research priorities over the coming decade.
    Type of Medium: Monograph available for loan
    Pages: X, 168 S. , Ill., graph. Darst., Kt.
    ISBN: 9783981363722
    Note: Table of Contents: EXECUTIVE SUMMARY. - KEY FINDINGS. - 1 INTRODUCTION. - 1.1 Background. - 1.2 The Circumpolar Arctic Coast. - 1.3 Rationale. - 1.4 Objectives and Organization of the Report. - 2 STATE OF THE ARCTIC COAST 2010 – A Thematic Assessment2.1. Physical State of the Circum-Arctic Coast. - 2.2. Ecological State of the Circum-Arctic Coast. - 2.3 Social, Economic and Institutional State of the Circum-Arctic Coast. - 3 INTEGRATED ASSESSMENT AND RESPONSE TO ARCTIC COASTAL CHANGE. - 3.1 Integrated Approaches to Coastal Change in the Arctic. - 3.2 Monitoring, Detecting and Modelling Coastal Change. - 3.3 Vulnerability, Adaptation, Adaptive Capacity and Resilience. - 3.4 Governance and Adaptation. - 4 SYNTHESIS AND FUTURE DIRECTION. - 4.1 Introduction. - 4.2 ICARP-II Science Plans. - 4.3 Knowledge Gaps and Research Priorities . - 4.4 Building a Road Map to Integrated Coastal Systems Research in the Arctic. - 4.5 Summary Discussion. - 5 REFERENCES. - List of Contributors. - Acknowledgements.
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  • 3
    Call number: AWI G3-19-92415
    Type of Medium: Dissertations
    Pages: VIII, 154, xv Seiten , Illustrationen, Diagramme, Karten
    Language: English
    Note: Table of contents Abstract Zusammenfassung 1 Motivation 2 Introduction 2.1 Arctic climate changes and their impacts on Coastal processes 2.2 Shoreline retreat along Arctic coasts 2.3 Impacts of Coastal erosion 2.3.1 Material fluxes 2.3.2 Retrogressive thaw slumps 2.3.3 Socio-economic impacts 2.4 Objectives 2.5 Study area 2.6 Thesis structure 2.7 Authors’ contributions 3 Variability in rates of Coastal change along the Yukon coast, 1951 to 2015 3.1 Introduction 3.2 Study Area 3.3 Data and Methods 3.3.1 Remote sensing data 3.3.2 Field survey data 3.3.3 Classification of shoreline 3.3.4 Transect-wise analyses of shoreline movements through time 3.4 Results 3.4.1 Temporal variations in shoreline change rates 3.4.2 Alongshore rates of change 3.4.3 Shoreline dynamics along field sites 3.4.4 Dynamics of lagoons, barrier Islands and spits (gravel features) 3.4.5 Yukon Territory land loss 3.5 Discussion 3.5.1 Temporal variations in shoreline change rates 3.5.2 Alongshore rates of change 3.5.3 Dynamics of lagoons, barrier Islands, and spits (gravel features) 3.5.4 Expected shoreline changes as a consequence of future climate warming 3.6 Conclusions Context 4 Coastal erosion of permafrost Solls along the Yukon Coastal Plain and Kuxes oforganic carbon to the Canadian Beaufort Sea 4.1 Introduction 4.2 Study Area 4.3 Methods 4.3.1 Sample collection and laboratory analyses 4.3.2 Soll organic carbon determinations 4.3.3 Flux of organic soil carbon and Sediments 4.3.4 Fate of the eroded soil organic carbon 4.4 Results 4.4.1 Ground lce 4.4.2 Organic carbon contents 4.4.3 Material fluxes 4.5 Discussion 4.5.1 Ground lce 4.5.2 Organic carbon contents 4.5.3 Material fluxes 4.5.4 Organic carbon in nearshore Sediments 4.6 Conclusion Context 5 Terrain Controls on the occurrence of Coastal retrogressive thaw slumpsalong the Yukon Coast, Canada 5.1 Introduction 5.2 Study Area 5.3 Methods 5.3.1 Mapping of RTSs and landform Classification 5.3.2 Environmental variables 5.3.3 Univariate regression trees 5.4 Results 5.4.1 Characteristics of RTS along the coast 5.4.2 Density and areal coverage od RTSs along the Yukon Coast 5.5 Discussion 5.5.1 Characteristics and distribution of RTSs along the Yukon Coast 5.5.2 Terrain factors explaining RTS occurrence 5.5.3 Coastal processes 5.6 Conclusions Context 6 Impacts of past and fiiture Coastal changes on the Yukon coast - threats forcultural sites, infrastructure and travel routes 6.1 Introduction 6.2 Study Area 6.3 Methods 6.3.1 Data for shoreline projections 6.3.2 Shoreline projection for the conservative scenario (S1) 6.3.3 Shoreline Projection for the dynamic scenario (S2) 6.3.4 Positioning and characterizing of cultural sites 6.3.5 Calculation of