Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F14%3A43887749" target="_blank" >RIV/60076658:12310/14:43887749 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985939:_____/14:00482866 RIV/86652079:_____/14:00482866

Výsledek na webu

<a href="http://www.esajournals.org/doi/full/10.1890/ES13-00393.1" target="_blank" >http://www.esajournals.org/doi/full/10.1890/ES13-00393.1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1890/ES13-00393.1" target="_blank" >10.1890/ES13-00393.1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands

Popis výsledku v původním jazyce

There is a growing demand for spatially explicit assessment of multiple ecosystem services (ES) and remote sensing (RS) can provide valuable data to meet this challenge. In this study, located in the Central French Alps, we used high spatial and spectralresolution RS images to assess multiple ES based on underpinning ecosystem properties (EP) of subalpine grasslands. We estimated five EP (green biomass, litter mass, crude protein content, species diversity and soil carbon content) from RS data using empirical RS methods and maps of ES were calculated as simple linear combinations of EP. Additionally, the RS-based results were compared with results of a plant trait-based statistical modelling approach that predicted EP and ES from land use, abiotic andplant trait data (modelling approach). The comparison between the RS and the modelling approaches showed that RS-based results provided better insight into the fine-grained spatial distribution of EP and thereby ES, whereas the modelling

Název v anglickém jazyce

Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands

Popis výsledku anglicky

There is a growing demand for spatially explicit assessment of multiple ecosystem services (ES) and remote sensing (RS) can provide valuable data to meet this challenge. In this study, located in the Central French Alps, we used high spatial and spectralresolution RS images to assess multiple ES based on underpinning ecosystem properties (EP) of subalpine grasslands. We estimated five EP (green biomass, litter mass, crude protein content, species diversity and soil carbon content) from RS data using empirical RS methods and maps of ES were calculated as simple linear combinations of EP. Additionally, the RS-based results were compared with results of a plant trait-based statistical modelling approach that predicted EP and ES from land use, abiotic andplant trait data (modelling approach). The comparison between the RS and the modelling approaches showed that RS-based results provided better insight into the fine-grained spatial distribution of EP and thereby ES, whereas the modelling

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

ECOSPHERE

ISSN

2150-8925

e-ISSN

—

Svazek periodika

5

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

29

Strana od-do

—

Kód UT WoS článku

000345096900007

EID výsledku v databázi Scopus

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