Hilly terrains characterized by the looseness of the surface soil layer and the slope of the topographic surface are subjected to diverse erosion processes, which could greatly impact forest management. An erosive process was identified along the unstable slopes in most of the Crveni potok catchment in the southeastern part of Serbia. The results of this study indicate two erosion types of variable intensity, dominated by weak and moderate erosion, while severe erosion is identified at several locations. Water pressure in the pores of loose soil in the Crveni potok catchment generates gully erosion in parts of the catchment that are geologically predisposed by the composition of the parent substrate. The paper discusses the factors influencing gully erosion, such as soil density, the slope of the topographic surface, rainfall (precipitation amount, annual distribution), and vegetation characteristics. The set of used methodologies, including remote sensing, nuclear, and geospatial ones, provided an integrated assessment of factors affecting erosion in unstable forest areas. The soil erosion rates in the study area are estimated from inventories of fallout radionuclide ¹³⁷Cs using conversion models. Airborne photogrammetry, terrestrial Structure-from-Motion 360-degree camera photogrammetry, and Terrestrial LiDAR Scanning (TLS) are used to model complex geomorphological features of forest terrain. These techniques are found to be convenient for the quantification of the existing state of erosion (length, width, volume, and density of the gully systems) in the forest gullies. The remote sensing techniques provided an insight into the physiographic characteristics of the studied area important for determining its parts affected by erosion. The models obtained using different techniques are compared, techniques are evaluated, and their advantages and disadvantages are pointed out. This approach enables the identification of priority targets for soil conservation and site-specific remediation measures to foster sustainable forest management.

This study assessed soil erosion intensity and soil properties across the Crveni Potok catchment in Serbia, a region of diverse morphology, geology, pedology, and vegetation. Soil samples were collected using a regular grid approach to identify the underlying factors contributing to erosion and the most vulnerable areas. Based on ¹³⁷Cs activities and the profile distribution (PD) model, severe erosion (>10 t ha⁻¹ y⁻¹) was predicted at nearly 60% of the studied locations. The highest mean erosion rates were detected for the lowest altitude range (300–450 m), Rendzic Leptosol soil, and grass-covered areas. A significant negative correlation was found between the erosion rates, soil organic matter, and indicators of soil structural stability (OC/clay ratio and S_t), indicating that the PD model successfully identifies vulnerable sites. The PD and RUSLE (revised universal soil loss equation) models provide relatively similar mean erosion rates (14.7 t ha⁻¹ y⁻¹ vs. 12.7 t ha⁻¹ y⁻¹) but significantly different median values (13.1 t ha⁻¹ y⁻¹ vs. 5.5 t ha⁻¹ y⁻¹). The model comparison revealed a positive trend. The observed inconsistencies were interpreted by the models’ spatiotemporal frameworks and RUSLE’s sensitivity to input data quality. Land use stands out as a significant factor modifying the variance of erosion rate, highlighting the importance of land management practices in mitigating erosion.

Among the most serious types of land degradation, soil erosion poses a major threat to agricultural productivity, water quality, and ecosystem stability. Using a multidisciplinary approach, this study aimed to identify the spatial patterns of soil erosion and dominant drivers influencing soil loss in the Toplica River Basin in southern Serbia. Soil properties, including texture and organic matter content, were analyzed in samples collected throughout the study area, accounting for variations in altitude, soil type, and land use, to determine the erodibility factor (K). The rainfall erosivity factor (R), topographic factor (LS), and cover management factor (C) were determined using available inputs on rainfall erosivity, topography, land use, and vegetation cover. The Revised Universal Soil Loss Equation (RUSLE) was used to estimate annual soil erosion rates, and GIS tools and cartographic techniques were used to create spatial layers for each RUSLE factor and to generate a detailed erosion risk map. The results showed a mean annual soil loss of 5.45 t ha⁻¹ year⁻¹, with values ranging from 0 to 397.09 t ha⁻¹ year⁻¹, indicating considerable spatial variability. The regression modeling revealed the dominant roles of factors LS (β = 0.828), C (β = 0.731), and their interaction (LS × C, β = 0.561), followed by rainfall-related interactions (R × C, β = 0.268 and R × LS, β = 0.261). Two dominant erosion regimes were distinguished: topography-controlled erosion in mountainous regions and land-use-controlled erosion in low- to moderately sloping agricultural areas. The maps and analyses presented in this study provide a process-based framework for interpreting spatial erosion patterns, identifying critical hotspots and areas with higher erosion risk, and supporting more focused and context-aware conservation strategies.

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Mapping the spatial distribution of fallout radionuclides in soil is essential for environmental assessments and evaluating potential future radiological risks, while also supporting the interpretation of spatial variability relevant to their use as soil redistribution tracers. However, model performance depends on data characteristics and spatial context, which limits the possibility of defining a universally optimal interpolation method. This study aimed to model the spatial distribution of ¹³⁷Cs at the national scale in Serbia by systematically applying and comparing 33 interpolation techniques, classified as deterministic or stochastic. A total of 193 soil samples, collected in accordance with International Atomic Energy Agency guidelines, were analyzed for ¹³⁷Cs activity using gamma spectrometry. The performance of interpolation techniques was evaluated through leave-one-out cross-validation (LOOCV) and an appropriateness index (AI) for comparison. Among the tested techniques, Empirical Bayesian Kriging Regression Prediction (EBKRP), which incorporates a digital elevation model (DEM) as an additional explanatory variable, achieved the best performance (AI = 0.8548). The resulting map of ¹³⁷Cs activity reveals a pronounced spatial gradient, with values ranging from below 22 Bq/kg to approximately 148 Bq/kg, reflecting the combined effects of initial ¹³⁷Cs deposition following the Chernobyl accident and subsequent long-term soil redistribution. Higher activities are restricted to the hilly and mountainous regions of western, eastern, and southeastern Serbia. Interpolation uncertainty increases in regions with elevated ¹³⁷Cs activities and should be accounted for in spatial interpretation.