Permafrost Landscape's Structure Categorisation Based on Land Cover, Digital Elevation Model and Land Surface Temperature on Verkhoyansk Mountain Range

• Main Authors: Zakharov, Moisei, Gadal, Sébastien, Danilov, Yuri
• Other Authors: North-Eastern Federal University, 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), Université Paris Diderot Paris 7, CNRS, Leibniz Institute of Ecological and Regional Development, IGN
• Format: Other/Unknown Material
• Language: English
• Published: HAL CCSD 2019
• Subjects: Permafrost landscape; Landscape analysis; Land cover; Land surface temperature LST; Digital elevation model; Verkhoyansk Mountain; Arctic; Russia; geo; envir; Sakha; Verkhoyansk; Climate change; permafrost; Republic of Sakha; Subarctic; taiga; Tundra; Siberia
• Online Access: https://doi.org/10.13140/RG.2.2.27406.72008; https://hal.archives-ouvertes.fr/hal-02396632/file/Poster-ILUS_Zakharov_Gadal_Danilov_sg.pdf; https://hal.archives-ouvertes.fr/hal-02396632

International audience Currently, the analysis and monitoring of the sustainability of subarctic landscapes in the context of global climate change and continuous permafrost, requires accurate methods for determining the spatial organization of each taxonomic of landscape units. In this article, we present an approach based on the recognition and characterizing the different landscapes by remote sensing in Verkhoyansk mountain range located in the Southern central Arctic region of the Republic of Sakha. The methodology developed combines land cover maps generated by classifications from Sentinel 2 data, land surface temperatures, and digital relief model. This methodology increases the accuracy of recognition of the landscape structures. The approach developed is especially important in the study of hard-to-reach high-mountainous areas and large territorial surfaces, such as the Verkhoyansk mountain range in North-East Siberia of Russia. Six landscapes categories have been identified after validation of the land field data collected during the expedition. Three altitudinal landscapes (arctic deserts, mountain tundra, mountain sparse forests), and three intra-zonal river valley landscapes of northern taiga, mountain-taiga and mountain-tundra in the region of Verkhoyansk Mountain Range.