Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine

• Published in: Land
• Main Authors: Zakharov, Moisei, Gadal, Sébastien, Kamičaitytė, Jūratė, Cherosov, Mikhail, Troeva, Elena
• Other Authors: Aix Marseille Université (AMU), Études 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), Institute of Biological Problems of Cryolithozone, Siberian Branch of the Russian Academy of Sciences (SB RAS), FMSH-RBSF OSAMA (development Of an optimal human Security Model for The Arctic), CNES TOSCA TRISHNA (Cryosphere), ANR-15-CE22-0006,PUR,Pôles URbains(2015)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: Google Earth Engine; Support Vector Machine; Time-series image classification; Terrain analysis; Landscape structure; Landscape mapping; Northeast Siberia; Arctic; Permafrost landscape; Permafrost Mountains; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [SHS.GEO]Humanities and Social Sciences/Geography; [SDE.ES]Environmental Sciences/Environment and Society; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; Climate change; permafrost; taiga; Tundra; Siberia
• Online Access: https://hal.science/hal-03751368; https://hal.science/hal-03751368/document; https://hal.science/hal-03751368/file/land-11-01187-v2.pdf; https://doi.org/10.3390/land11081187

International audience An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of ...