MAPPING LITHOLOGICAL AND MINERALOGICAL UNITS USING HYPERSPECTRAL IMAGERY

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Rayan Ghazi Thannoun

Abstract

 Hyperspectral images such as the Earth Observer-1 (EO-1) provides an efficient method of mapping surface mineralogy because it can measures the energy in narrower bands compared with multispectral sensors. The Kirkuk anticline northern Iraq is one of the most petroleum-rich provinces. It is characterized, that is an asymmetrical, cylindrical anticline, with a fold axis trend towards North West- East, South East. The study’s primary goal is to apply satellite processing and techniques on the Eo-1 imagery for identifying lithological and mineral units at a part of Kirkuk anticline northern Iraq. The EO-1 image was corrected at the beginning of atmospheric impacts using the FLAASH module in ENVI software. Processing of (Minimum Noise Fraction- MNF) processing was applied and then it reduced the dimensionality of data, as well as, the processing of (PPI) pixel purity index was applied to spatial reduction. This study tested the potential of (spectral angle mapper supervised classification-SAM) classification for mapping the lithological and mineral units using the Hyperion imagery. three different sources of endmembers or spectra are used for SAM classifications. The one:  is done by Analytical Spectral Devices (ASD) Spectrometer. The second: reference spectra have been taken from the spectral library of USGS. Third: extracting endmembers from the purest pixels of the hyperion image, which was done by applying (MNF and PPI). The endmembers were provided, generated as the training area for SAM classification.  The present results demonstrated the great potential of data used to map the distribution of alteration of minerals and lithological units in a part of Kirkuk anticline. The classified Hyperion image shows that Jarosite and illite are the most dominant altered minerals, as well as, the main lithological units of the upper member of Fatha formation are revealed in the core of the Kirkuk anticline with scattered and small outcrops towards the flanks.

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How to Cite
Thannoun, R. G. (2021). MAPPING LITHOLOGICAL AND MINERALOGICAL UNITS USING HYPERSPECTRAL IMAGERY. Malaysian Journal of Science, 40(1), 93–106. https://doi.org/10.22452/mjs.vol40no1.8
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Original Articles

References

Boardman, J. W., Kruse, F. A., & Green, R. O. (1995). Mapping target signatures via partial unmixing of AVIRIS data.

Crosta, A. P., Sabine, C., & Taranik, J. V. (1998). Hydrothermal alteration mapping at Bodie, California, using AVIRIS hyperspectral data. Remote Sensing of Environment, 65(3), 309-319.

Datt, B., McVicar, T. R., Van Niel, T. G., Jupp, D. L., & Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1246-1259.

Dunnington, H. V. (1958). Generation, migration, accumulation, and dissipation of oil in northern Iraq: Middle East.

Falcone, J. A., & Gomez, R. (2005). Mapping impervious surface type and sub‐pixel abundance using hyperion hyperspectral imagery. Geocarto International, 20(4), 3-10.

Farifteh, J., Nieuwenhuis, W., & García-Meléndez, E. (2013). Mapping spatial variations of iron oxide by-product minerals from EO-1 Hyperion. International journal of remote sensing, 34(2), 682-699.

Goetz, A. F., Kindel, B. C., Ferri, M., & Qu, Z. (2003). HATCH: Results from simulated radiances, AVIRIS and Hyperion. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1215-1222.

Goodenough, D. G., Dyk, A., Niemann, K. O., Pearlman, J. S., Chen, H., Han, T., ... & West, C. (2003). Processing Hyperion and ALI for forest classification. IEEE transactions on geoscience and remote sensing, 41(6), 1321-1331.

Green, A. A., Berman, M., Switzer, P., & Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on geoscience and remote sensing, 26(1), 65-74.

Hirano, A., Madden, M., & Welch, R. (2003). Hyperspectral image data for mapping wetland vegetation. Wetlands, 23(2), 436-448.

Iraq- Geosurv, (1993). Geological map of Kirkuk quadrangle, Sheet Ni-38-2, 1: 250,000. State establishment of survey and mining, Baghdad, Iraq.
Jafari, R., & Lewis, M. M. (2012). Arid land characterisation with EO-1 Hyperion hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 19, 298-307.

Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., & Goetz, A. F. H. (1993, August). The spectral image processing system (SIPS)‐interactive visualization and analysis of imaging spectrometer data. In AIP Conference Proceedings (Vol. 283, No. 1, pp. 192-201). American Institute of Physics.

Kumar, M. V., & Yarrakula, K. (2017). Comparison of efficient techniques of hyper-spectral image preprocessing for mineralogy and vegetation studies.


Magendran, T., & Sanjeevi, S. (2013). A study on the potential of satellite image-derived hyperspectral signatures to assess the grades of iron ore deposits. Journal of the Geological Society of India, 82(3), 227-235.

Mason, P. (2002). MMTG A-List Hyperspectral Data Processing Software. 920C.

Mazhari, N., Shafaroudi, A. M., & Ghaderi, M. (2017). Detecting and mapping different types of iron mineralization in Sangan mining region, NE Iran, using satellite image and airborne geophysical data. Geosciences Journal, 21(1), 137-148.

Miao, X., Gong, P., Pu, R., Carruthers, R. I., & Heaton, J. S. (2007). Applying class-based feature extraction approaches for supervised classification of hyperspectral imagery. Canadian Journal of Remote Sensing, 33(3), 162-175.

Perry, S. L., Kruse, F. A., & Carlston, C. (2011, May). Evidence of Hydrocarbon Seepage Using Multispectral Satellite Imagery, Kurdistan, Iraq. In 73rd EAGE Conference and Exhibition-Workshops 2011 (pp. cp-239). European Association of Geoscientists & Engineers.

Pervaiz, W., Uddin, V., Khan, S. A., & Khan, J. A. (2016). Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery. Journal of Applied Remote Sensing, 10(2), 026004.

Pervez, W., & Khan, S. A. (2015). Hyperspectral hyperion imagery analysis and its application using spectral analysis. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(3), 169.

Pu, R., Yu, Q., Gong, P., & Biging, G. S. (2005). EO‐1 Hyperion, ALI and Landsat 7 ETM+ data comparison for estimating forest crown closure and leaf area index. International Journal of Remote Sensing, 26(3), 457-474.

Rowan, L. C., Crowley, J. K., Schmidt, R. G., Ager, C. M., & Mars, J. C. (2000). Mapping hydrothermally altered rocks by analyzing hyperspectral image (AVIRIS) data of forested areas in the Southeastern United States. Journal of Geochemical Exploration, 68(3), 145-166.

Staenz, K., Neville, R. A., Clavette, S., Landry, R., White, H. P., & Hitchcock, R. (2002, June). Retrieval of surface reflectance from Hyperion radiance data. In IEEE International Geoscience and Remote Sensing Symposium (Vol. 3, pp. 1419-1421). IEEE.

Thannoun, R. Gh. (2012). Structural control evaluation of hydrocarbon seepages in northern Iraq using remote sensing techniques, Ph. D. Thesis, Mosul University, Iraq, 222 p. (In Arabic with English abstract, Unpublished)
Thannoun, R. Gh., Ali, S. H., and Abd Al-Munaem, N. (2018). Geobotanical Study of Some Areas South-Western Mosul Using Remote Sensing and ASD Dataset. Iraqi journal of earth sciences, Iraqi National Journal of Earth Sciences (Vol. 18, No. 1, pp. 9 – 26).
Tiwari, P. S., Pande, H., & Aye, M. N. (2010). Exploiting IKONOS and Hyperion data fusion for automated road extraction. Geocarto International, 25(2), 123-131.
White, J. C., Coops, N. C., Hilker, T., Wulder, M. A., & Carroll, A. L. (2007). Detecting mountain pine beetle red attack damage with EO‐1 Hyperion moisture indices. International Journal of Remote Sensing, 28(10), 2111-2121.
Zhang, X., & Pazner, M. (2007). Comparison of lithologic mapping with ASTER, hyperion, and ETM data in the southeastern Chocolate Mountains, USA. Photogrammetric Engineering & Remote Sensing, 73(5), 555-561.