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Institute
Thermal erosion is a major mechanism of permafrost degradation, resulting in characteristic landforms. We inventory thermo-erosional valleys in ice-rich coastal lowlands adjacent to the Siberian Laptev Sea based on remote sensing, Geographic Information System (GIS), and field investigations for a first regional assessment of their spatial distribution and characteristics. Three study areas with similar geological (Yedoma Ice Complex) but diverse geomorphological conditions vary in valley areal extent, incision depth, and branching geometry. The most extensive valley networks are incised deeply (up to 35 m) into the broad inclined lowland around Mamontov Klyk. The flat, low-lying plain forming the Buor Khaya Peninsula is more degraded by thermokarst and characterized by long valleys of lower depth with short tributaries. Small, isolated Yedoma Ice Complex remnants in the Lena River Delta predominantly exhibit shorter but deep valleys. Based on these hydrographical network and topography assessments, we discuss geomorphological and hydrological connections to erosion processes. Relative catchment size along with regional slope interact with other Holocene relief-forming processes such as thermokarst and neotectonics. Our findings suggest that thermo-erosional valleys are prominent, hitherto overlooked permafrost degradation landforms that add to impacts on biogeochemical cycling, sediment transport, and hydrology in the degrading Siberian Yedoma Ice Complex.
TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small arctic catchments
(2018)
The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt.