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.

Reforestation on nonforest lands (including abandoned agricultural lands) in Smolenskoe Poozerye National Park has been assessed from summer Landsat image series taken in 1984 to 2016. The boundaries...

The research shows the possibility of identifying a key biotope of boreal floodplain meadows in the Smolenskoe Poozerye National Park on the basis of spectral and topographic characteristics of the territory. The key biotope E3.47 according to EUNIS is a meadow along large rivers with areas of calm flow; it freeze every winter and affected by flooding in spring. This category includes areas that are open or poorly overgrown with woody and shrubby vegetation. Sentinel-2 multispectral satellite images (ESA Sentinel-2, 2019) are used to determine areas of floodplain meadows. The geospatial assessment of boreal floodplain meadows is carried out using the method of uncontrolled classification of “K-means” of ten spectral channels of Sentinel-2 satellite images obtained over six seasons, including the flood period. As a result of cluster analysis, classes that are spectrally close to meadow communities are selected. These classes located along flood-plains of rivers, with the elevation difference from the water line not exceeding 5 m. This elevation corresponds to the maximum possible rise of water in rivers during a flood for the studied area. A topographic index using a digital elevation model (DEM) and a hydrographic terrain network are used to correct the selected classes. The accuracy of recognition of the floodplain meadows class is determined using a matrix of error in 133 segments along the 4 transects located in floodplain and dry meadow communities. The convergence of result between the floodplain meadows classification and ground surveys data was 75% (Cohen’s kappa equals 0.53).