This research presents an approach for developing and applying automated mapping routines for mapping biophysical properties of riparian zones from high spatial resolution airborne multi-spectral UltraCam-D image data and LiDAR data. Object-oriented image analysis has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual information. This functionality increases the likelihood of generalizing classification rules, which may lead to the development of automated mapping approaches. The biophysical properties assessed in this work included riparian zone width and stream bank stability. The UltraCam-D and LiDAR data were captured in April 2008 and May 2005 respectively, covering parts of the Werribee Catchment in Victoria, Australia. Field data of streamside vegetation structure and physical form properties were obtained in April 2008. The field data were used both for calibration of the mapping routines and to validate the mapping results. The approach developed separates object-oriented mapping routines for individual subsets representing different riparian zone environments, i.e. urbanised areas, agricultural areas, and hilly forested areas. The mapping routines for mapping riparian zone properties were compared and merged to enable application of a single rule set for mapping biophysical properties within the three riparian zone environments. Results show the capacity to automate the process of mapping riparian zone with and stream bank stability from high spatial resolution airborne multi-spectral UltraCam-D image data and LiDAR data using object-oriented image analysis. This work lays the foundation for automatic feature extraction of biophysical properties of riparian zones to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies.