2015
DOI: 10.1139/cjfr-2014-0452
|View full text |Cite
|
Sign up to set email alerts
|

Hydraulic traits of Norway spruce sapwood estimated by Fourier transform near-infrared spectroscopy (FT-NIR)

Abstract: The potential of Fourier transform near-infrared (FT-NIR) spectroscopy to predict hydraulic traits in Norway spruce (Picea abies (L.) Karst.) sapwood was evaluated. Hydraulic traits tested were P 50 (applied air pressure causing 50% loss of hydraulic conductivity) and RWL 50 (applied air pressure causing 50% relative water loss). Samples came from 24-year-old spruce clones. FT-NIR spectra were collected from the axial (transverse) and radial surface of each solid wood sample for the prediction of P 50 and RWL … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
1
0
13

Year Published

2016
2016
2022
2022

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(14 citation statements)
references
References 54 publications
0
1
0
13
Order By: Relevance
“…Differences in the reflectance, absorbance and transmittance of light at different wavelengths by plant parts are tightly coupled to their chemical composition, cell structure and physiological properties (Curran, 1989;Elvidge, 1990;Kokaly et al, 2009), leading to the rapid recent development of spectroscopic methods as a general tool in plant ecophysiology and ecology. For example, spectroscopy has been used to estimate wood density and hydraulic traits (Acuna & Murphy, 2006;Petisco et al, 2006;Luss et al, 2015), accurately identify plant species from dried leaves (Durgante et al, 2013) across developmental stages (Lang et al, 2015), quantify nonstructural carbohydrate content of different plant organs (Ramirez et al, 2015) and characterize a broad suite of leaf biophysical traits (Clark et al, 2005;Serbin et al, 2012Serbin et al, , 2014Asner et al, 2014). Chavana-Bryant et al (2016), also in this special issue, was the first study to demonstrate that leaf reflectance spectra can successfully predict leaf age by using a partial least-squares regression (PLSR; Wold et al, 2001) approach applied to data from a Peruvian evergreen forest.…”
Section: Introductionmentioning
confidence: 99%
“…Differences in the reflectance, absorbance and transmittance of light at different wavelengths by plant parts are tightly coupled to their chemical composition, cell structure and physiological properties (Curran, 1989;Elvidge, 1990;Kokaly et al, 2009), leading to the rapid recent development of spectroscopic methods as a general tool in plant ecophysiology and ecology. For example, spectroscopy has been used to estimate wood density and hydraulic traits (Acuna & Murphy, 2006;Petisco et al, 2006;Luss et al, 2015), accurately identify plant species from dried leaves (Durgante et al, 2013) across developmental stages (Lang et al, 2015), quantify nonstructural carbohydrate content of different plant organs (Ramirez et al, 2015) and characterize a broad suite of leaf biophysical traits (Clark et al, 2005;Serbin et al, 2012Serbin et al, , 2014Asner et al, 2014). Chavana-Bryant et al (2016), also in this special issue, was the first study to demonstrate that leaf reflectance spectra can successfully predict leaf age by using a partial least-squares regression (PLSR; Wold et al, 2001) approach applied to data from a Peruvian evergreen forest.…”
Section: Introductionmentioning
confidence: 99%
“…Esta región del espectro abarca el rango de frecuencias entre 12.820 y 4.000 ondas/cm (cm -1 ) que corresponde a las longitudes de onda de 780 a 2.500 nm (Figura 6.1) (Schwanninger et al 2011, Hein 2012, Luss et al 2015. Este rango de ondas se ubica entre el espectro visible (VIS; 380 a 780 nm) y el infrarrojo medio (MIR: Mid-infrared; 2.500 a 25.000 nm) (Bokobza 1998;Pasquini et al 2003).…”
Section: Introductionunclassified
“…A su vez, en la región NIR, la radiación es absorbida de acuerdo con la concentración en que se hallan presentes los diferentes tipos de enlace. En consecuencia, el espectro NIR contiene información cualitativa y cuantitativa de la composición química de una muestra (Viana et al 2009, Luss et al 2015. En la madera, particularmente, la absorción es debida principalmente a sobretonos y bandas de combinación de las vibraciones fundamentales entre átomos de hidrógeno -H-y otros más grandes (carbono -C-, oxígeno -O-, nitrógeno -N-, azufre -S-) (Schimleck et al 1999;Luss et al 2015;Figura 6.2).…”
Section: Introductionunclassified
See 2 more Smart Citations