Determination of chlorophyll content in selected grass communities of Krkonoše mts. tundra based on laboratory spectroscopy and aerial hyperspectral dat

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F22%3A10449204" target="_blank" >RIV/00216208:11310/22:10449204 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022" target="_blank" >https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2022-381-2022" target="_blank" >10.5194/isprs-archives-XLIII-B3-2022-381-2022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Determination of chlorophyll content in selected grass communities of Krkonoše mts. tundra based on laboratory spectroscopy and aerial hyperspectral dat

Popis výsledku v původním jazyce

The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) - spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec (R) mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R(2) for all indices was 0.52 for all species together at leaf level and median R(2) = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R(2) was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.

Název v anglickém jazyce

Determination of chlorophyll content in selected grass communities of Krkonoše mts. tundra based on laboratory spectroscopy and aerial hyperspectral dat

Popis výsledku anglicky

The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) - spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec (R) mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R(2) for all indices was 0.52 for all species together at leaf level and median R(2) = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R(2) was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

<a href="/cs/project/LTAUSA18154" target="_blank" >LTAUSA18154: Hodnocení funkce ekosystémů na základě sledování kvantitativních parametrů vegetace z dat dálkového průzkumu Země vysokého prostorového, spektrálního a časového rozlišení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ISBN

—

ISSN

1682-1750

e-ISSN

2194-9034

Počet stran výsledku

8

Strana od-do

381-388

Název nakladatele

International Society for Photogrammetry and Remote Sensing

Místo vydání

France

Místo konání akce

Nice

Datum konání akce

6. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000855647800053