Connecting infrared spectra with plant traits to identify species : abstract + powerpoint

M.F. Buitrago Acevedo, A.K. Skidmore, T.A. Groen, C.A. Hecker

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Abstract

Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 mm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified speciesspecific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 mm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing.
Original languageEnglish
Pages1 p. s1-s13
Publication statusPublished - 26 Sep 2018
Event9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018 - Beurs van Berlage, Amsterdam, Netherlands
Duration: 23 Sep 201826 Sep 2018
Conference number: 9
http://www.ieee-whispers.com/2017/11/23/whispers-2018/

Conference

Conference9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018
Abbreviated titleWHISPERS 2018
CountryNetherlands
CityAmsterdam
Period23/09/1826/09/18
Internet address

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leaves
cellulose
wavelengths
lignin
forest plantations
mineralogy
health status
remote sensing
food industry
spectroscopy
chemistry
taxonomy
water content
nitrogen
crops
methodology
water
sampling

Cite this

Buitrago Acevedo, M. F., Skidmore, A. K., Groen, T. A., & Hecker, C. A. (2018). Connecting infrared spectra with plant traits to identify species : abstract + powerpoint. 1 p. s1-s13. Abstract from 9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018, Amsterdam, Netherlands.
Buitrago Acevedo, M.F. ; Skidmore, A.K. ; Groen, T.A. ; Hecker, C.A. / Connecting infrared spectra with plant traits to identify species : abstract + powerpoint. Abstract from 9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018, Amsterdam, Netherlands.
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Buitrago Acevedo, MF, Skidmore, AK, Groen, TA & Hecker, CA 2018, 'Connecting infrared spectra with plant traits to identify species : abstract + powerpoint' 9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018, Amsterdam, Netherlands, 23/09/18 - 26/09/18, pp. 1 p. s1-s13.

Connecting infrared spectra with plant traits to identify species : abstract + powerpoint. / Buitrago Acevedo, M.F.; Skidmore, A.K.; Groen, T.A.; Hecker, C.A.

2018. 1 p. s1-s13 Abstract from 9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018, Amsterdam, Netherlands.

Research output: Contribution to conferenceAbstractOther research output

TY - CONF

T1 - Connecting infrared spectra with plant traits to identify species : abstract + powerpoint

AU - Buitrago Acevedo, M.F.

AU - Skidmore, A.K.

AU - Groen, T.A.

AU - Hecker, C.A.

PY - 2018/9/26

Y1 - 2018/9/26

N2 - Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 mm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified speciesspecific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 mm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing.

AB - Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 mm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified speciesspecific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 mm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing.

M3 - Abstract

SP - 1 p. s1-s13

ER -

Buitrago Acevedo MF, Skidmore AK, Groen TA, Hecker CA. Connecting infrared spectra with plant traits to identify species : abstract + powerpoint. 2018. Abstract from 9th Workshop on Hyperspectral Image and Signal Processing :Evolution in Remote Sensing 2018, Amsterdam, Netherlands.