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

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    0467364 - ÚVGZ 2017 RIV LT eng J - Journal Article
    Samec, P. - Rychtecká, P. - Tuček, P. - Bojko, J. - Zapletal, Miloš - Cudlín, Pavel
    A Static Model of Abiotic Predictors and Forest Ecosystem Receptor Designed Using Dimensionality Reduction and Regression Analysis.
    Baltic Forestry. Roč. 22, č. 2 (2016), s. 259-274. ISSN 1392-1355. E-ISSN 1392-1355
    R&D Projects: GA MŠMT(CZ) LO1415
    Institutional support: RVO:67179843
    Keywords : forest state monitoring * EMEP-LRTAP * floodplain * mountain forests * canonical correlation analysis
    Subject RIV: EH - Ecology, Behaviour
    Impact factor: 0.635, year: 2016

    The closeness of dependence level between growth environment (abiotic predictors) and forest ecosystem (receptor) indicates
    accordance or discrepancy between site and forest state. Our forest ecosystem analysis was focused on static model approximation
    between abiotic predictors with the closest dependence and properties of the receptor at 1×1 km grid in the Czech Republic (Central
    Europe). The predictors have been selected from natural abiotic quantities sets of temperatures, precipitation, acid deposition, soil
    properties and relative site insolation. The receptor properties have been selected from remote sensing data, density and volume of
    above-ground biomass of forest stands according to the forest management plans, and from surface humus chemical properties. A
    selection of the most dependent quantities was made by combining factor analysis and cluster analysis. The static modelling of the
    dependences between selected predictors and receptor properties was conducted by canonical correlation analysis. Average temperature,
    annual precipitation, total potential acid deposition, soil base saturation, CEC, total acid elements and site insolation index
    closely corresponded to NDVI and surface humus base saturation, Corg and acid elements content at 30% of the analysed grid of
    forest soils and it indicated forest state within the confidence interval at 69% of the forest soil grid (rCCA = 0.79; P < 0.00001). The
    forest ecosystem state that corresponds to the selected abiotic predictors was demonstrated in hilly altitudes. The tested procedure is
    inconvenient for forest state analysis in floodplains and moorlands. Based on approximation deviations, highland and mountain forests
    were divided into areas with non-optimum or more optimum ecosystem state than as corresponds to the values of the predictors.

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