Ландшафтно-экологическое районирование города Оренбурга на основе геоморфометрических и эколого-хозяйственных особенностей территории

Abstract

The article is a complex of detailed studies of the natural landscape structure and the ecological and functional purpose of the urbanized territory. The relevance of the study is determined by the fact that it is necessary to introduce territorial planning documents (master plan) and landscape planning projects, with the allocation of specific sections of the natural-ecological framework of cities. The study was conducted with the aim of studying the natural-technical geosystem to optimize the interaction of nature transformed by human activities and the socio-economic needs of society. To achieve this goal, a typification scheme for landscape structures of urban space was developed for the first time, based on a digital terrain model and using the method of automated typological zoning of relief using its morphometric data using artificial neural networks. As a result of automated training of the neural network model and verification of the data obtained, 15 classes were obtained (taxa tracts) and established the correspondence of each type of landscape in space with an indication of geomorphometric characteristics. Based on the digitized model of the functional zoning of Orenburg and types of landscapes, for the first time, an integrated map of the landscape-ecological zoning of urban space was developed and classifications of types and types of landscape zones were presented: primary and mixed. Cartographic models of the natural-landscape component of Orenburg, which is the natural-ecological framework of the object of study – recreational zones and a hydrographic network are separately presented. The results obtained are important for maintaining the landscape functions of the urbanized area, forecasting changes, and minimizing the effects of anthropogenic impact. Key words: urban planning, natural-ecological framework, functional types of land use, technological systems, type of landscape purpose, landscape, geomorphometric features, neural network algorithm.