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

ABSTRACT

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.

KEYWORDS:

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

Acknowledgements

We thank Veli Juola for assistance in designing the measurement setup, and Daniel Schraik and Mait Lang for assistance in the Järvselja field campaign. We would also like to thank Nea Kuusinen and Iuliia Burdun for helpful comments in various stages of this study. The computations were implemented in the Puhti supercomputer, provided to us free-of-charge by CSC – IT Center for Science, Finland.

Disclosure statement

No potential conflict of interest was reported by the author.

Data availability statement

The data that support the findings of this study are available from the corresponding author, JJ, upon reasonable request.

Additional information

Funding

The study was partly funded by Academy of Finland (grant: DIMEBO 323004). This study has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 771049). The text reflects only the authors’ view and the Agency is not responsible for any use that may be made of the information it contains.