Modelling the spatial distribution of montane and subalpine forests in the central Alps using digital elevation models

The analysis of complex interactions between spatial distribution patterns of site factors and vegetation types is crucial for understanding high mountain ecosystems, especially in the view of a changing climate. Therefore, in the present study, a GIS and remote sensing-based approach is followed to produce a vegetation map for a study area in the Western Alps (Switzerland). Two major forest alliances are chosen for analysis: subalpine coniferous forest Vaccinio-Piceion/Larici-Pinetum cembrae and montane oak forest Quercion pubescenti-petraeae. As spatial information on site factors is commonly lacking in mountain areas, the use of a digital elevation model (DEM) is a potential substitute for use in vegetation analyses: it highly correlates with temperature, moisture, geomorphological processes and disturbance factors. Thus, it is important to analyse the capabilities of a DEM for indicating habitat conditions in a landscape characterised by high topodiversity and a patchwork of microclimatic habitats. e purpose of identifying the potential of landform parameters for the indication of forest habitat structures in the present study, 24 primary and secondary landform parameters have been derived, indicating temperature and moisture distribution, exposure towards wind, snow, etc. Quantitative analyses were performed using statistical means such as contingency correlation coefficients and principal components analysis. The results formed the basis for the development of parallel-epiped-vegetation models (PED) used to simulate the spatial distribution patterns of the subalpine coniferous and the montane oak forest. It can be shown that topographic variables derived from a DEM at a spatial resolution of 25 m are very useful for indicating habitats of large forest types. Additionally potential forest sites in the cultural landscape, removed by human logging, can be reconstructed. racies within the simulation results can partly be attributed to the insufficient parameterisation of geomorphologic activity and to poor spatial resolution of the DEM as compared to the vegetation data. Although the lack of information on the human dimension leads to some uncertainties in the interpretation of spatial patterns of vegetation, the exclusive use of topographic variables in vegetation models for the indication of forest habitats is very promising.