The urban vegetation is a principal biological component of the urban landscape. Identifying and mapping the urban vegetation are important to urban management and planning. This paper presents a new object-based two-stage method to classify urban vegetation using airborne LiDAR data and high resolution aerial photographs through a case study of downtown Houston

USA. By exploiting the spectral information plus 2D geometric attributes from high resolution aerial photographs and 3D morphological information from airborne LiDAR data

a detailed and accurate classification of urban vegetation has been achieved. In the first stage

the aerial photographs are segmented into image objects. Based on the spectral and 2D geometric attributes

these objects are divided into six categories: non-shaded vegetation

shaded vegetation

water

building

open space

and shade. Vegetation objects

including non-shaded and shaded vegetation

are derived separately. In the second stage

the normalized Digital Surface Model derived from airborne LiDAR data is introduced to characterize the 3D geometric properties (height and roughness) of each vegetation object. Based on these properties

the vegetation objects are further classified into trees

shrubs/hedges

and grass-covered lawns. The overall classification accuracy of vegetation is analyzed and reported as high as 93.46%. The sources of errors are ascribed to the shade in aerial photo and the miscalculation of Digital Terrain Model from LiDAR data. This research suggests that the combination of morphological information of LiDAR and the spectral information from image data renders a powerful tool for a detailed investigation of urban vegetation.