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Landsat 8 OLI Data for Identification of Hydrothermal Alteration Zone in Singhbhum Shear Zone using Successive Band Depth Difference Technique–A New Image Processing Approach


Affiliations
1 Indian Institute of Technology (ISM), Dhanbad 826 004, India
2 Regional Remote Sensing Centre West, National Remote Sensing Centre, Indian Space Research Organisation, Jodhpur 342 003, India
 

Recent advances in calculation algorithms have led to a new level of image processing for mineral identification and mapping. Mineral outcrop mapping has a decade’s history of using conventional methods like band combintion, band ratioing and relative absorption band depth (RBD) technique. Modification of these algorithms enriches the capabilities of object identification and mapping. Band combination and band ratioing help to locate the distribution of a hydrothermal altered zone. In the current study, an attempt has been made to modify the RBD approach. Newly introduced successive band depth difference (SBDD) measures the difference of reflectance values in successive bands by dividing the sum of the two highest successive shoulders by the shoulder of the lowest value before the starting shoulder. Band math function has been used in various bands of Landsat 8 operational land imager (OLI) data to access the precise distribution of points of the hydrothermal altered zone. SBDD method has achieved a kappa coefficient of 0.86 which depicts significant levels of accuracy.

Keywords

Relative Absorption Band Depth, RGB, Signal-To-Noise Ratio, SBDD, TIRS.
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  • Sabins, F. F., Remote sensing for mineral exploration. Ore Geol. Rev., 1999, 14, 157–183.

  • van der Meer, F., De Jong, S. and Bakker, W., Imaging Spectrometry: Basic Analytical Techniques, Imaging Spectrom, Springer, Dordrecht, 2002, pp. 17-61.

  • Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J. and Goetz, A. F. H., The spectral image processing system (Sips) – interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ., 1993, 44, 145–163; doi:10.1016/0034-4257(93)90013-N.

  • Clark, R. N., Swayze, G. A., Gallagher, A., Gorelick, N. and Kruse, F. A., Mapping with imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials. In Proceedings of the third airborne visible/infrared imaging spectrometer (AVIRIS) workshop, 1991, vol. 42, pp. 2–3.

  • Binzel, R. P., Rivkin, A. S., Bus, S. J., Sunshine, J. M. and Burbine, T. H., MUSES-C target asteroid (25143) 1998 SF36: a reddened ordinary chondrite. Meteorit. Planet. Sci., 2001, 36, 1167–1172; doi:10.1111/j.1945-5100.2001.tb01950.x.

  • Amin Beiranvand Pour and Mazlan Hashim, Hydrothermal alteration mapping using Landsat-8 data, Sar Cheshmeh copper mining district, SE Iran. J. Taibah Univ. Sci., 2014; http://dx.doi.org/10.1016/j.jtusci.2014.11.008.

  • Wang, J. N. and Zheng, L. F., The spectral absorption identification model and mineral mapping by imaging spectrometer data. Remote Sens. Environ., 1996, 1, 20–31.

  • Panda Surajit, Jain Manish Kumar and Jeyaseelan, A. T., A study and implications on the potential of satellite image spectral to assess the iron ore grades of Noamundi iron deposits area. J. Geol. Soc. India, 2018, 91, 227–231.

  • Crowley, J. K., Brickey, D. W. and Rowan, L. C., Airborne imaging spectrometer data of the Ruby Mountains, Montana: mineral discrimination using relative absorption band-depth images. Remote Sensing Environ., 1989, 29, 121–134.

  • Nikolakopoulos, K. G., Tsombos, P. I., Photiades, A., Psonis, K. and Zervakou, A., Using remote sensing multispectral data and GIS techniques for the geological mapping of Halki Island. Bull. Geol. Soc. Greece, 2013, 47, 1500–1509.

  • Zahra Yazdi, Ali Reza Jafari Rad and Kimiya Sadat Ajayebi, Analysis and modeling of geospatial datasets for porphyry copper prospectivity mapping in Chahargonbad area, Central Iran. Arab. J. Geosci., 2015, 8, 8237–8248.

  • Van Der Meer, F., Analysis of spectral absorption features in hyperspectral imagery. Int. J. Appl. Earth Obs. Geoinf., 2004, 5, 55–68.

  • Han, T. and Nelson, J., Mapping hydrothermally altered rocks with Landsat 8 imagery: A case study in the KSM and Snow field zones, northwestern British Columbia. British Columbia Geol. Surv., 2015, 103–112.

