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There are many kinds of landscape materials in Oze national park. Visitors of the Oze can be seen such materials as sequence landscapes due to walking on boardwalks. Some subjective evaluations of the landscape at individual positions were performed by image survey. Therefore, in order to extract interesting position with characteristic landscape, sequence landscape in Oze should be also evaluated. Moreover, the landscapes during the walking are analyzed continuously. On the other hand, landscape analysis is needed for some viewpoints which are not only subjective but also objective understanding. Particularly, in order to acquire stable data for landscape, acquisition of quantitative data is useful. Therefore, relation between such quantitative data and subjective data is concretized, and more reliable landscape analysis is become possible. As the method of quantitative data acquisition for landscape, fractal analysis is adopted by several situations. Relation between such fractal dimension and the subjective evaluation by human are analyzed, and it is expected that the fractal analysis can be proposed as an objective evaluation method for landscape. In these circumstances, the fractal analysis and image survey for sequence landscape were performed by using image sequences, and relation between results of both investigations in this paper.

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.

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