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  • 1
    Call number: AWI G3-22-94687
    Description / Table of Contents: Permafrost is warming globally, which leads to widespread permafrost thaw and impacts the surrounding landscapes, ecosystems and infrastructure. Especially ice-rich permafrost is vulnerable to rapid and abrupt thaw, resulting from the melting of excess ground ice. Local remote sensing studies have detected increasing rates of abrupt permafrost disturbances, such as thermokarst lake change and drainage, coastal erosion and RTS in the last two decades. All of which indicate an acceleration of permafrost degradation. In particular retrogressive thaw slumps (RTS) are abrupt disturbances that expand by up to several meters each year and impact local and regional topographic gradients, hydrological pathways, sediment and nutrient mobilisation into aquatic systems, and increased permafrost carbon mobilisation. The feedback between abrupt permafrost thaw and the carbon cycle is a crucial component of the Earth system and a relevant driver in global climate models. However, an assessment of RTS at high temporal resolution to determine the ...
    Type of Medium: Dissertations
    Pages: xxiv, 134 Seiten , Illustrationen, Diagramme, Karten
    Language: English
    Note: Dissertation, Universität Potsdam, 2021 , Table of Contents Abstract Zusammenfassung List of Figures List of Tables Abbreviations 1 Introduction 1.1 Scientific background and motivation 1.1.1 Permafrost and climate change 1.1.2 Permafrost thaw and disturbances 1.1.3 Abrupt permafrost disturbances 1.1.4 Remote sensing 1.1.5 Remote sensing of permafrost disturbances 1.2 Aims and objectives 1.3 Study area 1.4 General data and methods 1.4.1 Landsat and Sentinel-2 1.4.2 Google Earth Engine 1.5 Thesis structure 1.6 Overview of publications and authors’ contribution 1.6.1 Chapter 2 - Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions 1.6.2 Chapter 3 - Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions 1.6.3 Chapter 4 - Remote Sensing Annual Dynamics of Rapid Permafrost Thaw Disturbances with LandTrendr 2 Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions 2.1 Abstract 2.2 Introduction 2.3 Materials and Methods 2.3.1 Study Sites 2.3.2 Data 2.3.3 Data Processing 2.3.3.1 Filtering Image Collections 2.3.3.2 Creating L8, S2, and Site Masks 2.3.3.3 Preparing Sentinel-2 Surface Reflectance Images in SNAP 2.3.3.4 Applying Site Masks 2.3.4 Spectral Band Comparison and Adjustment 2.4 Results 2.4.1 Spectral Band Comparison 2.4.2 Spectral Band Adjustment 2.4.3 ES and HLS Spectral Band Adjustment 2.5 Discussion 2.6 Conclusions 2.7 Acknowledgements 2.8 Appendix Chapter 2 3 Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions 3.1 Abstract 3.2 Introduction 3.3 Materials and Methods 3.3.1 Study Sites 3.3.2 Data 3.3.3 Data Processing and Mosaicking Workflow 3.3.4 Data Availability Assessment 3.3.5 Mosaic Coverage and Quality Assessment 3.4 Results 3.4.1 Data Availability Assessment 3.4.2 Mosaic Coverage and Quality Assessment 3.5 Discussion 3.6 Conclusions 4 Remote Sensing Annual Dynamics of Rapid Permafrost Thaw Disturbances with LandTrendr 4.1 Abstract 4.2 Introduction 4.3 Study Area and Methods 4.3.1 Study area 4.3.2 General workflow and ground truth data 4.3.3 Data and LandTrendr 4.3.4 Index selection 4.3.5 Temporal Segmentation 4.3.6 Spectral Filtering 4.3.7 Spatial masking and filtering 4.3.8 Machine-learning object filter 4.4 Results 4.4.1 Focus sites 4.4.2 North Siberia 4.5 Discussion 4.5.1 Mapping of RTS 4.5.2 Spatio-temporal variability of RTS dynamics 4.5.3 LT-LS2 capabilities and limitations 4.6 Conclusion 4.7 Appendix 5 Synthesis and Discussion 5.1 Google Earth Engine 5.2 Landsat and Sentinel-2 5.3 Image mosaics and disturbance detection algorithm 5.4 Mapping RTS and their annual temporal dynamics 5.5 Limitations and technical considerations 5.6 Key findings 5.7 Outlook References Acknowledgements
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  • 2
    Call number: AWI G8-23-95167
