In the past two decades, the South part of Serbia has been affected by exploitive and illegal logging. As this trend is not decreasing to this day, there is a need to determine the area where this logging occurred precisely. The consequences of these actions are tremendous, causing the forest owners’ financial loss (regardless of whether it is private or state property) and a negative impact on the environment. Significant environmental and forest management problems deriving from these actions are erosion increase and more frequent torrential floods occurrence in the catchment. Since it is difficult to update the national forest inventories in remote areas, remote sensing techniques using different satellite imagery types can provide up-to-date data. The initial analysis that employed Normalized Difference Vegetation Index (created using Landsat 7 and Landsat 8 imagery) indicates massive deforestation in the research area between 1999 and 2021. Headwaters of the Štavska river catchment is selected as the research area to determine the amount of erosion in two periods—before and after deforestation occurred. Change in land cover (LC) is presented with two LC maps created applying supervised classification to Landsat 7 imagery from 1999 as a pre-deforestation LC state and Landsat 8 imagery acquired in 2021 as the current LC state. The erosion in the catchment for both periods is determined using the Soil and Water Assessment Tool (SWAT). The analysis results show the erosion change incurred as a deforestation effect in the river catchment. With the data obtained by remote sensing and SWAT analysis, it is possible to track changes in the area and acquire essential data, making the right and fast decisions to protect the natural resources economy and make sustainable development possible in this impoverished region.

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.

Wildfire has been one of the most dangerous environmental stressors nowadays. It is an important disturbance where ecosystem biomass is burned and where organisms are damaged or killed by fire. Therefore, the detecting and monitoring of this stressor are of great importance. During last decades, extensive forest fires have spread in Southern Europe, and they are registered in Serbia as well. During year 2007, several significant fires were registered in Stara Planina and Svrljiške Planine Mountains. The aims of this study were to detect land cover changes for the studied site from 2007-2017, to focus on monitoring the area affected by the wildfire, and to analyse the environment response to stressor. The study area is situated in East Serbia, partially covering the Mountains Stara Planina (western part) and Svrljiške planine (eastern part). The remote sensing techniques were used in the analysis and main satellite data were obtained via USGS Earth Explorer application. Six different classes were selected: Water, Forest, Pastures, Artificial area, Agriculture, and Bare soil. Results showed significant changes in two classes, Forest, and Pastures - the forest spread for more than 20% at the expense of pasture, which decreased more than 23%. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III-47007]

This research presents a comprehensive analysis of the production of terrain passability maps in southeastern Serbia, employing a multi-criteria decision-making (MCDM) analysis. The study integrates various geographical and infrastructural aspects, assigning coefficients to each input parameter, including rivers, roads, rails, CORINE Land Cover (CLC), soil, slope, and the Topographic Ruggedness Index (TRI). The introduction of the TRI marks an innovative advancement in terrain analysis and passability. By comparing wet and dry periods, the study provides critical insights into the dynamic nature of terrain passability, with implications for transportation planning and emergency response. The research's innovative approach and detailed examination set it apart, offering valuable contributions to scholarly comprehension and practical applications. The findings underscore the potential for interdisciplinary collaboration and the broad impact of geographic information systems (GIS) and terrain analysis in addressing real-world challenges. Future research may explore additional factors influencing terrain passability and expand the geographical scope of the study.

Soil erosion is a global environmental and economic problem that is significantly related to land-use changes. Over the last decades, several mountain areas in Serbia were exposed to strong human pressure caused by winter tourism development. The largest ski center in Serbia is situated on Kopaonik Mountain within the boundaries of Kopaonik National Park, where the conflict between economic and conservation goals is rapidly growing. In this study, we calculated the sedimentation and surface runoff in three sub-basins in the area of Kopaonik ski resort for two years (1984 and 2018) using the Soil and Water Assessment Tool (SWAT) and analyzed the changes that occurred during the observed period. The results show an increase in surface runoff and sediment yield in sub-basins 1 and 3 and a decrease in sub-basin 2. The analysis of land cover change shows an expansion of evergreen forests, appearance of barren soil and urban areas, reduction of mixed forests and pastures, and the appearance of deciduous forests. These findings indicate that in the area studied, the dominant processes are the development of tourism and natural revegetation of abandoned agricultural land. Application of remote sensing techniques and SWAT contributes to identifying and monitoring land degradation problems and improving conservation and management practices.