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Applicability of Developed Algorithm for Semi-automated Extraction and Morphotectonic Interpretation of Lineaments Using Remotely Sensed Data, Southwestern Tunisia

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Abstract

In this study, we performed semi-automatic extraction of lineaments using satellite imagery of Landsat-8 OLI (Operational Land Imager) and digital elevation model of SRTM (Shuttle Radar Topography Mission). The developed algorithm used for this purpose combines STA (Segment Tracing Algorithm), ALEGHT (Automatic Lineament Extraction by Generalized Hough Transform) and AERA (Alluvial Effect Reducing Algorithm) to achieve maximum precision in the output results. To evaluate the spatial distribution of lineaments, we performed multicriteria statistical analysis and mapping of the density and length. This methodology was applied to Segui region located in the southwestern part of Tunisia. It is characterized by folds made up by carbonate formations. The statistical analysis shows emergence of two major general lineament trends that represent extrados faults and has the same direction of folds axis. Density measurements of the spatial lineaments distribution indicate a high density in the centre of structures. High density is also observed along fold hinges and structural gaps. Measurements of lineaments lengths show that directional families with the same fold axis are the longest. The proposed approach is useful in other similar case studies for the morphotectonic and lineament interpretation.

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Acknowledgements

Data are available from the U.S. Geological Survey. The research was supported by Tunis El Manar University and the Research Unit UR13ES26.

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  1. UR13ES26, Paléoenvironnement, Géomatériaux et Risques Géologiques, Faculté des sciences de Tunis, Université de Tunis El Manar, 2092, Tunis, Tunisia

    Mohamed Haythem Msaddek, Yahya Moumni, Ismail Chenini & Mahmoud Dlala

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Msaddek, M.H., Moumni, Y., Chenini, I. et al. Applicability of Developed Algorithm for Semi-automated Extraction and Morphotectonic Interpretation of Lineaments Using Remotely Sensed Data, Southwestern Tunisia. Remote Sens Earth Syst Sci 2, 292–307 (2019). https://doi.org/10.1007/s41976-019-00028-4

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