Evaluation of Remote Sensing Techniques for Lithological Mapping in the Southeastern Pamir using Landsat 8 OLI Data

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Aminov Javhar
Xi Chen
Aminov Jovid
Mamadjanov Yunus
Aminov Jamshed
Duulatov Eldiiar
Bakhtiyorov Zulfiyor

Abstract

In this study we test the Landsat 8 OLI data potential for lithological mapping in the Southeastern Pamir. Discrimination of lithological units in the study area has been carried out by utilizing Landsat 8 OLI Satellite data and image enhancement techniques. The approaches consist of spectral enhancement such as independent component analysis (ICA), band ratioing, and false-color composition (FCC). The spectral enhancement techniques were applied in order to extract the initial lithological information, which shows a clear discrimination of granitic rocks from terrigenous and carbonate sedimentary successions. FCC image (OLI bands 6, 7 and 5), color-ratio composite image (OLI 6/5, OLI (7x5)/7, and OLI 6/7), and color composite of independent components (IC6, IC3, IC4) in red, green and blue respectively were found as combinations with more contrast on lithologic information and were used as the input data in supervised classification. Maximum likelihood classification was used to classify resultant images. The results, verified with field observations, demonstrate that different kind of granitoids, terrigenous and carbonaceous rocks can be distinguished and delineated, leading to constriction of geological maps with a better accuracy.

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How to Cite
Javhar, A., Xi Chen, Aminov Jovid, Mamadjanov Yunus, Aminov Jamshed, Duulatov Eldiiar, & Bakhtiyorov Zulfiyor. (2018). Evaluation of Remote Sensing Techniques for Lithological Mapping in the Southeastern Pamir using Landsat 8 OLI Data. International Journal of Geoinformatics, 11(1). Retrieved from https://journals.sfu.ca/ijg/index.php/journal/article/view/1111
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Author Biography

Aminov Javhar, State Key Laboratory of Remote Sensing and GIS, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

State Key Laboratory of Remote Sensing and GIS, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China