Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017

Su, Xiaojun; Yi, Zhang; Meng, Xiaohong; Yue, Dongxia; Ma, Jun; Guo, Fuyun; Zhou, Ziqiang; Rehman, Mohib ur; Khalid, Zainab; Chen, Guan; Zeng, Runqiang; Zhao, Fengying

doi:10.1007/s11629-021-6686-6

Cited by 24 publications

(24 citation statements)

References 55 publications

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“…There is an elevation boundary that makes the number of landslides reach the peak at that elevation boundary, whereupon the phenomenon of decreasing above that elevation boundary will occur. For example, the elevation boundary of the China-Pakistan Karakoram Highway is 2100 m [52]; along the Jinsha River Basin, the elevation boundary is 2000 m [13]. With increased altitude, due to human activity, river erosion, and rock weathering, the number of landslides is small and the degree of development is slow.…”

Section: Four General Rules Of Landslide Distributionmentioning

confidence: 99%

“…However, there are many exceptions. For example, along the China-Pakistan Karakoram Highway area, the rainfall range is 162-941 mm/a, with 84.3% of landslides occurring in areas in the semi-arid region (<400 mm/a) [52]; along the Sichuan-Tibet Railway Ya'an-Linzhi). No significant statistical relationship emerged between rainfall and landslide distribution [39].…”

Section: Four General Rules Of Landslide Distributionmentioning

confidence: 99%

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Wide Area Detection and Distribution Characteristics of Landslides along Sichuan Expressways

Chen

Zhang

et al. 2022

Remote Sensing

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Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future.

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Section: Four General Rules Of Landslide Distributionmentioning

confidence: 99%

Section: Four General Rules Of Landslide Distributionmentioning

confidence: 99%

Wide Area Detection and Distribution Characteristics of Landslides along Sichuan Expressways

Chen

Zhang

et al. 2022

Remote Sensing

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“…For engineers and geologists, determining landslide susceptibility with limited data is a problem in the support of planners and governmental organizations. To undertake landslide detection and mapping, optical remote sensing can be integrated with InSAR technology, which can overcome these limitations 12 . A strong tool for landslide detection and mapping at a wide scale is the use of SAR interferometric techniques, which can also help with the development and updating of landslide inventory maps 13 .…”

Section: Introductionmentioning

confidence: 99%

“…Interferometric Synthetic Aperture Radar (InSAR) techniques have gained widespread acceptance and use as tools for landslide mapping and monitoring over recent years. Although different techniques of InSAR have been successfully used in mapping slope deformations associated like 14 estimated surface deformation areas detected by SBAS-InSAR 12 , 15 , applied D-InSAR method for land subsidence and landslide monitoring 16 – 18 , generated SqeeInSAR method to check the ground movement 19 , used Stanford Method for Persistent Scatterers (StaMPS) to assess the deformation in dense vegetation area 20 – 22 , tested PSInSAR approach to calculate the movement of landslide. PSInSAR is effective in automatic slow-moving landslide mapping based on the use of a spatial statistical approach, the identification of individual landslides and the delineation of generalized unstable areas, the redefinition of the limits of ancient landslides, the identification of landslides based on the multi-temporal comparison of SAR imagery and the identification of the terrain elements responsible for slope deformation is made easier by the combined use of optical imagery 23 .…”

Section: Introductionmentioning

confidence: 99%

Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan

Hussain

Pan

Afzal

et al. 2023

Sci Rep

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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 (Vslope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (VSlope) 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 (Vslope) 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.

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“…There are already plethora of studies on earthquake and rainfall-triggered landslide inventories, as well as related landslide risk and hazard assessments, that have been carried out in the region, mainly in Pakistan (Dunning et al, 2007;Khattak et al, 2010;Ahmed and Rogers, 2015;Basharat et al, 2016;Khan et al, 2019;Imtiaz et al, 2022). In recent years, landslide-oriented research has been extended to the entire CPEC, especially for landslides along the KKH (Bacha et al, 2018;Rashid et al, 2020;Chang et al, 2021;Hussain et al, 2021;Maqsoom et al, 2022;Su et al, 2021). Hazard and risk assessments of some large-sized landslides were also carried out in this area, such as the Attabad landslide (Chen et al, 2017), where landslide dam breach and its hazards to the KKH project construction site downstream was discussed.…”

Section: Introductionmentioning

confidence: 99%

Zonation-based landslide hazard assessment using artificial neural networks in the China-Pakistan Economic Corridor

Zhang

Yong

et al. 2022

Front. Earth Sci.

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Distribution of landslide is controlled by various causative factors that have different impacts on the occurrence of landslide in different regions. Using one single model to build the hazard assessment is not enough to fully reflect the spatial differences of landslide controlling factors especially for large area. Landslide hazard assessment based on zonation was therefore proposed in this study with an attempt to take effective measures to address this problem. The China–Pakistan Economic Corridor was taken as the study area where landslide hazard assessment was carried out. Based on the features of geological structure, topography, and climate, the study area was divided into three zones. The controlling factors were further analyzed by the geographical detectors method. It was found that the main controlling factors for landslides in these three zones were related to the site’s topography (altitude, slope gradient, and relief amplitude), land use, and distance to an earthquake epicenter. Furthermore, different factors for landslide hazard assessment were selected based on the result of a controlling factor analysis. An artificial neural network model was employed to build the hazard assessment models, and hazard assessment maps were generated. Validations were conducted, showing that the accuracy of hazard assessment maps by zones was higher than that by the whole study area, despite there was no significant difference during the modeling process.

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Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017

Cited by 24 publications

References 55 publications

Wide Area Detection and Distribution Characteristics of Landslides along Sichuan Expressways

Wide Area Detection and Distribution Characteristics of Landslides along Sichuan Expressways

Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan

Zonation-based landslide hazard assessment using artificial neural networks in the China-Pakistan Economic Corridor

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