Mapping Siberian Arctic Mountain Permafrost Landscapes by Machine Learning Multi-Sensors Remote Sensing: Example of Adycha River Valley

• Main Authors: Zakharov, Moisei, Gadal, Sébastien, Danilov, Yuri, Kamičaitytė, Jūratė
• Other Authors: Aix Marseille Université (AMU), Étude des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), North-Eastern Federal University, Kaunas University of Technology (KTU), Polar Urban Centers PUR, INSTICC, SCITEPRESS: Science and Technology Publications, FMSH-RBSF OSAMA (development Of an optimal human Security Model for The Arctic), PEPS CNRS RICOCHET, Cédric Grueau, Robert Laurini, Lemonia Ragia
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2021
• Subjects: Permafrost Landscape; Remote Sensing Modeling; Landscape mapping; Digital Terrain Model; Permafrost map dynamics; Machine learning; Data fusion; Geographic databases; Local knowledge; ASTER GDEM; Landsat 8 OLI / TIRS; Sentinel 2; Multi-sensors; Arctic Mountains; Yakutia; Russia; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; [SDE.ES]Environmental Sciences/Environment and Society; [SHS.GEO]Humanities and Social Sciences/Geography; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; Adycha; Arctic; Verkhoyansk; permafrost; Tundra; Siberia
• Online Access: https://hal.science/hal-03207301; https://hal.science/hal-03207301/document; https://hal.science/hal-03207301/file/GISTAM2021_22_Final_.pdf

International audience The landscape taxonomy has a complex structure and hierarchical classification with indicators of their recognition, which is based on a variety of heterogeneous geographic territorial and expert knowledge. This inevitably leads to difficulties in the interpretation of remote...