The paper introduces a method of digital mapping of spatial distribution of soil typological units. It implements fuzzy k-means to classify the soil profile data (study area from the Považský Inovec Mountains, Slovakia) and regression-kriging with the selected digital terrain and remote sensing data to draw membership maps of soil typological units. Totally three soil typological units were identified: Haplic Cambisols (Skeletic, Dystric), Albic Stagnic Luvisols, and Haplic Stagnosols (Albic, Dystric). We analysed the membership values to these units with respect to terrain and remote sensing data. The membership values appeared as spatially smoothly dependant on the terrain gradients (linearly or exponentially) whereas the residua showed spatial autocorrelation. Based on regression and kriging analyses, the regression-kriging model was successfully deployed to draw raster membership maps. These maps yield coefficients of determination between R<sup>2</sup> = 56% (Albic Stagnic Luvisols) to R<sup>2</sup>= 79% (Haplic Cambisols (Skeletic, Dystric)) when evaluated by cross validation. The grid-based continuous soil map represents an alternative to the classical polygon soil maps and can offer a wide range of interpretations for landscape studies.
A fine-scaled approach for predicting soil acidity using plant species in a spatially limited area (Čepúšky Nature Reserve, Slovakia) is presented here. This approach copes with some specific limitations: i) a limited pool of vegetation data may make the predictions too sensitive to the lack of species information, and ii) the predictions may be sensitive to the narrow pH gradient. Vegetation relevés and soil reaction (pH-H 2 O and pH-CaCl 2 ) were systematically recorded. A set of species indicator values and amplitudes was calibrated with physical pH data using the Weighted Averaging (WA), HOF modelling and Non-Metric Multidimensional Scaling (NMDS) methods, along with Ellenberg indicator values. Two prediction methods were tested: i) WA and ii) Amplitude Overlap (AO). WA prediction with Ellenberg's and WA-calibrated species indicator values were the most powerful technique (R 2 =68.4-68.7% and 53.4-59.1% for pH-CaCl 2 and pH-H 2 O, respectively). WA-prediction with HOF-based indicator values was less effective (R 2 =61.7% and 50.7%) due to the decrease in species' information because with HOF modelling many species are assumed indifferent or too rare. The NMDS method does not bring any significant gain to the calibration, though it avoids the lack of species information. The AO method was proven to be less powerful under studied circumstances, because it is sensitive both to the lack of species' information and to the truncation of species responses. The results prove that a spatially explicit approach can provide significant indices to estimate changes in soil acidity -pHCaCl 2 better than pH-H 2 O.
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