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The proposed study aims to determine the influence of geological structures on the features of the channels of the largest right-bank tributaries of the Dniester River - the Stryi, Bystrytsia, and Limnytsia rivers. For this purpose, we conducted zoning of the river based on morphometric and hydrological characteristics. Three parts were identified: mountainous, pre-mountainous area and plain area, which differ significantly in channel properties, their changes over time, and deformation processes. Objective.

Purpose. The aim of this research is monitoring of coniferous forests of Tukhlya forestry in Precarpathian region using medium and high resolution satellite images and images obtained from an unmanned aerial vehicle (UAV). Methodology. To monitor the condition of forests of Tukhlya Forestry, a technique based on using satellite images with different spectral characteristics and resolutions, images obtained from UAVs and, accordingly, their processing by different methods, was used.

Aim of work is to investigate the influence of the Precarpathian bend and the Volynian-Podolian upland for the nature of displacement of the Dnister River tributaries and to determine stability of river channels. The object of this research is the Dnister River and its left and right tributaries. Considering the main factors influencing the nature of the horizontal riverbed displacements caused by both natural and anthropogenic factors, special attention is focused on the geological structures in the area on which Dnister River and its tributaries flow. Methods.

The actuality of the present paper is substantiated by the need in preservation the landscape and biological diversity of nature-protected territories to achieve the ecologically optimal structure of land use as one of the main targets of their sustainable development. The paper describes methodological approaches to analysis of the nature-protected territory landscape diversity, presents the landscape metrics, calculated using the FRAGSTATS software as well as images of their spatial distribution.

In this paper the technique for monitoring the displacement channel of rivers in urban areas. Monitoring conducted by the example of the river Stryy within the city and nearby villages, covering a 30-year period – from 1979 to 2010. The main causes of riverbed displacement is described. The monitoring methodology is based on usage data from space imaging, cartographic materials, including special maps – the soil and geologic maps of quaternary sediments and, also engineering and surveying data.

In this paper we study the technique of channel displacement of the Dniester River. Monitoring of the rivers was done for the territory within the villages Tershev and Busovisko, and covers a 30-year period – from 1979 to 2010. The main causes of river bed displacement. We propose a conceptual model of the study of channel displacement, which is based on usage data from space imaging, aerial photography, laser scanning, cartographic materials, including special maps – the soil and geologic maps of quaternary sediments and, as well as data engineering and surveying..

Data about results of earthquake are important when evaluation of damages and can be used by rescuers in emergency situations. Technological scheme of quantitative determination of destroyed objects caused by natural disaster on the example of fragmental space images of the city Senday (Japan) is presented in the paper.

Activities and results achieved of the European Union financed project in Tajikistan is discussed.

Tourist route for the recreational area in the upper part of the river Dniester is designed. This tourist route, its characteristics and main tourist objects are combined with space image GeoEye.

In this paper a researches of methods for the controlled classification by the spectral distance, Mahalanobis distance and maximum probability to identify forests was conducted. As a materials for the research space images of the satellites Ikonos, QuickBird and test information from ground-based methods were used. As an outcome it was identified that the maximum probability method is the most efficient one for the forest classification issue.