    Description / Table of Contents: The Arctic nearshore zone plays a key role in the carbon cycle. Organic-rich sediments get eroded off permafrost affected coastlines and can be directly transferred to the nearshore zone. Permafrost in the Arctic stores a high amount of organic matter and is vulnerable to thermo-erosion, which is expected to increase due to climate change. This will likely result in higher sediment loads in nearshore waters and has the potential to alter local ecosystems by limiting light transmission into the water column, thus limiting primary production to the top-most part of it, and increasing nutrient export from coastal erosion. Greater organic matter input could result in the release of greenhouse gases to the atmosphere. Climate change also acts upon the fluvial system, leading to greater discharge to the nearshore zone. It leads to decreasing sea-ice cover as well, which will both increase wave energy and lengthen the open-water season. Yet, knowledge on these processes and the resulting impact on the nearshore zone is scarce, because access to and instrument deployment in the nearshore zone is challenging. Remote sensing can alleviate these issues in providing rapid data delivery in otherwise non-accessible areas. However, the waters in the Arctic nearshore zone are optically complex, with multiple influencing factors, such as organic rich suspended sediments, colored dissolved organic matter (cDOM), and phytoplankton. The goal of this dissertation was to use remotely sensed imagery to monitor processes related to turbidity caused by suspended sediments in the Arctic nearshore zone. In-situ measurements of water-leaving reflectance and surface water turbidity were used to calibrate a semi-empirical algorithm which relates turbidity from satellite imagery. Based on this algorithm and ancillary ocean and climate variables, the mechanisms underpinning nearshore turbidity in the Arctic were identified at a resolution not achieved before. The calibration of the Arctic Nearshore Turbidity Algorithm (ANTA) was based on in-situ measurements from the coastal and inner-shelf waters around Herschel Island Qikiqtaruk (HIQ) in the western Canadian Arctic from the summer seasons 2018 and 2019. It performed better than existing algorithms, developed for global applications, in relating turbidity from remotely sensed imagery. These existing algorithms were lacking validation data from permafrost affected waters, and were thus not able to reflect the complexity of Arctic nearshore waters. The ANTA has a higher sensitivity towards the lowest turbidity values, which is an asset for identifying sediment pathways in the nearshore zone. Its transferability to areas beyond HIQ was successfully demonstrated using turbidity measurements matching satellite image recordings from Adventfjorden, Svalbard. The ANTA is a powerful tool that provides robust turbidity estimations in a variety of Arctic nearshore environments. Drivers of nearshore turbidity in the Arctic were analyzed by combining ANTA results from the summer season 2019 from HIQ with ocean and climate variables obtained from the weather station at HIQ, the ERA5 reanalysis database, and the Mackenzie River discharge. ERA5 reanalysis data were obtained as domain averages over the Canadian Beaufort Shelf. Nearshore turbidity was linearly correlated to wind speed, significant wave height and wave period. Interestingly, nearshore turbidity was only correlated to wind speed at the shelf, but not to the in-situ measurements from the weather station at HIQ. This shows that nearshore turbidity, albeit being of limited spatial extent, gets influenced by the weather conditions multiple kilometers away, rather than in its direct vicinity. The large influence of wave energy on nearshore turbidity indicates that freshly eroded material off the coast is a major contributor to the nearshore sediment load. This contrasts results from the temperate and tropical oceans, where tides and currents are the major drivers of nearshore turbidity. The Mackenzie River discharge was not identified as a driver of nearshore turbidity in 2019, however, the analysis of 30 years of Landsat archive imagery from 1986 to 2016 suggests a direct link between the prevailing wind direction, which heavily influences the Mackenzie River plume extent, and nearshore turbidity around HIQ. This discrepancy could be caused by the abnormal discharge behavior of the Mackenzie River in 2019. This dissertation has substantially advanced the understanding of suspended sediment processes in the Arctic nearshore zone and provided new monitoring tools for future studies. The presented results will help to understand the role of the Arctic nearshore zone in the carbon cycle under a changing climate.