losses under the S1 and S2 scenarios 6.3.6 Estimation of future dynamics in very dynamic areas 6.4 Results and discussion 6.4.1 Past and future shoreline change rates 6.4.2 Cultural sites 6.4.3 Infrastructure and travel routes 6.5 Conclusions 7 Discussion 7.1 The importance of understanding climatic drivers of Coastal changes 7.2 The influence of shoreline change rates on retrogressive thaw slump activity 7.3 On the calculation of carbon fluxes from Coastal erosion along the Yukon coast 7.4 Impacts of present and future Coastal erosion on the natural and human environment 7.5 Synthesis 8 Summary and Conclusions Bibliography Supporting Material Data Set ds01 Table S1 Table S3 Abbreviations and Nomendature Acknowledgements
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  • 4
    Call number: ZS-090(506) ; ZSP-168-506
    In: Berichte zur Polar- und Meeresforschung
    Type of Medium: Series available for loan
    Pages: X, 131 S. , Ill., graph. Darst., Kt.
    ISSN: 1618-3193
    Series Statement: Berichte zur Polar- und Meeresforschung 506
    Classification: D.3.
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  • 5
    Call number: AWI G3-19-92460
    Description / Table of Contents: The Yukon Coast in Canada is an ice-rich permafrost coast and highly sensitive to changing environmental conditions. Retrogressive thaw slumps are a common thermoerosion feature along this coast, and develop through the thawing of exposed ice-rich permafrost on slopes and removal of accumulating debris. They contribute large amounts of sediment, including organic carbon and nitrogen, to the nearshore zone. The objective of this study was to 1) identify the climatic and geomorphological drivers of sediment-meltwater release, 2) quantify the amount of released meltwater, sediment, organic carbon and nitrogen, and 3) project the evolution of sediment-meltwater release of retrogressive thaw slumps in a changing future climate. The analysis is based on data collected over 18 days in July 2013 and 18 days in August 2012. A cut-throat flume was set up in the main sediment-meltwater channel of the largest retrogressive thaw slump on Herschel Island. In addition, two weather stations, one on top of the undisturbed tundra and one on the…
    Type of Medium: Monograph available for loan
    Pages: 163 Seiten , Illustrationen, Diagramme
    Language: English
    Note: Table of Contents Abstract Kurzfassung Abbreviations and nomenclature 1. Introduction 2. Scientific Background 2.1. Permafrost 2.2.Retrogressive Thaw Slumps 2.3. Inputs of Freshwater, Sediment and Carbon into the Canadian Beaufort Sea 3. Study Area 3.1. Regional Setting: Yukon Coast and Herschel Island 3.2. Retrogressive Thaw Slumps 4. Material and Methods 4.1. Field Work 4.1.1. Terrain Photography 4.1.2. Differential Global Positioning System (DGPS) 4.1.3. Light Detection And Ranging (LiDAR) and Digital Elevation Model (DEM) 4.1.4. Micrometeorology 4.1.5. Discharge Measurement 4.1.6. Multiple Regression-Statistical Relationships between Micrometeorological Variables and Discharge 4.1.7. Sampling 4.2. Laboratory Analyses 4.2.1. Sedimentological Analyses 4.2.2. Hydrochemical Analyses 4.3. Fluxes of Sediment and (In-) Organic Matter 5. Results 5.1. Field Work 5.1.1. Terrain Photography 5.1.2. Differential Global Positioning System (DGPS) 5.1.3. Light Detecting And Ranging (LiDAR) and Digital Elevation Model (DEM) 5.1.4. Micrometeorology 5.1.5. Discharge 5.1.6. Multiple Regression - Statistical Relationships between Micrometeorology and Discharge 5.2. Laboratory Analyses 5.2.1. Sedimentological Analyses 5.2.2. Hydrochemical Analyses 5.3. Fluxes of Sediment-meltwater 6. Discussion 6.1. Microclimatological and Geomorphological Factors Controlling Discharge 6.1.1. Diurnal Variations 6.1.2. Seasonal Variations 6.2. Contribution of Retrogressive Thaw Slumps to the Sediment Budget of the Yukon Coast 6.2.1. Origin of Outflow Material 6.2.2. Slump D in the Regional Context 6.2.3. Seasonal Sediment Budget Compilation for Slump D 6.2.4. Retrogressive Thaw Slump Occurrence along the Yukon Coast 6.2.5. Input to the