Spatial Information Research

Korea Spatial Information Society (대한공간정보학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Individual Trees and Estimation of Mean Tree Height using Airborne LIDAR Data

항공 라이다데이터를 이용한 개별수목탐지 및 평균수고추정

  • 황세란 (서울시립대학교 공간정보공학과) ;
  • 이미진 (서울시립대학교 공간정보공학과) ;
  • 이임평 (서울시립대학교 공간정보공학과)
  • Received : 2012.01.06
  • Accepted : 2012.06.26
  • Published : 2012.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

As the necessity of forest conservation and management has been increased, various forest studies using LIDAR data have been actively performed. These studies often utilize the tree height as an important parameter to measure the forest quantitatively. This study thus attempt to apply two representative methods to estimate tree height from airborne LIDAR data and compare the results. The first method based on the detection of the individual trees using a local maximum filter estimates the number of trees, the position and heights of the individual trees, and the mean tree height. The other method estimates the maximum and mean tree height, and the crown mean height for each grid cell or the entire area from the canopy height model (CHM) and height histogram. In comparison with the field measurements, 76.6% of the individual trees are detected correctly; and the estimated heights of all trees and only conifer trees show the RMSE of 1.91m and 0.75m, respectively. The tree mean heights estimated from CHM retain about 1~2m RMSE, and the histogram method underestimates the tree mean height with about 0.6m. For more accurate derivation of diverse forest information, we should select and integrate the complimentary methods appropriate to the tree types and estimation parameters.

산림의 보존과 관리에 대한 필요성이 점차 증가하면서 항공 라이다데이터를 이용한 산림연구가 활발히 수행되고 있다. 이러한 산림연구에서 수목고도는 정량적인 산림측정을 위한 중요한 변수로 이용된다. 이에 본 연구는 항공 라이다데이터로부터 수목고도 추정을 위한 대표적인 두 종류의 방법을 적용하고 그 결과를 비교분석한다. Local maximum 필터링에 기반한 개별수목탐지 방법으로 개별수목의 수, 위치, 높이 및 평균수고를, 수목고도모델 또는 히스토그램을 이용한 평균수고 추정방법으로 개별격자 또는 전체영역에 대한 최대, 평균수고, 평균 수관고를 추정한다. 현장에서 실측한 검증데이터와 비교한 결과 개별 수목은 76.6%의 정확도로 탐지되었으며 개별수고는 전체 수종의 경우 1.91m, 침엽수종에 대해서는 0.75m의 RMSE로 추정되었다. 반면 수목고도모델을 이용하여 추정된 평균수고는 약 1~2m의 RMSE를 보였으며, 히스토그램을 이용하여 추정된 평균수고는 약 0.6m 과소 추정되었다. 정확하고 다양한 산림정보 추출을 위해 수종 및 추정인자에 따라 적합한 상호보완적인 방법을 선택하고 융합하는 것이 필요하다.

