The objective of this paper is to spatially predict tree/shrub genera using generalized linear models (GLM), color-infrared (CIR) aerial images, ADS40 images, digital surface models (DSM)s and field samples. The present study was carried out in the framework of the Swiss Mire Protection Program, where extraction of forest parameters for description of present state of a mire ecosystem and as indicators for changes are of high importance. In a first step, high-quality DSMs were automatically generated from CIR aerial images for two test sites, both located in the Pre-alpine zone of Central Switzerland. In a second step, tree layers were then generated combining canopy height models derived from the DSMs and LWAR DTM with a fuzzy classification of CIR aerial images. In a third step, on the basis of these tree layers, fractional tree/shrub covers were generated using explanatory variables derived from the DSMs and logistic regression models. Then tree genera were predicted for the pixel values (tree/shrub probability > 0.3) of the fractional covers using a multinomial regression model and additional spectral information as provided by Leica ADS40 data for one test site and CIR aerial images for the other test site. Overall, prediction of tree genera was less satisfactory when only using CIR aerial images. In contrary, up to six different tree genera were predicted with high accuracy using explanatory variables derived from ADS40 images. The study stresses the importance of high-resolution and high-quality DSMs and highlights the potential airborne remotes sensing data for ecological modeling purposes.