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.

Site index is the most commonly used measure of potential site productivity, providing key information for forest management practices. It is determined using species-specific site index models that take into account climatic and edaphic factors. To reliably estimate the site index, appropriate models are necessary. In Poland, however, outdated guidelines, i.e., yield tables, are used to determine site classes, which result in the inappropriate estimation of height growth and increments of stands. Therefore, the aim of this study is to develop new site index models for the main forest forming tree species in Poland, in a total of eight species. For the development of site index models, we used growth trajectories of 3052 sample trees, representing the whole range of geographic locations and site conditions. Five dynamic models were selected and parametrized to develop the site index models. The models were evaluated using quantitative measures of goodness of fit (MAE, R2, and AIC), the analysis of residuals, and the assessment of how the model reflects the biological phenomena of height growth. Results showed that depending on the species, different models have the highest predictive ability. There are significant differences in results using traditional yield tables and developed site index models. For most of the species, the largest differences characterized either the youngest or the oldest age classes. These differences can be attributed to the changes in growth conditions from the time when yield tables were developed. Growth dynamics of forest stands may also show spatial variability, thus, in future research additional site variables and, regional variability should be taken into account.In recent years, forest ecosystems in Europe have experienced changes due to both the mean climate variables and the higher risk of extreme weather events [14]. Therefore, for effective long-term planning, forest management has to take into account the impact of climate change on stand growth and site productivity. In general, recent climate changes seem to have had a positive impact on forest productivity, although, this may vary at regional scales [15]. In northern and western Europe, a positive effect on forest growth and wood production is expected [14]. The site index considers the impact of climate conditions, expressing how the climatic and edaphic factors influence site productivity. Therefore, it is frequently used to analyze how environmental changes affect forest ecosystems [16][17][18][19][20]. Climate-sensitive site productivity models provide key information to develop forest management guidelines for adaptation steps and apply mitigation strategies against changing climatic conditions [21]. Site index models also play a crucial role in estimating the dynamics of forest carbon pools. According to the good practice guidance (GPG) of the Intergovernmental Panel on Climate Change (IPCC), the past and future dynamics of forest carbon pools should be calculated using stand volume. Stand volume is most commonly es...

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AbstractThis paper deals with design characteristics-oriented approach to architectural design based on the combination of three methods – recognition, generation, and evaluation. Design characteristics are understood as a set of specific features which constitute a discriminant of a class of architectural forms. The Biederman recognition-by-components theory is used to recognize the design structure. An evolutionary algorithm, which serves as a generative tool, is driven by the fuzzy evaluation based on Birkhoff's aesthetic measure. Phenotypes of architectural objects are seen as configurations of Biederman's basic components essential for visual perception. Genotypes of these objects are represented by graphs with bonds, where nodes represent object components, node bonds represent component surfaces, while graph edges represent relations between surfaces. Graph evolutionary operators, that is, crossover and mutation, are defined in such a way that they preserve characteristic features seen as design requirements specified for designed objects. The fitness function is determined by the fuzzy evaluation of designs based on Birkhoff's aesthetic measure for polygons adapted for three-dimensional solids. The approach is illustrated by examples of designing objects with the use of a fuzzy evaluation mechanism, which takes into account both aesthetic criteria and the degree to which design requirements corresponding to object characteristic features are satisfied.