The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 995–1000, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-995-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 995–1000, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-995-2020

  21 Aug 2020

21 Aug 2020

APPLICATION OF 3D TREE MODELING USING POINT CLOUD DATA BY TERRESTRIAL LASER SCANNER

R. Kumazaki and Y. Kunii R. Kumazaki and Y. Kunii
  • Department of Landscape Architecture Science, Tokyo University of Agriculture, 1-1-1 Sakuragaoka, Setagaya, Tokyo, 156-8502, Japan

Keywords: Japanese Garden, tree point cloud data, 3D tree model, TLS-QSM method, reproducibility

Abstract. Constructing 3D models for trees such as those found in Japanese gardens, in which many species exist, requires the generation of tree shapes that combine the characteristics of the tree's species and natural diversity. Therefore, this study proposes a method for constructing a 3D tree model with highly-accurate tree shape reproducibility from tree point cloud data acquired by TLS. As a method, we attempted to construct a 3D tree model using the TreeQSM, which is open source for TLS-QSM method. However, in TreeQSM, since processing is based on the assumption that the tree point cloud consists of data related to trunks and branches, measuring trees in which leaves have fallen is recommended. To solve this problem, we proposed an efficient classification process that mainly uses thresholds for deviation and reflectance, which are the adjunct data of the object that can be acquired by laser measurement. Furthermore, to verify accuracy of the model, position coordinates from the constructed 3D tree model were extracted. The extracted coordinates were compared with the those of the tree point cloud data to clarify the extent to which the 3D tree model was constructed from the tree point cloud data. As a result, the 3D tree model was constructed within the standard deviation of 0.016 m from the tree point cloud data. Therefore, the reproducibility of the tree shape by the TLS-QSM method was also effective in terms of accuracy.