Geoinformation modeling of permafrost landscapes of North-Eastern Siberia

• Main Authors: Zakharov, Moisei, Gadal, Sébastien, Danilov, Yuri
• Other Authors: Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), North-Eastern Federal University, Aix Marseille Université (AMU), Russian Science Foundation No. 21-17-00250., V.B. Sochava Institute of Geography SB RAS, Valdimitrov Igor Nikolaevich
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2022
• Subjects: Landscape permafrost; Remote sensing; Machine learning; GEE; Land cover change; Arctic Mountains; ASTER GDEM; Siberian Eastern; Yakutia; [SHS.GEO]Humanities and Social Sciences/Geography; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [SDE.MCG]Environmental Sciences/Global Changes; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; Arctic; permafrost; Siberia
• Online Access: https://hal.science/hal-03897646; https://hal.science/hal-03897646/document; https://hal.science/hal-03897646/file/Zakharov%20et%20al.,%202022Irkutsk.pdf

International audience The landscape-indicative approach makes it possible to determine permafrost landscapes based on the identification of two physiognomic indicators-variables of relief and vegetation, as well as stratigraphic-genetic sediment complexes. For permafrost characteristics of landscapes, multilevel combinations of environmental variables (criteria) for identifying landscapes are used. All this is the result of the analysis and processing of a huge array of field data in various landscapes of Yakutia (Fedorov, 2022). One of the main methods for obtaining these variables is geoinformation modeling as a methodological solution based on the construction and use of models of spatial objects, their relationships, and the dynamics of processes using GIS tools (Zhurkin et al., 2012). The aim of our study is to develop a geoinformation modeling technique for studying mountainous permafrost landscapes using the Orulgan ridge as the study case. The Orulgan Ridge is characterized by a meridional orographic structure and the presence of all varieties of mountain permafrost landscapes in the Arctic, which makes it a reasonable choice among the other mountain regions in terms of coverage and diversity. Cloud calculator platforms such as Google Earth Engine(GEE) are becoming the main tool for land cover-based landscape indication (DeLancey et al, 2019). The core of geoinformation modeling is the data of multitemporal multi-zone satellite images and a digital elevation model, the synthesis and processing of which make it possible to carry out landscape indication. The methodological workflow for obtaining data on the spatial structure of permafrost landscapes consists of the sequential compilation and synthesis of vegetation cover data, using the supervised phenology-based classification of the time series of Sentinel 2 MSI and Landsat 8 OLI data. Landform classification is performed on the basis of GIS-based terrain analysis by the ASTER GDEM scenes. The characteristics of the relief and genetic deposits were ...