Advertisement

Application of ASTER Remote Sensing for Lithological Mapping in the Udaipur District of Rajasthan, India

  • S. S. Salaj
  • S. K. Srivastava
  • Rahul Dugal
  • Richa Upadhyay
  • D. S. Suresh Babu
  • S. KalirajEmail author
Chapter

Abstract

Remote sensing applications for earth studies such as lithological discrimination, geological mapping and potential mineral exploration have shown great success worldwide. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level-1B image includes visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands that have been analysed to discriminate lithology features in meta-sedimentary terrains of Aravalli Supergroup in Udaipur area of Rajasthan, India. The area comprises various types of geological settings and rock types composed of economic valuable deposits of lead, zinc, copper, micas and marbles; they show spectral reflectance distinctly in bands of VNIR and SWIR. The unique spectral signature reflected by lithological unit shows effectiveness in lithological mapping. The reflectance spectra of various rock types, namely, phyllitic dolomite, siliceous dolomite, metagreywacke, quartzite and gneiss, were collected in situ using spectroradiometer and used as reference of ASTER image for the preparation of spectral signature of different lithological units. The image is applied to analysis atmospheric correction using Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and empirical line calibration techniques to convert pixel radiance values into reflectance. A minimum noise fraction (MNF) transform is applied to identify the inherent variance of spectral reflectance and effectively discriminates various lithological units. The different types of lithological units are clearly discriminated using MNF method. Spectral Angle Mapper (SAM) classification is an effective tool for differentiating rock types and its distinct mineralogical composition from associated terrains. Spectral Angle Mapper (SAM) classification uses field-derived spectral signature to demarcate various lithological features with its spatial extent. The result shows different lithological units under Aravalli Supergroup, Banded Gneissic Complex and intrusive formations that are composed of meta-arkose, conglomerate, phyllite, mica schist, dolomite, metagreywacke and migmatites in various locations. The extracted geological features using ASTER image show strong resampling with the district resource map and validated using ground truth verification. The overall accuracy of SAM-classified map of lithological units is 73.39% and Kappa coefficient of 0.59. Mapping the lithological features using ASTER image, data coupled with MNF and SAM techniques provides relatively accurate result, and this study may be used for discrimination of lithological units with its spatial characteristics.

Keywords

ASTER Lithological mapping FLAASH Minimum noise fraction Spectral Angle Mapper Remote sensing and GIS 

Notes

Acknowledgement

The authors are grateful to the directors of the Indian Institute of Remote Sensing (IIRS), Dehradun, and National Centre for Earth Science Studies (NCESS), Thiruvananthapuram, for constant support and encouragement. Thanks are also due to Dr. Rabi N. Sahoo, Indian Agricultural Research Institute (IARI), New Delhi, for lab support and Dr. T. N. Prakash, NCESS, for extending XRD facilities. We are also grateful to Dr. R. R. Chowdhary, Dr. P. R. Golani, Director of GSI FTC, Zawar and Col. Kakkad for their support during fieldwork.

