Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.

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We produced the first spatially explicit, cross-border, digital map of long-term (160 years) land use in the Carpathian Ecoregion, the Hungarian part of the Pannonian plains and the historical region of Moravia in the Czech Republic. We mapped land use in a regular 2 × 2 km point grid. Our dataset comprises of 91,310 points covering 365,240 km 2 in seven countries (Czechia,

Flooding is a major environmental hazard in Poland with risks that are likely to increase in the future. Land use and land cover (LULC) have a strong influencing on flood risk. In the Polish Carpathians, the two main projected land use change processes are forest expansion and urbanization. These processes have a contradictory impact on flood risk, which makes the future impact of LULC changes on flooding in the Carpathians hard to estimate. In this paper, we investigate the impact of the projected LULC changes on future flood risk in the Polish Carpathians for the test area of Ropa river basin. We used three models of spatially explicit future LULC scenarios for the year 2060. We conduct hydrological simulations for the current state and for the three projected land use scenarios (trend extrapolation, 'liberalization' and 'self-sufficiency'). In addition, we calculated the amount of flood-related monetary losses, based on the current flood plain area and both actual and projected land use maps under each of the three scenarios. The results show that in the Ropa river, depending on scenario, either peak discharge decreases due to the forest expansion or the peak discharge remains constant-the impact of LULC changes on the hydrology of such mountainous basins is relatively low. However, the peak discharges are very diverse across sub-catchments within the modeling area. Despite the overall decrease of peak discharge, there are areas of flow increase and there is a substantial projected increase in flood-related monetary losses within the already flood-prone areas, related to the projected degree of urbanization.