  • Mwaniki, M. W., Moeller, M. S. and Schellmann, G., A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: a case study of central region Kenya. Int. Arch Photogramm. Remote Sens. Spat. Inf. Sci., 2015, 40, 897.

  • Roy, D. P. et al., Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing Environ., 2014, 145, 154–172.

  • Geological Quadrangle Map (Jamshedpur Quadrangle, Bihar, Orissa and West Bengal) of Geological Survey of India.

  • Dunn, J. A., Geology and petrology of Eastern Singhbhum and surrounding areas. Mem. Geol. Surv. India, 1942, 69, 261–456.

  • Agus, A. J. L., Mapping white mica in milled porphyry copper pebbles using hyperspectral imagery: an exploratory study. GeoInf. Earth Obs., 2011, 26–28.

  • Anon, GSI, ER, Unpublished GSI report on Project Singhbhum – Synthesis of data of Singhbhum Copper Belt, Singhbhum District, Bihar: Part I & II, Unpublished, 1991.

  • Banerjee, K., Panda, S. and Kumar Jain, M., Identification and mapping of copper mining area in Singhbhum copper belt using advance image processing techniques. Int. J. Sci. Res., 2012, 1404–1407.

  • Pal, D. C., Sarkar, S., Mishra, B. and Sarangi, A. K., Chemical and sulphur isotope compositions of pyrite in the Jaduguda U (–Cu–Fe) deposit, Singhbhum shear zone, eastern India: implications for sulphide mineralization. J. Earth Syst. Sci., 2011, 120, 475–488.

  • Bhattacharya, C., Talapatra, A. and Bose, S. S., Integrated geochemical approach for tracing gold mineralisation in parts of Singhbhum and Ranchi Districts, Bihar, India. Rec. Geol. Surv. India, 1984, 114, 1–14; https://eurekamag.com/research/019/214/019214244.php

  • Sarkar, S. N., Pre-cambrian Stratigraphy and Geochronology of Peninsular India, Dhanbad Publishers, India, 1968, vol. 33.

  • Banerji, A. K., Ore genesis and its relationship to volcanism, tectonism, granitic activity, and metasomatism along the Singhbhum shear zone, eastern India. Econ. Geol., 1981, 76, 905–912.

  • National Aeronautics and Space Administration; http://science.nasa.gov/missions/ldcm/ (accessed on 18 February 2017).

  • Prost, G. L., Remote Sensing for Geologists: A Guide to Image Interpretation, CRC Press, 2002, 2nd edn.

  • Miyatake, S., Regional Lineament analysis and alteration mineral mapping for intrusive related copper exploration in the Myanmar central volcanic belt. Proc. 23rd Asian Conf. on Remote Sensing, 2002, pp. 1–4 (CD-ROM).

  • Pour, A. B. and Hashim, M., Alteration mineral mapping using ETM+ and hyperion remote sensing data at Bau Gold Field, Sarawak, Malaysia. In IOP Conference Series: Earth Environ. Sci., 2014, 18, 12–149.

  • Ren, D. and Abdelsalam, M. G., Optimum index factor (OIF) for ASTER data: examples from the Neoproterozoic Allaqi Suture, Egypt. Proc. Geol. Soc. Am., 2001, p. 123.

  • Ali, A. S. and Pour, A. B., Lithological mapping and hydrothermal alteration using Landsat 8 data: a case study in ariab mining district, red sea hills, Sudan. Int. J. Basic Appl. Sci., 2014, 3, 199.

  • da Cunha Frutuoso, R. M., Mapping hydrothermal gold mineralization using Landsat 8 data. A case of study in Chaves license, Portugal, 2015.

  • Pour, A. B. and Hashim, M., Regional hydrothermal alteration mapping using Landsat-8 data. In Space Sci. Commun. (Icon Space), Int. Conf., 2015, 199–202.

  • Ducart, D. F., Silva, A. M., Toledo, C. L. B. and Assis, L. M. D., Mapping iron oxides with Landsat-8/OLI and EO-1/Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil. Braz. J. Geol., 2016, 46, 331–349.

  • Arunachalam, M., Udhayaraj, A. D., Jacob, A., Naren Prabakaran, V. P., Vasanth, M. S. and Saravanavel, J., Hydrothermal Mineral Alteration Mapping in parts of Northwestern Tamil Nadu, Indiausing Geospatial Technology. Int. Symp. Oper. Remote Sens., 2014.

