Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index

ABSTRACT This study analysed the changing pattern of the spectral features of copper-stressed leaves for several vegetation types, and explored the mechanism of the Copper Stress Vegetation Index (CSVI). First, the change of seven key spectral features (Green Peak, Red Valley, Red Shoulder, NIR (Near Infrared) Reflectance Platform, Blue-Edge, Yellow-Edge, and Red-Edge) with copper stress level from low to high, were presented and analysed. Second, the chlorophyll contents in leaves were investigated to explain the spectral characteristics at the visible band. Third, the leaf structure and absorption related to copper were analysed to explore the reason of changing pattern at NIR band. The results showed that there are significant changing trends at Blue-Edge, Green Peak, and Red-Edge while the changing pattern at NIR band depends on the vegetation type. The analysis on chlorophyll content, leaf structure, and absorption related to copper, gave an overall mechanism explanation for the spectral characteristics of copper-stressed vegetation and the wavelengths used in CSVI. The results and conclusions in this paper, contribute new knowledge of copper-stressed vegetation reflectance and the CSVI, and provide mechanism basement for the remote sensing of copper-stressed vegetation.

[1]  P. C. Nagajyoti,et al.  Heavy metals, occurrence and toxicity for plants: a review , 2010 .

[2]  Ying Qu,et al.  [A novel method for extracting leaf-level solar-induced fluorescence of typical crops under Cu stress]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[3]  Alireza Sharifi,et al.  Estimation of biophysical parameters in wheat crops in Golestan province using ultra-high resolution images , 2018 .

[4]  Timothy A. Warner,et al.  Mapping tree stress associated with urban pollution using the WorldView-2 Red Edge band , 2013 .

[5]  Si-Ning Chen,et al.  [Study on the spectrum response of Brassica Campestris L leaf to the zinc pollution]. , 2007, Guang pu xue yu guang pu fen xi = Guang pu.

[6]  A Eshel,et al.  Photosynthesis, chlorophyll integrity, and spectral reflectance in lichens exposed to air pollution. , 2001, Journal of environmental quality.

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

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

[9]  C. Polan,et al.  Electrophoretic analysis of ruminal degradability of corn proteins. , 1994, Journal of dairy science.

[10]  Huazhong Ren,et al.  A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI) , 2017 .

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

[12]  Jie Zhao,et al.  Comparing the effects of different spectral transformations on the estimation of the copper content of Seriphidium terrae-albae , 2018, Journal of Applied Remote Sensing.

[13]  Yonghua Qu,et al.  A reflectance spectra model for copper-stressed leaves: advances in the PROSPECT model through addition of the specific absorption coefficients of the copper ion , 2014 .

[14]  [Prediction of Cadmium Content in the Leaves of Navel Orange in Heavy Metal Contaminated Soil Using VIS-NIR Reflectance Spectroscopy]. , 2015, Guang pu xue yu guang pu fen xi = Guang pu.

[15]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

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

[17]  G. Jancsó,et al.  Effect of D and 18O isotope substitution on the absorption spectra of aqueous copper sulfate solutions , 2005 .

[18]  Lei Liu,et al.  Detecting Sulfuric and Nitric Acid Rain Stresses on Quercus glauca through Hyperspectral Responses , 2018, Sensors.

[19]  Suhong Liu,et al.  Advancing the PROSPECT-5 Model to Simulate the Spectral Reflectance of Copper-Stressed Leaves , 2017, Remote. Sens..

[20]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[21]  Prasad S. Thenkabail,et al.  Hyperspectral Data in Long-Term, Cross-Sensor Continuity Studies , 2016 .

[22]  Moon Sung Kim.  THE USE OF NARROW SPECTRAL BANDS FOR IMPROVING REMOTE SENSING ESTIMATIONS OF FRACTIONALLY ABSORBED PHOTOSYNTHETICALLY ACTIVE RADIATION , 1994 .

[23]  Dawei Liu,et al.  Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China , 2018, Remote. Sens..

[24]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[25]  Katsuaki Koike,et al.  A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area , 2015 .

[26]  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.

[27]  Junjie Wang,et al.  A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance , 2015, Remote. Sens..

[28]  Juan Hou,et al.  Study on the spectral response of Brassica Campestris L. leaf to the copper pollution , 2008 .

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

[30]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[31]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[32]  Stéphane Jacquemoud,et al.  PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .

[33]  Liu Xinhui,et al.  Study on the spectral response of Brassica Campestris L . leaf to the copper pollution , 2008 .

[34]  Peter J Ralph,et al.  Towards a more ecologically relevant assessment of the impact of heavy metals on the photosynthesis of the seagrass, Zostera capricorni. , 2002, Marine pollution bulletin.