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This research studied the changes in leaf reflectance spectra (350-2500 nm) due to metal phytoextraction into barley plants grown in metal-spiked soils (3 levels of Cd, Pb, As and their metal-mixture treatments). Growth of barley was adversely affected due to 100 mg As kg-1 and metal-mixture (10 Cd+150 Pb+100 As; mg kg-1) treatments. Metal phytoextraction were in order of: root >straw ≥leaves >grains. Results of reflectance spectra of leaves show the influence of As-treatment only, causing spectral changes in visible and infrared domains mostly, as apparent from the significant correlation between leaf-As and leaf-spectra. Chlorophyll and water stress indices and band depths analyses showed significant correlations to leaf-As, and can be used to distinguish metal-stressed plants. Finally, regression models demonstrate the potential use of hyperspectral reflectance data to monitor plant health during phytoremediation process and to estimate leaf-As in barley, particularly in this study. [ABSTRACT FROM AUTHOR]

A method to visualize multiple membership maps, called 'Colour mixture' (CM) is described and compared with alternative techniques: defuzzification and Pixel mixture. Six landform parameters were used to derive the landform classes using supervised fuzzy k -means classification. The continuous categorical map is derived by GIS calculations with colours, where colour values are considered to represent the taxonomic space spanned by the attribute variables. Coordinates of the nine class centres (landform facets) were first transformed from multivariate to two-dimensional attribute space using factor analysis, and then projected on the Hue Saturation Intensity (HSI) colourwheel. The taxonomic value was coded with the Hue and confusion with Saturation. To improve visual impression, saturation was replaced with whiteness. Classes that were closer in attribute space were merged into similar generic colours. The CM technique limits the derived mixed-colour map to seven generic hues independently of the total number of classes, which provides a basis for automated generalization. The confusion index derived from the mixed-colour map was used to derive primary boundaries and to locate areas of higher taxonomic confusion. [ABSTRACT FROM AUTHOR]