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Study on Heavy Metal in Soil Based on Spectral Second-Order Differential Gabor Transform

  • Pingjie Fu
  • Keming YangEmail author
  • Feisheng Feng
Research Article
  • 56 Downloads

Abstract

Topsoil was collected from the surroundings of a mining area in Xilin Gol League which was located in Xilinhot of Inner Mongolia Autonomous Region, and measurement was conducted to obtain soil spectral curves and the concentrations of plumbum (Pb), zinc (Zn), copper (Cu), and nickel (Ni) in soil, so as to study the relationship between transform spectrum expansion coefficient contour distribution of different soil samples in this area and heavy metal concentration in soil. The method was based on frequency domain. First, soil spectra were converted to sparse spectra. Then, by combining soil sparse spectra with Gabor transform theory, a conversion to the frequency domain is conducted to detect the subtle difference between soil spectra of heavy metal at different concentrations. This approach helps to get rid of studies which aim to infer the content of heavy metal in soil simply through the information of spectral reflectance in soil, and a frequency domain transform-based analysis of spectral information concerning heavy metal overproof in soil is carried out instead, ultimately achieving the purpose of detecting the existence of transient spectra of heavy metal overproof in soil. This study was to provide a basis for the hyperspectral frequency domain study of soil heavy metal overproof. According to the study, this method could be used to detect the threshold concentration of Pb overproof in the surrounding soil of the mining area in Xilin Gol League. Furthermore, when Pb and Ni concentrations in soil exceeded the background value of soil environment in Inner Mongolia Autonomous Region, an over-standard Zn concentration in this area would lead to changes in transform spectrum expansion coefficient contour distribution and, thus, could be used to detect the threshold concentration of Zn overproof in this area.

Keywords

Hyperspectral remote sensing Soil heavy metals Frequency domain transform spectrum Spectral second-order differential Gabor expansion 

Notes

Acknowledgements

This work was supported by the State Key Laboratory of Coal Resources and Safe Mining 2017 Open Foundation (SKLCRSM17KFA09) and the National Natural Science Foundation of China (41271436).

