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This scenario study is focused on the analysis and causal explanation of the effects of regional climate change on the productivity and water-use efficiency of a pure European beech Fagus sylvatica (L.) forest in western Germany. An ensemble of 9 regional climate model (RCM) simulation results based on the A1B emissions scenario from the ENSEMBLES project was used, along with local observations to drive the physiological forest growth model BALANCE for a 10 yr period. It simulates biomass increments, as well as productivity and efficiency, under different climatic conditions. Despite the RCM ensemble's large bandwidth, the results clearly indicate a decrease in total living biomass under future climate conditions compared to initial conditions and BALANCE control simulations. Net primary productivity was significantly reduced by 30% and water-use efficiency-though more uncertain-by 13% for end-of-the-century climate conditions compared to the control runs. In the A1B scenario, the lowered productivity and efficiency of the beech stand under future climate conditions are caused by higher mortality rates, lower water availability and higher drought stress, though partly counterbalanced by longer growing seasons.

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One of the most common applications of remote sensing in forestry is the production of thematic maps, depicting e.g. tree species or stand age, by means of image classification. Nevertheless, the absolute quantification of stand variables is even more essential for forest inventories. For both issues, satellite data are attractive for their large-area and up-to-date mapping capacities. This study followed two steps, and at first a supervised parametric classification was performed for a German test site based on a radiometrically corrected Landsat-5 TM scene. There, eight forest classes were identified with an overall accuracy of 87.5%. In the following, the study focused on the estimation of one key stand variable, the stem number per hectare (SN), which was carried out for a number of Norway spruce stands that had been clearly identified in the multispectral classification. For the estimation of SN, the approach of Linear Spectral Mixture Analysis (LSMA) was found to be clearly more effective than spectral indices. LSMA is based on the premise that measured reflectances can be linearly modelled from a set of so-called endmember spectra. In this study, the endmember sets were held variable to decompose pixel values to abundances of a vegetation, a background (soil, litter, bark) and a shade fraction. Forest structure determines the visible portions of these fractions, and therefore, a multiple regression using them as predictor variables provided the best SN estimates. LSMA allows a pixel-by-pixel quantification of SN for complete satellite images. This opens the view to exploit these data for an improved calibration of large-scale multi-parameter assessment strategies (e.g. statistical modelling or the kNN method for satellite data interpretation).