Mapping cryogenic processes and assessing the sustainability of permafrost landscapes in the North-East Arctic Siberian taiga and tundra from Landsat 8, Sentinel 2, and DEM data
International audience Permafrost landscapes are one of the most sensitive ecosystems that humans inhabit. Geocryological conditions determined by the presence of ice content, the genetic type of sediments, and the active layer are one of the most important variables for classifying the vulnerabilit...
Main Authors: | , , |
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Other Authors: | , , , , , , , |
Format: | Conference Object |
Language: | English |
Published: |
HAL CCSD
2022
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Subjects: | |
Online Access: | https://hal.science/hal-03678952 https://hal.science/hal-03678952/document https://hal.science/hal-03678952/file/Poster_LPS_2022.pdf |
Summary: | International audience Permafrost landscapes are one of the most sensitive ecosystems that humans inhabit. Geocryological conditions determined by the presence of ice content, the genetic type of sediments, and the active layer are one of the most important variables for classifying the vulnerability of an ecosystem to disturbances in vegetation and soil cover. These variables indicate cryogenic processes that can be activated during the degradation of permafrost. Cryogenic mapping is an important parameter for assessing the state of permafrost and infrastructure design in permafrost landscapes. However, the methods of remote sensing spatial modelling for understanding the distribution of cryogenic processes in the Arctic Siberian mountainous areas with continuous permafrost are still insufficient. The cartographies at the regional scales of 1:500 000 are inexistent. We need for permafrost landscape maps is increasing with the development of the North-East Siberian Arctic for the infrastructures and urban centres' risk assessments. Orulgan Ridge in North-East Siberia is one of these territories. This study examines the Orulgan Ridge region, as a case area, where we developed maps of the distribution of cryogenic processes based on the detailed landscape structure (including classification of environmental variables, vegetation covers and genetic type of sediments) with time-series Sentinel 2 MSI and Landsat 8 OLI, and stereogrammetric digital elevation model of the ArcticDEM data. The combination of Random Forest classifier and geomorphological GIS terrain analysis has successfully distinguished 6 classes of boreal mountain taiga and 3 classes of arctic tundra and mountain desert. Based on the indicator parameters of the interrelation of ecological variables (such as vegetation and topographic position) adopted in permafrost-landscape cartography, we carried out the regionalization of cryogenic processes. We made a classification of the genetic type of deposits, which determines the likelihood of the ... |
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