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

International audience; For the past ten years, energy issues – alongside the policies implemented to address them – have once again been at the heart of development issues in the Arctic and circumpolar regions. The reasons for this situation are based on the intensification of the exploitation of natural resources, for which the impacts of climate change (warming) are perceived as opportunities, thereby accelerating economic development and the reterritorialization of the Arctic space. The policies linked to the energy transition are driven by both ecology and the need to have autonomous production unit grids reinforcing the interrelations between territorial development and energy. Energy production units – whether wind, solar, gas, coal, nuclear, or sometimes hydroelectric – are key drivers of economic development in the Arctic and circumpolar space, determining the exploitation of future natural resources and human security. Without energy planning, there can be no economic growth, no human development, and no territorial development. Energy safety is fundamental for the territorial development of the Russian Arctic. The analysis of the Russian Arctic space by remote sensing shows intense urbanization processes accompanying the exploitation of natural resources.

International audience; In the last twenty years, the Metropolis of Yakutsk has experienced significant changes characterised by intense urban growth, densification, and urban structure changes in a complex geographical environment: extremely low temperature during six to seven months, impacts of permafrost dynamics and relative melting, seasonal exposure to ice breakup on the suburban areas. The urban structure and land-use changes conditioned by the urban growth and environmental impacts are analysed at two geographic scales: the meso-urban level with the use of the Landsat-5 TM, Landsat-8 OLI, and Sentinel-2 MSI satellites images covering the period from 2010 to 2020; and at the regional level with the DMSP-OLS PL (1995-2013) and VIIRS-DNB (2015-2020) sensors. The recognition of the urban structures and land use transformations at both scales are based on the use of combined machine learning data processing. The simulations of the urban structures and land use evolutions to 2030 at the meso-urban and regional scales by Markov chain cellular automata give comparable results of the future trends of the Yakutsk metropolis: reduction of vegetation, forests areas (due to forest fires) and agriculture zones; the increase of bare soil, water surfaces and new urban areas.

International audience; The process of metropolisation of Yakutsk is one of the most unique geographic phenomena. Since the end of the Soviet Union, the city of 193000 inhabitants growing fastly to cross in 2021 up to 33000 people covering today almost 1 million square kilometres in the extreme Arctic environment and climate. The population of the Yakutsk metropolis developed strategies of adaptation we are going to analyse. Scenarios of possible transformations of the metropolis including the impacts of global warming have been developed at different scales and temporalities (Landsat Series, Sentinel 2, DMSP, and VIIRS) based on machine learning and deep learning processing.

International audience; One of the challenges of human security in the Arctic regions, and more specifically in the Eastern Siberian Arctic regions, concerns the assessment of inhabitants’ exposure to recurrent environmental risks for the urban population in a geophysical environment characterized by notable change dynamics. The latter is known as permafrost degradation, water flooding (river, geomorphologic canals, and plateau erosion flooding) and ice breakup, erosion, and forest fires. Urban centers and villages are confronted with a multiplicity of natural hazards. However, the evaluation of their exposure is partial or non-existent; hence there is the need to create indices of potential exposure to environmental risks. They are built by spatial analysis of survey data on the perception and knowledge of the local population and remote sensing that are integrated into Geographic Information Systems. Knowledge and understanding of how local people perceive the natural and anthropogenic environment in the context of climate change and its consequences are some of the key factors in creating effective urban planning strategies for the cities in the high-risk zones of environmental hazards. The urban region of Yakutsk is the test study area for the construction of these indicators of human security in the Siberian Russian Arctic. The large urban area with more than 350,000 people in one of the coldest regions in the world is confronted by the acceleration of global warming and urbanization on a recurrent and seasonal basis. This hazardous occurrence is combined with the risks of forest fires, erosion, floods, and permafrost degradation affecting infrastructures, settlements, industrial and agricultural areas.

International audience; The importance, or rather the multiplicity of human security issues in the Russian Arctic is fundamental for the development of this region, whose growing economic significance with environmental problems linked to the adaptation of populations and mobilization of the humans' competencies. The Russian Federation has a significant lead in training and education in the Arctic, however, in some areas such as geomatics, there are significant gaps. This is mainly due to the positioning of geomatics and remote sensing in Russia and to a technical approach to the discipline. The International Franco-Russian Master's Degree in Geomatic and Environment (MIGE), the first Master's Degree in this field in Siberia, responds to this demand: to train specialists in geomatics applied to the management of the Arctic environment and the exploitation of natural resources. Academic cooperation makes sense because of the common issues. The built cooperation is based on the complementarity and cross-mobilization of competencies and knowledge.

