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A New Hyperspectral Index for Estimating Copper Content in an Indicative Plant for the Exploration of Copper Deposit

  • Shichao Cui
  • Rufu Ding
  • Kefa Zhou
Article
  • 118 Downloads

Abstract

With the rapid development of hyperspectral technology, estimating metal content in plants by establishing the relationship between vegetation indices and metal content has become popular. However, few researchers have studied whether the vegetation index can be used to estimate the metal content in Seriphidium terrae-albae. It is essential to estimate the metal content in S. terrae-albae because the geochemical information gathered in its body can provide reference for the exploration of concealed deposits. To propose a new vegetation index with which to estimate copper content, this study first analyzes the relationships between three types of vegetation indices, including the ratio, difference, and normalized vegetation index, constructed by the combination of two arbitrary bands in the range of 400–1300 nm and the copper content in S. terrae-albae. The results show that (R747.5 − R572.5)/(R747.5 + R572.5) and R752.5/R517.5 have greater relationships with copper content than other vegetation indices. This method’s applicability for estimating copper content using these two types of vegetation indices is compared from three different angles: the accuracy of the metal content estimation, the sensitivity of the vegetation index to the metal content, and the influence of spectral scale. The results reveal that R752.5/R517.5 has a more stable spectral width than (R747.5 − R572.5)/(R747.5 + R572.5), and it can also be used to easily detect subtle changes in copper content. Finally, we determine that R752.5/R517.5 is the best vegetation index for estimating the copper content in S. terrae-albae.

