Application of Landsat-7 satellite data and a DEM for the quantification of thermokarst-affected terrain types in the periglacial Lena-Anabar coastal lowland
|Title||Application of Landsat-7 satellite data and a DEM for the quantification of thermokarst-affected terrain types in the periglacial Lena-Anabar coastal lowland|
|Publication Type||Journal Article|
|Year of Publication||2006|
|Authors||Grosse, G, Schirrmeister, L, Malthus, TJ|
|Date Published||Jan 2006|
|Keywords||Classification, GIS, Laptev Sea coastal lowland, Mamontov Klyk, Mapping, quantification, Remote sensing, Siberia, terrain analysis, Thermokarst|
Extensive parts of Arctic permafrost-dominated lowlands were affected by large-scale permafrost degradation, mainly through Holocene thermokarst activity. The effect of thermokarst is nowadays observed in most periglacial lowlands of the Arctic. Since permafrost degradation is a consequence as well as a significant factor of global climate change, it is necessary to develop ef.cient methods for the quantification of its past and current magnitude. We developed a procedure for the quantification of periglacial lowland terrain types with a focus on degradation features and applied it to the Cape Mamontov Klyk area in the western Laptev Sea region. Our terrain classification approach was based on a combination of geospatial datasets, including a supervised maximum likelihood classification applied to Landsat-7 ETM+ data and digital elevation data. Thirteen final terrain surface classes were extracted and subsequently characterized in terms of relevance to thermokarst and degradation of ice-rich deposits. 78 % of the investigated area was estimated to be affected by permafrost degradation. The overall classification accuracy was 79 %. Thermokarst did not develop evenly on the coastal plain, as indicated by the increasingly dense coverage of thermokarst-related areas from south to north. This regionally focused procedure can be extended to other areas to provide the highly detailed periglacial terrain mapping capabilities currently lacking in global-scale permafrost datasets.