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Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data

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An Erratum to this article was published on 01 September 2010

Abstract

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

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References

  1. Smith B, Knorr W, Widlowski J L, et al. Combining remote sensing data with process modeling to monitor boreal conifer forest carbon balances. For Ecol Manag, 2008, 255: 3985–3994, 10.1016/j.foreco.2008.03.056

    Article  Google Scholar 

  2. Kim T M, Song C C, Lee W K, et al. Estimation of vegetation carbon storage of Pinus densiflora in watershed level using quickbird imagery. Korean J For Meas, 2007, 10: 33–38

    Google Scholar 

  3. Labrecque S, Fournier R A, Luther J E, et al. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. For Ecol Manag, 2006, 226: 129–144, 10.1016/j.foreco.2006.01.030

    Article  Google Scholar 

  4. Popescu S C. Estimating biomass of individual pine trees using airborne lidar. Biom Bioen, 2007, 31: 646–655, 10.1016/j.biombioe.2007.06.022

    Article  Google Scholar 

  5. Kwak D A, Lee W K, Lee J H, et al. Detection of individual trees and estimation of tree height using LiDAR data. J For Res, 2007, 12: 425–434, 10.1007/s10310-007-0041-9

    Article  Google Scholar 

  6. Bortolot Z J, Wynne R H. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. Remote Sen, 2005, 59: 342–360

    Google Scholar 

  7. Packalén P. Using airborne laser scanning data and digital aerial photographs to estimate growing stock by tree species. Dissertation for Doctoral Degree. Joensuu: University of Joensuu, 2009

    Google Scholar 

  8. Caylor J. Aerial photography in the next decade. J For Res, 2000, 21: 1093–1114

    Google Scholar 

  9. Hall R J. The roles of aerial photographs in forestry remote sensing image analysis. In: Wulder M A, Franklin S E, eds. Methods and Applications for Remote Sensing of Forests: Concepts and Case Studies. Boston, MA: Kluwer Academic Publishers, 2003. 47–75

    Google Scholar 

  10. Myles S. The use of aerial photographs in forestry. J For, 1945, 43: 252–257

    Google Scholar 

  11. Koch B, Heyder U, Welnacker H. Detection of individual tree crowns in airborne LiDAR data. Photogramm Eng Remote Sens, 2006, 72: 357–363

    Article  Google Scholar 

  12. Gougeon F A. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can J Remote Sens, 1995, 54: 83–94

    Google Scholar 

  13. Brandtberg T, Walter F. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple scale analysis. Machine Vision Appl, 1998, 11: 64–73, 10.1007/s001380050091

    Article  Google Scholar 

  14. Pollock R. The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown model. Dissertation for Doctoral Degree. Vancouver: Department of Computer Science, University of British Columbia, 1996

    Google Scholar 

  15. Schardt M, Ziegler M, Wimmer A, et al. Assessment of forest parameters by means of laser scanning. In: Proceedings of the IRPRS Commun part 3A, Int Arch Photogramm Remote Sens vol 34, Graz, Austria, September 9–13, 2002. 302–309

  16. Dralle K, Rudemo M. Stem number estimation by kernel smoothing of aerial photos. Can J Forest Res, 1996, 26: 1228–1236, 10.1139/x26-137

    Article  Google Scholar 

  17. Beucher S, Lantuejoul C. Use of watersheds in contour detection. International Workshop on Image Processing, Realtime Edge and Motion Detection/Estimation, Rennes, France, 1979

  18. Soille P. Morphological Image Analysis: Principles and Applications, 2nd ed. Berlin: Springer-Verlag, 2003. 391

    Google Scholar 

  19. Chen Q, Baldocchi D, Gong P. Isolating individual trees in a savanna woodland using small footprint LiDAR data. Photogramm Eng Rem S, 2006, 72: 923–932

    Article  Google Scholar 

  20. Meyer F, Beucher S. Morphological segmentation. J Vis Commun Image R, 1990, 1: 21–46, 10.1016/1047-3203(90)90014-M

    Article  Google Scholar 

  21. Suarez J C, Ontiveros C, Smith S, et al. Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry. Comput Geosci, 2005, 31: 253–262, 10.1016/j.cageo.2004.09.015

    Article  Google Scholar 

  22. KFRI. Forest Management for Mitigation of Greenhouse Gas Emissions. Forest Research Institute, Seoul, South Korea. 1998

    Google Scholar 

  23. Dralle K, Rudemo M. Stem number estimation by kernel smoothing of aerial photos. Can J For Res, 1996, 26: 1228–1236, 10.1139/x26-137

