Estimating canopy spectral invariants from ground reference and remote sensing data

Miina Rautiainen; Pauline Stenberg; Petr Lukes; Matti Mõttus; Janne Heiskanen

doi:10.1109/WHISPERS.2010.5594861

Estimating canopy spectral invariants from ground reference and remote sensing data

Miina Rautiainen^*
, Pauline Stenberg
, Petr Lukes
, Matti Mõttus
, Janne Heiskanen

^*Corresponding author for this work

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

Physically-based remote sensing methods have progressively become more attractive for monitoring the vertical and horizontal structure of vegetation. A relatively recent development in modeling the radiation field of a vegetation canopy is the spectral invariants theory. The theory states that the radiation budget of a vegetation canopy can be parameterized using only spectrally invariant parameters which depend on canopy structure in a complex manner. In this paper, we briefly review how spectral invariants can be estimated from hyperspectral remote sensing data or in situ vegetation canopy gap fraction measurements.

Original language	English
Title of host publication	2nd Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publication	Evolution in Remote Sensing, WHISPERS 2010 - Workshop Program
DOIs	https://doi.org/10.1109/WHISPERS.2010.5594861
Publication status	Published - 29 Nov 2010
MoE publication type	A4 Article in a conference publication
Event	2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Reykjavik, Iceland Duration: 14 Jun 2010 → 16 Jun 2010

Conference

Conference	2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010
Country/Territory	Iceland
City	Reykjavik
Period	14/06/10 → 16/06/10

Keywords

Forest
Remote sensing
Spectral invariants

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10.1109/WHISPERS.2010.5594861

Cite this

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title = "Estimating canopy spectral invariants from ground reference and remote sensing data",

abstract = "Physically-based remote sensing methods have progressively become more attractive for monitoring the vertical and horizontal structure of vegetation. A relatively recent development in modeling the radiation field of a vegetation canopy is the spectral invariants theory. The theory states that the radiation budget of a vegetation canopy can be parameterized using only spectrally invariant parameters which depend on canopy structure in a complex manner. In this paper, we briefly review how spectral invariants can be estimated from hyperspectral remote sensing data or in situ vegetation canopy gap fraction measurements.",

keywords = "Forest, Remote sensing, Spectral invariants",

author = "Miina Rautiainen and Pauline Stenberg and Petr Lukes and Matti M{\~o}ttus and Janne Heiskanen",

year = "2010",

month = nov,

day = "29",

doi = "10.1109/WHISPERS.2010.5594861",

language = "English",

isbn = "9781424489077",

booktitle = "2nd Workshop on Hyperspectral Image and Signal Processing",

note = "2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 ; Conference date: 14-06-2010 Through 16-06-2010",

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Rautiainen, M, Stenberg, P, Lukes, P, Mõttus, M & Heiskanen, J 2010, Estimating canopy spectral invariants from ground reference and remote sensing data. in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Workshop Program., 5594861, 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010, Reykjavik, Iceland, 14/06/10. https://doi.org/10.1109/WHISPERS.2010.5594861

Estimating canopy spectral invariants from ground reference and remote sensing data. / Rautiainen, Miina; Stenberg, Pauline; Lukes, Petr et al.
2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Workshop Program. 2010. 5594861.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Estimating canopy spectral invariants from ground reference and remote sensing data

AU - Rautiainen, Miina

AU - Stenberg, Pauline

AU - Lukes, Petr

AU - Mõttus, Matti

AU - Heiskanen, Janne

PY - 2010/11/29

Y1 - 2010/11/29

N2 - Physically-based remote sensing methods have progressively become more attractive for monitoring the vertical and horizontal structure of vegetation. A relatively recent development in modeling the radiation field of a vegetation canopy is the spectral invariants theory. The theory states that the radiation budget of a vegetation canopy can be parameterized using only spectrally invariant parameters which depend on canopy structure in a complex manner. In this paper, we briefly review how spectral invariants can be estimated from hyperspectral remote sensing data or in situ vegetation canopy gap fraction measurements.

AB - Physically-based remote sensing methods have progressively become more attractive for monitoring the vertical and horizontal structure of vegetation. A relatively recent development in modeling the radiation field of a vegetation canopy is the spectral invariants theory. The theory states that the radiation budget of a vegetation canopy can be parameterized using only spectrally invariant parameters which depend on canopy structure in a complex manner. In this paper, we briefly review how spectral invariants can be estimated from hyperspectral remote sensing data or in situ vegetation canopy gap fraction measurements.

KW - Forest

KW - Remote sensing

KW - Spectral invariants

UR - https://www.scopus.com/pages/publications/78649256050

U2 - 10.1109/WHISPERS.2010.5594861

DO - 10.1109/WHISPERS.2010.5594861

M3 - Conference article in proceedings

AN - SCOPUS:78649256050

SN - 9781424489077

BT - 2nd Workshop on Hyperspectral Image and Signal Processing

T2 - 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010

Y2 - 14 June 2010 through 16 June 2010

ER -

Estimating canopy spectral invariants from ground reference and remote sensing data

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Keywords

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