With increased interest in the environmental, psychological and social benefits provided by urban forests, the need for accurate and cost-effective methods for monitoring tree condition within an urban landscape is becoming critical. Light Detection and Ranging (LiDAR) has been used as an efficient tool for measuring tree and forest stand structure in commercial forestry applications for more than a decade, however its application in urban forestry remains nascent. In this paper, we present an approach to detect and delineate individual trees from high density discrete return LiDAR data in an urban context. To do so, the approach exploits tree inventories maintained by city managers to overcome the unique challenges presented by an urban forest, such as a broad range of tree species both native and exotic and age classes. Using tree inventory data to “seed” automated detection and delineation processes, we are able to detect 88.3% of a set of reference trees, and achieve an average similarity ratio of 0.66 between the automatically-delineated and reference crown outlines, with a ratio of 1 indicating a perfect match. By accurately delineating tree crowns, various tree metrics can be extracted from the LiDAR point cloud, which can be used to create maps of tree condition across the city for use in management and monitoring activities.