한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제25권2호
  • /
  • Pages.48-58
  • /
  • 2022
  • /
  • 1226-9719(pISSN)
  • /
  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
권호 표지
DOI QR코드

DOI QR Code

산림지역 수목의 기하학적 구조 측정을 위한 휴대용 라이다 장비의 활용성 평가

Assessment on the Applicability of a Handheld LiDAR for Measuring the Geometric Structures of Forest Trees

  • 최승운 (국립공원공단 국립공원연구원) ;
  • 김태근 (국립공원공단 국립공원연구원) ;
  • 김종필 (국립공원공단 국립공원연구원) ;
  • 김성재 ((주)신우아이씨티)
  • 투고 : 2022.02.23
  • 심사 : 2022.04.06
  • 발행 : 2022.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 흉고직경, 수고 등 산림지역 수목의 기하학적 구조 측정을 위한 휴대용 라이다 장비의 활용성을 평가하였다. 휴대용 라이다 장비의 정확도를 파악하기 위하여 태백산국립공원 내 대상지역(30m×30m)을 선정하고 매목조사를 통해 흉고직경과 수고를 측정하였다. 또, 라이다 장비의 활용성에 대한 객관적인 평가를 위해 편향, 평균제곱근오차, 절대평균오차, 상관계수 등 4가지 통계지표를 활용하였다. 분석결과, 흉고직경의 경우 매목조사와 휴대용 라이다 장비를 이용한 측정치가 거의 유사한 것으로 나타났으며, 휴대용 라이다 장비는 기존의 조사방식을 대체하기에 충분하였다. 그러나, 매목조사에 의한 수고 측정치는 다양한 오차를 포함하고 있어 라이다의 정확도를 파악하기에는 한계가 있었다. 향후 보다 신뢰성 있는 수고 측정방법을 통해 수집된 자료로부터 라이다 장비의 정확도를 평가하는 연구가 필요할 것으로 생각된다.

This study tried to assess the applicability of a hand-held LiDAR for measuring the geometric structures of forest trees including diameters at a breast height(DBH) and tree height(H). A traditional method using tapelines was conducted to analyze the accuracy of the LiDAR instrument in the Taebaeksan national park in South Korea. Four statistical indices which are bias, root mean square error, mean absolute error, and correlation coefficient were employed to compare the measurements by the LiDAR instrument and traditional method. The DBHs from the LiDAR were very similar to those from the traditional method. And it indicated that the LiDAR is sufficient to be a alternative of a traditional method. However, there was a limitation in assessing the accuracy of LiDAR for measuring tree height by comparing the measurements by observer's eyes since they included different error sources. Further study is needed to assess the accuracy of LiDAR instrument for tree height through more reliable measurements.

키워드

과제정보

이 논문은 국립공원공단 국립공원연구원의 '라이다 기반의 식생구조 측정방법 도입 연구'의 지원으로 수행되었음.

