Forests play a significant role in the global biogeochemical and -physical cycles and particularly the complex three-dimensional forest canopy structure influences the fluxes of energy and matter between the atmosphere and forests. Assessing this structure quantitatively using conventional fieldwork or traditional remote sensing methods is difficult, whereas airborne laser scanning (ALS) systems have proven to be suitable for providing explicit vertical information for large areas. However, most existing ALS based approaches include manual processing steps or need additional data about stand characteristics. To solve these issues, a robust and automatic multi-dimensional clustering method was developed to derive forest canopy structure types (CSTs) based on full-waveform ALS data. The results show that it is possible to develop an automatic, self-sustained and transferable method for: the extraction of CSTs without any previous knowledge about the forest stand; and the extraction of bio-physical parameters based on the resulting CSTs.