Estimation of copper concentration of rocks using hyperspectral technology

doi:10.1007/s11707-019-0753-0

Front. Earth Sci.

2019, Vol. 13

Issue (3) : 563-574 https://doi.org/10.1007/s11707-019-0753-0

RESEARCH ARTICLE

Estimation of copper concentration of rocks using hyperspectral technology

Shichao CUI^1,^2,^3,⁴, Kefa ZHOU^1,^2,^3,⁴(

), Rufu DING⁵, Guo JIANG^1,^2,^3,⁴

¹. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
². Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
³. Xinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China
⁴. University of the Chinese Academy of Sciences, Beijing 100049, China
⁵. China Non-Ferrous Metals Resources Geological Survey, Beijing 100012, China

Download: PDF(2061 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Rock geochemical information is important for mineral exploration and provides a theoretical basis for the rapid delineation of hidden minerals. Remote sensing technology provides the possibility of rapid and large-scale extraction of geochemical information from the earth’s surface. This study analyzed the relationship between copper concentration and rock spectra by first collecting 222 rock samples, and then measuring the copper concentration of rock samples in the laboratory and reflectance spectra using an ASD FieldSpec3 portable spectrometer. It finally established quantitative relationships between the original spectra, first-order derivative spectra and second-order derivative spectra and copper concentration, respectively, using the partial least squares support vector machine method (PLS-SVM). The results show that 1) The estimation accuracy of using second-order derivatives spectra as input parameters to establish a model for estimating copper concentration is the highest, and the determined coefficient (R²) between the predicted value and real value reaches 0.54. 2) When the copper concentration is less than 80 mg/kg, the inversion model of copper concentration established using PLS-SVM obtains a good result. The R² between the predicted copper concentration and the real copper concentration reached 0.70248. When the copper concentration is greater than 80 mg/kg, the inversion model of copper concentration established using partial least squares (PLS) obtains a good result. The R² between the predicted copper concentration and the real copper concentration reached 0.49. The R² between real copper concentration and copper predicted by the method of piecewise separate modeling reaches 0.816. Therefore, the method of segmental modeling has great potential to improve the accuracy of copper concentration inversion.

Keywords copper concentration rock geochemical information PLS-SVM remote sensing

Corresponding Author(s): Kefa ZHOU

Just Accepted Date: 01 August 2019 Online First Date: 04 September 2019 Issue Date: 15 October 2019

Cite this article:

Shichao CUI,Kefa ZHOU,Rufu DING, et al. Estimation of copper concentration of rocks using hyperspectral technology[J]. Front. Earth Sci., 2019, 13(3): 563-574.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0753-0
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I3/563

Fig.1 Study area and sampling location; (b) Distribution of sampling points in Google Map; (a) field photographs of study area.

Fig.2 Changes of the spectra of different types of rock with different concentration on copper. (a) Slate; (b) granite diorite; (c) granite; (d) hornblende pyroxenite; (e) hornblende gabbro; (f) mudstone; (g) diorite-porphyrite; (h) diorite; (i) gabbro.

Fig.3 Histogram of copper concentration of rock samples.

Fig.4 Correlations between three types of transformed spectra and the copper concentration of rock samples.

Tab.1 Comparison of inversion accuracy of copper concentration using three different types spectral transformations

Fig.5 Scatter plots between the predicted value of model established using different spectral transformations and the true value. (a) Original spectra; (b) first-order derivative spectra; (c) second-order derivatives spectra.

Fig.6 Comparison of inversion accuracy under different levels of copper concentration. (a) Copper concentration of rock samples is less than 80 mg/kg; (b) copper concentration of rock samples is more than 80 mg/kg; (c) the scatter plot between real copper concentration and copper predicted by the method of piecewise separate modeling.

Fig.7 Ultra low altitude detection platform.

