Abstract& Context Projections of species distribution models under future climate are usually based on long-term averages. However, singular extreme drought events presumably contribute to the shaping of distribution limits at the retreating low-elevation xeric limits.& Methods The objectives of this study were to set up a distribution model based on extreme drought events (EDM), which uses sanitary logging information as a proxy of vitality response of beech, and compare it with the results of classical species distribution models (SDMs).& Results Predictions of the EDM for 2025 were in agreement with those of the SDM, but EDM predicted a more serious decline in all regions of Hungary towards the end of the century.& Conclusion These results suggest that the predicted increase in frequency and severity of drought events may further limit the distribution of beech in the future.

Climate change particularly threatens the xeric limits of temperate-continental forests. In Hungary, annual temperatures have increased by 1.2 °C–1.8 °C in the last 30 years and the frequency of extreme droughts has grown. With the aim to gain stand-level prospects of sustainability, we have used local forest site variables to identify and project effects of recent and expected changes of climate. We have used a climatic descriptor (FAI index) to compare trends estimated from forest datasets with climatological projections; this is likely for the first time such a comparison has been made. Four independent approaches confirmed the near-linear decline of growth and vitality with increasing hot droughts in summer, using sessile oak as model species. The correlation between droughts and the expansion of pest and disease damages was also found to be significant. Projections of expected changes of main site factors predict a dramatic rise of future drought frequency and, consequently, a substantial shift of forest climate classes, especially at low elevation. Excess water-dependent lowland forests may lose supply from groundwater, which may change vegetation cover and soil development processes. The overall change of site conditions not only causes economic losses, but also challenges long-term sustainability of forest cover at the xeric limits.

Location, spread, abundance and density of forest regeneration are key factors in understanding forest dynamics as well as in operational management of uneven-aged stands. Simulation of forest growth, silviculture and planning of skid road networks require accurate and objective methods for locating forest regeneration. Terrestrial laser scanning has high potential for tree mapping, however, the development of automatic processing methods has been focused on mature trees so far. This study introduces an automatic procedure to locate individual trees with 3-6 meter height from terrestrial laser scanner data. The method has been validated on three sample quadrates representing different stand structures and it succeeded in detecting 79-90% of trees extracted manually from the point cloud. Out of the investigated stand features, stem density had the strongest impact on the performance, while branching intensity slightly affected the detection rate. The results highlight that terrestrial laser scanning has the ability for the quantitative evaluation of regeneration, providing a prospective tool for surveying forests of contiguous cover.

Geographic Information System (GIS) uses geospatial databases as a model of the real world. Since we are speaking of the real world this entails that in many cases the information about the Earth’s surface is highly important. Therefore, the generation of a surface model is significant. Basically, the quality of the Digital Elevation Model (DEM) depends on the source data or techniques used to obtain them. However, different spatial interpolation methods used for the same data may provide different results. This paper compares the accuracy of different spatial interpolation methods such as IDW, Kriging, Natural Neighbor and Spline. Since interpolation is essential in DEM generation, then is important to do a comparative analysis of such methods to find out which one provides more accurate results. The DEM data set used is from an aero photogrammetric surveying. According to this data set, three scenarios are performed for each of the methods. Selected random control points are derived from the base data set. The first example includes 10% of randomly selected control points, the second example includes 20%, and the third example includes 30%. The Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) are calculated. We find out that results do not have much difference; however, the most accurate results are derived from the Spline and Kriging interpolation methods.

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