Semi-supervised methods to identify individual crowns of lowland tropical canopy species using imaging spectroscopy and lidar

Jean Baptiste Féret, Gregory P. Asner

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy height and use them to assess the identification accuracy. We found that semi-supervised Support Vector Machine classification using tensor summation kernel was superior to supervised classification, with demonstrable accuracy for at least eight out of nine species, and for all combinations of data types tested. We also found that the combination of hyperspectral imagery and LiDAR data usually improved species classification. Both LiDAR intensity and LiDAR canopy height proved useful for classification of certain species, but the improvements varied depending upon the species in question. Our results pave the way for target-species identification in tropical forests and other ecosystems.

Original languageEnglish (US)
Pages (from-to)2457-2476
Number of pages20
JournalRemote Sensing
Volume4
Issue number8
DOIs
StatePublished - Aug 1 2012
Externally publishedYes

Keywords

  • Biodiversity
  • Canopy height
  • Carnegie airborne observatory
  • Hyperspectral imagery
  • Lidar intensity
  • Pecies mapping
  • Semi-supervised classification
  • Tropical forests

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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