1	E M Abdel-Rahman, O Mutanga, J Odindi, E Adam, A Odindo, R Ismail (2014). A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of swiss chard grown under different irrigation water sources using hyperspectral data. Comput Electron Agric, 106: 11–19 https://doi.org/10.1016/j.compag.2014.05.001
2	Y Altmann, A Halimi, N Dobigeon, J Y Tourneret (2012). Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery. IEEE Trans Image Process, 21(6): 3017–3025 https://doi.org/10.1109/TIP.2012.2187668 pmid: 22345533
3	H Azizi, M A Tarverdi, A Akbarpour (2010). Extraction of hydrothermal alterations from ASTER SWIR data from east Zanjan, Northern Iran. Adv Space Res, 46(1): 99–109 https://doi.org/10.1016/j.asr.2010.03.014
4	C C Borel, S A W Gerstl (1994). Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sens Environ, 47(3): 403–416 https://doi.org/10.1016/0034-4257(94)90107-4
5	E Choe, K W Kim, S Bang, I H Yoon, K Y Lee (2009). Qualitative analysis and mapping of heavy metals in an abandoned Au–Ag mine area using NIR spectroscopy. Environ Geol, 58(3): 477–482 https://doi.org/10.1007/s00254-008-1520-9
6	C Cortes, V N Vapnik (1995). Support vector networks. Mach Learn, 20(3): 273–297 https://doi.org/10.1007/BF00994018
7	A C Fassoniandrade, D C Zanotta, L A Guasselli, A M de Andrade(2017). Linear spectral mixing model for estimating optically active components in estuarine waters of Patos Lagoon, Brazil. Int J Remote Sens, 38(17): 4767–4781 https://doi.org/10.1080/01431161.2017.1323281
8	R M D Freitas, V Haertel, Y E Shimabukuro (2008). Linear spectral mixture model in moderate spatial resolution image data. Bol Ciênc Geod, 14(1): 55–71
9	S Gannouni, N Rebai, S Abdeljaoued (2012). A spectroscopic approach to assess heavy metals contents of the mine waste of Jalta and Bougrine in the north of Tunisia. J Geogr Inf Syst, 4(3): 242–253 https://doi.org/10.4236/jgis.2012.43029
10	P Gong, B Yu (2001). Conifer species recognition: effects of data transformation. Int J Remote Sens, 22(17): 3471–3481 https://doi.org/10.1080/01431160110034654
11	V F Haertel, Y E Shimabukuro (2005). Spectral linear mixing model in low spatial resolution image data. IEEE Trans Geosci Remote Sens, 43(11): 2555–2562 https://doi.org/10.1109/TGRS.2005.848692
12	A N H Hede, K Kashiwaya, K Koike, S Sakurai (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
13	M Honarmand, H Ranjbar, J Shahabpour (2012). Application of principal component analysis and spectral angle mapper in the mapping of hydrothermal alteration in the Jebal–Barez area, Southeastern Iran. Resour Geol, 62(2): 119–139 https://doi.org/10.1111/j.1751-3928.2012.00184.x
14	R Heylen, P Scheunders (2016). A multilinear mixing model for nonlinear spectral unmixing. IEEE Trans Geosci Remote Sens, 54(1): 240–251 https://doi.org/10.1109/TGRS.2015.2453915
15	W S Ibrahim, K Watanabe, K Yonezu (2016). Structural and litho-tectonic controls on Neoproterozoic base metal sulfide and gold mineralization in north Hamisana shear zone, south eastern desert, Egypt: the integrated field, structural, Landsat 7 ETM+ and ASTER data approach. Ore Geol Rev, 79: 62–77 https://doi.org/10.1016/j.oregeorev.2016.05.012
16	T Kemper, S Sommer (2002). Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Technol, 36(12): 2742–2747 https://doi.org/10.1021/es015747j pmid: 12099473
17	M Khaleghi, H Ranjbar, J Shahabpour, M Honarmand (2014). Spectral angle mapping, spectral information divergence, and principal component analysis of the ASTER SWIR data for exploration of porphyry copper mineralization in the Sarduiyeh area, Kerman Province, Iran. Appl Geomat, 6(1): 49–58 https://doi.org/10.1007/s12518-014-0125-0
