A novel technique for tree stem height estimation by fusing low density LiDAR data and optical images

Light detection and ranging (LiDAR) is one of the most efficient remote sensing technologies for the estimation of forest parameters. However, when acquired with a low laser sampling density, LiDAR data fail in providing accurate tree height measures. In order to address this issue, in this paper we propose a novel technique for the reconstruction of tree-top height based on the joint use of low-density LiDAR data and high resolution optical images. The proposed method is based on the following steps: i) detection of all the tree crowns present in the scene by fusing the two remotely sensed data sources; ii) reconstruction of the tree-top height for those crown hit by at least one LiDAR point; iii) estimation of the tree-top height for those crowns without LiDAR points. The proposed technique has been tested on a coniferous forest located in the Italian Alps. The experimental results points out the effectiveness of the proposed method.