Classification of tree species based on hyperspectral reflectance images of stem bark

Jussi Juola*, Aarne Hovi, Miina Rautiainen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
2 Downloads (Pure)


Automatization of tree species identification in the field is crucial in improving forest-based bioeconomy, supporting forest management, and facilitating in situ data collection for remote sensing applications. However, tree species recognition has never been addressed with hyperspectral reflectance images of stem bark before. We investigated how stem bark texture differs between tree species using a hyperspectral camera set-up and gray level co-occurrence matrices and assessed the potential of using reflectance spectra and texture features of stem bark to identify tree species. The analyses were based on 200 hyperspectral reflectance data cubes (415–925 nm) representing ten tree species. There were subtle interspecific differences in bark texture. Using average spectral features in linear discriminant analysis classifier resulted in classification accuracy of 92–96.5%. Using spectral and texture features together resulted in accuracy of 93–97.5%. With a convolutional neural network, we obtained an accuracy of 94%. Our study showed that the spectral features of stem bark were robust for classifying tree species, but importantly, bark texture is beneficial when combined with spectral data. Our results suggest that in situ imaging spectroscopy is a promising sensor technology for developing accurate tree species identification applications to support remote sensing.

Original languageEnglish
Article number2161420
Number of pages15
JournalEuropean Journal of Remote Sensing
Issue number1
Early online date28 Dec 2022
Publication statusPublished - 2023
MoE publication typeA1 Journal article-refereed


  • forestry
  • Hyperspectral
  • reflectance image
  • stem bark
  • texture
  • tree species


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