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  • 2015 - 2019  (2)
  • [2019]  (2)
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  • 2015 - 2019  (2)
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  • 1
    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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  • 2
    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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