Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China

This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

[1]  M. Schlerf,et al.  Remote sensing of forest biophysical variables using HyMap imaging spectrometer data , 2005 .

[2]  C. Elvidge Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[3]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[4]  S. Prasad,et al.  Growth, photosynthetic electron transport, and antioxidant responses of young soybean seedlings to simultaneous exposure of nickel and UV-B stress , 2005, Photosynthetica.

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

[6]  J. Clevers The use of imaging spectrometry for agricultural applications , 1999 .

[7]  Meiling Liu,et al.  A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[8]  E. Terrence Slonecker,et al.  Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties , 2018, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation.

[9]  Terje Gobakken,et al.  Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data , 2005 .

[10]  Christian Götze,et al.  Spectrometric analyses in comparison to the physiological condition of heavy metal stressed floodplain vegetation in a standardised experiment , 2010 .

[11]  Sarah C Dunagan,et al.  Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). , 2007, Environmental pollution.

[12]  G. Guyot,et al.  Utilisation de la Haute Resolution Spectrale pour Suivre L'etat des Couverts Vegetaux , 1988 .

[13]  Barry Haack,et al.  Spectroscopic Analysis of Arsenic Uptake in Pteris Ferns , 2009, Remote. Sens..

[14]  B. Sridhar,et al.  Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium , 2007 .

[15]  A. Skidmore,et al.  Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features , 2004 .

[16]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[17]  Freek D. van der Meer,et al.  Indicator kriging applied to absorption band analysis in hyperspectral imagery: A case study from the Rodalquilar epithermal gold mining area, SE Spain , 2006 .

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

[19]  Martin Spiller,et al.  In situ detection of heavy metal substituted chlorophylls in water plants , 1998, Photosynthesis Research.

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

[21]  G. Bonham-Carter Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data , 1988 .

[22]  Clement Atzberger,et al.  LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .

[23]  Byun-Woo Lee,et al.  Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression , 2006 .

[24]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[25]  J Zhang,et al.  Zinc and cadmium accumulation and tolerance in populations of Sedum alfredii. , 2007, Environmental pollution.

[26]  Lhotáková Zuzana,et al.  Detection of multiple stresses in Scots pine growing at post-mining sites using visible to near-infrared spectroscopy. , 2013, Environmental science. Processes & impacts.

[27]  Bo-Ching Chen,et al.  Model evaluation of plant metal content and biomass yield for the phytoextraction of heavy metals by switchgrass. , 2012, Ecotoxicology and environmental safety.

[28]  A. Skidmore,et al.  Red edge shift and biochemical content in grass canopies , 2007 .

[29]  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.

[30]  Lalit Kumar,et al.  Imaging Spectrometry and Vegetation Science , 2001 .

[31]  Tingchen Jiang,et al.  Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China , 2017 .

[32]  K. Tansey,et al.  Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. , 2015, Environmental pollution.

[33]  Xinde Cao,et al.  Accumulation of Pb, Cu, and Zn in native plants growing on a contaminated Florida site. , 2006, The Science of the total environment.

[34]  Zhongxin Chen,et al.  Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance. , 2014, Marine pollution bulletin.

[35]  Qiuming Cheng,et al.  On the relationship between the early spring Indian Ocean's sea surface temperature (SST) and the Tibetan Plateau atmospheric heat source in summer , 2018 .

[36]  Xuezheng Shi,et al.  Hyper-spectral remote sensing to monitor vegetation stress , 2008 .

[37]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[38]  Robert K. Vincent,et al.  Remote sensing of soybean stress as an indicator of chemical concentration of biosolid amended surface soils , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[39]  Moses Azong Cho,et al.  Hyperspectral reflectance features of water hyacinth growing under feeding stresses of Neochetina spp. and different heavy metal pollutants , 2014 .

[40]  F. Küpper,et al.  Environmental relevance of heavy metal-substituted chlorophylls using the example of water plants , 1996 .

[41]  A. Skidmore,et al.  Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species , 2005 .

[42]  B. Turner,et al.  Estimating foliage nitrogen concentration from HYMAP data using continuum, removal analysis , 2004 .

[43]  W. Cohen,et al.  Hyperspectral versus multispectral data for estimating leaf area index in four different biomes , 2004 .

[44]  M. Cho,et al.  A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method , 2006 .

[45]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[46]  David G. Rossiter,et al.  Spectral changes in the leaves of barley plant due to phytoremediation of metals—results from a pot study , 2015 .

[47]  Qiuming Cheng,et al.  Spatiotemporal shoreline dynamics of Namibian coastal lagoons derived by a dense remote sensing time series approach , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[48]  Guofeng Wu,et al.  Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—A case study along Le’an River, China , 2011, Geo spatial Inf. Sci..

[49]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[50]  Sheng-bo Chen,et al.  [Vegetation stress spectra and their relations with the contents of metal elements within the plant leaves in metal mines in Heilongjiang]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[51]  Ting Li,et al.  Estimating regional heavy metal concentrations in rice by scaling up a field-scale heavy metal assessment model , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[52]  Ruiliang Pu,et al.  Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..

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

[54]  Raymond F. Kokaly,et al.  Investigating a Physical Basis for Spectroscopic Estimates of Leaf Nitrogen Concentration , 2001 .

[55]  Jay Gao,et al.  Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges , 2018 .

[56]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[57]  Andrew K. Skidmore,et al.  Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[58]  L. Lazăr,et al.  Assessment of physiological state of Betula pendula and Carpinus betulus through leaf reflectance measurements , 2015 .

[59]  T. Thangaradjou,et al.  Geochemical and geo-statistical assessment of heavy metal concentration in the sediments of different coastal ecosystems of Andaman Islands, India , 2010 .

[60]  N. Milton,et al.  Arsenic- and selenium-induced changes in spectral reflectance and morphology of soybean plants , 1989 .

[61]  Masahiko Nagai,et al.  Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy , 2012, Remote. Sens..

[62]  P. Curran Remote sensing of foliar chemistry , 1989 .

[63]  F. Sabins,et al.  Remote sensing for mineral exploration , 1999 .

[64]  P. Curran,et al.  A new technique for interpolating the reflectance red edge position , 1998 .

[65]  Guofeng Wu,et al.  Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. , 2016, Journal of hazardous materials.

[66]  Thomas Kemper,et al.  Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. , 2002, Environmental science & technology.

[67]  J. Evans Straightforward Statistics for the Behavioral Sciences , 1995 .