Cryogenic Land Surface Process Detection in Siberian High Latitude Mountain Permafrost Landscape by Time Series Landsat Thermal Imagery

• Published in: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium pp. 237 - 240
• Main Authors: Gadal, Sebastien, Zakharov, Moisei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.07.2023
• Subjects: Clouds; Cryogenics; Infrared imaging; Land surface; land surface cryogenic process; land surface temperature; Landsat; split-window algorithm; thermal anomaly; Time series analysis; Vegetation mapping
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS52108.2023.10282807

In recent years, the accumulation of cloud-free remote sensing thermal infrared data for high-latitude permafrost regions makes it possible to use them to identify active cryogenic land surface processes (ACP) spurred by climate change and human activities. ACP is an extremely important indicator of excitations in the energy cycle of permafrost landscapes and changes in the carbon budget. In this paper, we test the time series of land surface temperature (LST) extracted from Landsat 8 OLI/TIRS datasets using a split window algorithm (SWA) to identify ACP for the study area in the mountains of Northeastern Siberia. We identified active cryogenic processes from thermal anomalies detected using standard deviation thresholds on 5 datasets for selected dates. Our results show that multi-year verified high-resolution LST data for the second half of the summer period can be applicable for identifying an area with active cryogenic processes in valley and disturbed permafrost landscapes, provided they can indicate local effects of processes associated with drainage degradation and vegetation change.