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Global climate change and sustained urban growth have increased the necessity of assessing the role that urban vegetation plays in urban dwellers' lives, as well as in urban ecosystem services. Urban environmental studies, however, still lack methods to characterize urban vegetation with adequate detail and across large areas. To remedy this gap, we apply a Support-Vector-Machine approach to classify eight frequent tree genera in the capital city of Berlin, Germany. We investigate different spectral and temporal band combinations of five RapidEye images acquired during the 2009 phenological season, and use ancillary surface and terrain models for orthorectification and improved tree masking. Results show that intra-annual time-series of RapidEye data can be used for high-precision tree genera classification in an urban environment. Differences within RapidEye time-series correlate well with empirical …