Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 327867
Title Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains
Author(s) Kooistra, L.; Salas, E.A.L.; Clevers, J.G.P.W.; Wehrens, H.R.M.J.; Leuven, R.S.E.W.; Nienhuis, P.H.; Buydens, L.M.C.
Source Environmental Pollution 127 (2004)2. - ISSN 0269-7491 - p. 281 - 290.
DOI https://doi.org/10.1016/S0269-7491(03)00266-5
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2004
Keyword(s) near-infrared reflectance - red-edge - leaf reflectance - heavy-metals - spectroscopy - rhine - calibration - trends
Abstract This study investigated the relation between vegetation reflectance and elevated concentrations of the metals Ni, Cd, Cu, Zn and Pb in river floodplain soils. High-resolution vegetation reflectance spectra in the visible to near-infrared (400-1350 nm) were obtained using a field radiometer. The relations were evaluated using simple linear regression in combination with two spectral vegetation indices: the Difference Vegetation Index (DVI) and the Red-Edge Position (REP). In addition, a multivariate regression approach using partial least squares (PLS) regression was adopted. The three methods achieved comparable results. The best R-2 values for the relation between metals concentrations and vegetation reflectance were obtained for grass vegetation and ranged from 0.50 to 0.73. Herbaceous species displayed a larger deviation from the established relationships, resulting in lower R-2 values and larger cross-validation errors. The results corroborate the potential of hyperspectral remote sensing to contribute to the survey of elevated metal concentrations in floodplain soils under grassland using the spectral response of the vegetation as an indicator. Additional constraints will, however, have to be taken into account, as results are resolution- and location-dependent. (C) 2003 Elsevier Ltd. All rights reserved.
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