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Landslide Occurrences Along Lineaments on NH-154A, Chamba, Himachal Pradesh; Extracted from Satellite Data Landsat 8, India

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Abstract

The Lineament analysis is an important factor to assess its control on landslide occurrences and also to analyze the general tectonics of the terrain. Lineament may signify the vulnerable weak planes where slope material strength is being reduced and causing slope failures. The present work is carried along the parts of NH-154A, Chamba–Bharmour road, Chamba, Himachal Pradesh to establish the association between landslide occurrences and lineaments. Approximately 60 km stretch of road section from Kuranh to Hadsar has been studied for this analysis. The Landsat 8 OLI/TIRS C1 Level-1data suggests that the presence of 1129 lineaments, which are classified into very high, high, moderate, low and very low density classes applying natural break techniques in Arc GIS 10.4.1 software. The total study area is 446.17 km2 in which 26 km2 area is under very high, 66 km2 area into high, 100 km2 into moderate, 138 km2 into low and 116 km2 area into very low risk zones. The lineament intersection point map shows a direct relationship between it and the existing landslide. Two major directions, i.e., NW–SE which is of tectonic origin coincides with the Chamba Thrust and a less prominent, NE–SW directed lineaments are present. The results also indicate a direct association between lineament and landslide distribution.

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Acknowledgements

The authors are thankful to Head, CAS in Geology, University of Lucknow, Lucknow for providing working facilities and wishes to acknowledge the fellowship grant received from the Rajeev Gandhi National Fellowship, UGC New Delhi for financial support to carry out the present work. Authors also express gratitude to the anonymous reviewers, whose comment and suggestions improved the quality of this manuscript.

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Correspondence to Ajay Kumar Arya.

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Singh, K., Arya, A.K. & Agarwal, K.K. Landslide Occurrences Along Lineaments on NH-154A, Chamba, Himachal Pradesh; Extracted from Satellite Data Landsat 8, India. J Indian Soc Remote Sens 48, 791–803 (2020). https://doi.org/10.1007/s12524-020-01113-8

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