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.

Traditionally in Hungary the soil cover under agricultural and forestry management is typically characterized independently and just approximately identically. Soil data collection is carried out and the databases of soil features are managed irrespectively. As a consequence, nationwide soil maps cannot be considered homogeneously predictive for soils of croplands and forests, plains and hilly/mountainous regions. In order to compile a national soil type map with harmonized legend as well as with spatially relatively homogeneous predictive power and accuracy, the authors unified their resources. Soil profile data originating from the two sources (agriculture and forestry) were cleaned up and harmonized according to a common soil type classification. Various methods were tested for the compilation of the target map: segmentation of a synthesized image consisting of the predictor variables, multi stage classification by Classification and Regression Trees, Random Forests and Artificial Neural Networks. Evaluation of the results showed that the object based, multi-level mapping approach performs significantly better than the simple classification techniques. A combination of best performing classifiers, when each classifier's vote on the same object is weighted according to its confidence in the voted class, led to the final product: a unified, national, soil type map with spatially consistent predictive capabilities.

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This paper analyses the recent recurring dieback and growth decline of Black pine (P. nigra Arn. var austriaca) in the Keszthely mountains of south-west Hungary, and their relations to water deficits due to droughts. These relations were studied in five stands with low soil water storage capacity for the period 1981-2016. The vitality was assessed using 60 tree-ring samples and changes in remotely sensed vegetation activity indices, i.e., the normalized difference vegetation index (NDVI) and the normalized difference infrared index (NDII). Water deficit was estimated by using meteorological drought indices such the standardized precipitation-evapotranspiration index (SPEI) and the forestry aridity index (FAI), as well as the relative extractable water (REW), calculated by the Brook90 hydrological model. Results revealed a strong dependency of annual tree ring width on the amount of water deficit as measured by all the above estimators, with the highest correlation shown by the summer REW. Droughts also showed a long-term superimposed effect on tree growth. NDII seemed to be more sensitive to drought conditions than NDVI. The robust dependency of tree growth on the summer water availability combined with the projected increasing aridity might lead to decreasing growth of Black pine in Hungary towards the end of the century. We thus argue that the suggestion by several papers that Black pine can be a possible substitute species in the Alpine and Mediterranean region in the future should be revisited.

Due to former soil surveys and mapping activities signifi cant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfi lled with these available datasets either by their direct usage or aft er certain specifi c and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project we would like to signifi cantly extend the potential, how soil information requirements could be satisfi ed in Hungary. We started to compile digital soil maps, which fulfi l optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. Regression kriging has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matt er content. Classifi cation and regression trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identifi ed and expressed in decision rules made the compilation of spatially refi ned category-type soil maps (like genetic soil type and soil productivity maps) possible with the aid of high resolution environmental auxiliary variables. In our paper, we give a short introduction to soil mapping and information management concentrating on the driving forces for the renewal of soil spatial data infrastructure provided by the framework of Digital Soil Mapping. The fi rst results of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project are presented in the form of brand new national and regional soil maps.

Illés, G., Kovács, G. and Heil, B. 2011. Comparing and evaluating digital soil mapping methods in a Hungarian forest reserve. Can. J. Soil Sci. 91: 615–626. To investigate applications of widespread digital soil mapping methods in forestry management, soil maps for a Hungarian forest reserve were developed using general discriminant and classification tree analysis as predictive tools. Soil samples were collected applying an unaligned semi-systematic grid. Second level units of the World Reference Base of Soil Resources and their yield capacity were determined. Terrain attributes were derived using a digital elevation model, and they were assigned to soil data to be used as predictors for second level units of the World Reference Base for Soil Resources (SLU) maps. A comparison was made of prediction accuracy. Both the discriminant analysis and the classification tree-based prediction were able to derive SLU maps; however, the classification accuracies were uneven. The methods used provided 63–65% average classification accuracy for dominant SLUs, but only 0–18% in the case of less common SLUs. One of the major issues of digital soil mapping that needs to be addressed is that the same inputs may result in different output maps depending on the use of spatial predictions. To overcome this problem we created a new combination of these methods in which the classification accuracies were used to select the most appropriate prediction. For each location, the method that gave higher prediction accuracy was used to extend the soil map to unknown areas. In this way we improved the overall accuracy of output maps as well as the prediction accuracies of individual SLUs.

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The understanding of spatial distribution patterns of native riparian tree species in Europe lacks accurate species distribution models (SDMs), since riparian forest habitats have a limited spatial extent and are strongly related to the associated watercourses, which needs to be represented in the environmental predictors. However, SDMs are urgently needed for adapting forest management to climate change, as well as for conservation and restoration of riparian forest ecosystems. For such an operative use, standard large-scale bioclimatic models alone are too coarse and frequently exclude relevant predictors. In this study, we compare a bioclimatic continent-wide model and a regional model based on climate, soil, and river data for central to south-eastern Europe, targeting seven riparian foundation species—Alnus glutinosa, Fraxinus angustifolia, F. excelsior, Populus nigra, Quercus robur, Ulmus laevis, and U. minor. The results emphasize the high importance of precise occurrence data and environmental predictors. Soil predictors were more important than bioclimatic variables, and river variables were partly of the same importance. In both models, five of the seven species were found to decrease in terms of future occurrence probability within the study area, whereas the results for two species were ambiguous. Nevertheless, both models predicted a dangerous loss of occurrence probability for economically and ecologically important tree species, likely leading to significant effects on forest composition and structure, as well as on provided ecosystem services.