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Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan
Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their l...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170121/ https://www.ncbi.nlm.nih.gov/pubmed/37161025 http://dx.doi.org/10.1038/s41598-023-34030-0 |
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author | Hussain, Sajid Pan, Bin Afzal, Zeeshan Ali, Muhammad Zhang, Xianlong Shi, Xianjian Ali, Muhammad |
author_facet | Hussain, Sajid Pan, Bin Afzal, Zeeshan Ali, Muhammad Zhang, Xianlong Shi, Xianjian Ali, Muhammad |
author_sort | Hussain, Sajid |
collection | PubMed |
description | Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (V(slope)). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (V(Slope)) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (V(slope)) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies. |
format | Online Article Text |
id | pubmed-10170121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101701212023-05-11 Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan Hussain, Sajid Pan, Bin Afzal, Zeeshan Ali, Muhammad Zhang, Xianlong Shi, Xianjian Ali, Muhammad Sci Rep Article Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (V(slope)). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (V(Slope)) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (V(slope)) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies. Nature Publishing Group UK 2023-05-09 /pmc/articles/PMC10170121/ /pubmed/37161025 http://dx.doi.org/10.1038/s41598-023-34030-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hussain, Sajid Pan, Bin Afzal, Zeeshan Ali, Muhammad Zhang, Xianlong Shi, Xianjian Ali, Muhammad Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan |
title | Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan |
title_full | Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan |
title_fullStr | Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan |
title_full_unstemmed | Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan |
title_short | Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan |
title_sort | landslide detection and inventory updating using the time-series insar approach along the karakoram highway, northern pakistan |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170121/ https://www.ncbi.nlm.nih.gov/pubmed/37161025 http://dx.doi.org/10.1038/s41598-023-34030-0 |
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