| Title |
Reconfiguration of Physical Structure of Vegetation by Voxelization Based on 3D Point Clouds |
| Authors |
안명희(Ahn, Myeonghui) ; 장은경(Jang, Eun-kyung) ; 배인혁(Bae, Inhyeok) ; 지운(Ji, Un) |
| DOI |
https://doi.org/10.12652/Ksce.2020.40.6.0571 |
| Keywords |
3D 지상 레이저 스캔;복셀;식생;재구현;포인트 클라우드 3D terrestrial laser scan;Point cloud;Reconfiguration;Vegetation;Voxel |
| Abstract |
Vegetation affects water level change and flow resistance in rivers and impacts waterway ecosystems as a whole. Therefore, it is important to have accurate information about the species, shape, and size of any river vegetation. However, it is not easy to collect full vegetation data on-site, so recent studies have attempted to obtain large amounts of vegetation data using terrestrial laser scanning (TLS). Also, due to the complex shape of vegetation, it is not easy to obtain accurate information about the canopy area, and there are limitations due to a complex range of variables. Therefore, the physical structure of vegetation was analyzed in this study by reconfiguring high-resolution point cloud data collected through 3-dimensional terrestrial laser scanning (3D TLS) in a voxel. Each physical structure was analyzed under three different conditions: a simple vegetation formation without leaves, a complete formation with leaves, and a patch-scale vegetation formation. In the raw data, the outlier and unnecessary data were filtered and removed by Statistical Outlier Removal (SOR), resulting in 17%, 26%, and 25% of data being removed, respectively. Also, vegetation volume by voxel size was reconfigured from post-processed point clouds and compared with vegetation volume; the analysis showed that the margin of error was 8%, 25%, and 63% for each condition, respectively. The larger the size of the target sample, the larger the error. The vegetation surface looked visually similar when resizing the voxel; however, the volume of the entire vegetation was susceptible to error. |