Ecology and Resilient Infrastructure

Korean Society of Ecology and Infrastructure Engineering (응용생태공학회)
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Estimation of Single Vegetation Volume Using 3D Point Cloud-based Alpha Shape and Voxel

3차원 포인트 클라우드 기반 Alpha Shape와 Voxel을 활용한 단일 식생 부피 산정

  • Jang, Eun-kyung (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Ahn, Myeonghui (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 장은경 (한국건설기술연구원 수자원하천연구본부) ;
  • 안명희 (한국건설기술연구원 수자원하천연구본부)
  • Received : 2021.11.15
  • Accepted : 2021.12.01
  • Published : 2021.12.31
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Abstract

In this study, information on vegetation was collected using a point cloud through a 3-D Terrestrial Lidar Scanner, and the physical shape was analyzed by reconfiguring the object based on the refined data. Each filtering step of the raw data was optimized, and the reference volume and the estimated results using the Alpha Shape and Voxel techniques were compared. As a result of the analysis, when the volume was calculated by applying the Alpha Shape, it was overestimated than reference volume regardless of data filtering. In addition, the Voxel method to be the most similar to the reference volume after the 8th filtering, and as the filtering proceeded, it was underestimated. Therefore, when re-implementing an object using a point cloud, internal voids due to the complex shape of the target object must be considered, and it is necessary to pay attention to the filtering process for optimal data analyzed in the filtering process.

본 연구에서는 3차원 지상 라이다 스캐너를 통해 수집되는 포인트 클라우드를 활용하여 식생의 정보를 수집하였으며, 수집된 데이터를 기반으로 객체를 재구현하여 물리적 형상을 분석하였다. 이를 위해 원시 데이터의 필터링 단계별 최적의 데이터를 구축하였으며, 구축된 데이터를 활용하여 실제 부피와 Alpha Shape 및 Voxel 기법을 활용한 부피 산정 결과를 산정한 후 각각 비교하였다. 분석 결과, Alpha Shape를 적용하여 부피를 산정한 경우 데이터 필터링과 관계없이 실제 부피보다 과다 산정되는 것으로 나타났다. 또한 Voxel 기법을 활용할 경우 8차 필터링 후 실제 부피와 가장 유사한 것으로 나타났으며, 이후 필터링이 진행될수록 실제 부피에 비해 과소 산정되는 것을 알 수 있었다. 따라서 포인트 클라우드를 활용하여 객체를 재구현 할 경우, 대상이 되는 객체의 복잡한 형상으로 인한 내부 공극을 고려해야 하며, 필터링 과정에서 최적의 데이터 구축을 위한 필터링 과정에 반드시 주의할 필요가 있다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(NRF-2019R1C1C1009719)을 받아 수행된 연구임.