참고문헌

  1. Balenovic, I., X. Liang, L. Jurjevic, J. Hyyppa, A. Seletkovic and A. Kukko. 2021. Hand-held personal laser scanning - Current status and perspectives for forest inventory application. Croatian Journal of Forest Engineering 42:165-183. https://doi.org/10.5552/crojfe.2021.858
  2. Bauwens, S., H. Bartholomeus, K. Calders and P. Lejeune. 2016. Forest inventory with terrestrial LiDAR: A comparison of static and hand-held mobile laser scanning. Forests 7(6):127. https://doi.org/10.3390/f7060127
  3. Bitelli, G., M. Dubbini and A. Zanutta. 2004. Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies. Proceedings of XXth ISPRS Congress Commission V. pp.246-251.
  4. Donager, J. J., T. T. Sankey, J. B. Sankey, A. E. Sanchez Meador, A. J. Springer and J. D. Bailey. 2018. Examining forest structure with terrestrial lidar: Suggestions and novel techniques based on comparisons between scanners and forest treatments. Earth and Space Science 5:753-776. https://doi.org/10.1029/2018EA000417
  5. Farr, T. G. and M. Kobrick. 2000. Shuttle Radar Topography Mission produces a wealth of data. Eos, Transanctions American Geophysical Union 81(48):583-585. https://doi.org/10.1029/EO081i048p00583
  6. Hopkinson, C., L. Chasmer, C. Young-Pow and P. Treitz. 2004. Assessing forest metrics with a gound-based scanning LiDAR. Canadian Journal of Remote Sensing 34:573-583.
  7. Hyyppa, E., X. Yu, H. Kaartinen, T. Hakala, A. Kukko, M. Vastaranta, J. Hyyppa. 2020. Comparison of backpack, handheld, under-canopy UAV, and above-canopy UAV laser scanning for field reference data collection in boreal forests. Remote Sensing 12(20):3327. https://doi.org/10.3390/rs12203327
  8. Kang, D.B., J.C. Huh and K.N. Ko. 2017. Analysis of factors influencing the measurement error of ground-based LiDAR. Journal of the Korean Solar Energy Society 37(6):25-37. https://doi.org/10.7836/KSES.2017.37.6.025
  9. Kankare, V., M. Holopainen, M. Vastaranta, E. Puttonen, X. Yu, J. Hyyppa, M. Vaaja, H. Hyyppa and P. Alho. 2013. Individual tree biomass estimation using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 75:64-75. https://doi.org/10.1016/j.isprsjprs.2012.10.003
  10. Karagianni, A. 2017. Terrestrial Laser Scanning in Building Documentation. Civil Engineering and Architecture 5(6):215-221. https://doi.org/10.13189/cea.2017.050603
  11. Ko, C.U., J.S. Kim, D.G. Kim and J.T. Kang. 2021. Analysis of optimal pathways for terrestrial LiDAR scanning the establishment of digital inventory of forest resources. Korean Journal of Remote Sensing 37(2):245-256. https://doi.org/10.7780/KJRS.2021.37.2.6
  12. Liang, X., V. Kankare, J. Hyyppa, Y. Wang, A. Kukko, H. Haggren, X. Yu, H. Kaartinen, A. Jaakkola, F. Guan, M. Holopainen and M. Vastaranta. 2016. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 115:63-77. https://doi.org/10.1016/j.isprsjprs.2016.01.006
  13. Lim, K., P. Treitz, M. Wulder, B. St-Onge and M. Flood. 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography 27:88-106. https://doi.org/10.1191/0309133303pp360ra
  14. Maas, H.G., A. Bienert, S. Scheller and E. Keane. 2008. Automatic forest inventory parameter determination from terrestrial laser scanner data. International Journal of Remote Sensing 29:1579-1593. https://doi.org/10.1080/01431160701736406
  15. Proudman, A., M. Ramezani and M. Fallon. 2021. Online estimation of diameter at breast height(DBH) of forest trees using a handheld LiDAR. Proceedings of 2021 European Conference on Mobile Robots(ECMR).
  16. Pu, S. 2008. Generating building outlines from terrestrial laser scanning. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 37(B5): 451-456.
  17. Seidel, D., S. Fleck and C. Leuschner. 2012. Analyzing forest canopies with ground-based laser scanning: A comparison with hemispherical photography. Agricultural and Forest Meteorology 154:1-8. https://doi.org/10.1016/j.agrformet.2011.10.006
  18. Stal, C., J. Verbeurgt, L. De Sloover and A. De Wulf. 2021. Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters. Journal of Forest Research 32:1503-1513. https://doi.org/10.1007/s11676-020-01214-7
  19. Teo, T.A. and C.M. Chiu. 2015. Pole-like road object detection from mobile lidar system using a coarse-to-fine approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(10):4805-4818. https://doi.org/10.1109/JSTARS.2015.2467160
  20. Valenca, J. I. Puente, E. Julio, H. Gonzalez-Jorge and P. Arias-Sanchez. 2017. Assessment of cracks on concrete bridges using image processing supported by laser scanning survey. Construction and Building Materials 146:668-678. https://doi.org/10.1016/j.conbuildmat.2017.04.096
  21. Van Leeuwen, M. and M. Nieuwenhuis. 2010. Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research 129:749-770. https://doi.org/10.1007/s10342-010-0381-4
  22. Vastaranta, M., T. Melkas, M. Holopainen, H. Kaartinen, J. Hyyppa, H. Hyyppa. 2009. Laser-based field measurements in tree-level forest data acquisition. Photogrammetric Journal of Finland 21:51-61.
  23. Xie, Y., J. Zhang, X. Chen, S. Pang, H. Zeng and Z. Shen. 2020. Accuracy assessment and error analysis for diameter at breast height measurement of trees obtained using a novel backpack LiDAR system. Forest Ecosystems 7:33-43. https://doi.org/10.1186/s40663-020-00237-0
  24. Zhang, K. and H. C. Frey. 2006. Road grade estimation for on-road vehicle emissions modeling using light detection and ranging data. Journal of the Air & Waste Management Association 56(6):777-788. https://doi.org/10.1080/10473289.2006.10464500