Recently, very high-resolution sensors as Light Detection and Ranging (LiDAR) data have permitted significant improvements in vegetation mapping in urban areas. This paper presents an evaluation of a new generation of airborne LIDAR bi-spectral discrete point (Optech titan) for mapping and characterizing urban vegetation. The methodology is based on a four-step approach: 1) the analysis of the quality of data in order to estimate noise between the green and near-infrared LIDAR point clouds; 2) this enables to remove the topographic effects and 3) a first classification, devoted to the elimination of the non-vegetation class, is performed based on the intensity value of the two channels; finally, in 4), the tree coverage is classified into seven categories of strata combination. To this end specific descriptors related to the organization of the point clouds are used.
These first results show that compared to monospectral LiDAR data, bi-spectral LiDAR enables to improve significantly both the extraction and the characterization of urban objects. This reveals new perspectives for mapping and characterizing urban patterns and other complex structures.