18	L Kooistra, R Wehrens, R S E W Leuven, L M C Buydens (2001). Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal Chim Acta, 446(1–2): 97–105 https://doi.org/10.1016/S0003-2670(01)01265-X
19	M Liu, Q Z Lin, Q J Wang, H Li (2010). Study on the geochemical anomaly of copper element based on reflectance spectra. Spectrosc Spect Anal, 30(5): 1320–1323 pmid: 20672626
20	D F Malley, P C Williams (1997). Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ Sci Technol, 31(12): 3461–3467 https://doi.org/10.1021/es970214p
21	J Moros, S F O Vallejuelo, A Gredilla, A Diego, J M Madariaga, S Garrigues, M Guardia. (2009). Use of reflectance infrared spectroscopy for monitoring the metal content of the estuarine sediments of the Nerbioi-Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country). Environ Sci Technol, 43(24): 9314–9320 https://doi.org/10.1021/es9005898 pmid: 20000524
22	A M Qaid, H T Basavarajappa, H Ranjbar (2009). Application of principal component analysis to ASTER and ETM+ data for mapping the alteration zones in north east of Hajjah, Yemen. Southeast Asian J Surg, 9(2): 15–21
23	N A Quarmby, J R G Townshend, J J Settle, K H White, M Milnes, T L Hindle, N Silleos (1992). Linear mixture modelling applied to AVHRR data for crop area estimation. Int J Remote Sens, 13(3): 415–425 https://doi.org/10.1080/01431169208904046
24	T W Ray, B C Murray (1996). Nonlinear spectral mixing in desert vegetation. Remote Sens Environ, 55(1): 59–64 https://doi.org/10.1016/0034-4257(95)00171-9
25	A M Rady, D E Guyer, W Kirk, I R Donis-González (2014). The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes. J Food Eng, 135(2): 11–25 https://doi.org/10.1016/j.jfoodeng.2014.02.021
26	H Ranjbar, M Honarmand, Z Moezifar (2004). Application of the Crosta technique for porphyry copper alteration mapping, using ETM data in the southern part of the Iranian volcanic sedimentary belt. J Asian Earth Sci, 24(2): 237–243 https://doi.org/10.1016/j.jseaes.2003.11.001
27	S M Salem, S A Arafa, T M Ramadan, E S A El Gammal(2013). Exploration of copper deposits in Wadi El Regeita area, Southern Sinai, Egypt, with contribution of remote sensing and geophysical data. Arab J Geosci, 6(2): 321–335 https://doi.org/10.1007/s12517-011-0346-z
28	M Sawut, A Ghulam, T Tiyip, Y J Zhang, J L Ding, F Zhang, M Maimaitiyiming (2014). Estimating soil sand content using thermal infrared spectra in arid lands. Int J Appl Earth Obs Geoinf, 33(12): 203–210 https://doi.org/10.1016/j.jag.2014.05.010
29	A C Schuerger, G A Capelle, J A Di Benedetto, C Mao, C N Thai, M D Evans, et al. (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
30	M B Seasholtz, B Kowalski (1993). The parsimony principle applied to multivariate calibration. Anal Chim Acta, 277(2): 165–177 https://doi.org/10.1016/0003-2670(93)80430-S
31	L Serrano, J Peñuelas, S L Ustin (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens Environ, 81(2–3): 355–364 https://doi.org/10.1016/S0034-4257(02)00011-1
32	A Malekzadeh Shafaroudi, M H Karimpour, C R Stern, S A Mazaheri (2009). Hydrothermal alteration mapping in SW Birjand, Iran, using the advanced spaceborne thermal emission and reflection radiometer (ASTER) image processing. J Appl Sci (Faisalabad), 9(5): 829–842 https://doi.org/10.3923/jas.2009.829.842
33	T Shi, H Liu, Y Chen, J Wang, G Wu (2016). Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. J Hazard Mater, 308: 243–252 https://doi.org/10.1016/j.jhazmat.2016.01.022 pmid: 26844405
