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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
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    PANGAEA
    In:  Supplement to: Widhalm, Barbara; Bartsch, Annett; Heim, Birgit (2015): A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data. International Journal of Remote Sensing, 36(22), 5537-5556, https://doi.org/10.1080/01431161.2015.1101505
    Publication Date: 2019-02-11
    Description: A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized. Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1-7%.
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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  • 3
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    PANGAEA
    In:  Supplement to: Noerling, Caroline (2017): Short-term changes of permafrost degradation triggered by anthropogenic impacts and climatic events in Western Siberia 2010-2013. Master Thesis, University of Potsdam, 74 pp, hdl:10013/epic.51479.d001
    Publication Date: 2018-10-08
    Description: The data set presents results from geospatial analyses of a region in Central Yamal, Western Siberia, which was affected by the construction of the Bovanenkovo railway line and by high Retrogressive Thaw Slumps (RTS) occurrence in consequence of the extremely warm and wet year 2012. A change detection was performed using high resolution optical satellite images from 2010 (GeoEye-1) and 2013 (QuickBird). The preprocessing of the satellite data (orthorectification and atmospheric correction) was performed by and is described in Dvornikov et al., 2016. The degree of disturbance change from 2010 and 2013 around the railway line was classified into three major disturbance levels - low, medium and high, based on the width of disturbance and magnitude of change over the course of three years. A kernel density raster file illustrates RTS distribution. The highest disturbance along the railway can be observed, where the RTS activity is the highest in 2013.
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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  • 4
    Publication Date: 2018-10-08
    Type: Dataset
    Format: application/zip, 11.0 MBytes
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  • 5
    Publication Date: 2018-12-12
    Type: Dataset
    Format: text/tab-separated-values, 16 data points
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  • 6
    Publication Date: 2018-12-12
    Type: Dataset
    Format: application/zip, 316.0 kBytes
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  • 7
    Publication Date: 2018-09-27
    Type: Dataset
    Format: text/tab-separated-values, 168 data points
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  • 8
    Publication Date: 2019-02-11
    Type: Dataset
    Format: application/zip, 3308.0 kBytes
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  • 9
    Publication Date: 2018-09-27
    Description: Principal Component Analysis (PCA) is a well-established technique in remote sensing for the visualization of multidimensional data. It reduces redundancy in multiband or multitemporal imagery, increases the signal-to-noise ratio and provides an opportunity to use multitemporal datasets for change detection. PCA transforms the axes of multidimensional data in such way that the new axes (the principal components) account for variances within the data, with the first PC accounting for the largest variance and the last PC accounting for the smallest variance. In our study PCA of TerraSAR-X time stacks of backscatter intensity and interferometric coherence provided a good spatial overview of the essential information contained within the multiple time slices. The PC1 for both stacks showed the most common features of the contributing images and represented the means of the temporal stacks. The PC1 of the coherence stack accounted for 29% of the variance (or unique information) and mapped (i) water bodies (lakes and river), (ii) rocky outcrops, and (iii) the remaining land surfaces. The PC1 of the backscatter stack accounted for 35% of the variance and was contaminated by such effects as the presence or absence of lake ice and shadow/layover in the rocky outcrops region. Anomalies in seasonal patterns were demonstrated by the higher PCs. The PC2 of the backscatter stack accounted for 22% of the variance and delineated water bodies. The PC3 of backscatter stack accounted for only 4% of the variance in the dataset and represented the spatial variance in river ice conditions during spring. The PC2 of coherence, which accounted for 9.5% of the variance in the coherence stack, represented the spatially variable snow conditions in spring (snowmelt to the south and stable snow cover to the north).
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
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  • 10
    Publication Date: 2018-09-27
    Description: Time series of TerraSAR-X backscatter intensity and 11-day interferometric coherence with high temporal resolution have been used to interpret major seasonal land surface changes in a variety of tundra environments, namely an area of wet polygonal tundra, a drier Ice Complex upland area, a recently drained well-vegetated lake basin, a partly well-vegetated floodplain, a bare sandbank, and a very dry area of rocky outcrops. Seasonal variations in intensity and coherence were evaluated in the context of meteorological conditions such as air temperatures, precipitation and snow cover status. The TSX signal appeared to have very limited penetration through vegetation and the observed variations in backscatter and coherence were therefore mainly attributed to processes in the upper layer of vegetation. Variations in the TSX backscatter intensities were mostly moderate throughout the annual cycle. Backscatter was found to be insensitive to ground freezing and thawing as well as being generally insensitive to precipitation, but it was sensitive to (i) an individual rain event at the time of SAR acquisition, (ii) an individual snow shower coinciding with unusually high air temperature, and (iii) the spring melt of the snowpack (likely with a refrozen icy crust on the surface). Flooding of the sandbank was clearly detectable from extremely low backscatter values. The selected regions of interest (ROIs) demonstrated generally good separability on the basis of differences in their backscatter intensities: rough and very sparsely vegetated rocky outcrops yielded the highest backscatter and the smooth barren sandbank yielded the lowest backscatter. The backscatter from the vegetated ROIs yielding intermediate values, with the less vegetated ROIs returning lower backscatter. Interferometric coherence comprises both amplitude and phase signal components and should therefore be more sensitive to surface changes than backscatter intensity alone, especially at the X-band frequency, an assumption that is strongly supported by the results of our investigations. The coherence decreased dramatically with the onset of snow cover in all of the landscape types. The snow melt period was also clearly identified by another reduction in coherence. The snow shower that affected the backscatter also caused a reduction in coherence. January and February yielded the highest coherence values for all of the ROIs (with mean values of up to 0.9 for the rocky outcrops).
    Type: Dataset
    Format: application/zip, 3349.0 kBytes
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