Identification and monitoring landslides in Longitudinal Range-Gorge Region with InSAR fusion integrated visibility analysis

Guo, Rui; Li, Sumin; Chen, Ya’nan; Li, Xiangxin; Yuan, Liwei

doi:10.1007/s10346-020-01475-7

Cited by 48 publications

(28 citation statements)

References 34 publications

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“…In order to associate it with real-world deformation, many scholars have attempted to transform the deformation along the LOS direction into the deformation along the slope direction [ 52 , 53 ]. However, such a practice was not used in this study because slope-oriented deformation can play its role only in the case of translational landslides [ 54 ]. Through the field survey, it was discovered that most of the landslides that occurred in the study area involved the deformation caused in the vertical direction.…”

Section: Methodsmentioning

confidence: 99%

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

Zhu

Gan

Yuan

et al. 2022

Sensors

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Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after correction were compared. Moreover, the correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides.

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Section: Methodsmentioning

confidence: 99%

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

Zhu

Gan

Yuan

et al. 2022

Sensors

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“…The distribution of geometric distortion regions in the SAR images depends upon the local terrain characteristics and the acquisition direction of the satellite radar. To avoid this problem, SAR visibility analysis is often performed on multi-track SAR images, then effective ground deformation data in nongeometric distortion areas that are obtained from the TS-InSAR analysis are adopted for landslide identification [15,26,27]. Further, the technique of InSAR could only obtain the component of the ground deformation along the line-of-sight (LOS) direction of the satellite radar [18,23,28,29].…”

Section: Introductionmentioning

confidence: 99%

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

et al. 2022

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The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

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“…The InSAR Stacking technology overcomes the limitations of time incoherence and avoids the long temporal separation, spatial incoherence, and atmospheric effects in traditional interferometry methods, producing land deformation results that are more continuous in time and space (Hong et al 2009;Luo et al 2019). Therefore, this method has been widely used in landslide identification and deformation monitoring (Wang et al 2019a;Guo et al 2021b).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning

Wang

Zhuang

et al. 2021

Preprint

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The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.

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Identification and monitoring landslides in Longitudinal Range-Gorge Region with InSAR fusion integrated visibility analysis

Cited by 48 publications

References 34 publications

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China)

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning

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