Minerals identification and mapping using ASTER satellite image

Khunsa Fatima; Muhammad Umar Khan Khattak; Allah Bakhsh Kausar; Muhammad Toqeer; Naghma Haider; Asid Ur Rehman

doi:10.1117/1.JRS.11.046006

17 October 2017 Minerals identification and mapping using ASTER satellite image

Khunsa Fatima, Muhammad Umar Khan Khattak, Allah Bakhsh Kausar, Muhammad Toqeer, Naghma Haider, Asid Ur Rehman

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046006 (October 2017). https://doi.org/10.1117/1.JRS.11.046006

Abstract

Advanced spaceborn thermal emission and reflection radiometer (ASTER) has fine spectral bands in short-wave infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum. The purpose behind the study is to explore the potential of ASTER for lithological and minerals detection; in comparison with Landsat-ETM+ Khaira Murat range (KMR) of Gali Jagir area, district Attock was selected as a test site; enriched with industrial minerals of the Eocene age. Maximum likelihood classification was applied on Landsat-ETM+ and ASTER images. Maximum likelihood classification on ASTER satellite image exhibits better discrimination among various lithologies as compared to Landsat-ETM+. Classified image of ASTER showed a correlation coefficient of 0.6 with the geological survey of Pakistan’s map while a classified image of Landsat-ETM+ exhibited a correlation of only 0.43. Landsat-ETM+ and ASTER satellite images were further investigated for minerals detection. Landsat-ETM+ band ratio detected clay. ASTER SWIR band ratios detected various clay and carbonate minerals. X’PertPRO diffractometer and differential thermal analysis of field samples verified the detected minerology. The results suggest that ASTER can be successfully used for lithological and minerals mapping of less-examined areas.

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Khunsa Fatima, Muhammad Umar Khan Khattak, Allah Bakhsh Kausar, Muhammad Toqeer, Naghma Haider, and Asid Ur Rehman "Minerals identification and mapping using ASTER satellite image," Journal of Applied Remote Sensing 11(4), 046006 (17 October 2017). https://doi.org/10.1117/1.JRS.11.046006

Received: 1 February 2017; Accepted: 21 September 2017; Published: 17 October 2017

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