J. For. Sci., 2006, 52(4):181-187 | DOI: 10.17221/4500-JFS

Object-oriented classification of Ikonos satellite data for the identification of tree species composition

F. Hájek
Faculty of Forestry and Environment, Czech University of Agriculture in Prague, Prague, Czech Republic

This paper describes the automated classification of tree species composition from Ikonos 4-meter imagery using an object-oriented approach. The image was acquired over a man-planted forest area with the proportion of various forest types (conifers, broadleaved, mixed) in the Krušné hory Mts., Czech Republic. In order to enlarge the class signature space, additional channels were calculated by low-pass filtering, IHS transformation and Haralick texture measures. Employing these layers, image segmentation and classification were conducted on several levels to create a hierarchical image object network. The higher level separated the image into smaller parts regarding the stand maturity and structure, the lower (detailed) level assigned individual tree clusters into classes for the main forest species. The classification accuracy was assessed by comparing the automated technique with the field inventory using Kappa coefficient. The study aimed to create a rule-base transferable to other datasets. Moreover, the appropriate scale of common image data and utilisation in forestry management are evaluated.

Keywords: automated image analysis; eCognition; median filters; texture; forestry management

Published: April 30, 2006  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Hájek F. Object-oriented classification of Ikonos satellite data for the identification of tree species composition. J. For. Sci. 2006;52(4):181-187. doi: 10.17221/4500-JFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. BAATZ M., SHÄPE A., 1999. Object-oriented and multi-scale image analysis in semantic networks. In: Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing, August 16-20th, International Institute for GeoInformation Science and Earth Observation, Enschede.
    2. BAATZ M., BENZ U.C., DEHGHANI S., HEYNEN M., HÖLTJE A., HOFFMANN P., LINGENFELDER I., MIMLER M., SOHLBACH M.,WEBER M., WILHAUCK G., 2003. eCognition object oriented image analysis user guide. Munchen, Definiens Inc.
    3. BENZ U.C., HOFFMANN P., WILHAUCK G., LINGENFELDER I., HEYNEN M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58: 239-258. Go to original source...
    4. BLASCHKE T., STROBL J., 2001. What's wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS, 6: 12-17.
    5. BRANDTBERG T., 1999. Automatic individual tree-based analysis of high spatial resolution aerial images on naturally regenerated boreal forests. Canadian Journal of Forestry, 29: 1464-1478. Go to original source...
    6. GOPAL S., WOODCOCK C., 1996. Remote sensing of forest change using artificial neural networks. IEEE Control Systems Magazine, 8: 42-48.
    7. HALOUNOVÁ L., 2003. Textural classification of B &W serial photos for the forest classification. In: Proceedings of the 23 rd Symposium of European Association of Remote Sensing Laboratories, June 2-5, 2003, Gent: 173-179.
    8. HARALICK R., SHAPIRO L., 1992. Computer and Robot Vision, Vol. I. Chap. 9. Texture. Reading, Addison-Wesley: 453-494.
    9. LECKIE D.G., GOUGEON F.A., WALSWORTH N., PARADINE D., 2003. Stand delineation and composition estimation using semi-automated individual tree crown analysis. Remote Sensing of Environment, 8: 355-369. Go to original source...
    10. ZHANG Y., 2001. A spectral and spatial information integrated approach for object extraction from high-resolution digital imagery. In: Second Digital Earth Conference, June 24-28, 2001, Fredericton. Acknowledgements I would like to thank my supervisor Lena Halounová from Remote Sensing Lab, Czech Technical

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content