Keywords

Seriphidium terrae-albae Copper content Vegetation indices Spectral scale 

Notes

Acknowledgements

This research is funded by National Natural Science Foundation of China (Grant Nos. U1503291, 41402296), International cooperation project of the Xinjiang Uygur Autonomous Region (Grant No. 20156017), Key Laboratory fund of Xinjiang Uygur Autonomous Region (Grant No. 2016D03006). The “one belt and one road” team of the Chinese Academy of Sciences (2017-XBZG-BR-002), Key R&D Program of Xinjiang Uygur Autonomous Region (2017B03017-2).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Asmaryan S, Warner TA, Muradyan V, Nersisyan G (2013) Mapping tree stress associated with urban pollution using the worldview-2 red edge band. Remote Sens Lett 4(2):200–209.  https://doi.org/10.1080/2150704x.2012.715771 CrossRefGoogle Scholar
  2. Baret F, Jacquemoud S, Guyot G, Leprieur C (1992) Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sens Environ 41(2–3):133–142.  https://doi.org/10.1016/0034-4257(92)90073-s CrossRefGoogle Scholar
  3. Chi GY, Shi Y, Chen X, Ma J, Zheng TH (2012) Effects of metal stress on visible/near-infrared reflectance spectra of vegetation. Adv Mater Res 347–353:2735–2738.  https://doi.org/10.4028/www.scientific.net/amr.347-353.2735 CrossRefGoogle Scholar
  4. Christian F, Wolfgang B (2002) Monitoring of Environmental changes caused by hard coal mining. Proceedings of SPIE 4545: 64–72.  https://doi.org/10.1117/12.453691
  5. Collins W, Chang SH, Raines GL, Canney F, Ashley R (1983) Airborne biogeophysical mapping of hidden mineral deposits. Econ Geol 78(4):737–749.  https://doi.org/10.2113/gsecongeo.78.4.737 CrossRefGoogle Scholar
  6. Filippidis A, Papastergios G, Kantiranis N, Michailidis K, Chatzikirkou A, Katirtzoglou K (2012) The species of Silene compacta, Fischer as indicator of zinc, iron and copper mineralization. Chem der Erde—Geochem 72(1):71–76.  https://doi.org/10.1016/j.chemer.2011.11.003 CrossRefGoogle Scholar
  7. Götze C, Jung A, Merbach I, Wennrich R, Gläßer C (2010) Spectrometric analyses in comparison to the physiological condition of heavy metal stressed floodplain vegetation in a standardised experiment. Cent Eur J Geosci 2(2):132–137.  https://doi.org/10.2478/v10085-010-0002-y CrossRefGoogle Scholar
  8. Hede ANH, Kashiwaya K, Koike K, Sakurai S (2015) A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area. Remote Sens Environ 171:83–97.  https://doi.org/10.1016/j.rse.2015.10.006 CrossRefGoogle Scholar
  9. Horler DNH, Barber J, Barringer AR (1980) Effects of heavy metals on the absorbance and reflectance spectra of plants. Int J Remote Sens 1(2):121–136.  https://doi.org/10.1080/01431168008547550 CrossRefGoogle Scholar
  10. Li XW (2005) Retrospect prospect and innovation in quantitative remote sensing. J Henan Univ (Nat Sci) 35(4):49–56 (In Chinese)Google Scholar
  11. Li XW, Wang WT (2013) Prospects on future developments of quantitative remote sensing. Acta Geogr Sin 68(9):1163–1169 (In Chinese)Google Scholar
  12. Liu Y, Chen H, Wu G, Wu X (2010) Feasibility of estimating heavy metal concentrations in Phragmites austrakis using laboratory-based hyperspectral data—a case study along Le’an River, China. Int J Appl Earth Obs Geoinf 12:S166–S170.  https://doi.org/10.1016/j.jag.2010.01.003 CrossRefGoogle Scholar
  13. Liu M, Liu X, Ding W, Wu L (2011a) Monitoring stress levels on rice heavy metal pollution from hyperspectral reflectance using wavelet-fractal analysis. Int J Appl Earth Obs Geoinf 13(2):246–255.  https://doi.org/10.1016/j.jag.2010.12.006 CrossRefGoogle Scholar
  14. Liu M, Liu X, Wu L, Duan L, Zhong B (2011b) Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance. Comput Geosci 37(9):1254–1263.  https://doi.org/10.1016/j.cageo.2010.11.019 CrossRefGoogle Scholar
  15. Lottermoser BG, Ashley PM, Munksgaard NC (2008) Biogeochemistry of Pb–Zn gossans, northwest Queensland, Australia: implication for mineral exploration and mine site rehabilitation. Appl Geochem 23(4):723–742.  https://doi.org/10.1016/j.apgeochem.2007.12.001 CrossRefGoogle Scholar
  16. Miller JR, Hare EW, Wu J (1990) Quantitative characterization of the vegetation red edge reflectance 1. an inverted-Gaussian reflectance model. Int J Remote Sens 11(10):1755–1773.  https://doi.org/10.1080/01431169008955128 CrossRefGoogle Scholar
  17. Özdemir Z (2005) Pinus brutia as a biogeochemical medium to detect iron and zinc in soil analysis, chromite deposits of the area Mersin. Turk Chem der Erde—Geochem 65(1):79–88.  https://doi.org/10.1016/j.chemer.2003.09.001 CrossRefGoogle Scholar
  18. Özdemi̇r Z, Sağıroğlu A (2000) Salix acmophylla, tamarix smyrnensis and phragmites australis as biogeochemical indicators for copper deposits in Elazığ, Turkey. J Asian Earth Sci 18(5):595–601.  https://doi.org/10.1016/s1367-9120(99)00065-6 CrossRefGoogle Scholar
  19. Pratas J, Prasad MNV, Freitas H, Conde L (2005) Plants growing in abandoned mines of Portugal are useful for biogeochemical exploration of arsenic, antimony, tungsten and mine reclamation. J Geochem Explor 85(3):99–107.  https://doi.org/10.1016/j.gexplo.2004.11.003 CrossRefGoogle Scholar
  20. Reid N, Hill SM (2010) Biogeochemical sampling for mineral exploration in arid terrains: Tanami gold province, Australia. J Geochem Explor 104(3):105–117.  https://doi.org/10.1016/j.gexplo.2010.01.004 CrossRefGoogle Scholar
  21. Ren HY, Zhuang DF, Pan JJ, Shi XZ, Wang HJ (2008) Hyper-spectral remote sensing to monitor vegetation stress. J Soils Sediments 8(5):323–326.  https://doi.org/10.1007/s11368-008-0030-4 CrossRefGoogle Scholar
  22. Schuerger AC, Capelle GA, Di Benedetto JA, Mao C, Thai CN, Richards JT, Blank TA, Stryjewski EC (2003) Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum, Flugge.). Remote Sens Environ 84(4):572–588.  https://doi.org/10.1016/s0034-4257(02)00181-5 CrossRefGoogle Scholar
  23. Smith KL, Steven MD, Colls JJ (2004) Use of hyperspectral derivative ratios in the red-edge region to identify plants stress response to gas leak. Remote Sens Environ 92(2):207–217.  https://doi.org/10.1016/j.rse.2004.06.002 CrossRefGoogle Scholar
  24. Song W, Lei LQ, Song CA, Ding RF (2016) Characteristics of phytogeochemical and prospecting choice of effective plants and elements in Kalatongke Cu–Ni ore field, Xinjiang. J Guilin Univ Technol 36(2):195–206 (In Chinese)Google Scholar
  25. Song CA, Song W, Ding RF, Lei LQ (2017) Phytogeochemical characteristics of Seriphidium terrae-albae (Krash) Poljak in the metallic ore deposits in North part of East Junggar desert area, Xinjinag and their prospecting significance. Geotecton et Metallog 41(1):122–132 (In Chinese)Google Scholar
  26. Sridhar BBM, Han FX, Diehl SV, Monts DL, Su Y (2007) Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium. Int J Remote Sens 28(5):1041–1054.  https://doi.org/10.1080/01431160500075832 CrossRefGoogle Scholar
  27. Viña A, Gitelson AA (2005) New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys Res Lett 32(17):195–221.  https://doi.org/10.1029/2005gl023647 CrossRefGoogle Scholar
  28. Viña A, Gitelson AA, Nguy-Robertson AL, Peng Y (2011) Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens Environ 115(12):3468–3478.  https://doi.org/10.1016/j.rse.2011.08.010 CrossRefGoogle Scholar
  29. Wang JJ, Wang TJ, Shi TZ, Wu GF, Skidmore AK (2015) A wavelet-based area parameter for indirectly estimating copper concentration in carex leaves from canopy reflectance. Remote Sens 7:15340–15360.  https://doi.org/10.3390/rs71115340 CrossRefGoogle Scholar
  30. Zhang C, Ren H, Qin Q, Ersoy OK (2017) A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI). Remote Sens Lett 8(6):576–585.  https://doi.org/10.1080/2150704X.2017.1306135 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  2. 2.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  3. 3.Xinjiang Key Laboratory of Mineral Resources and Digital GeologyUrumqiChina
  4. 4.University of the Chinese Academy of SciencesBeijingChina
  5. 5.China Non-Ferrous Metals Resources Geological SurveyBeijingChina

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