Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Carbon Dioxide Stocks in Forest Using Airborne LiDAR Data

항공 LiDAR 데이터를 이용한 산림의 이산화탄소 고정량 추정

  • 이상진 (서울시립대학교 도시과학대학 공간정보공학과) ;
  • 최윤수 (서울시립대학교 도시과학대학 공간정보공학과) ;
  • 윤하수 (서울시립대학교 도시과학대학 공간정보공학과)
  • Received : 2012.03.02
  • Accepted : 2012.06.27
  • Published : 2012.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.

본 논문은 Pulsed LiDAR 시스템에 의해 취득된 고밀도 항공 LiDAR 데이터를 이용하여 산림의 이산화탄소 고정량의 객관적이고 과학적인 추정을 목적으로 한다. 이를 위해, 산림지형의 라이다 필터링, 효율적인 개개목 탐지 알고리즘를 통해 취득된 수목의 생장인자를 이용하여 바이오매스 및 이산화탄소 고정량을 추정하는 일련의 방법을 개선하고 통합하여 연구대상지에 적용하였다. 그 결과, 추출된 연구대상지의 DTM은 3.32%의 Type-II 에러를 가진 것으로 나타났고, 개개목 탐지 알고리즘에 의해 식별된 개개목 위치 및 개체수 추정결과는 66.26%의 정확도를 나타냈다. 이와 같은 3차원 산림구조를 이용하여 산출된 연구대상지의 이산화탄소 고정량은 연구대상지의 약 15%에 이르는 면적을 현장조사하여 산출된 이산화탄소 고정량과 비교해 볼 때 약 7.2%의 차이를 나타냈다. 이러한 결과로 미루어 볼 때, 항공 LiDAR 기술이 전통적인 산림조사방법을 대체할 수 있는 가능성을 확인하였다.

Keywords

References

  1. 손영모, 김종찬, 이경학, 김래현 (2007), 우리나라 산림 바이오매스 자원평가, 연구보고 07-22, 국립산림과학연구원, pp. 11-26.
  2. Aldred, A. H. and bonnor, G. M. (1985), Application of airborne lasers to forest surveys, Information Report PI-X-51, Canadian Forestry Service of Petawawa National Forestry Centre, p. 62.
  3. Baltsavias, E. (2000), A comparison of btween photogrammetry and laser scanning, Journal of Photogrammetry and Remote Sensing, ISPRS, Vol. 54, pp. 83-94.
  4. Brouad, J. S., 2009, "Genetic Gain", http://www.abtreegene.com/toolkit/#
  5. Clark, M. L., Clark, D. B. and Roberts, D. A. (2004), Smallfootprint LiDAR estimation of sub-canopy elevation and tree height in a tropical rain forest landscape, Remote Sensing of Environment, Vol. 81(2-3), pp. 68-89.
  6. Girard, M. C. (2003), Processing of Remote Sensing Data, Taylor & Francis, London, pp.306-307.
  7. Gonzalez, R. C. and Richard E. W. and Steven L. E. (2004), Matlab을 이용한 디지털 영상처리(Digital Image Processing using MATLAB), 유현중, 김태우 옮김, ITC, pp. 358.
  8. Hese, S. and Lehmann, F. (2000), Comparison of digital surface models of HRSC-A and LASER scanner for forest stand characteristics, X IXth ISPRS Congress, ISPRS, Amsterdam, pp. 525-532.
  9. Holmgren, J., Nilsson, M. and Olsson, H. (2003), Estimation of tree height and stem volume on plots using airborne laser scanning, Forest Science, Vol. 49, pp. 419-428.
  10. Hyyppa, J., Hyyppa, H., Inkinen, H., Engdahl, M., Linko, S. and Zhu, Y.H. (2000), Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, Vol. 128, pp. 109-120. https://doi.org/10.1016/S0378-1127(99)00278-9
  11. Karel, W., Pfeifer, N. and Briese, C. (2006), DTM QUALITY ASSESSMENT, Proceedings of ISPRS Technical Commission II Symposium., Vienna,
  12. Kraus, K. and Pfeifer, N. (1998), Determination of terrain models in wooded areas with airborne laser scanner data, Journal of Photogrammetry and Remote Sensing, ISPRS, Vol 53, pp. 193-203. https://doi.org/10.1016/S0924-2716(98)00009-4
  13. Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A. and Harding, D. (1999), Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests, Remote sensing of Environment, Vol. 70, pp. 339-361. https://doi.org/10.1016/S0034-4257(99)00052-8
  14. MacLean, G. A. and Krabill, W. B. (1986), Gross-merchabtable timber volume estimation using an airborne LiDAR system, Canadian Journal of Remote Sensing, Vol. 12, pp. 7-18. https://doi.org/10.1080/07038992.1986.10855092
  15. Means, J, E., Acker, S. A., Harding, D. J., Blair, J. B., Lefsky, M. A., Cohen, W. B., Harmon, M. E. and McKee, W. A. (1999), Use of large-footprint scanning airborne airborne LiDAR to estimate forest stand characteristics in the Western Cascade of Oregon, Remote Sensing of environment, Vol. 67, pp. 298-308. https://doi.org/10.1016/S0034-4257(98)00091-1
  16. Nilson, M. (1996), Estimation of tree heights and stand volume using an airborne laser data, Remote Sensing of Environment, Vol. 56, pp. 1-7. https://doi.org/10.1016/0034-4257(95)00224-3
  17. Persson, A.,. Holmgren, J. and Soderman, U. (2002), Detecting and Measuring Individual Trees Using an Airborne Laser Scanner, Photogrammetric Engineering & Remote Sensing, Vol. 68, pp. 925-932.
  18. Schreier, H., Lougheed, J. Tucker, C. and Leckie, D. (1985), Automated measurements of terrain reflection and height variations using an airborne infrared laser system, International Journal of Remote Sensing, Vol. 6, pp. 101-113. https://doi.org/10.1080/01431168508948427
  19. Sithole, G. and Vosselman, G (2003), Report : ISPRS Comparison of Filters, Working Group III/3 of ISPRS Commission III, http://www.itc.nl/isprswgIII-3/filtertest/index.html.
  20. Onge, B. A (1999), Estimating individual tree height of the boreal forest using airborne laser altimetry and digital videography, ISPRS workshop on "Mapping surface structure and topography by airborne and spaceborne lasers", Commission III of working Group 3, pp. 179-184.
  21. Vincent, L. (2003), Morphological Grayscale Reconstruction in Image Analysis : Applications and Efficient Algorithms, IEEE Transactions on image processing, Vol. 2, No. 2, pp. 176-201.

Cited by

  1. Comparative Evaluation between Administrative and Watershed Boundary in Carbon Sequestration Monitoring - Towards UN-REDD for Mt. Geum-gang of North Korea - vol.22, pp.5, 2013, https://doi.org/10.14249/eia.2013.22.5.439
  2. 고해상도 위성영상과 LiDAR 자료를 활용한 해안지역에 인접한 농경지 추출에 관한 연구 vol.18, pp.1, 2012, https://doi.org/10.11108/kagis.2015.18.1.170
  3. A Comparative Study of Carbon Absorption Measurement Using Hyperspectral Image and High Density LiDAR Data in Geojedo vol.35, pp.4, 2012, https://doi.org/10.7848/ksgpc.2017.35.4.231
  4. 지상라이다를 활용한 소나무 산불피해지의 임목 피해특성 분석 vol.36, pp.6, 2012, https://doi.org/10.7780/kjrs.2020.36.6.1.2