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This article presents several case studies in southwest Germany, which aimed to support land use management decisions by a process-oriented statistical upscaling of point-related environmental monitoring data to the landscape scale. When techniques of data subsetting were used in a sensible way and corresponding to the appropriate scale for the evaluation envisaged, multiple linear regression offered a data mining technique which was able to spatially predict relatively complex environmental patterns with parsimonious, interpretable and accurate models, whereby different evaluation scales were best represented by different DTM resolutions. Scenario models based upon the regression formulas were a valuable tool for visualizing management options and evaluating management impacts (tree species selection) on soil functions (carbon storage), which qualifies the presented methodology as a useful aid in decision making. Such upscaling techniques may be used for forecasting long-term effects of ecosystem management, but they provided no information on temporal dynamics. Therefore, time trends of point information on soil solution data were scaled by linking them to soil chemical data which was available in higher spatial resolution, using both statistical and process-oriented methods.

The article presents results of a case study in northeastern Germany, where magnetic susceptibility assessment was carried out at grid-wise field measurements. The measurements were clustered into three different depth levels, which represent the humus layer, the transition zone between humus layer and mineral horizon, and the mineral horizon. Taking these three depth levels, a multiple regression-based regionalization approach was applied, testing and using additional environmental parameters derived from geology, topography, and stand type with the aim to develop a comprehensive model for spatial variability of magnetic susceptibility. Spatial variation of magnetic susceptibility was predicted with a high precision by the multiple linear regression models. A slightly differing set of model parameters was selected for the single depth levels. In tendency, magnetic susceptibility values in depth level 6-10 cm were best explained by the distance to Bitterfeld and by soil properties. In depth level 11-15 cm, variables which describe the orographic conditions and stand properties gain in importance. In depth level 21-25 cm, variables indicating soil and site properties disappear completely. Here, aspect and land surface characteristics play a major role together with stand properties. A spatial stratification of the model for a distance of up to 25 km to the former emitters provided a further improvement of the model quality considering the prediction of small-scale variations of magnetic susceptibility.