The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 297–302, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-297-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 297–302, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-297-2022
 
30 May 2022
30 May 2022

COMPREHENSIVE QUANTITATIVE UNDERSTANDING OF THE LANDSCAPE USING TLS POINT CLOUD DATA

R. Tachikawa1 and Y. Kunii2 R. Tachikawa and Y. Kunii
  • 1Department of Landscape Architecture Science, Graduate School of Tokyo University of Agriculture 1-1-1 Sakuragaoka, Setagaya-ku, Tokyo, 156-8502, Japan
  • 2Department of Landscape Architecture Science, Tokyo University of Agriculture 1-1-1 Sakuragaoka, Setagaya-ku, Tokyo, 156- 8502, Japan

Keywords: Terrestrial Laser scanner, Point Cloud data, Landscape evaluation, VQM, Quantification, Sequence

Abstract. Landscape spaces such as gardens and parks are composed of various landscape components, creating diverse landscapes. In general, the quality of the landscape in these spaces is often judged subjectively by visitors. On the other hand, if landscapes can be evaluated objectively, they can be used to create better spaces in the management and creation of landscaped spaces. In recent years, point cloud data has been acquired in urban and natural spaces. In landscaped spaces, point cloud data is increasingly used for landscape simulation and current state planning. In this study, point cloud data acquired with a terrestrial laser scanner (TLS) in the target space were used to quantitatively characterize the entire landscape using fractal analysis and visual and ecological environmental quality models (VQM). We also segmented these data into components of the point cloud data and calculated the relationship between the data and the occupancy of the components. On the other hand, focusing on environmental visual information received passively from a wide range of environments, we conducted an analysis based on panoramic images created from point cloud data. As a result, both fractal analysis and VQM showed a high correlation with previous research methods in understanding the landscape using point cloud data. In addition, the analysis of the landscape was made more efficient than the conventional photographic analysis by segmenting the components in advance at the data processing stage, demonstrating the usefulness of landscape analysis from data acquired by laser scanners.