Monitoring spatial and temporal variability of sea-ice processes in the Laptev-Sea, using ASAR data
Thomas Krumpen(1) , Christian Haas(1) , and Busche Thomas(1)
Christian Haas, Thomas Busche, Thomas Krumpen
The ice regime of the 500 km wide shelf of the Laptev-Sea is controlled by strong offshore Siberian winds that generate one of the main flaw recurring polynya in the Laptev sea, the West New Siberian polynya (WNS) and a second recurring coastal polynya, which can be observed along the north-western coast of the Lena-Delta.
To monitor small scale processes of the NWS polynya, the Lena coastal polynya and the sea-ice in general, ENVISAT ASAR images have been acquired for the period between October 2003 and June 2004.
Parallel, two sea floor observations have been moored to derive ice thickness (ADCP based), ice drift and salinity, temperature and currents in the water body at a depth of approximately 25m.
The sea floor observations and the Lena Delta are covered by WSM products, while APP and IMP products are used to monitor the region around the moorings with higher spatial resolution.
WSM images could be obtained 0.9 times a week. The temporal coverage of IMP and APP is less, due to a lack of satellite ground station visibility.
Aim of this project is a first investigation of sea-ice processes in the Laptev Sea and the derivation of ice/ocean variables based on SAR data.
For ice-regime monitoring, ice drift-speed and direction has been extracted manually from the imagery. Opening and closing events of polynya formation, fast ice boarder etc. have been mapped based on a combination of a texture-based classifier and a seed growing threshold methodology.
Additionally, the derived ice/ocean variables were evaluated using a statistical landscape pattern analysis program (FRAGSTATS) and finally linked to climate data.
The second challenge was to identify a phenomenological cycle of the seasonal evolution of σº at 5.3GHz for different samples throughout the imagery. The presented thesis shows that ENVISAT ASAR images can be successfully used to extract beginning and end of freeze-up, winter, early-melt and melt-onset period in the study area.