Arctic extensively urbanized centers are subject to the impact of many negative environmental phenomena progressing in terms of global climate change and regional development in Yakutia in the context of poor and missing databases. For this reason, the modeling of the risk exposures is based on combining the remote sensing, and local knowledge of inhabitants. According to the occurrences of the natural hazards, the territorial management and the decision-making system require the identification and assessment of natural risks to which the rural populations localized in the towns and villages are exposed, for example, in the urban center of Khamagatta located at 70km to the North from Yakutsk near the Lena River. The main environmental vulnerability exposures are seasonal: springtime floods between May and June, the forest fires from June to August, the cyclic permafrost degradation, and river erosion impacts.The current vulnerability impacts, damages to the lands and the settlements, and the populations risk exposures are analyzed from the maps of vulnerabilities created from remote sensing satellite Sentinel 2A/B series, with the local knowledge of the inhabitants of Khamagatta who lived and perceived all events. All the data generated, maps, models of vulnerability exposures, and local knowledge are integrated, combined, and merged into the geographic information system (GIS). The GIS modeling combines the risk of natural hazards and the damages, and the risk knowledge and perceptions of the inhabitants. Land uses, Landscape classification, and the land cover is made by Object-Based Image Analysis (OBIA) using an optical time series of Sentinel 2 images (2015-2020) including the population knowledge for the recognition of the environmental vulnerabilities. The methodological approach included the participation of local people in workshops through discussion and participatory mapping, questionnaires, and interviews in two stages. The first stage included the development of the knowledge database for a comprehensive understanding of the life of the local population, including the forms of adaptation to the negative natural phenomena. The collected information is delocalized and integrated into the GIS. The second stage consisted of validation and discussion, including stakeholders (municipality and rescue services) to increase the reliability and legitimacy of the research results.Perceptions of the inhabitants of Khamagatta are correlated with the maps of risk exposures generated by remote sensing to increase the accuracy of the environmental process modeling and landscape classification. The combination of the environmental change dynamics, the impacts on the towns and villages with the human perception and experience constitutes the main base supporting the prevention mapping of the natural hazards. This data could be very useful in planning the development of Arctic towns and villages and proposing evolution scenarios and urban planning models and strategies for increasing their resilience and adaptation to climate change consequences.

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 sensing data and image analysis in landscape research in the field of classification and mapping. This article examines an approach to the analysis of intra-season Landsat 8 OLI images and modeling of ASTER GDEM data for mapping of mountain permafrost landscapes of Northern Siberia at the scale of 1: 500,000 as well as its methods of classification and geographical recognition. This approach suggests implementing the recognition of terrain types and vegetation types of landscape types. The 8 types of landscape have been identified by using the classification of the relief applying Jenness's algorithm and the assessment of the geomorphological parameters of the valley. The 6 vegetation types have been identified in mountain tundra, mountain woodlands, and valley complexes of the Adycha river valley in the Verkhoyansk mountain range. The results of mapping and the proposed method for the interpretation of remote sensing data used at regional and local levels of studying the characteristics of the permafrost distribution. The work contributes to the understanding of the landscape organization of remote mountainous permafrost areas and to the improvement of methods for mapping the permafrost landscapes for territorial development and rational environmental management.

International audience; The importance, or rather the multiplicity of human security issues in the Russian Arctic is fundamental for the development of this region, whose growing economic significance with environmental problems linked to the adaptation of populations. Despite the distance, the direct impacts of global warming in the Russian Arctic are significant in France and in Western Europe. For example, the evolution of permafrost landscapes (methane, CO2 emissions, macro viruses, etc.) have directly affected human infrastructures in Yakutia Arctic and sub-Arctic zones, then at the regional level in Western Europe. Scientific cooperation makes sense because of the common issues. The built cooperation is based on the complementarity and cross-mobilization of methodologies and knowledge. This has resulted in significant advances in modeling risk exposure and the consequences for urban development in terms of understanding geophysical processes and for the development of the Russian Arctic territories in Yakutia. The research is carried out within the framework of the Franco-Russian project of FMSH-RFFI.

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.

International audience; Approaches of geographic ontologies can help to overcome the problems of ambiguity and uncertainty of remote sensing data analysis for modeling the landscapes as a multidimensional geographic object of research. Image analysis based on the geographic ontologies allows to recognize the elementary characteristics of the alas landscapes and their complexity. The methodology developed includes three levels of geographic object recognition: (1) the landscape land cover classification using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifiers; (2) the object-based image analysis (OBIA) used for the identification of alas landscape objects according to their morphologic structures using the Decision Tree Learning algorithm; (3) alas landscape's identification and categorization integrating vegetation objects, territorial organizations, and human cognitive knowledge reflected on the geo-linguistic object-oriented database made in Central Ya-kutia. The result gives an ontology-based alas landscape model as a system of geographic objects (forests, grasslands, arable lands, termokarst lakes, rural areas, farms, repartition of built-up areas, etc.) developed under conditions of permafrost and with a high sensitivity to the climate change and its local variabilities. The proposed approach provides a multidimensional reliable recognition of alas landscape objects by remote sensing images analysis integrating human semantic knowledge model of Central Yakutia in the subarctic Siberia. This model requires to conduct a multitemporal dynamic analysis for the sustainability assessment and land management.