Beaufort Sea 6.3. Projected Climatic Change and its Impact on Retrogressive Thaw Slump Outflow 6.4. Uncertainties and Limitations 6.5. Future Research 7. Conclusion 8. Appendix 8.1. Field Work 8.1.1. Slump D's northern headwall profile 8.1.2. Collinson Head slump 8.1.3. Herschel Island West Coast slump 8.1.4. Roland Bay slump 8.1.5. Kay Point slump 8.2. Laboratory Work 8.2.1. Volumetric Ice Content 8.2.2. Grain Size 8.3. Evolution of Slump D 8.3.1. Geo Eye satellite of Slump D 8.3.2. Aerial Oblique Photography of Slump D 8.3.3. LiDAR of Slump D 8.3.4. Time Lapse Photography of Slump D's Headwall 9. References 10. Financial and technical support 11. Acknowledgement - Danksagung
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  • 6
    Call number: AWI G8-19-92586
    Description / Table of Contents: Arctic warming has implications for the functioning of terrestrial Arctic ecosystems, global climate and socioeconomic systems of northern communities. A research gap exists in high spatial resolution monitoring and understanding of the seasonality of permafrost degradation, spring snowmelt and vegetation phenology. This thesis explores the diversity and utility of dense TerraSAR-X (TSX) X-Band time series for monitoring ice-rich riverbank erosion, snowmelt, and phenology of Arctic vegetation at long-term study sites in the central Lena Delta, Russia and on Qikiqtaruk (Herschel Island), Canada. In the thesis the following three research questions are addressed: • Is TSX time series capable of monitoring the dynamics of rapid permafrost degradation in ice-rich permafrost on an intra-seasonal scale and can these datasets in combination with climate data identify the climatic drivers of permafrost degradation? • Can multi-pass and multi-polarized TSX time series adequately monitor seasonal snow cover and snowmelt in small Arctic catchments and how does it perform compared to optical satellite data and field-based measurements? • Do TSX time series reflect the phenology of Arctic vegetation and how does the recorded signal compare to in-situ greenness data from RGB time-lapse camera data and vegetation height from field surveys? To answer the research questions three years of TSX backscatter data from 2013 to 2015 for the Lena Delta study site and from 2015 to 2017 for the Qikiqtaruk study site were used in quantitative and qualitative analysis complimentary with optical satellite data and in-situ time-lapse imagery. The dynamics of intra-seasonal ice-rich riverbank erosion in the central Lena Delta, Russia were quantified using TSX backscatter data at 2.4 m spatial resolution in HH polarization and validated with 0.5 m spatial resolution optical satellite data and field-based time-lapse camera data. Cliff top lines were automatically extracted from TSX intensity images using threshold-based segmentation and vectorization and combined in a geoinformation system with manually digitized cliff top lines from the optical satellite data and rates of erosion extracted from time-lapse cameras. The results suggest that the cliff top eroded at a constant rate throughout the entire erosional season. Linear mixed models confirmed that erosion was coupled with air temperature and precipitation at an annual scale, seasonal fluctuations did not influence 22-day erosion rates. The results highlight the potential of HH polarized X-Band backscatter data for high temporal resolution monitoring of rapid permafrost degradation. The distinct signature of wet snow in backscatter intensity images of TSX data was exploited to generate wet snow cover extent (SCE) maps on Qikiqtaruk at high temporal resolution. TSX SCE showed high similarity to Landsat 8-derived SCE when using cross-polarized VH data. Fractional snow cover (FSC) time series were extracted from TSX and optical SCE and compared to FSC estimations from in-situ time-lapse imagery. The TSX products showed strong agreement with the in-situ data and significantly improved the temporal resolution compared to the Landsat 8 time series. The final combined FSC time series revealed two topography-dependent snowmelt patterns