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Using airborne laser scanning for mountain forests mapping: support vector regression for stand parameters estimation and unsupervised training for treetop detection

Abstract : Numerous studies have shown the potential of airborne laser scanning for the mapping of forest resources. However, the application of this remote sensing technique to complex forests encountered in mountainous areas requires further investigation. In this thesis, the two main methods used to derive forest information are tested with airborne laser scanning data acquired in the French Alps, and adapted to the constraints of mountainous environments. In particular, a framework for unsupervised training of treetop detection is proposed, and the performance of support vector regression combined with dimension reduction for forest stand parameters estimation is evaluated.
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  • HAL Id : tel-02596609, version 1
  • IRSTEA : PUB00034625

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J.M. Monnet. Using airborne laser scanning for mountain forests mapping: support vector regression for stand parameters estimation and unsupervised training for treetop detection. Environmental Sciences. Doctorat de l'Université de Grenoble, spécialité Signal, Image, Parole et Télécom, 2011. English. ⟨tel-02596609⟩

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