Keywords

References

  1. A. Persson, J. Holmgren, and U. Soderman, 2002, "Detecting and Measuring Individual Trees Using an Airborne Laser Scanner," Photogrammetric Engineering and Remote Sensing, vol. 68, no. 9, pp. 925-932.
  2. B. Koch, U. Heyder, and H. Weinacker, 2006, "Detection of Individual Tree Crowns in Airborne Lidar Data," Photogrammetric Engineering and Remote Sensing, vol. 72, no. 4, pp. 357-363. https://doi.org/10.14358/PERS.72.4.357
  3. C. Lin, G. Thomson, C. S. Lo, and M. S. Yang, 2011, "A Multi-level Morphological Active Contour Algorithm for Delineating Tree Crowns in Mountatinous Forest," Photogrammetric Engineering and Remote Sensing, vol. 77, no. 3, pp. 241-249. https://doi.org/10.14358/PERS.77.3.241
  4. E. Næsset, 2002, "Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data," Remote Sensing of Environment, vol. 80, no. 1, pp. 88-99. https://doi.org/10.1016/S0034-4257(01)00290-5
  5. G. Sithole, 2001, "Filtering of Laser Altimetry Data using A Slope Adaptive Filter," International Archives of Photogrammetry and Remote Sensing, Annapolis, MD, Oct. 22-24, vol.34(3/W4), pp. 203-210.
  6. G. Vosselman, 2000, "Slope based filtering of laser altimetry data," International Archives of Photogrammetry and Remote Sensing, vol. 33(B3/2), pp. 935-942.
  7. J. Hyyppa, O. Kelle, M. Lehikoinen, and M. Inkinen, 2001, "A Segmentation-Based Method to Retrieve Stem Volume Estimates from 3-D Tree Height Models Produced by Laser Scanners," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 5, pp. 969-975. https://doi.org/10.1109/36.921414
  8. K. Zhao, S. Popescu, and R. Nelson, 2009, "Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers," Remote Sensing of Environment, vol. 113, no. 1, pp. 182-196. https://doi.org/10.1016/j.rse.2008.09.009
  9. M. Maltamo, P. Packalen, X. Yu, K. Eerikainen, J. Hyyppa, and J. Pitkanen, 2005, "Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data," Forest Ecology and Management, vol. 216, no. 1-3, pp. 41-50. https://doi.org/10.1016/j.foreco.2005.05.034
  10. M. Schardt, M. Ziegler, A. Wimmer, R. Wack, and J. Hyyppa, 2002, "Assessment of forest parameters by means of laser scanning," International Archives of Photogrammetry and Remote Sensing, Graz, Austria, vol. 34, pp. 302-309.
  11. P. Axelsson, 2000, "DEM Generation from Laser Scanner Data using Adaptive TIN Models," International Archives of Photogrammetry and Remote Sensing, vol. 33(B4/1), pp. 110-117.
  12. P. Soille, 1999, Morphological Image Analysis, p. 316, Springer, Berlin.
  13. S. A. Hall, I. C. Burke, D.O. Box, M. R. Kaufmann, and J. M. Stoker, 2005, "Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests," Forest Ecology and Management, vol. 208, no. 1-3, pp. 189-209. https://doi.org/10.1016/j.foreco.2004.12.001
  14. S. Hwang and I. Lee, 2011, "Current status of tree height estimation from airborne LiDAR data," Korean Journal of Remote Sensing, vol. 27, no. 3, pp. 389-401. https://doi.org/10.7780/kjrs.2011.27.3.389
  15. S. C. Popescu, R. H. Wynne, and R. F. Nelson, 2002, "Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size," Computers and Electronics in Agriculture, vol. 37, no. 1-3, pp. 71-95. https://doi.org/10.1016/S0168-1699(02)00121-7
  16. S. P. Lloyd, 1982, "Least Squares Quantization in PCM," IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129-137. https://doi.org/10.1109/TIT.1982.1056489
  17. T. Brandtberg, T. A. Warner, R. E. Landenberger, and J. B. McGraw, 2003, "Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America," Remote Sensing of Environment, vol. 85, no. 3, pp. 290-303. https://doi.org/10.1016/S0034-4257(03)00008-7
  18. X. Yu, J. Hyyppa, A. Kukko, M. Maltamo, and H. Kaartinen, 2006, "Change detection Techniques for canopy height growth measurements using airborne laser scanner data," Photogrammetric Engineering and Remote Sensing, vol. 72, no. 12, pp. 1339-1348. https://doi.org/10.14358/PERS.72.12.1339
  19. Y. Wang, H. Weinacker, and B. Koch, 2008, "A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest," Sensors, vol. 8, no. 6, pp. 3938-3951. https://doi.org/10.3390/s8063938
  20. 송철철, 이우균, 곽두안, 곽한빈, 2008, "산림조사에서의 항공라이다 취득인자에 따른 영향분석을 위한 시뮬레이션 모델 개발," 한국GIS학회, 제22권, 제6호, pp. 310-317.
  21. 우충식, 윤정숙, 신정일, 이규성, 2007, "항공 Lidar데이터를 이용한 산림지역의 개체목 자동 인식 및 수고 추출," 한국임학회지, 제96권, 제3호, pp.251-258.
  22. 장안진, 김대성, 김용일, 김경옥, 2005, "LiDAR 데이터와 컬러항공사진을 이용한 수목의 개체 추출 및 높이 추정," 한국GIS학회 추계학술대회, 한국공간정보학회, pp. 67-74.
  23. 황세란, 김성준, 이임평, "항공 라이다데이터를 이용한 산림영역 탐지," 한국공간정보학회지, 제18권, 제3호, pp. 23-32.
  24. 황세란, 이임평, 2011, "산림지형 모델링을 위한 항공 라이다 데이터의 지면점 필터링 비교분석과 정확도 개선," 한국측량학회논문지, 제29권, 제6호, pp. 641-650.