Estimating regional heavy metal concentrations in rice by scaling up a field-scale heavy metal assessment model

a b s t r a c t The objective of this study was to determine the levels of heavy metals cadmium (Cd) and copper (Cu) in rice by upscaling a field-scale heavy metal assessment (FHMA) model from field to regional scale. The FHMA model was established on the basis of spectral parameters in combination with soil parameters by employing a generalized dynamic fuzzy neural network. The piecewise function and ordinary kriging were developed to suit the upscaled spectral parameters and soil parameters, respectively. In addition, the network structure and fuzzy rules, which had already been developed in the FHMA model, would be subsequently extracted as those of the regional-scale heavy metal assessment (RHMA) model. The results showed that the latter performed well at prediction with a correlation coefficient (R 2 ) and model efficiency (ME) greater than 0.70, and can be applied to other areas, perhaps universally. This study suggests that it is feasible to accurately estimate regional heavy-metal concentrations in rice by scaling up the FHMA if such a strategy is appropriately selected and finds that the piecewise function is well suited to transferring spectral data from a field to a regional scale.

[1]  Malcolm Taberner,et al.  Assessment of biophysical vegetation properties through spectral decomposition techniques , 1996 .

[2]  Ted M. Zobeck,et al.  Scaling up from field to region for wind erosion prediction using a field-scale wind erosion model and GIS , 2000 .

[3]  Peter M. Atkinson,et al.  Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data , 2007, Comput. Geosci..

[4]  Peter M. Atkinson,et al.  Evaluating a thermal image sharpening model over a mixed agricultural landscape in India , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

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

[7]  F. J. García-Haro,et al.  Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site , 2009 .

[8]  Hongrui Wang,et al.  Generalized dynamic fuzzy neural network-based tracking control of robot manipulators , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[9]  A. Chave,et al.  A bounded influence regression estimator based on the statistics of the hat matrix , 2003 .

[10]  Nirmal Keshava,et al.  Angle-based band selection for material identification in hyperspectral processing , 2003, SPIE Defense + Commercial Sensing.

[11]  Martha C. Anderson,et al.  Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship , 2003 .

[12]  Jan M. H. Hendrickx,et al.  Effect of scaling transfer between evapotranspiration maps derived from LandSat 7 and MODIS images , 2005, SPIE Defense + Commercial Sensing.

[13]  Yasushi Yamaguchi,et al.  Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area , 2006 .

[14]  A. Becker,et al.  Disaggregation, aggregation and spatial scaling in hydrological modelling , 1999 .

[15]  H. Keulen,et al.  Effects of modelling detail on simulated potential crop yields under a wide range of climatic conditions , 2011 .

[16]  K. Kok,et al.  Evaluating impact of spatial scales on land use pattern analysis in Central America , 2001 .

[17]  J. Schmidt,et al.  Application of wavelet analysis for monitoring the hydrologic effects of dam operation: Glen Canyon Dam and the Colorado River at Lees Ferry, Arizona , 2005 .

[18]  Andrew K. Skidmore,et al.  Continuum removed band depth analysis for detecting the effects of natural gas, methane and ethane on maize reflectance , 2006 .

[19]  M. Van Meirvenne,et al.  Kriging soil texture under different types of nonstationarity , 2003 .

[20]  Alan B. Anderson,et al.  Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with Landsat TM image and polynomial regression , 2002 .

[21]  J. Clevers,et al.  Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data , 2004 .

[22]  Peter M. Atkinson,et al.  Assessing uncertainty in estimates with ordinary and indicator kriging , 2001 .

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

[24]  Martin K. van Ittersum,et al.  Scale changes and model linking methods for integrated assessment of agri-environmental systems , 2011 .

[25]  Yizong Huang,et al.  Combined toxicity of copper and cadmium to six rice genotypes (Oryza sativa L.). , 2009, Journal of environmental sciences.

[26]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[27]  Na Li,et al.  Transformation From Hyperspectral Radiance Data to Data of Other Sensors Based on Spectral Superresolution , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[28]  F. Zengin,et al.  Effects of some heavy metals on content of chlorophyll, proline and some antioxidant chemicals in bean [Phaseolus vulgaris L.] seedlings , 2005 .

[29]  W. Schröder,et al.  A methodological approach of site selection and data analysis to provide model input data for an up-scaling of population effects of transgenetic oilseed rape in Northern Germany , 2006 .

[30]  Jianguo Wu,et al.  A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications , 2002 .

[31]  Meiling Liu,et al.  Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices , 2010 .

[32]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[33]  Gérard Balent,et al.  The spatial scale mismatch between ecological processes and agricultural management: do difficulties come from underlying theoretical frameworks? , 2010 .

[34]  Yong Li,et al.  Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information , 2010 .

[35]  Meiling Liu,et al.  Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model , 2011, Comput. Geosci..

[36]  Meng Joo Er,et al.  Control of a mobile robot using generalized dynamic fuzzy neural networks , 2004, Microprocess. Microsystems.

[37]  M. Parrya,et al.  Effects of climate change on global food production under SRES emissions and socio-economic scenarios , 2004 .

[38]  Asim Biswas,et al.  Application of Continuous Wavelet Transform in Examining Soil Spatial Variation: A Review , 2011 .

[39]  M. Weissa,et al.  Review of methods for in situ leaf area index ( LAI ) determination Part II . Estimation of LAI , errors and sampling , 2003 .

[40]  S. Gerstl,et al.  Coupled atmosphere/canopy model for remote sensing of plant reflectance features. , 1985, Applied optics.

[41]  N. Giesen,et al.  Scale effects in Hortonian surface runoff on agricultural slopes in West Africa: Field data and models , 2011 .

[42]  R Font,et al.  Use of near-infrared spectroscopy for determining the total arsenic content in prostrate amaranth. , 2004, The Science of the total environment.

[43]  K. Clint Slatton,et al.  A Scalable Approach to Fusing Spatiotemporal Data to Estimate Streamflow via a Bayesian Network , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[44]  I. Odeha,et al.  Mapping of salinity risk in the lower Namoi valley using non-linear kriging methods , 2004 .

[45]  Mui Lay,et al.  Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. , 2005, Environmental pollution.

[46]  George B. Arhonditsis,et al.  A Bayesian hierarchical framework for calibrating aquatic biogeochemical models , 2009 .

[47]  Harald van der Werff,et al.  Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage , 2012, Ecol. Informatics.

[48]  Jan M. H. Hendrickx,et al.  Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale , 2009 .

[49]  L. Buydens,et al.  Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. , 2004, Environmental pollution.

[50]  Meiling Liu,et al.  Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[51]  Xin Chen,et al.  Spectral response of rice (Oryza sativa L.) leaves to Fe2+ stress , 2009, Science in China Series C: Life Sciences.

[52]  Guangxing Wang,et al.  Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images , 2002 .

[53]  Martin Volk,et al.  Integrated ecological-economic modelling of water pollution abatement management options in the Upper Ems River Basin , 2008 .

[54]  Nirmal Keshava,et al.  Best bands selection for detection in hyperspectral processing , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[55]  G. Fischer,et al.  Effects of climate change on global food production under SRES emissions and socio-economic scenarios , 2004 .

[56]  Giovanni Zurlini,et al.  Multi-scale vulnerability of natural capital in a panarchy of social–ecological landscapes , 2010 .