References

  1. Al Maliki, A., Bruce, D., & Owens, G. (2014). Prediction of lead concentration in soil using reflectance spectroscopy. Environmental Technology & Innovation, 1, 8–15.CrossRefGoogle Scholar
  2. Chen, T., Chang, Q., Liu, J., Clevers, J. G. P. W., & Kooistra, L. (2016). Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China. Science of the Total Environment, 565, 155–164.CrossRefGoogle Scholar
  3. Du, H. S., Zhang, X. D., Jin, Y., & Hou, Y. D. (2014). Face image recognition method via Gabor low-rank recovery sparse representation-based classification. Acta Electronica Sinica, 42(12), 2386–2393.  https://doi.org/10.3969/j.issn.0372-2112.2014.12.008.Google Scholar
  4. Fard, R. S., & Matinfar, H. R. (2016). Capability of vis-NIR spectroscopy and Landsat 8 spectral data to predict soil heavy metals in polluted agricultural land (Iran). Arabian Journal of Geosciences, 9(20), 745.CrossRefGoogle Scholar
  5. Feng, P., & Cao, X. B. (2011). An empirical study on the stock price analysis and prediction based on ARMA model. Mathematics in Practice and Theory, 41(22), 84–90.Google Scholar
  6. Ghasemi, A., Manesh, S. M. T., Tabatabaei, S. H., & Mokhtari, A. R. (2015). Geoenvironmental studies and heavy metal mapping in soil: The case of Ghohroud area, Iran. Environmental Earth Sciences, 74(6), 5221–5232.CrossRefGoogle Scholar
  7. Gholizadeh, A., Borùvka, L., Saberioon, M. M., Kozák, J., Vašát, R., & Nemecek, K. (2015a). Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil and Water Research, 10(4), 218–227.  https://doi.org/10.17221/113/2015-SWR.CrossRefGoogle Scholar
  8. Gholizadeh, A., Borůvka, L., Vašát, R., Saberioon, M., Klement, A., Kratina, J., et al. (2015b). Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: A case study. PLoS ONE, 10(2), e0117457.CrossRefGoogle Scholar
  9. Gong, S. Q., Wang, X., Shen, R. P., Liu, Z. B., & Yun-Mei, L. I. (2010). Study on heavy metal element content in the coastal saline soil by hyperspectral remote sensing. Remote Sensing Technology & Application, 25(2), 169–177.Google Scholar
  10. He, X., Lin, J. Z., Zhang, Y., & Chen, Z. P. (2011). Signal detection and features analysis in electronic warfare against space-borne SAR based on Gabor representation. Signal Processing, 27(11), 1734–1738.Google Scholar
  11. He, J. L., Zhang, S. Y., Zha, Y., & Jiang, J. J. (2015). Review of retrieving soil heavy metal content by hyperspectral remote sensing. Remote Sensing Technology and Application, 03(30), 407–412.  https://doi.org/10.11873/j.issn.1004-0323.2015.3.0407.Google Scholar
  12. Hou, L. M., Wei-Qi, W. U., & Zhang, X. P. (2014). Audio re-sampling detection in audio forensics based on second-order derivative. Journal of Shanghai University, 20(03), 304–312.  https://doi.org/10.3969/j.issn.1007-2861.2013.07.028.Google Scholar
  13. Li, R. (2016). Sparse time-frequency representation based on discrete Gabor transform. Hefei: Anhui University.Google Scholar
  14. Li, X. H., Hu, X. Q., Yin, J. X., & Yan, H. R. (2014). Feature extraction based on Gabor transform. Modular Machine Tool & Automatic Manufacturing Technique, 01, 29–30+34.  https://doi.org/10.13462/j.cnki.Mmtamt.2014.01.008.CrossRefGoogle Scholar
  15. Liu, Y., Li, W., Wu, G., & Xu, X. (2011). Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—A case study along Le’an River, China. Geo-spatial Information Science, 14(1), 10–16.CrossRefGoogle Scholar
  16. Liu, J. B., Zhang, Y., Wang, H. Y., & Du, Y. C. (2018). Study on the prediction of soil heavy metal elements content based on visible near-infrared spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 199, 43–49.  https://doi.org/10.1016/j.saa.2018.03.040.CrossRefGoogle Scholar
  17. Nan, C. Q., Liu, H. H., Fan, J., & Lu, Q. D. (2012). Comprehensive evaluation of heavy metal elements in soil of Washi Town based on GIS. Soil and Water Conservation in China, 10, 70–72.  https://doi.org/10.14123/j.cnki.swcc.2012.Google Scholar
  18. Niazi, N. K., Singh, B., & Minasny, B. (2015). Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site. International Journal of Environmental Science and Technology, 12(6), 1965–1974.CrossRefGoogle Scholar
  19. Pandit, C. M., Filippelli, G. M., & Li, L. (2010). Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression. International Journal of Remote Sensing, 31(15), 4111–4123.  https://doi.org/10.1080/01431160903229200.CrossRefGoogle Scholar
  20. Peng, Y., Kheir, R. B., Adhikari, K., Malinowski, R., Greve, M. B., Knadel, M., et al. (2016). Digital mapping of toxic metals in Qatari soils using remote sensing and ancillary data. Remote Sensing, 8(12), 1003.CrossRefGoogle Scholar
  21. Shen, W. J., Jiang, C. Q., Shi, H., Wang, C. H., Li, M. S., & Jiangsu, P. E. M. C. (2014). Progress in soil heavy metal pollution monitoring via remote sensing technology. Remote Sensing Information, 29, 112–117.  https://doi.org/10.3969/j.issn.1000-3177,2014.06.022.Google Scholar
