Evaluation of various spectral inputs for estimation of forest biochemical and structural properties from airborne imaging spectroscoopy data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F16%3A00461345" target="_blank" >RIV/86652079:_____/16:00461345 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5194/isprs-archives-XLI-B7-961-2016" target="_blank" >http://dx.doi.org/10.5194/isprs-archives-XLI-B7-961-2016</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/isprs-archives-XLI-B7-961-2016" target="_blank" >10.5194/isprs-archives-XLI-B7-961-2016</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of various spectral inputs for estimation of forest biochemical and structural properties from airborne imaging spectroscoopy data

Popis výsledku v původním jazyce

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

Název v anglickém jazyce

Evaluation of various spectral inputs for estimation of forest biochemical and structural properties from airborne imaging spectroscoopy data

Popis výsledku anglicky

In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method – support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 – 710 nm, c) CR 705 – 780 nm, and d) CR 680 – 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 – 710 nm, whereas CR bands in range of 680 – 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

Druh

D - Stať ve sborníku

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

<a href="/cs/project/LO1415" target="_blank" >LO1415: CzechGlobe 2020 - Rozvoj Centra pro studium dopadů globální změny klimatu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

The international archives of the Photogrammetry, Remote sensing and spatial information sciences

ISBN

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ISSN

1682-1750

e-ISSN

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Počet stran výsledku

6

Strana od-do

961-966

Název nakladatele

International Society of Photogrammetry and Remote Sensing

Místo vydání

s. l.

Místo konání akce

Prague

Datum konání akce

12. 7. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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