References

  1. Boardman JW, Kruse FA (1994) Automated spectral analysis: a geologic example using AVIRIS data, north Grapevine Mountains, Nevada. In: Proceedings, Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor, MI, I-407–I-418Google Scholar
  2. Borengasser M, Hungate WS, Watkins R (2008) Hyperspectral remote sensing principles and applications. Taylor & Francis, Boca Raton, pp 119–130Google Scholar
  3. Chandan Kumar, Amba Shetty, Simit Raval, Richa Sharma PK, Champati Ray (2015) Lithological discrimination and mapping using ASTER SWIR data in the Udaipur area of Rajasthan, India. Proc Earth Planet Sci 11:180–188CrossRefGoogle Scholar
  4. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46CrossRefGoogle Scholar
  5. Di Tommaso I, Rubinstein N (2007) Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geol Rev 32(1):275–290CrossRefGoogle Scholar
  6. Gabr S, Ghulam A, Kusky T (2010) Detecting areas of high-potential gold mineralization using ASTER data. Ore Geol Rev 38(1):59–69CrossRefGoogle Scholar
  7. Gad S, Kusky T (2007) ASTER spectral ratioing for lithological mapping in the Arabian–Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Res 11(3):326–335CrossRefGoogle Scholar
  8. Gomez C, Delacourt C, Allemand P, Ledru P, Wackerle R (2005) Using ASTER remote sensing data set for geological mapping, in Namibia. Phys Chem Earth, Parts A/B/C 30(1):97–108CrossRefGoogle Scholar
  9. GSI (1997) District resource map of Udaipur District, Rajasthan on 1:250,000 scale. Geological Survey of India, CalcuttaGoogle Scholar
  10. Hewson RD, Cudahy TJ, Mizujiko S, Ueda K, Mauger AL (2005) Seamless geological map generation using ASTER in the Broken Hill-Curnmona province of Australia. Remote Sens Environ 99:159–172CrossRefGoogle Scholar
  11. Hosseinjani M, Tangestani MH (2011) Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area, SE Kerman, Iran. Int J Digital Earth 4(6):487–504CrossRefGoogle Scholar
  12. Kruse FA, Lefkoff AB, Boardman JB, Heidebreicht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The Spectral Image Processing System (SIPS) – interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44:145–163CrossRefGoogle Scholar
  13. Kruse, FA, Dietz JB (1991) Integration of visible­through microwave-range multispectral image data sets for geologic mapping. In: Proceedings of the Cinquieme Colloque International, Mesures Physiques et Signatures En Teledetection, 14–18 January, Couchevel, France, European Space Agency, Paris, France, ESA SP-319, vol 2, pp 481–486Google Scholar
  14. Pour AB, Hashim M (2011) Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. J Asian Earth Sci 42(6):1309–1323CrossRefGoogle Scholar
  15. Pour AB, Hashim M (2012) Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh – Dokhtar Volcanic Belt, Iran. Adv Space Res 49(4):753–769CrossRefGoogle Scholar
  16. Qiu F, Abdelsalam M, Thakkar P (2006) Spectral analysis of ASTER data covering part of the Neoproterozoic Allaqi-Heiani suture, Southern Egypt. J Afr Earth Sci 44(2):169–180CrossRefGoogle Scholar
  17. Rajendran S, Al-Khirbash S, Pracejus B, Nasir S, Al-Abri AH, Kusky TM, Ghulam A (2012) ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: exploration strategy. Ore Geol Rev 44:121–135CrossRefGoogle Scholar
  18. Rajendran S, Nasir S, Kusky TM, Ghulam A, Gabr S, El-Ghali MA (2013) Detection of hydrothermal mineralized zones associated with listwaenites in Central Oman using ASTER data. Ore Geol Rev 53:470–488CrossRefGoogle Scholar
  19. Rowan L, Hook SJ, Abrams MJ, Mars JC (2003) Mapping hydrothermally altered rocks at Cuprite, Nevada, using the Advanced Spaceborne thermal emission and reflection radiometer (ASTER), a new satellite-imaging system. Econ Geol Bull Soc Econ Geol 98(5):1019–1027CrossRefGoogle Scholar
  20. Rowan LC, Mars JC (2003) Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sens Environ 84(3):350–366CrossRefGoogle Scholar
  21. Roy AB, Paliwal BS (1981) Evolution of lower Proterozoic epicontinental deposits: stromatolite-bearing Aravalli rocks of Udaipur, Rajasthan, India. Precamb Res 14:49–74CrossRefGoogle Scholar
  22. Roy SS, Malthottra G, Mohanty M (1988) Geology of Rajasthan. Geology Society of India, BangaloreGoogle Scholar
  23. Sabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev 14(3):157–183CrossRefGoogle Scholar
  24. Sharma RS (2009) Cratons and Fold Belts of India, Lecture notes in earth sciences. Springer-Verlag, Berlin, pp 304–320Google Scholar
  25. Sinha-Roy S, Malhotra G, Mohanty M (1998) Geology of Rajasthan, 1st edn. Geological Society of India, Bangalore, pp 278–290Google Scholar
  26. Tangestani MH, Jaffari L, Vincent RK, Sridhar BM (2011) Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: a case study from Neyriz ophiolite, SW Iran. Remote Sens Environ 115(9):2243–2254CrossRefGoogle Scholar
  27. Van der Meer FD, Van der Werff HM, van Ruitenbeek FJ, Hecker CA, Bakker WH, Noomen MF, Woldai T (2012) Multi-and hyperspectral geologic remote sensing: a review. Int J Appl Earth Observ Geoinfor 14(1):112–128CrossRefGoogle Scholar
  28. Youssef AM, Hassan AM, EL-Haddad AAA (2009) Mapping of Prerift – Synrift sedimentary units using Enhanced Thematic Mapper Plus (ETM+): Sidri-Ferian Area, Southwestern Sinai Peninsula, Egypt. J Indian Soc Remote Sens 37:377–393CrossRefGoogle Scholar
  29. Zhang X, Pazner M, Duke N (2007) Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California). ISPRS J Photogramm Remote Sens 62(4):271–282CrossRefGoogle Scholar
  30. Zhang X, Yang H, Shuai T (2009) Comparison of FLAASH versus Empirical Line Approach for Atmospheric Correction of OMIS-II Imagery. In: Proceedings of 30th Asian Conference on Remote Sensing (ACRS), Beijing, ChinaGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • S. S. Salaj
    • 1
  • S. K. Srivastava
    • 2
  • Rahul Dugal
    • 3
  • Richa Upadhyay
    • 2
  • D. S. Suresh Babu
    • 1
  • S. Kaliraj
    • 1
    Email author
  1. 1.Central Geomatics Laboratory (CGL), ESSO-National Centre for Earth Science Studies (NCESS)Ministry of Earth Sciences, Government of IndiaThiruvananthapuramIndia
  2. 2.Indian Institute of Remote SensingDehradunIndia
  3. 3.University of PunePuneIndia

Personalised recommendations