Single tree species classification from Terrestrial Laser Scanning data for forest inventory

  • Authors:

  • Ahlem Othmani;Lew F. C. Lew Yan Voon;Christophe Stolz;Alexandre Piboule

  • Affiliations:

  • Laboratoire LE2I - UMR CNRS 6306, 12 rue de la fonderie, 71200 Le Creusot, France and Office National des Forêts, Pôle R&D de Nancy, 11 rue de l'Ile de Corse, 54000 Nancy, France;Laboratoire LE2I - UMR CNRS 6306, 12 rue de la fonderie, 71200 Le Creusot, France;Laboratoire LE2I - UMR CNRS 6306, 12 rue de la fonderie, 71200 Le Creusot, France;Office National des Forêts, Pôle R&D de Nancy, 11 rue de l'Ile de Corse, 54000 Nancy, France

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2013

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Abstract

Due to the increasing use of Terrestrial Laser Scanning (TLS) systems in the forestry domain for forest inventory, the development of software tools for the automatic measurement of forest inventory attributes from TLS data has become a major research field. Numerous research work on the measurement of attributes such as the localization of the trees, the Diameter at Breast Height (DBH), the height of the trees, and the volume of wood has been reported in the literature. However, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. Most of the research work uses Airborne Laser Scanning (ALS) data and measures tree species attributes on large scales. In this paper we propose a method for individual tree species classification of five different species based on the analysis of the 3D geometric texture of the bark. The texture features are computed using a combination of the Complex Wavelet Transforms (CWT) and the Contourlet Transform (CT), and classification is done using the Random Forest (RF) classifier. The method has been tested using a dataset composed of 230 samples. The results obtained are very encouraging and promising.