  • Liew, S. C., Principles of Remote Sensing-Centre for Remote Imaging, Sensing and Processing, CRISP, 2001.

  • Goetz, A. F. and Rowan, L. C., Geol remote sensing. Science, 1981, 211, 781–791.

  • Rowan, L. C. and Mars, J. C., Lithologic mapping in the Mountain Pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sens. Environ., 2003, 84, 350–366.

  • Mohan, B. K. and Porwal, A., Hyperspectral image processing and analysis. Curr. Sci., 2015, 108, 833–841.

  • Clark, R. N., King, T. V., Klejwa, M., Swayze, G. A. and Vergo, N., High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. Solid Earth, 1990, 95, 12653–12680.

  • Mars, J. C. and Rowan, L. C., Regional mapping of phyllic-and argillic-altered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere, 2006, 2, 161–186.

  • Clark, R. N. et al., Surface reflectance calibration of terrestrial imaging spectroscopy data: a tutorial using AVIRIS. Proc. 10th Airb. Earth Sci. Works, 2002, pp. 1–2.

  • Mukerji, B. and Sarkar, B. C., An integrated GIS modeling approach to mineral potential mapping of copper deposits of Singhbhum belt, India. Appl. Comput. Oper. Res. Miner. India, 2005, 235–245.

  • Clark, R. N., Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual Remote Sensing, 1999, 3, 3–58.

  • Soe, M., Kyaw, T. A. and Takashima, I., Application of remote sensing techniques on iron oxide detection from ASTER and Landsat images of Tanintharyi coastal area, Myanmar. Eng. Resour. Sci. Res. Rep., 2005, 26, 21–28.

  • Zhang, T. et al., Integrating data of ASTER and Landsat-8 OLI (AO) for hydrothermal alteration mineral mapping in Duolong Porphyry Cu–Au deposit, Tibetan Plateau, China. Remote Sensing, 2016, 8, 890.

  • Ben-Dor, E., Kruse, F. A., Lefkoff, A. B. and Banin, A., Comparison of three calibration techniques for utilization of GER 63channel aircraft scanner data of Makhtesh Ramon, Negev, Israel. Photogramm. Eng. Remote Sens., 1994, 60, 1339–1354.

  • Brown, A. J., Walter, M. R. and Cudahy, T. J., Hyperspectral imaging spectroscopy of a Mars analogue environment at the North Pole Dome, Pilbara Craton, Western Australia. Aust. J. Earth Sci., 2005, 52, 353–364.

  • Pour, A. B. and Hashim, M., Hydrothermal alteration mapping from Landsat-8 data, Sar Cheshmeh copper mining district, southeastern Islamic Republic of Iran. J. Taibah Univ. Sci., 2015, 9, 155–166.


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  • Landsat 8 OLI Data for Identification of Hydrothermal Alteration Zone in Singhbhum Shear Zone using Successive Band Depth Difference Technique–A New Image Processing Approach

Abstract Views: 409  |  PDF Views: 117

Authors

Krishnendu Banerjee
Indian Institute of Technology (ISM), Dhanbad 826 004, India
Manish Kumar Jain
Indian Institute of Technology (ISM), Dhanbad 826 004, India
A. T. Jeyaseelan
Regional Remote Sensing Centre West, National Remote Sensing Centre, Indian Space Research Organisation, Jodhpur 342 003, India
Surajit Panda
Indian Institute of Technology (ISM), Dhanbad 826 004, India

Abstract


Recent advances in calculation algorithms have led to a new level of image processing for mineral identification and mapping. Mineral outcrop mapping has a decade’s history of using conventional methods like band combintion, band ratioing and relative absorption band depth (RBD) technique. Modification of these algorithms enriches the capabilities of object identification and mapping. Band combination and band ratioing help to locate the distribution of a hydrothermal altered zone. In the current study, an attempt has been made to modify the RBD approach. Newly introduced successive band depth difference (SBDD) measures the difference of reflectance values in successive bands by dividing the sum of the two highest successive shoulders by the shoulder of the lowest value before the starting shoulder. Band math function has been used in various bands of Landsat 8 operational land imager (OLI) data to access the precise distribution of points of the hydrothermal altered zone. SBDD method has achieved a kappa coefficient of 0.86 which depicts significant levels of accuracy.

Keywords


Relative Absorption Band Depth, RGB, Signal-To-Noise Ratio, SBDD, TIRS.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi10%2F1639-1647