@phdthesis{Siegmund2018,
  author    = {Siegmund, Jonatan Frederik},
  title     = {Quantifying impacts of climate extreme events on vegetation},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407095},
  school      = {Universit{\"a}t Potsdam},
  pages     = {129},
  year      = {2018},
  abstract  = {Together with the gradual change of mean values, ongoing climate change is projected to increase frequency and amplitude of temperature and precipitation extremes in many regions of Europe. The impacts of such in most cases short term extraordinary climate situations on terrestrial ecosystems are a matter of central interest of recent climate change research, because it can not per se be assumed that known dependencies between climate variables and ecosystems are linearly scalable. So far, yet, there is a high demand for a method to quantify such impacts in terms of simultaneities of event time series. In the course of this manuscript the new statistical approach of Event Coincidence Analysis (ECA) as well as it's R implementation is introduced, a methodology that allows assessing whether or not two types of event time series exhibit similar sequences of occurrences. Applications of the method are presented, analyzing climate impacts on different temporal and spacial scales: the impact of extraordinary expressions of various climatic variables on tree stem variations (subdaily and local scale), the impact of extreme temperature and precipitation events on the owering time of European shrub species (weekly and country scale), the impact of extreme temperature events on ecosystem health in terms of NDVI (weekly and continental scale) and the impact of El Ni{\~n}o and La Ni{\~n}a events on precipitation anomalies (seasonal and global scale). The applications presented in this thesis refine already known relationships based on classical methods and also deliver substantial new findings to the scientific community: the widely known positive correlation between flowering time and temperature for example is confirmed to be valid for the tails of the distributions while the widely assumed positive dependency between stem diameter variation and temperature is shown to be not valid for very warm and very cold days. The larger scale investigations underline the sensitivity of anthrogenically shaped landscapes towards temperature extremes in Europe and provide a comprehensive global ENSO impact map for strong precipitation events. Finally, by publishing the R implementation of the method, this thesis shall enable other researcher to further investigate on similar research questions by using Event Coincidence Analysis.},
  language  = {en}
}
@phdthesis{Beamish2019,
  author    = {Beamish, Alison Leslie},
  title     = {Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation},
  doi       = {10.25932/publishup-42592},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-425922},
  school      = {Universit{\"a}t Potsdam},
  pages     = {v, 102},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}