34	G Siebielec, G W McCarty, T I Stuczynski, J B Reeves 3rd (2004). Near- and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. J Environ Qual, 33(6): 2056–2069 https://doi.org/10.2134/jeq2004.2056 pmid: 15537928
35	Y Song, F Li, Z Yang, G A Ayoko, R L Frost, J Ji (2012). Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang River Delta, China. Appl Clay Sci, 64(4): 75–83 https://doi.org/10.1016/j.clay.2011.09.010
36	Y S Son, M K Kang, W J Yoon (2014). Lithological and mineralogical survey of the Oyu Tolgoi region, Southeastern Gobi, Mongolia using ASTER reflectance and emissivity data. Int J Appl Earth Obs Geoinf, 26(1): 205–216 https://doi.org/10.1016/j.jag.2013.07.004
37	B B M Sridhar, F X Han, S V Diehl, D L Monts, Y Su (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
38	L L Tang, X F Zhang, Y H Liu (2006). Research progress in the foundation and technology of remote sensing lithology information extraction. Mining Research and Development, 26(3): 68–73 (in Chinese)
39	M H Tangestani, F Moore (2001). Comparison of three principal component analysis techniques to porphyry copper alteration mapping: a case study, Meiduk Area, Kerman, Iran. Can J Rem Sens, 27(2): 176–182 https://doi.org/10.1080/07038992.2001.10854931
40	L Wang, Y L Bai, Y Lu, H Wang (2012). Effect on retrieval precision for corn N content by spectrum data transformation. Remote Sensing Technology and Application, 26(2): 220–225 (in Chinese)
41	X Wen, G Hu, X Yang (2007). A Simplified Method for Extracting Mineral Information from Hyperspectral Remote Sensing Image Using SAM Algorithm. In: Zhao P D, Agterberg F, Cheng Q M, eds. The 12th Conference of the international Association for mathematical Geology. China University of Geosciences, 526–529
42	S Wold, M Sjöström, L Eriksson (2001). PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst, 58(2): 109–130 https://doi.org/10.1016/S0169-7439(01)00155-1
43	Y Wu, J Chen, J Ji, P Gong, Q Liao, Q Tian, H Ma (2007). A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Sci Soc Am J, 71(3): 918–926 https://doi.org/10.2136/sssaj2006.0285
44	Y Xue, T G Dai, L Zou, H D Xia, J L Liu (2007). Extracting alteration information based on SVM—taking Jidi area as an example. Geological Survey Re, 30(4): 315–320 (in Chinese)
45	J Yan, K Zhou, J Wang, S Wang, W Wang, L I Dong (2013). Extraction of hyper-spectral remote sensing alteration information based on SAM and SVM. Computer Engineering and Applications, 49(19): 141–146 (in Chinese)
46	T Yang, Y Xue, T G Dai (2008). Extracting alteration information by SVM based on spectrum and texture—taking Jidi area as the example. Earth & Environment, 36(1): 81–86 (in Chinese)
47	C B Yang, C X Zhang, F Liu, Q G Jiang (2015). Study on the relationship between the depth of spectral absorption and the content of the mineral composition of biotite. Spectrosc Spect Anal, 35(9): 2583–2587 pmid: 26669172
48	B J Yoder, R E Pettigrewcrosby (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens Environ, 53(3): 199–211 https://doi.org/10.1016/0034-4257(95)00135-N
49	C Zhang, H Ren, Q Qin, O K Ersoy (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
50	K Zhou, N Zhang (2017). Extraction of alteration mineral information from moderate remote sensing images using MPS method. Photonirvachak (Dehra Dun), 46(2): 1–8
51	G Z Zhou, C Z Wang, F J Yang, Y M Li (2009). Field collected plant spectrum denoising by logarithm transform and wavelet transform. J Infrared Millim W, 28(4): 316–320 https://doi.org/10.3724/SP.J.1010.2009.00316
52	Y Zhu, G R Shen, Q Q Xiang, Y Wu (2017). Spectral characteristics of soil salinity based on different pre-processing methods. Chinese Journal of Soil Science, 48(3): 560–568 (in Chinese)