that corresponded to in-situ measurements. Additionally TSX was able to detect snow patches longer in the season than Landsat 8, underlining the advantage of TSX for detection of old snow. The TSX-derived snow information provided valuable insights into snowmelt dynamics on Qikiqtaruk previously not available. The sensitivity of TSX to vegetation structure associated with phenological changes was explored on Qikiqtaruk. Backscatter and coherence time series were compared to greenness data extracted from in-situ digital time-lapse cameras and detailed vegetation parameters on 30 areas of interest. Supporting previous results, vegetation height corresponded to backscatter intensity in co-polarized HH/VV at an incidence angle of 31°. The dry, tall shrub dominated ecological class showed increasing backscatter with increasing greenness when using the cross polarized VH/HH channel at 32° incidence angle. This is likely driven by volume scattering of emerging and expanding leaves. Ecological classes with more prostrate vegetation and higher bare ground contributions showed decreasing backscatter trends over the growing season in the co-polarized VV/HH channels likely a result of surface drying instead of a vegetation structure signal. The results from shrub dominated areas are promising and provide a complementary data source for high temporal monitoring of vegetation phenology. Overall this thesis demonstrates that dense time series of TSX with optical remote sensing and in-situ time-lapse data are complementary and can be used to monitor rapid and seasonal processes in Arctic landscapes at high spatial and temporal resolution.
    Type of Medium: Dissertations
    Pages: XIII, 131 Seiten , Illustrationen
    Language: Undetermined
    Note: Dissertation, Universität Potsdam, 2019 , TABLE OF CONTENTS Abstract Zusammenfassung Table of contents List of figures List of tables List of abbreviations 1 Introduction 1.1 Scientific background and motivation 1.1.1 Permafrost degradation 1.1.2 Snow cover 1.1.3 Vegetation phenology 1.2 Remote sensing of rapid changes 1.2.1 SAR remote sensing 1.2.2 TerraSar-X 1.3 Data and methods 1.4 Aims and objectives 1.5 Study areas and data 1.6 Thesis structure and author contributions 1.6.1 Chapter 2 – Monitoring inter-and intra-seasonal dynamics of rapidly degrading ice-rich permafrost riverbanks in the Lena Delta with TerraSAR-X time series 1.6.2 Chapter 3 – TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small Arctic catchments 1.6.3 Chapter 4 – Estimation of Arctic tundra vegetation phenology with TerraSAR-X 2 Monitoring inter-and intra-seasonal dynamics of rapidly degrading ice-rich permafrost riverbanks in the Lena Delta with TerraSAR-X time series 2.1 Abstract 2.2 Introduction 2.3 Study area 2.4 Data and methods 2.4.1 SAR data and processing 2.4.2 Automated cliff-top line extraction from SAR data 2.4.3 Quantification of cliff-top erosion with the Digital Shoreline Analysis System 2.4.4 Cliff top mapping from optical satellite data 2.4.5 In-situ observations of cliff top erosion 2.4.6 Climate data 2.4.7 Statistical data analysis 2.5 Results 2.5.1 TSX erosion versus in-situ and optical datasets 2.5.2 Inter-and intra-annual cliff-top erosion and climate data 2.5.3 Backscatter time series 2.6 Discussion 2.6.1 Inter-annual dynamics of cliff-top erosion 2.6.2 Intra-annual dynamics of cliff-top erosion 2.6.3 Backscatter dynamics of tundra and cliff surfaces 2.7 Conclusions 2.8 Acknowledgments 3 TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small Arctic catchments 3.1 Abstract 3.2 Introduction 3.3 Study area 3.4 Data and methods 3.4.1 SAR satellite data 3.4.2 Optical satellite data 3.4.3 In-situ time-lapse camera data 3.4.4 Snow Cover Extent from TerraSAR-X 3.4.5 Snow Cover Extent from Landsat 8 3.4.6 Accuracy assessment of TerraSAR-X Snow Cover Extent 3.4.7 Fractional Snow Cover time series analysis 3.5 Results 3.5.1 Evaluation of TSX Snow Cover Extent 3.5.2 Time series of Fractional Snow Cover in all catchments 3.5.3 Time series of Fractional SnowCover in small catchments 3.6 Discussion 3.6.1 Spatiotemporal