Generating the accurate and real time information on the position of urban objects is essential for the management, planning, and automation of three-dimensional modeling of urban lands. Trees and low altitude vegetation cover (shrubs and meadows) are the most important urban objects because they play an important role in sustainable urban planning and development and environmental management and affect the urban temperature, air quality and noise levels in the urban environment. For this reason, in recent decades, identification and detection of trees low altitude vegetation cover in urban areas using remote sensing data has become one of the important research. So, in this research, a method is presented to identify trees and low altitude vegetation cover from aerial images with high spatial resolution and aerial laser scanning data. For this purpose, the first Orthorectified images of the three study areas were generated from aerial imagery and the noise in the LiDAR data was identified and eliminated. Then, Digital Elevation Model (DEM) is generated using a developed method based on the Scan Labeling Algorithm (SLA). In addition, normalized Digital Surface Model (nDSM) has been obtained by differentiating the Digital Elevation Model (DEM) from the Digital Surface Model (DSM). In the following, high and low altitude areas of the study areas have been identified by thresholding on the normalized Digital Surface Model (nDSM). Then, an Enriched Vegetation Index (EVI) in shadow areas was produced from aerial image to separate vegetation and non- vegetation areas. Eventually, trees and low altitude vegetation cover identified by overlapping the vegetation areas with high and low altitude areas, respectively. In this research, detected trees and low altitude vegetation areas evaluated by Working Group IV, Commission III of International Society for Photogrammetry and Remote Sensing (ISPRS-WGIII/4). In this study, average pixel-based completeness, correctness and quality metrics in three study areas for detected trees are 74.00%, 63.50% and 52.10%. The mentioned average metrics for detected low-altitude vegetation cover are 58.00%, 69.40%, 46.30%. The evaluation results indicates that average object-based quality metric for detected trees has highest value with respect to other methods which introduced by other researchers. Also, average pixel-based and object based completeness, correctness and quality metrics for detected trees and low altitude vegetation metrics have acceptable level than other introduced methods.