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Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.

Abstract:This paper tends to show potential usage of GIS in educational process. Contemporary educational process is unimaginable without involving innovations in all forms of teaching processes. These innovations tend to develop dialectical approach in observation of real world objects, phenomenon and processes. The practical implementations of GIS as well as requirements are considered regarding foreign experience and solutions.

Cultural assets in the area of the Danube Limes in Serbia are an integral part of the world heritage “Roman Empire Borders”. The research presented in this paper includes the tourist and cartographic visualization of 19 Roman sites in the Danube Limes region of Golubac–Radujevac, to determine the real possibilities of tourism development in this area. The historical and cultural heritage of this area is among the most attractive tourist destinations in Serbia, Djerdap National Park and Djerdap Geopark. Despite its diverse cultural and historical values and the specific and unique natural environment, this area is not sufficiently used for tourism. The research included the evaluation of localities, which may serve as the basis to establish which activities should be undertaken in order to plan, use, preserve, and protect such important cultural assets, under the principles of sustainable tourism development. Information based on spatially referenced data in the research process requires cartographic support, in order to understand the geospatial relations of the site significance. Cartographic visualization enabled efficiently systematized data organization, spatial identification, presentation, and the use of complex information from the mapped area in the data analysis in this paper.

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