monitoring of snowmelt dynamics using TSX 3.6.2 Technical considerations for using TSX for wet snow detection 3.7 Conclusions 3.8 Acknowledgements 3.9 Appendix 4 Relationships between X-Band SAR and vegetation phenology in a low Arctic ecosystem 4.1 Abstract 4.2 Introduction 4.3 Study area 4.4 Data and methods 4.4.1 In-situ time-lapse phenological cameras 4.4.2 Time-lapse image analysis 4.4.3 SAR satellite data 4.4.4 Backscatter and coherence time series 4.4.5 In-situ vegetation and climate data 4.5 Results 4.5.1 Phenocams 4.5.2 Backscatter dynamics 4.5.3 Coherence dynamics 4.6 Climate data 4.7 Backscatter and vegetation height 4.8 Discussion 4.9 Conclusion 4.10 Acknowledgments 5 Synthesis 5.1 Rapid permafrost disturbance 5.2 Snowmelt dynamics 5.3 Arctic tundra vegetation phenology 5.4 Seasonality and complementarity of TSX 5.5 Limitations and technical considerations 5.6 Key findings and outlook References Acknowledgements
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  • 7
    Publication Date: 2019-01-26
    Description: This dataset contains the results of granulometric and bulk geochemical analyses of Van Veen surface samples obtained by the Alfred Wegener Institute (AWI) in the course of the 2012 and 2013 summer field seasons. The sampling was performed along transects in depths generally 〈13 m, to a distance of about 〈5 km off Herschel Island. In 2012, 75 samples in Pauline Cove and in the vicinity of Simpson Point were obtained. Sample collection was expanded in 2013, on transects established the previous year, with additional locations in Tetris Bay and Workboat Passage. Samples consisted of approximately 100 g of the top 3-6 cm of sediment, and were frozen in the field and freeze dried at the AWI before undergoing analytical procedures. Sample locations were recorded with the onboard global positioning system (GPS) unit. Grain size distributions in our study were obtained using laser diffractometry at the AWI (Beckman Coulter LS200) on the 〈1 mm fraction of samples oxidized with 30% H2O2 until effervescence ceased to remove organics. Some samples were also sieved using a sieve stack with 1 phi intervals. GRADISTAT (Blott and Pye, 2001) was used to calculate graphical grain size statistics (Folk and Ward, 1957). Grain diameters were logarithmically transformed to phi values, calculated as phi=-log2d, where d is the grain diameter in millimeters (Blott and Pye, 2001; Krumbein, 1934). Freeze dried samples were ground and ground using an Elemetar Vario EL III carbon-nitrogen-sulphur analyzer at the AWI to measure total carbon (TC) and total nitrogen (TN). Tungsten oxide was added to the samples as a catalyst to the pyrolysis. Following this analysis, total organic carbon (TOC) was determined using an Elementar VarioMax. Stable carbon isotope ratios of 13C/12C of 118 samples were determined on a DELTAplusXL mass spectrometer (ThermoFisher Scientific, Bremen) at the German Research Centre for Geosciences (GFZ) in Potsdam, Germany . An additional analysis on 69 samples was carried out at the University of Hamburg with an isotope ratio mass spectrometer (Delta V, Thermo Scientific, Germany) coupled to an elemental analyzer (Flash 2000, Thermo Scientific, Germany). Prior to analysis, soil samples were treated with phosphoric acid (43%) to release inorganic carbon. Values are expressed relative to Vienna Peedee belemnite (VPDB) using external standards (USGS40, -26.4 per mil VPDB and IVA soil 33802153, -27.5 per mil VPDB).
    Type: Dataset
    Format: text/tab-separated-values, 24069 data points
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  • 8
    Publication Date: 2018-09-27
    Type: Dataset
    Format: text/tab-separated-values, 60 data points
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  • 9
    Publication Date: 2019-04-18
    Type: Dataset
    Format: text/tab-separated-values, 675 data points
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  • 10
    Publication Date: 2018-10-08
    Type: Dataset
    Format: text/tab-separated-values, 18 data points
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