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Self-Organizing Feature Maps and selected conventional numerical methods for assessment of environmental quality

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Author(s): Piotr Kosiba

Journal: Acta Societatis Botanicorum Poloniae
ISSN 0001-6977

Volume: 78;
Issue: 4;
Start page: 335;
Date: 2009;
Original page

Keywords: SOFM | "neural networks" | "tar-spot" | pollution | bioindication | modelling

ABSTRACT
The investigations concerned sites of Acer platanoides L. infected or not by Rhytisma aceriniu (Pers.) Fr. The aim of the study was to check the occurrence of R. acerinium, and whether it reflects the environmental status. Furthermore, an analysis was carried out to find out whether the applied SOFM offers additional advantages to solve problems in relation to conventional methods. Concentrations of selected elements in soils and leaves, and leaf and "tar-spot" morphometric traits were also measured. A significant differentiation was found between sites in relation to the analyzed traits. It appeared, that sites showing lower concentrations of chemical elements and proper developmental habitat conditions massive infections take place. The study showed that R. acerinium is a good biological indicator for assessment of environmental status. The applied, conventional statistical methods, SOFM and image techniques showed similar, but not identical results for assessment of environmental quality using R. acerinium. SOFM appeared to be more useful for ordination of results and ought to be taken into account as a proper tool of estimation of various plants and their biotopes.

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