This article proposes a three-dimensional (3D) vegetation extraction workflow to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. Results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

Article describes the efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. One major limitation of the traditional methods of remote sensing to map canopy gaps is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. The authors proposed an object-oriented classification method that integrates multi-source information.