Remote Sensing of Mountain Permafrost Landscape by Multi-Fusion Data Modeling. Example of Verkhoyansk Ridge (Russia)

• Main Authors: GADAL, Sébastien, Zakharov, Moisei, Danilov, Yuri, Kamičaitytė, Jūratė
• Other Authors: Aix Marseille Université (AMU), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), North-Eastern Federal University, Kaunas University of Technology (KTU), Polar Urban Centers PUR, IEEE International Geoscience and Remote Sensing Symposium, CNRS PEPS RICOCHET
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2020
• Subjects: Russia; Verkhoyansk; Sentinel 2; Multi-fusion methods; Land surface temperature; Landform classification; Permafrost landscape mapping; Permafrost mountains; Arctic; Support vector Machine; ASTER; Landsat 8 TIRS; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; [SDE.ES]Environmental Sciences/Environmental and Society; [SHS.GEO]Humanities and Social Sciences/Geography; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; permafrost
• Online Access: https://hal.archives-ouvertes.fr/hal-02951488; https://hal.archives-ouvertes.fr/hal-02951488/document; https://hal.archives-ouvertes.fr/hal-02951488/file/0003082.pdf

International audience Mapping of permafrost mountain landscape of Verkhoyansk in the Arctic zone is based on the recognition by remote sensing and GIS modeling of the landscape permafrost-objects resulting from the combination of the Milkov’s taxonomic classifications. The methodology developed integrates three types of modeling: the first one is mapping the vegetation repartition using Sentinel 2A with SVM classifier during the summer vegetative period; second is the landform classification using Jenness’s algorithm from ASTER data; and the third one is land surface temperature calculus from Landsat 8 OLI/TIRS images identifying the permafrost characteristics categories. Results are merged using a native index equation of permafrost landscape objects in GIS. This original mapping approach improves significantly the understanding of complexity of the permafrost mountain structures and processes with the annual monitoring by remote sensing.