[1]	Conghui LI, Lili LIN, Zhenbang HAO, Christopher J. POST, Zhanghao CHEN, Jian LIU, Kunyong YU. Developing a USLE cover and management factor (C) for forested regions of southern China[J]. Front. Earth Sci., 2020, 14(3): 660-672.
[2]	Emre ÇOLAK, Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
[3]	Jianhong LIU, Clement ATZBERGER, Xin HUANG, Kejian SHEN, Yongmei LIU, Lei WANG. *Modeling grass yields in Qinghai Province, China, based on MODIS NDVI* data—an empirical comparison**[J]. Front. Earth Sci., 2020, 14(2): 413-429.
[4]	Xia LEI, Jiayi PAN, Adam DEVLIN. An ultraviolet to visible scheme to estimate chromophoric dissolved organic matter absorption in a Case-2 water from remote sensing reflectance[J]. Front. Earth Sci., 2020, 14(2): 384-400.
[5]	Tong LI, Huadong GUO, Li ZHANG, Chenwei NIE, Jingjuan LIAO, Guang LIU. Simulation of Moon-based Earth observation optical image processing methods for global change study[J]. Front. Earth Sci., 2020, 14(1): 236-250.
[6]	Kaiguo FAN, Huaguo ZHANG, Jianjun LIANG, Peng CHEN, Bojian XU, Ming ZHANG. Analysis of ship wake features and extraction of ship motion parameters from SAR images in the Yellow Sea[J]. Front. Earth Sci., 2019, 13(3): 588-595.
[7]	Ying XIONG, Fen PENG, Bin ZOU. Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area[J]. Front. Earth Sci., 2019, 13(3): 614-627.
[8]	Carmine APOLLARO, Francesco PERRI, Emilia LE PERA, Ilaria FUOCO, Teresa CRITELLI. Chemical and minero-petrographical changes on granulite rocks affected by weathering processes[J]. Front. Earth Sci., 2019, 13(2): 247-261.
[9]	Igor Appel. Uncertainty in satellite remote sensing of snow fraction for water resources management[J]. Front. Earth Sci., 2018, 12(4): 711-727.
[10]	Donal O’Leary III, Dorothy Hall, Michael Medler, Aquila Flower. Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps[J]. Front. Earth Sci., 2018, 12(4): 693-710.
[11]	Hongyan WANG, Qiangzi LI, Xin DU, Longcai ZHAO. Quantitative extraction of the bedrock exposure rate based on unmanned aerial vehicle data and Landsat-8 OLI image in a karst environment[J]. Front. Earth Sci., 2018, 12(3): 481-490.
[12]	Zhen LI, Jinghu PAN. Spatiotemporal changes in vegetation net primary productivity in the arid region of Northwest China, 2001 to 2012[J]. Front. Earth Sci., 2018, 12(1): 108-124.
[13]	Mohamed Abd Salam EL-VILALY, Kamel DIDAN, Stuart E. MARSH, Willem J.D. VAN LEEUWEN, Michael A. CRIMMINS, Armando Barreto MUNOZ. Vegetation productivity responses to drought on tribal lands in the four corners region of the Southwest USA[J]. Front. Earth Sci., 2018, 12(1): 37-51.
[14]	Duanyang XU, Alin SONG, Xiao SONG. Assessing the effect of desertification controlling projects and policies in northern Shaanxi Province, China by integrating remote sensing and farmer investigation data[J]. Front. Earth Sci., 2017, 11(4): 689-701.
[15]	Sizhang TANG,Chaomin SHEN,Guixu ZHANG. Adaptive regularized scheme for remote sensing image fusion[J]. Front. Earth Sci., 2016, 10(2): 236-244.

Viewed

Full text

Abstract

Cited

Shared

Discussed