Abstract ID: 3.12102 | Accepted as Poster | Talk/Oral | TBA | TBA

The transformation of mountain socio-ecological systems is influenced by the interaction of natural, primarily climatic, and social, political and economic factors. The ratio of the contribution of different factors is not always obvious due to their overlap and the manifestation of the effects of acceleration or deceleration of transformation. In this paper, we focused on the main shifts in the transformation of the nature, population and economy of the mountainous regions of the North Caucasus over the past 30 years, based on modern data and methods of geographic information modelling. For this purpose, the current diversity of land cover within the main high-altitude landscape belts was reflected. Based on statistical data, field research and analysis of remote sensing data, an assessment is made of changes in the development of regions and landscapes of the North Caucasus. A widespread reduction in the nival-glacial zone has been noted, which reduces the potential for ski tourism. Trends in forest change are extremely uneven. Forest cover varies from 41.8% in Adygea to 11.7% in Dagestan. The greatest increase in forest area is observed near villages (as a result of gasification), the decrease is in the zone of intensive grazing livestock in the middle mountains. Within the mountainous territory there is a relatively small number of arable land areas, but these areas are also abandoned due to low competitiveness with agricultural holdings located on the plain. The widespread (in the mountains, foothills and plains) decrease in the share of arable land is also associated with the development of former agricultural lands for housing construction and other objects. The use of remote sensing data makes it possible to assess the transformation of natural landscapes and changes in land use types. In general, natural landscape zones that fit into four generalized groups (foothills, low mountains, middle mountains and high mountains) are subject to multidirectional trends, the correct accounting of which requires the use of modern methods of geographic information modelling and verification in the field.