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Lithological discrimination and structural lineaments extraction using Landsat 8 and ASTER data: a case study of Tiwit (Anti-Atlas, Morocco)

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Abstract

The present work reveals the potential of Landsat 8 and ASTER imagery in the lithological discrimination and structural lineaments extraction in the Tiwit region (Jbel Saghro). Various remote sensing and image processing techniques were applied to the Landsat 8 and ASTER scenes: False-color composites (RGB 751 & 531), Principal Component Analysis (PCA 653 & 821), Minimum Noise Fraction (MNF 643 & 541), and Independent Component Analysis (ICA 137 & 235). These techniques discriminate the granitic formations (Isk-n-Alla, Mimasmarane, Ibantarn, and Ikniwn), the rhyolitic and ignimbrite formation (Amtattouch, Ouzarzamand Assaka), and other various rock types (aphanitic basalts, sandstones, conglomerates, etc.). Maximum Likelihood, Spectral Angle Mapper, and Mahalanobis distance classifiers show an overall accuracy of 88%, 56%, and 82.6%, respectively, for Landsat 8. ASTER data show a better result in classification with an overall accuracy of 90.6%, 84%, and 88% for the same classifiers. Structural lineaments are extracted using manual (visual interpretation) and automatique (LINE algorithm) approaches on both datasets. The extracted lineaments show NE-SW, E-W, and ENE-WSW trends for ASTER and Landsat 8 data, these results are consistent with the geologic map of Tiwit. Ultimately, the study demonstrates that ASTER data is more effective for lithological discrimination and structural lineaments mapping.

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Acknowledgements

We gratefully thank Ms. Kawtar Ech-Charay for providing high-resolution geologic maps of the Moroccan Anti-Atlas.

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AM: conceptualization, methodology, resources, investigation, writing—original draft. AD: project administration, supervision, validation.

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Correspondence to Anass Marzouki.

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Marzouki, A., Dridri, A. Lithological discrimination and structural lineaments extraction using Landsat 8 and ASTER data: a case study of Tiwit (Anti-Atlas, Morocco). Environ Earth Sci 82, 125 (2023). https://doi.org/10.1007/s12665-023-10831-4

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