Recognizing Amazonian tree species in the field using bark tissues spectra

Abstract

The identification of tree species in the field is often a subjective process and misidentifications cause many problems for forest management in the Amazon Forest. Near infrared spectra from dried leaves of herbarium specimens are able to distinguish species in tropical forests. However, toolsto improve species identification directly in the field are needed. In this study, we tested whether spectral reflectance of bark tissues (rhytidome and phloem) collected with a portable spectrometer in the field can be used for the discrimination of tree species. Spectral data was collected for 254 trees of 8 families, 10 genera and 11 species from terra firme forests in Central Amazon with an ASD field spectrometer. Data consisted of reflectance values within 350-2500 nm wavelengths. We compared the rate of correct species recognition for different datasets using linear discriminant models. The rate of correct species assignment using this technique was 98% when using spectra from the inner bark (phloem) and 94% with outer bark (rhytidome) spectra. We suggest that the application of this technique can improve the quality of species identification directly during field inventories, fostering better forest management practices.

Effectiveness of Fourier transform near‐infrared spectroscopy spectra for species identification of anurans fixed in formaldehyde and conserved in alcohol: A new tool for integrative taxonomy

“…Recent studies have shown the effectiveness of FT‐NIR spectroscopy in botanical species identifications (Durgante et al., 2013; Fan et al., 2010; Hadlich et al., 2018; Lang et al., 2017; Lang et al, 2015; Prata et al, 2018). The method has also been used in vertebrate and invertebrate studies (Almeida de Azevedo et al., 2019; Rigby et al., 2014; Rodriguez‐Fernandez et al., 2011; Vance et al., 2014, 2016) and may be a viable alternative to characteristics used in classical taxonomy as it is highly cost‐effective, rapid, and can be non‐destructive (Rodriguez‐Fernandez et al., 2011; Vance et al., 2016).…”

Multiangular spectra of tree bark for common boreal tree species in Europe

“…Furthermore, previous studies have concluded that ignoring the contribution of woody tree parts of the canopy in imaging spectroscopy data (i.e., satellite images) may lead to less accurate estimates of, for example, chlorophyll (Verrelst et al 2010) and nadir-viewed canopy reflectance (Asner 1998). Knowledge of species-specific stem bark spectra can also be used in developing accurate tree species identification algorithms (e.g., machine learning classification models) (Hadlich et al 2018). Accurate classification models could be valuable in the future for the forest industry and the development of autonomous forestry machinery.…”

Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest

“…In general, the high variability of bark spectra, and the absorption features that can be found in these groups could be attributed to a high diversity of trees in the region (Hilje et al, 2015). Higher variability of bark spectra associated with a high diversity of trees has also been reported in other forest communities (Hadlich et al, 2018).…”