    Type of Medium: Dissertations
    Pages: xv, ii, 85, xvii Seiten , Illustrationen, Diagramme, Karten
    Language: English
    Note: Dissertation, Universität Potsdam, 2022 (kumulative Dissertation) , TABLE OF CONTENTS Abstract Zusammenfassung Allgemeinverständliche Zusammenfassung List of Figures List of Tables Funding Chapter 1 Introduction 1.1 Scientific Background 1.1.1 Arctic Climate Change 1.1.2 The Arctic Nearshore Zone 1.1.3 Ocean Color Remote Sensing 1.2 Objectives 1.3 Study Area 1.4 Methods 1.4.1 Field Sampling 1.4.2 Data Processing 1.4.3 Satellite Imagery Processing 1.5 Thesis Structure 1.6 Author Contributions Chapter 2 Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters retrived using 30-year Landsat Archive Imagery 2.1 Abstract 2.2 Introduction 2.3 Material and Methods 2.3.1 Regional Setting 2.3.2 Landsat Images Acquisition and Processing 2.3.3 Landsat Turbidity Retrieval 2.3.4 Transects in the nearshore zone 2.3.5 Wind Data 2.4 Results 2.4.1 Brightness Temperature 2.4.2 Surface Reflectance and Turbidity Mapping 2.4.3 Gradients in the nearshore zone 2.5 Discussion 2.6 Conclusion Appendix A Chapter 3 The Arctic Nearshore Turbidity Algorithm (ANTA) - A Multi Sensor Turbidity Algorithm for Arctic Nearshore Environments 3.1 Abstract 3.2 Introduction 3.3 Methods 3.3.1 Regional setting 3.3.2 In-situ sampling 3.3.3 Optical data processing 3.3.4 Algorithm tuning 3.3.5 Satellite imagery processing 3.4 Results and Discussion 3.4.1 Turbidity and SPM 3.4.2 ANTA performance 3.4.3 Comparison with the Dogliotti et al., (2015) algorithm 3.4.4 Test and transfer of the ANTA 3.5 Conclusion Chapter 4 Drivers of Turbidity and its Seasonal Variability in the Nearshore Zone of Herschel Island Qikiqtaruk (western Canadian Arctic) 4.1 Abstract 4.2 Introduction 4.3 Methods 4.3.1 Study Area 4.3.2 Satellite Imagery 4.3.3 In-situ data 4.3.4 Reanalysis data 4.4 Results and Discussion 4.4.1 Time Series Analysis 4.4.2 Drivers of turbidity 4.5 Conclusion Chapter 5 Synthesis 5.1 Applicability of Remote Sensing Algorithms in the Arctic Nearshore Zone 5.2 Drivers of Nearshore Turbidity 5.3 Spatial Variations of Nearshore Turbidity 5.4 Challenges and Outlook List of Acronyms Bibliography Danksagung
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  • 3
    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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  • 4
    Publication Date: 2015-11-12
    Print ISSN: 2364-9453
    Electronic ISSN: 2364-9461
    Topics: Geosciences
    Published by Springer
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  • 5
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  • 7
    Publication Date: 2020-05-15
    Electronic ISSN: 2296-665X
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Frontiers Media
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  • 8
    Publication Date: 2015-11-16
    Print ISSN: 0143-1161
    Electronic ISSN: 1366-5901
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Taylor & Francis
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  • 9
    Publication Date: 2016-09-01
    Print ISSN: 0034-4257
    Electronic ISSN: 1879-0704
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Elsevier
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  • 10
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