Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Use of Crown Feature Analysis to Separate the Two Pine Species in QuickBird Imagery

  • Kim, Cheon (Department of Forest Resources/Department of Applied Information Technology, Kookmin University)
  • Published : 2008.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Tree species-specific estimates with spacebome high-resolution imagery improve estimation of forest biomass which is needed to predict the long term planning for the sustainable forest management(SFM). This paper is a contribution to develop crown distinguishing coniferous species, Pinus densiflora and Pinus koraiensis, from QuickBird imagery. The proposed feature analysis derived from shape parameters and first and second-order statistical texture features of the same test area were compared for the two species separation and delineation. As expected, initial studies have shown that both formfactor and compactness shape parameters provided the successful differentiating method between the pine species within the compartment for single crown identification from spaceborne high resolution imagery. Another result revealed that the selected texture parameters - the mean, variance, angular second moment(ASM) - in the infrared band image could produce good subset combination of texture features for representing detailed tree crown outline.

Keywords

References

  1. Haralick, R. M., K. Shanmugam and I. Dinstein, 1973. Texture features for image classification, IEEE Transactions on Systems, Man and Cybernetic, SMC-3(6): 610-621 https://doi.org/10.1109/TSMC.1973.4309314
  2. Kayitakire, F., C. Hamael, and P. Defourny, 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery, Remote Sensing of Environment, 102: 390-401 https://doi.org/10.1016/j.rse.2006.02.022
  3. Kim, C. and S. -H Hong, 2007. Estimating crown parameters from spaceborne high resolution imagery, Proc. of ISRS 2007, pp. 247-249
  4. Larsen, M., 2007. Single tree species classification with a hypothetical multi-spectral satellite, Remote Sensing of Environment, 110: 523-532 https://doi.org/10.1016/j.rse.2007.02.030
  5. Leckie, D. G., F. A. Gougeon, N. Walsworth and D. Pardine, 2003. Stand delineation and composition estimation using semi-automated individual tree crown analysis, Remote Sensing of Environment, 85: 355-369 https://doi.org/10.1016/S0034-4257(03)00013-0
  6. Ministerial Conference for the Protection of Forests in Europe (MCPFE), 1990. General declaration and resolutions Strasbourg, France
  7. Montreal Process Working Group, 2005. The Montreal Process, http://www.mpci.org/ home (viewed January 2008)
  8. Pearlstine, L., K. M. Portier and S. E. Smith, 2005. Textural discrimination of an invasive plant, shinus terebinthifolius, from low altitude digital imagery, Photoframmetric Engineering & Remote Sensing, 71(3): 289-298 https://doi.org/10.14358/PERS.71.3.289
  9. Russ, J. C., 2002. The Image Processing Handbook, 4th ed. CRC Press, Boca Raton
  10. Sun, C. and W. G. Wee, 1983. Neighboring grey level dependence matrix for texture classification, Computer Vision, Graphics, and Image Processing, 23: 341-352
  11. Tuominen, S. and A. Pekkarinen, 2005. Performance of different spectral and textural aerial photograph features in multi-source forest inventory, Remote Sensing of Environment, 94(2): 256-268 https://doi.org/10.1016/j.rse.2004.10.001
  12. Van Coillie, F. M. B., L. P. C. Verbeke and R. R. De Wulf, 2007. Feature selection by genetic algorithms in object-based classification of IKNOS imagery for forest mapping in Flanders, Belgium, Remote Sensing of Environment, 110: 476-487 https://doi.org/10.1016/j.rse.2007.03.020