  22. Shi, Z. F., & Wang, L. (2013). Contents of soil heavy metals and evaluation on the potential pollution risk in Shenmu mining area. Journal of Agro-Environment Science, 32(6), 1150–1158.  https://doi.org/10.11654/jaes.2013.06.010.Google Scholar
  23. Shi, Z., Wang, Q., Peng, J., Ji, W., Liu, H., Li, X., et al. (2014). Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations. Science China. Earth Sciences, 57(7), 1671.Google Scholar
  24. Shokr, M. S., El Baroudy, A. A., Fullen, M. A., El-Beshbeshy, T. R., Ali, R. R., Elhalim, A., et al. (2016). Mapping of heavy metal contamination in alluvial soils of the Middle Nile Delta of Egypt. Journal of Environmental Engineering and Landscape Management, 24(3), 218–231.CrossRefGoogle Scholar
  25. Stazi, S. R., Antonucci, F., Pallottino, F., Costa, C., Marabottini, R., Petruccioli, M., et al. (2014). Hyperspectral visible–near infrared determination of arsenic concentration in soil. Communications in Soil Science and Plant Analysis, 45(22), 2911–2920.CrossRefGoogle Scholar
  26. Tian, L., Chen, Y. P., & Liang, H. L. (2013). The application of smoothing pseudo Wigner–Ville distribution in seismic signal processing. Journal of Xinjiang Normal University, 32(03), 1–4.  https://doi.org/10.3969/j.issn.1008-9659.2013.03.001.Google Scholar
  27. Todorova, M., Mouazen, A. M., Lange, H., & Atanassova, S. (2014). Potential of near-infrared spectroscopy for measurement of heavy metals in soil as affected by calibration set size. Water, Air, and Soil pollution, 225(8), 2036.  https://doi.org/10.1007/s11270-014-2036-4.CrossRefGoogle Scholar
  28. Tong, J. J., Li, L., Lin, Q. G., & Zhu, D. H. (2017). SSVEP brain–computer interface (BCI) system using smoothed pseudo Wiener–Ville distribution. Journal of Zhejiang University (Engineering Science), 51(03), 598–604.  https://doi.org/10.3785/j.issn.1008-973x.2017.03.023.Google Scholar
  29. Wang, F., Bian, H. Y., Zhang, Y. H., Duan, C. W., & Chen, G. (2016). Hilbert–Huang transform combined with smoothed pseudo Wigner–Vine time-frequency distribution to identify reservoir fluid properties. Geophysical Prospecting for Petroleum, 55(06), 851–860.  https://doi.org/10.3969/j.issn.1000-1441.2016.06.010.Google Scholar
  30. Wang, Q. R., Cai, Q. X., Ma, C. A., & Li, F. Y. (2006). Assessment on heavy metal pollution in Shengli Open Pit Mine. Coal Science and Technology, 34(10), 72–73+78.Google Scholar
  31. Wang, X. H., Deng, K. Z., & Yang, H. C. (2013). Buildingup of remote sensing models for heavy metal pollution in soil: Take the pollution of lead and zinc mine in Shuikou mountain as an example. Bulletin of Surveying and Mapping, 3, 29–31.Google Scholar
  32. Woźniak, M., & Połap, D. (2017). Voice recognition through the use of Gabor transform and heuristic algorithm. International Journal of Electronics & Telecommunications, 63(2), 159–164.  https://doi.org/10.1515/eletel-2017-0021.CrossRefGoogle Scholar
  33. Wu, Y., Chen, J., Ji, J., Gong, P., Liao, Q., Tian, Q., et al. (2007). A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Science Society of America Journal, 71(3), 918–926.  https://doi.org/10.2136/sssaj2006.0285.CrossRefGoogle Scholar
  34. Wu, Y. Z., Chen, J., Ji, J. F., Tian, Q. J., & Wu, X. M. (2005). Feasibility of reflectance spectroscopy for the assessment of soil mercury contamination. Environmental Science and Technology, 39(3), 873–878.CrossRefGoogle Scholar
  35. Wu, J. S., Song, J., Zheng, M. K., Xie, J., Li, J. J., & Huang, X. L. (2011). Review of methods for monitoring soil heavy metal concentrations. Journal of Northeast Agricultural University, 42(5), 133–139.Google Scholar
  36. Xiao, J. Y., Wang, Y., Zang, Q., Li, X., Zhao, P., & Wan, Y. L. (2013). Review on methods of monitoring soil heavy metal based on hyperspectral remote sensing data. Hubei Agricultural Sciences, 6, 003.  https://doi.org/10.14088/j.cnki.issn0439-8114.2013.06.019.Google Scholar
  37. Yang, Y., Liu, A. J., Chao, L. M. Q. Q. G., Shan, Y. M., Wu, N. T., Chen, H. J., et al. (2016). Spatial distribution of soil heavy metals of opencut coal mining in inner Mongolia Xilingol typical Steppe. Ecology and Environmental Sciences, 25(5), 885–892.  https://doi.org/10.16258/j.cnki.1674-5906.2016.05.023.Google Scholar
  38. Ye, Z., Bai, L., & Yongjian, N. (2016). Hyperspectral image classification algorithm based on Gabor feature and locality-preserving dimensionality reduction. Acta Optica Sinica, 36(10), 1028003.  https://doi.org/10.3788/AOS201636.1028003.CrossRefGoogle Scholar
  39. Zheng, Y. G., Zhang, Z. G., Yao, D. X., & Chen, X. Y. (2013). Characteristics of temporal spatial distribution and enrichment of heavy metals in coal mine reclaimed soil. Journal of China Coal Society, 38(8), 1476–1483.  https://doi.org/10.13225/j.cnki.jccs.2013.08.033.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Coal Resources and Safe MiningChina University of Mining and TechnologyBeijingChina

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