ArticleEcology and evolution2022
A spectral analysis of stem bark for boreal and temperate tree species.
Article in Ecology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Intra- and interspecific variation in spectral properties of dominantEcology and evolution · 2023Article
- A spectral analysis of stem bark for boreal and temperate tree species.Ecology and evolution · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The woody material of forest canopies has a significant effect on the total forest reflectance and on the interpretation of remotely sensed data, yet research on the spectral properties of bark has been limited. We developed a novel measurement setup for acquiring stem bark reflectance spectra in field conditions, using a mobile hyperspectral camera. The setup was used for stem bark reflectance measurements of ten boreal and temperate tree species in the visible (VIS) to near-infrared (NIR) (400-1000 nm) wavelength region. Twenty trees of each species were measured, constituting a total of 200 hyperspectral reflectance images. The mean bark spectra of species were similar in the VIS region, and the interspecific variation was largest in the NIR region. The intraspecific variation of bark spectra was high for all studied species from the VIS to the NIR region. The spectral similarity of our study species did not correspond to the general phylogenetic lineages. The hyperspectral reflectance images revealed that the distributions of per-pixel reflectance values within images were species-specific. The spectral library collected in this study contributes toward building a comprehensive understanding of the spectral diversity of forests needed not only in remote sensing applications but also in, for example, biodiversity or land surface modeling studies.
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