The detection and evaluation of changes in vegetation patterns is a prerequisite for monitoring programs. The Swiss mire monitoring program aims to assess the changes in mire vegetation in order to examine the efficiency of the management measures. A promising way to explore and detect vegetation structure and vegetation change is the application of predictive vegetation mapping that combines image classification and predictive habitat distribution models. These models deal with predictor variables derived from remotely sensed spectral data and from environmental variables such as a digital surface model (DSM). Low accuracy of environmental data to predict vegetation at the local scale is due to the difficulties to capture dominant fine-scale enironmental gradients. Using high resolution spectral and topographical data sets of 50 cm pixel size and below, the study presented here aims to improve the simulation of local-scale vegetation properties. The spectral data for fine-scale modelling are based on CIR orthoimages with a ground resolution of 32 cm. Various spectral variables and spectral-textural variables were derived for the modelling process. A new method to reduce the number of predictor variables, the composite modelling is presented in this paper. In comparison to existing methods, composite modelling has the advantage of being independent of the scale of the predictor variables, and at the same time being transferable among various data sets. Mean indicator values for moisture, nutrients and light derived from vegetation data are used as response variables. Results show that the topographical variables based on relief features are less powerful predictors than the spectral variables but that combining them enhances the overall predictive power. Stratification of the data according to the tree layer and the shadow areas increases the accuracy of the model.

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Mires are highly threatened ecosystems in the lowlands of Central Europe. Reduced water levels and eutrophication promote shrub encroachment and the expansion of tall species, such as common reed (Phragmites australis). In the ''Burgmoos'', a Swiss mire of national importance, attempts have been made to reverse these developments through cattle grazing in parts of the mire area. To monitor overall vegetation change and to assess the influence of grazing (which started in 2004), the vegetation was surveyed in 1995, 2001 and 2007. Ecological indicator values of the vegetation changed considerably between 1995 and 2007: mean indicator values for nutrients and soil pH increased in 80 and 72% of the relevés, respectively, while mean indicator values for moisture, humus and light decreased in 81, 86 and 76% of the relevés, respectively. Plant species from bogs, transition mires and fens decreased, while trees, pasture species and P. australis increased. Grazing had a weak effect on P. australis and did not prevent an increase in abundance of this species. The abundance of transition mire species was maintained in the grazed area between 2001 and 2007, whereas it continued to decrease in the ungrazed areas. This positive effect of grazing was, however, compensated by several adverse effects: In the non-forested parts of the mire, grazing accelerated the increase of nutrient indicator values, the decrease of bog species and the increase of pasture species. We conclude that grazing has not been effective in preventing undesirable vegetation changes in the Burgmoos.

Aims: Resurveys of vegetation plots are prone to several errors that can result in misleading conclusions. Minimizing such errors and finding alternative approaches for analyzing resurvey data are therefore important. We focused on inter-observer error and excluded other sources of variation. Our main questions were: How large is the inter-observer error (i.e. pseudoturnover) in vegetation surveys, and can it be reduced by simple data aggregation approaches? Which factors are affecting pseudoturnover and does it vary between morphological species groups or change over time? Is ecological inference robust against inter-observer differences? Location: Switzerland. Methods:Over seven years, we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover.Results: Average pseudoturnover was 29% when raw species lists were compared. However, by applying simple aggregation steps to the species list, pseudoturnover was reduced to 17%. Pseudoturnover further varied among habitat types and declined over the years, indicating a training effect among observers. Most overlooked taxa, responsible for pseudoturnover, had low cover values. Mean ecological indicator values were robust against inter-observer differences. Conclusions:To minimize pseudoturnover, we suggest continuous training of observers and species-list aggregation prior to analysis. As mean ecological indicator values were robust against inter-observer differences, we conclude that they can provide a reliable estimate of temporal vegetation and ecological changes.