Reconstructing Stem Cross Section Shapes From Terrestrial Laser Scanning

: Wang, Di; Kankare, Ville; Puttonen, Eetu; Hollaus, Markus; Pfeifer, Norbert

Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

: PISCATAWAY

: 2017

: IEEE Geoscience and Remote Sensing Letters

: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

: IEEE GEOSCI REMOTE S

: 14

: 2

: 272

: 276

: 5

: 1545-598X

: 1558-0571

DOI: https://doi.org/10.1109/LGRS.2016.2638738

Terrestrial laser scanning (TLS) is an effective tool for retrieving forest attributes. For example, stem diameters can be estimated from the TLS point cloud by applying automatic algorithms, which often approximate the stem cross section by using a circle or a cylinder. However, the cross section of a tree stem is never exactly a circle. Moreover, the cross section provides other economically important attributes related to, for example, the wood quality and growth environment. Thus, advanced curve fitting and other geometric fitting methods should be explored further. In this letter, a Fourier series curve approximation approach is proposed for modeling stem cross section shapes. The routine uses iterative Fourier series approximation in polar coordinates to remove gross errors. Three different diameter approximations are tested: circle fitting, Fourier series fitting, and combined Fourier series and circle fitting. The proposed approach is tested for approximating the diameter at breast height (DBH) with the use of two data sets: the first from an Alpine mixed and landslide-affected forest with multiscan TLS, and the second from a mature Scots pine forest in Finland with single-scan TLS. The results showed that for the multiscan data, the use of the combined Fourier series and circle fitting improved the root mean square error of DBH by 12.4% compared with direct circle fitting. The DBH accuracy for the single-scan data resulted in similar accuracy compared with that of the circle fitting. The results imply that the new approach is able to accurately reconstruct stem cross sections, especially for multiscan data.