This article discusses how urban tree canopy cover is often mapped by classifying high-resolution multispectral imagery. However, it can be difficult to differentiate low-lying vegetation from tree cover using optical data alone. Combining a lidar-derived normalized digital surface model (NDSM) improves classification accuracy, but the optical imagery is often imperfectly aligned with the NDSM. Aerial imagery is normally orthorectified using the ground elevation. However, tall objects in the orthorectified imagery still suffer from relief displacement and this can cause classification errors when lidar and the aerial imagery are combined. This article presents an approach for urban tree cover mapping that is composed of two parts. The first part is a method for correcting the relief displacement of trees in previously orthrectified aerial imagery, and the second part is an object-based classification method that combines relief-corrected multispectral aerial imagery with a lidar-derived NDSM. Using these methods, tree cover was mapped for a 1,600 ha region of London, Ontario, Canada with improved positional and classification accuracy.