Identification and deformation analysis of potential landslides after the Jiuzhaigou earthquake by SBAS-InSAR

Chang, Ming; Sun, Wenjing; Xu, Hengzhi; Tang, Liangliang

doi:10.1007/s11356-022-25055-5

Cited by 11 publications

(10 citation statements)

References 63 publications

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“…The early identification of potential landslide hazards is to identify the possible location and range of landslides before the occurrence of such disasters. The possibility of occurrence of potential landslides is determined, and the corresponding prevention and control measures are taken to effectively avoid the risk of landslide disasters Chang et al, 2023). Just like the research conducted by Yannick Thiery et al (2020) in France, the use of practical mapping tools, remote sensing, network services and other new technologies can provide higher quality landslide data information, thereby improving prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Review article: Research progress on influencing factors, data, and methods for early identification of landslide hazards

Lu,

Yang,

Song

et al. 2024

Preprint

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Abstract. The early identification of potential landslide hazards has always been a hot and difficult issue in the field of international landslide research. In recent years, many scholars have conducted extensive and beneficial explorations in this field, making significant contributions to the effective prevention of landslide disasters. However, until now, there are very few review documents on summarizing such valuable experience in the system, which makes it difficult to meet the ever-increasing demand of researchers in scientific documents. To address the gap, this paper systematically reviews 843 documents collected by the two data platforms of Web of Science (WOS) and Scopus from 1971 to 2023 by using the bibliometric analysis software. This paper first figures out the internal relationship between documents by analysing their spatial and temporal distribution characteristics, and then emphatically analyses the application, advantages and disadvantages of different early identification methods based on the influencing factors of landslide disaster formation and multi-source data acquisition links. And finally, this paper discusses the challenges and development trends in this field from four aspects of cooperative analysis, multi-source data, topic analysis and research trends, and puts forward some suggestions. This research can help researchers to use various early identification methods reasonably and provide summary and integration services of scientific document achievements for efficient research in this field.

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Section: Introductionmentioning

confidence: 99%

Review article: Research progress on influencing factors, data, and methods for early identification of landslide hazards

Lu,

Yang,

Song

et al. 2024

Preprint

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“…The InSAR technique has proved to be a powerful tool for landslide studies [44,45]. However, previous studies mainly focused on landslide deformation analysis [46,47], potential landslide detection [48,49], and improving the InSAR method [50]. Only a few studies have discussed the role of the InSAR technique in understanding the development of potential landslides at the regional scale.…”

Section: Introductionmentioning

confidence: 99%

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

Zhou,

Guo,

Huang

et al. 2023

Applied Sciences

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Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.

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“…Therefore, the identification, monitoring, and assessment of geological hazards can be carried out in conjunction with surface deformation monitoring using the MT-InSAR technology. Nowadays, MT-InSAR techniques, such as PS-InSAR [11], SBAS-InSAR [12], and SqueeSAR [13], are widely used in the monitoring and identification of geological hazards such as landslides, unstable slopes, and so on.…”

Section: Introductionmentioning

confidence: 99%

Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR

Lu,

Yang,

Zeng

et al. 2023

Remote Sensing

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Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%.

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Identification and deformation analysis of potential landslides after the Jiuzhaigou earthquake by SBAS-InSAR

Cited by 11 publications

References 63 publications

Review article: Research progress on influencing factors, data, and methods for early identification of landslide hazards

Review article: Research progress on influencing factors, data, and methods for early identification of landslide hazards

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR

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