    Article  Google Scholar 

  24. Foisy L, Wang S, Hervet E, et al. Automatic individual delineation of trees by anisotropic LM filtering applied to high spatial resolution satellite images. In: Proceeding of IEEE International Geosciences and Remote Sensing Symposium & 27th Canadian Symposium on Remote Sensing, 2006

  25. Shapiro L G, Stockman G C. Computer Vision, London: Prentence Hall, 2001. 137–150

    Google Scholar 

  26. Mark N, Alberto A. Feature Extraction and Image Processing. New York: Academic Press, 2008. 88

    Google Scholar 

  27. Wang L, Gong P, Biging G S. Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery. Photogramm Eng Remote Sens, 2004, 70: 351–357

    Article  Google Scholar 

  28. Lu Y, Jain R C. Behavior of edges in scale space. IEEE Trans Patt Anal Machine Intell, 1989, 11: 337–356, 10.1109/34.19032

    Article  Google Scholar 

  29. Nabuurs G J, Paivinen R, Sikkema R, et al. The role of the European forests in the global carbon cycle-a review. Biom Bioen, 1997, 13: 345–358, 10.1016/S0961-9534(97)00036-6, 1:CAS:528:DyaK1cXivVKhur0%3D

    Article  CAS  Google Scholar 

  30. Leckie D, Gougeon F, Hill D, et al. Combined high-density LiDAR and multispectral imagery for individual tree crown analysis. Can J Remote Sens, 2003, 29: 633–649

    Article  Google Scholar 

  31. Besl P J, Jain R C. Segmentation through variable-order surface fitting. IEEE Trans Patt Anal Machine Intell, 1988, 10: 167–192, 10.1109/34.3881

    Article  Google Scholar 

  32. Definiens. Definiens Professional 5 User Guid. Definiens AG, München, Germany, 2006. 15

    Google Scholar 

  33. Chen Q, Gong P, Baldocchi D, et al. Estimating basal area and stem volume for individual trees from LiDAR data. Photogramm Eng Remote Sens, 2007, 73: 1355–1365

    Article  Google Scholar 

  34. Kwak D A, Lee W K, Kim S W, et al. Regional differences in height and taper form of Pinus densiflora in Korea. Korean J For Meas, 2004, 7: 49–60

    Google Scholar 

  35. Son Y M, Lee K H, Kwon S D, et al. Evaluation and prediction system of main tree species forest resources. KFRI, Seoul, Korea, 2004. 125

  36. Olson J S, Watts J A, Allison L J. Carbon in live vegetation of major world ecosystems. ORNL-5862, Oak Ridge National Laboratory, Oak Ridge, TN, USA, 1983

  37. IPCC. Reveised 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Kanagawa, Japan, 2006

  38. Petrou M, Bosdogianni P. Image Processing: the Fundamentals. Wi-Wiley, UK, 2004

  39. Gonzalez R C, Woods R E. Digital Image Processing 2nd ed. New Jersey: Prentice Hall, 2002

    Google Scholar 

  40. Parvati K, Prakasa Rao B S, Mariya Das M. Image segmentation using gray-scale morphology and marker-controlled watershed transformation. Dis Dyn Nat Soc, 2008, 2008: 1–8, 10.1155/2008/384346

    Article  Google Scholar 

  41. Koukoulas S, Blcakburn G A. Quantifying the spatial properties of forest canopy gap using LiDAR imagery and GIS. Int J Remote Sen, 2004, 25: 3049–3071, 10.1080/01431160310001657786

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 136-701, Korea

    So-Ra Kim, Doo-Ahn Kwak, Woo-Kyun oLee, Yowhan Son & Seongjin Yoo

  2. Forest Practice Research Center, Korea Forest Research Institute, Kyeonggi-Do, 487-821, Korea

    Sang-Won Bae

  3. Departement of Forest Resources, Jinju National University, Jinju, 660-758, Korea

    Choonsig Kim

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  1. So-Ra Kim
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  2. Doo-Ahn Kwak
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  3. Woo-Kyun oLee
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  4. Yowhan Son
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Correspondence to Woo-Kyun oLee.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s11427-010-4064-7.

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Kim, SR., Kwak, DA., oLee, WK. et al. Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data. Sci. China Life Sci. 53, 885–897 (2010). https://doi.org/10.1007/s11427-010-4017-1

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  • DOI: https://doi.org/10.1007/s11427-010-4017-1

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