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

• Published in: Land (Basel) Vol. 11; no. 8; p. 1187
• Main Authors: Zakharov, Moisei, Gadal, Sébastien, Kamičaitytė, Jūratė, Cherosov, Mikhail, Troeva, Elena
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022; MDPI
• Subjects: Anthropogenic factors; Arctic zone; Artificial intelligence; Classification; Climate change; Climate variability; Computer Science; Environment and Society; Environmental aspects; Environmental monitoring; Environmental Sciences; Forests; Frozen ground; Geographic information systems; Geography; Geomorphology; Google Earth Engine; Human influences; Humanities and Social Sciences; Ice environments; Image Processing; Landforms; Landsat; Landscape; landscape structure; Machine learning; Mapping; Methods and statistics; Modeling and Simulation; Moisture effects; Moisture index; Mountains; Normalized difference vegetative index; Observations; Permafrost; permafrost landscape; Polar environments; Regions; Remote sensing; River valleys; Rivers; Spatial data; Structural analysis; Support Vector Machine; Support vector machines; Taiga; Taiga & tundra; Terrain analysis; Time series; time-series image classification; Topography; Tundra; Valleys; Vegetation; Vegetation cover; Russia; Arctic region; Siberia; Terrain analysis; Landscape structure; Permafrost landscape; Permafrost Mountains; Google Earth Engine; Northeast Siberia; Support Vector Machine; Time-series image classification; Arctic; Landscape mapping
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land11081187

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 vegetation and relief, can be of key importance for environmental monitoring and the study of regional variability of climatic changes.