A comparison of methods to relate grass reflectance to soil metal contamination

Grass-dominated vegetation covers large areas of the Dutch river floodplains. Remotely sensed data on the conditions under which this vegetation grows may yield information about the degree of soil contamination. This paper explores the relationship between grassland canopy reflectance and zinc (Zn) contamination in the soil under semi-field conditions. A field radiometer was used to record reflectance spectra of perennial ryegrass (Lolium perenne) in an experimental field with Zn concentrations in the soil ranging from 32 to 1800mgkg−1. Several spectral vegetation indices (VIs) and a multivariate approach using partial least squares (PLS) regression were investigated to evaluate their potential use in estimating Zn contamination levels. Compared to the best PLS model (RMSEP = 181.4 mg kg−1), the narrow band vegetation index MSAVI2mm performed better (RMSEP = 162.9 mg kg−1). Both MSAVI2mm and PLS gave a high user accuracy for the strongly contaminated soil class (100% and 91%, respectively), while the total accuracy was satisfactory (60% and 55%, respectively). Results from this feasibility study indicate the potential of using remote sensing techniques for the classification of contaminated areas in river floodplains. But as the results from this study may be both resolution- and location-dependent, research on field and image scale is now required to test the established relations and to assess their susceptibility to seasonal influences, species heterogeneity, and increased levels of spectral noise.

[1]  R. Leuven,et al.  Application of geographic information systems and remote sensing in river studies , 2002 .

[2]  Johanna Smeyers-Verbeke,et al.  Handbook of Chemometrics and Qualimetrics: Part A , 1997 .

[3]  S. Sommer,et al.  The potential of remote sensing for monitoring rural land use changes and their effects on soil conditions , 1998 .

[4]  L. Buydens,et al.  A Procedure for Incorporating Spatial Variability in Ecological Risk Assessment of Dutch River Floodplains , 2001, Environmental management.

[5]  O. Shimizu,et al.  Effects of heavy metals on the , 1971 .

[6]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[7]  Riccardo Leardi,et al.  Application of genetic algorithm–PLS for feature selection in spectral data sets , 2000 .

[8]  Sheng-Huei Chang,et al.  Airborne biogeophysical mapping of hidden mineral deposits , 1983 .

[9]  Eric R. Ziegel,et al.  Handbook of Chemometrics and Qualimetrics, Part B , 2000, Technometrics.

[10]  Lutgarde M. C. Buydens,et al.  Evolutionary optimisation : a tutorial , 1998 .

[11]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[12]  F. Csillag,et al.  The Influence of Vegetation Index and Spatial Resolution on a Two-Date Remote Sensing-Derived Relation to C4 Species Coverage , 2001 .

[13]  Lutgarde M. C. Buydens,et al.  Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains , 2001 .

[14]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[15]  James Barber,et al.  Effects of heavy metals on the absorbance and reflectance spectra of plants , 1980 .

[16]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[17]  Mark Cutler,et al.  Estimating Canopy Chlorophyll Concentration from Field and Airborne Spectra , 1999 .

[18]  C. A. V. van Gestel,et al.  Development of zinc bioavailability and toxicity for the springtail Folsomia candida in an experimentally contaminated field plot. , 1997, Environmental pollution.

[19]  Kevin P. Price,et al.  Calibration of broad- and narrow-band spectral variables for rangeland cover component quantification , 1999 .

[20]  R. W. Birnie,et al.  Spectral reflectance response of big sagebrush to hydrocarbon-induced stress in the Bighorn Basin, Wyoming , 1994 .

[21]  Mary E. Martin,et al.  Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance : a comparison of statistical methods , 1996 .

[22]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[23]  William J. Collins,et al.  Confirmation of the airborne biogeophysical mineral exploration technique using laboratory methods , 1983 .

[24]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[25]  C. B. Lucasius,et al.  Genetic algorithms in wavelength selection: a comparative study , 1994 .

[26]  Pablo J. Zarco-Tejada,et al.  Natural and stress-induced effects on leaf spectral reflectance in Ontario species , 2000 .

[27]  F. D. van der Meer,et al.  Spectral characteristics of wheat associated with hydrocarbon microseepages , 1999 .