학술논문

Time-lapse electrical resistivity tomography and ground penetrating radar mapping of the active layer of permafrost across a snow fence in Cambridge Bay, Nunavut Territory, Canada: correlation interpretation using vegetation and meteorological data
Document Type
Original Paper
Source
Geosciences Journal. 25(6):877-890
Subject
snow fence
geophysical survey
active layer
normalized difference vegetation index (NDVI)
permafrost
Language
English
ISSN
1226-4806
1598-7477
Abstract
The active layer thickness (ALT) is a key parameter for permafrost studies. Changes in the ALT are affected mainly by air and ground temperatures, physical and thermal properties of the surface and subsurface materials, soil moisture, vegetation, and the duration and thickness of snow cover. Ground penetrating radar (GPR) and electrical resistivity tomography (ERT) were employed across a snow fence during the thawing season to delineate and monitor the active layer of permafrost in Cambridge Bay, Nunavut, Canada. The variation of the ALT is well captured by the high-resolution time-lapse radargram. At the position of the fence, the active layer thickens over the thawing period from 0.5 m depth at the beginning to 1.0 m depth at the end. The active layer is thicker in the pre-fence area (C zone) than in the post-fence area (H zone). As the air temperature increases with time, the difference in thickness between the two zones decreases, eventually becoming almost equal. Changes in the ALT are represented in the ERT by low resistivities (< 200 Ωm), which decrease gradually with time. This occurs most significantly in the H zone due to the rapidly increasing temperature in the absence of snow cover. The electrical resistivity structure of the active layer is well correlated with the vegetation activity, as measured by the normalized difference vegetation index, air/ground temperatures, soil moisture, snow cover, and snow accumulation controlled by the fence. Geophysical data interpretation and correlation schemes with vegetation and meteorological data explored in this paper can be applied to monitor the active layer, which is expected to thin during the freezing season.