Exploring the seasonality of rapid Arctic changes from space - Stettner, Samuel (2018)
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 ...