<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Genetic diversity is an important indicator of forest sustainability requiring particular attention and new methods to obtain fast and cheap estimates of genetic diversity. We assessed the differences in visible (VIS) and near infrared (NIR) spectral reflectance properties of detached shoots of several distant Scots pine provenances aiming to identify the most informative spectral wavebands and the seasonal time for the genetic diversity scoring. Shoots of five trees per provenance were sampled at two week intervals during the active growth and fall. The samples were scanned using a hyperspectral camera, equipped with a highly sensitive spectrometer capable of covering the spectral range of 400-1000 nm with a sampling interval of 0.6 nm. The ANOVAs revealed significant provenance effects on the spectral reflectance at variable spectral intervals depending on the sampling occasion. During the active growth, PCA identified the most informative wavebands over whole spectral range investigated. During the shoot/needle hardiness development, NIR was the most informative. Provenance ranking in spectral reflectance returned geographically interpretable pattern. We conclude that there are significant provenance attributable and interpretable differences in spectral reflectance of Scots pine needles providing a good opportunity for detecting this spectral variation with the hyperspectral imaging technique.</p></abstract>

Genetic diversity is an important indicator of forest sustainability requiring particular attention and new methods to obtain fast and cheap estimates of genetic diversity. We assessed the differences in visible (VIS) and near infrared (NIR) spectral reflectance properties of detached shoots of several distant Scots pine provenances aiming to identify the most informative spectral wavebands and the seasonal time for the genetic diversity scoring. Shoots of five trees per provenance were sampled at two week intervals during the active growth and fall. The samples were scanned using a hyperspectral camera, equipped with a highly sensitive spectrometer capable of covering the spectral range of 400-1000 nm with a sampling interval of 0.6 nm. The ANOVAs revealed significant provenance effects on the spectral reflectance at variable spectral intervals depending on the sampling occasion. During the active growth, PCA identified the most informative wavebands over whole spectral range investigated. During the shoot/needle hardiness development, NIR was the most informative. Provenance ranking in spectral reflectance returned geographically interpretable pattern. We conclude that there are significant provenance attributable and interpretable differences in spectral reflectance of Scots pine needles providing a good opportunity for detecting this spectral variation with the hyperspectral imaging technique.

Visible and near infrared hyperspectral imaging reveals significant differences in needle reflectance among Scots pine provenances

Silvae Genetica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Genetic diversity is an important indicator of forest sustainability requiring particular attention and new methods to obtain fast and cheap estimates of...

Visible and near infrared hyperspectral imaging reveals significant differences in needle reflectance among Scots pine provenances

Published Online: Jun 01, 2017

Page range: 169 - 180

Received: Apr 30, 2014

DOI: https://doi.org/10.1515/sg-2014-0022

Keywords
Pinus sylvestris, forest inventory, genetic diversity, phenology, remote sensing

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Visible and near infrared hyperspectral imaging reveals significant differences in needle reflectance among Scots pine provenances

Published Online: Jun 01, 2017

Page range: 169 - 180

Received: Apr 30, 2014

DOI: https://doi.org/10.1515/sg-2014-0022

KeywordsPinus sylvestris, forest inventory, genetic diversity, phenology, remote sensing

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Pinus sylvestris, forest inventory, genetic diversity, phenology, remote sensing