References

  1. Ahn, M., Jang, E.-K., Bae, I. and Ji, U. 2020. Reconfiguration of Physical Structure of Vegetation by Voxelization Based on 3D Point Clouds. KSCE Journal of Civil and Environmental Engineering Research 40(6): 571-581. doi: 10.12652/Ksce.2020.40.6.0571
  2. Bienert, A., Hess, C., Maas, H.G. and Von Oheimb, G. 2014. A Voxel-based technique to estimate the volume of trees from terrestrial laser scanner data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40(5): 101-106. doi: 10.5194/isprsarchives-XL-5-101-2014
  3. Bienert, A., Queck, R., Schmidt, A., Bernhofer, C. and Maas, H.-G. 2010. Voxel space analysis of terrestrial laser scans in forests for wind field modeling. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 38(PART 5).
  4. Boothroyd, R. and James. (2017). Flow-vegetation interactions at the plant-scale: the importance of volumetric canopy morphology on flow field dynamics [Durham University]. Retrieved from https://pdfs.semanticscholar.org/82b7/1f0302e83632ee076f40169f14b5cd272318.pdf?_ga=2.200340270.1671434650.1566237458-639639868.1565275494
  5. Dassot, M., Colin, A., Santenoise, P., Fournier, M. and Constant, T. 2012. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Computers and Electronics in Agriculture 89: 86-93. doi: 10.1016/j.compag.2012.08.005
  6. Grau, E., Durrieu, S., Fournier, R., Gastellu-Etchegorry, J. P. and Yin, T. 2017. Estimation of 3D vegetation density with Terrestrial Laser Scanning data using voxels. A sensitivity analysis of influencing parameters. Remote Sensing of Environment 191: 373-388. doi: 10.1016/j.rse.2017.01.032
  7. Hosoi, F., Nakai, Y. and Omasa, K. 2013. Voxel tree modeling for estimating leaf area density and woody material volume using 3-D LIDAR data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(5W2), 115-120. doi: 10.5194/isprsannals-II-5-W2-115-2013
  8. Jalonen, J., Jarvela, J., Koivusalo, H. and Hyyppa, H. 2014. Deriving Floodplain Topography and Vegetation Characteristics for Hydraulic Engineering Applications by Means of Terrestrial Laser Scanning. Journal of Hydraulic Engineering 140(11): 04014056. doi: 10.1061/(asce)hy.1943-7900.0000928
  9. Jalonen, J., Jarvela, J., Virtanen, J.P., Vaaja, M., Kurkela, M. and Hyyppa, H. 2015. Determining characteristic vegetation areas by terrestrial laser scanning for floodplain flow modeling. Water (Switzerland) 7(2): 420-437. doi: 10.3390/w7020420
  10. Jang, E., Ahn, M. and Ji, U. 2020. Introduction and Application of 3D Terrestrial Laser Scanning for Estimating Physical Structurers of Vegetation in the Channel. Ecology and Res ilient Infras tructure 7(2): 90-96. doi: 10.17820/eri.2020.7.2.090
  11. Jin, S.-N. and Cho, K.-H. 2016. Expansion of Riparian Vegetation Due to Change of Flood Regime in the Cheongmi-cheon Stream, Korea. Ecology and Resilient Infrastructure 3(4): 322-326. doi: 10.17820/eri.2016.3.4.322
  12. Kankare, V., Holopainen, M., Vastaranta, M., Puttonen, E., Yu, X., Hyyppa, J., Vaaja, M., Hyyppa, H. and Alho, P. 2013. Individual tree biomass estimation using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 75: 64-75. doi: 10.1016/j.isprsjprs.2012.10.003
  13. Lee, C. D. G. S.-Y. Y. S. S. H. 2019. Dataset of Long-term Investigation on Change in Hydrology, Channel Morphology, Landscape and Vegetation Along the Naeseong Stream (II). Ecology and Resilient Infrastructure 6(1): 34-48.
  14. Li, Y., Hess, C., Von Wehrden, H., Hardtle, W. and Von Oheimb, G. 2014. Assessing tree dendrometrics in young regenerating plantations using terrestrial laser scanning. Annals of Forest Science 71(4): 453-462. doi: 10.1007/s13595-014-0358-4
  15. Maas, H.G., Bienert, A., Scheller, S. and Keane, E. 2008. Automatic forest inventory parameter determination from terrestrial laser scanner data. International Journal of Remote Sensing 29(5): 1579-1593. doi: 10.1080/01431160701736406
  16. Rutzinger, M., Pratihast, A.K., Oude Elberink, S. and Vosselman, G. 2010. Detection and modelling of 3D trees from mobile laser scanning data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 38(PART 5).
  17. Wikipedia. 2006. Voxel. Retrieved from https://wikipedia.org/wiki/voxel
  18. Woo, H., Cho, K.-H., Jang, C.L. and Lee, C.J. 2019. Fluvial processes and vegetation-research trends and implications. Ecology and Resilient Infrastructure 6(2): 89-100.
  19. Woo, H. and Park, M. 2016. Cause-based Categorization of the Riparian Vegetative Recruitment and Corresponding Research Direction. Ecology and Resilient Infrastructure 3(3): 207-211. doi: 10.17820/eri.2016.3.3.207
  20. Yan, Z., Liu, R., Cheng, L., Zhou, X., Ruan, X. and Xiao, Y. 2019. A Concave Hull Methodology for Calculating the Crown Volume of Individual Trees Based on Vehicle-Borne LiDAR Data. Remote Sensing 11(6): 623. doi: 10.3390/rs11060623