Target-free ULS-TLS point-cloud registration for alpine forest lands

Abstract

Modern laser scanning techniques have been commonly used for forest land studies. Complementary scanning capabilities can be particularly obtained using unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) technologies. The registration of the ULS and TLS data can thus lead to more comprehensive data acquisition and information extraction in forest lands. However, this registration process is typically hindered by problems of data density and inconsistency of the scanned forest canopy shape. In this paper, we propose a tree-height registration (TR) method for ULS-TLS point-cloud registration, and apply this method to the alpine forest land of the Shangri-La City of the Northwestern Yunnan Province in China. The tree height points are obtained from a digital surface model (DSM), which contains isosurface points of the forest structures. Rotation and translation matrices are then calculated through singular value decomposition (SVD), and rough registration is completed. Finally, fine registration is achieved through nearest-neighbor iterative SVD. The results show that the proposed method can effectively register ULS and TLS forest data samples, with an average accuracy of 0.43 m. The method has an average running time of less than 21 s through 3m-by-3m window size to search tree height point for registration, and also shows good applicability in forest lands with slopes in the range of 2-18°. Moreover, the best outcomes were obtained for a 3m-by-3m window size.