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Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method's details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus' district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels. [ABSTRACT FROM AUTHOR]

Linking vegetation, soil biota, and soil carbon stocks in forests has a high predictive value. The specific aim of this study was to identify the relationships between vegetation, earthworms, and soil carbon stocks in nine types of forests dominating autonomous landscape positions in a coniferous–broadleaf forest zone of the European part of Russia. Mountain forests were selected in the Northwest Caucasus, while plain forests were selected in Bryansk Polesie and on the Moskva-Oka plain. One-way analysis of variance (ANOVA) and v-tests were used to assess the impact of different factors on soil C stocks. To assess the contribution of vegetation, litter quality, and earthworms to variation of carbon stocks in organic (FH-layer) and mineral layer (0–50 cm), the method of hierarchical partitioning was performed. The highest C stocks in the organic horizons were associated with the low-quality litter, i.e., with a low base saturation, high acidity, and wide C/N ratio. The highest soil C stocks in the mineral layers were found in mixed forests with the highest richness of plant species, producing litterfall of different quality. The C stock in the organic horizon was negatively related to the biomass of worms that process the litter, while the carbon stock in the mineral layers was positively related to the biomass of worms whose life activity is related to the mineral layers. These findings demonstrated the substantial influence of plants producing a litter of different quality, and of earthworms, belonging to different functional groups, on soil C stocks in coniferous–broadleaf forests. [ABSTRACT FROM AUTHOR]