A Static Model of Abiotic Predictors and Forest Ecosystem Receptor Designed Using Dimensionality Reduction and Regression Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F16%3A00467364" target="_blank" >RIV/86652079:_____/16:00467364 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43410/16:43910894 RIV/47813059:19240/16:N0000201 RIV/61989592:15310/16:33161845

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Static Model of Abiotic Predictors and Forest Ecosystem Receptor Designed Using Dimensionality Reduction and Regression Analysis

Popis výsledku v původním jazyce

The closeness of dependence level between growth environment (abiotic predictors) and forest ecosystem (receptor) indicatesnaccordance or discrepancy between site and forest state. Our forest ecosystem analysis was focused on static model approximationnbetween abiotic predictors with the closest dependence and properties of the receptor at 11 km grid in the Czech Republic (CentralnEurope). The predictors have been selected from natural abiotic quantities sets of temperatures, precipitation, acid deposition, soilnproperties and relative site insolation. The receptor properties have been selected from remote sensing data, density and volume ofnabove-ground biomass of forest stands according to the forest management plans, and from surface humus chemical properties. Anselection of the most dependent quantities was made by combining factor analysis and cluster analysis. The static modelling of thendependences between selected predictors and receptor properties was conducted by canonical correlation analysis. Average temperature,nannual precipitation, total potential acid deposition, soil base saturation, CEC, total acid elements and site insolation indexnclosely corresponded to NDVI and surface humus base saturation, Corg and acid elements content at 30% of the analysed grid ofnforest soils and it indicated forest state within the confidence interval at 69% of the forest soil grid (rCCA = 0.79; P < 0.00001). Thenforest ecosystem state that corresponds to the selected abiotic predictors was demonstrated in hilly altitudes. The tested procedure isninconvenient for forest state analysis in floodplains and moorlands. Based on approximation deviations, highland and mountain forestsnwere divided into areas with non-optimum or more optimum ecosystem state than as corresponds to the values of the predictors.n

Název v anglickém jazyce

A Static Model of Abiotic Predictors and Forest Ecosystem Receptor Designed Using Dimensionality Reduction and Regression Analysis

Popis výsledku anglicky

The closeness of dependence level between growth environment (abiotic predictors) and forest ecosystem (receptor) indicatesnaccordance or discrepancy between site and forest state. Our forest ecosystem analysis was focused on static model approximationnbetween abiotic predictors with the closest dependence and properties of the receptor at 11 km grid in the Czech Republic (CentralnEurope). The predictors have been selected from natural abiotic quantities sets of temperatures, precipitation, acid deposition, soilnproperties and relative site insolation. The receptor properties have been selected from remote sensing data, density and volume ofnabove-ground biomass of forest stands according to the forest management plans, and from surface humus chemical properties. Anselection of the most dependent quantities was made by combining factor analysis and cluster analysis. The static modelling of thendependences between selected predictors and receptor properties was conducted by canonical correlation analysis. Average temperature,nannual precipitation, total potential acid deposition, soil base saturation, CEC, total acid elements and site insolation indexnclosely corresponded to NDVI and surface humus base saturation, Corg and acid elements content at 30% of the analysed grid ofnforest soils and it indicated forest state within the confidence interval at 69% of the forest soil grid (rCCA = 0.79; P < 0.00001). Thenforest ecosystem state that corresponds to the selected abiotic predictors was demonstrated in hilly altitudes. The tested procedure isninconvenient for forest state analysis in floodplains and moorlands. Based on approximation deviations, highland and mountain forestsnwere divided into areas with non-optimum or more optimum ecosystem state than as corresponds to the values of the predictors.n

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

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 periodika

Baltic Forestry

ISSN

1392-1355

e-ISSN

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Svazek periodika

22

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

14

Strana od-do

259-274

Kód UT WoS článku

000392367900009

EID výsledku v databázi Scopus

2-s2.0-85008485191