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Tree Species Classification Based on 3D Bark Texture Analysis

  • Ahlem Othmani
  • Alexandre Piboule
  • Oscar Dalmau
  • Nicolas Lomenie
  • Said Mokrani
  • Lew Fock Chong Lew Yan Voon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D point clouds from which forest inventory attributes are calculated. In the case of mixed plantings where the 3D point clouds contain data from several different tree species, it is important to be able to automatically recognize the tree species in order to analyze the data of each of the species separately. Although automatic tree species recognition from TLS data is an important problem, it has received very little attention from the scientific community. In this paper we propose a method for classifying five different tree species using TLS data. Our method is based on the analysis of the 3D geometric texture of the bark in order to compute roughness measures and shape characteristics that are fed as input to a Random Forest classifier to classify the tree species. The method has been evaluated on a test set composed of 265 samples (53 samples of each of the 5 species) and the results obtained are very encouraging.

Keywords

Tree species classification 3D pattern recognition 3D bark texture analysis forest inventory 

References

  1. 1.
    Dassot, M., Constant, T., Fournier, M.: The Use of Terrestrial LiDAR Technology in Forest Science: Application fields, Benefits and Challenges. Annals of Forest Science 6, 959–974 (2011)CrossRefGoogle Scholar
  2. 2.
    Othmani, A., Piboule, A., Krebs, M., Stolz, C., Lew Yan Voon, L.F.C.: Towards Automated and Operational Forest Inventories with T-LiDAR. In: SilviLaser - 11th International Conference on LiDAR Applications for Assessing Forest Ecosystems, Hobart, Australia, October 16-19 (2011)Google Scholar
  3. 3.
    Puttonen, E., Suomalainen, J., Hakala, T., Rikknen, E., Kaartinen, H., Kaasalainen, S., Litkey, P.: Tree species classification from fused active hyperspectral reflectance and LIDAR measurements. Forest Ecology and Management 260, 1843–1852 (2010)CrossRefGoogle Scholar
  4. 4.
    Haala, N., Reulke, R., Thies, M., Aschoff, T.: Combination of terrestrial Laser Scanning with high resolution panoramic Images for Investigations in Forest Applications and tree species recognition. In: Proceedings of the ISPRS Working Group V/1, IAPRS - XXXIV (PART 5/W16), Dresden, Deutschland, February 19-22. Panoramic Photogrammetry Workshop (2004)Google Scholar
  5. 5.
    Reulke, R., Haala, N.: Tree species recognition with fuzzy texture parameters. In: Klette, R., Žunić, J. (eds.) IWCIA 2004. LNCS, vol. 3322, pp. 607–620. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Alliez, P., Tayeb, S., Wormser, C.: AABB Tree, CGAL 3.5 edn. (2009)Google Scholar
  7. 7.
    Taubin, G.: Geometric Signal Processing on Polygonal Meshes. State of the Art Report, Eurographics (2000)Google Scholar
  8. 8.
    Weinberger, K.Q., Packer, B.D., Saul, L.K.: Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization. In: Proceedings of the Tenth International Workshop on AI and Statistics (AISTATS 2005), Barbados, WI (2005)Google Scholar
  9. 9.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), Portland, OR, USA, pp. 226–231 (1996)Google Scholar
  10. 10.
    Edelsbrunner, H., Kirkpatrick, D.G., Seidel, R.: On the Shape of a Set of Points in the Plane. IEEE Transactions on Information Theory IT-29(4) (July 1983)Google Scholar
  11. 11.
    Breiman, L.: Random Forests. Machine Learning, 5–32 (October 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ahlem Othmani
    • 1
    • 2
  • Alexandre Piboule
    • 2
  • Oscar Dalmau
    • 3
  • Nicolas Lomenie
    • 4
  • Said Mokrani
    • 1
  • Lew Fock Chong Lew Yan Voon
    • 1
  1. 1.Laboratory LE2I - UMR CNRS 6306Le CreusotFrance
  2. 2.Office National des Forêts, Pôle R&D de NancyNancyFrance
  3. 3.Centro de Investigacion en Matematicas A.CGuanajuatoMexico
  4. 4.Laboratory LIPADE - EA 2517Université Paris DescartesParisFrance

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