With growing importance of the global economy, one of the basic natural resources − intensity of land use − is also increasing, which often is the cause of land degradation processes. The causes and manifestations of brownfields are diverse, and their elimination is the first prerequisite for the sustainable use of land resources and development of each region. Improvement, maximal and efficient engagement in economic activity of brownfields is one of the key challenges for sustainable resource use that makes significant contribution to regional development. The reuse of brownfields has significant impact on sustainable development as it meets all three of its objectives: improving the economy, improving social cohesion and the environment. The aim of the article is on the basis of special literature examples to examine issues of sustainable development, evaluation and restoration of brownfields, transformation of brownfields into recreational areas, as well as further use of brownfields in cities and rural areas.

According to the official statistics the areas of abandoned agricultural land in Lithuania are gradually decreasing, but very slightly. The aim of this study is to research spatial determination and abandoned land classification in the territory of Vilnius District Municipality. Vilnius District Municipality was chosen for the research because it, although located near the capital of the country and has a high population density, it is still the district having the largest percent of abandoned land plots. A fast, cost-effective and sufficiently accurate method for determination of abandoned land plots would allow to constantly monitoring, to fix changes and foresee the abandoned land plots reduction possibilities. In the study there was used the multispectral RGB and NIR colour Sentinel-2 satellite images, the layer of the administrative boundary of Vilnius County and layer of abandoned agriculture land, which is available in Lithuanian Spatial Information Portal (www.geoportal.lt). The data was processed by Geographic Information System (GIS) techniques using classical classification Region Growing Algorithm. The research shows that NIR image classification result is more reliable than the result from RGB images.