Potential loess landslide deformation monitoring using L-band SAR interferometry

Liu, Yuzhou; Liao, Mingsheng; Shi, Xuguo; Zhang, Lu; Cunningham, Cory

doi:10.1080/10095020.2016.1258202

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…It is made of 14 translational slides, seven rotational landslides, and 10 loess flows, detected according to the decomposition of interferometric vectors and following the proposed procedure. No human activities (e.g., excavations, loading, deforestation) were detected from historical Google Earth image archive, thus, slope movements most likely are triggered by the precipitations, as also stated by previous studies [58,59]. In terms of deformation magnitude, translational slides range from values of 4 to 30 mm/year along the horizontal direction.…”

Section: Discussionsupporting

confidence: 58%

Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data

et al. 2020

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Identification and classification of landslides is a preliminary and crucial work for landslide risk assessment and hazard mitigation. The exploitation of surface deformation velocity derived from satellite synthetic aperture radar interferometry (InSAR) is a consolidated and suitable procedure for the recognition of active landslides over wide areas. However, the calculated displacement velocity from InSAR is one-dimensional motion along the satellite line of sight (LOS), representing a major hurdle for landslide type and failure mechanism classification. In this paper, different velocity datasets derived from both ascending and descending Sentinel-1 data are employed to analyze the surface ground movement of the Huangshui region (Northwestern China). With global warming, precipitation in the Huangshui region, geologically belonging to the loess basin in the eastern edge of Qing-Tibet Plateau, has been increasing, often triggering a large number of landslides, posing a potential threat to local citizens and natural and anthropic environments. After processing both SAR data geometries, the surface motion was decomposed to obtain the two-dimensional displacements (vertical and horizontal E–W). Thus, a classification criterion of the loess landslide types and failure mode is proposed, according to the analysis of deformation direction, velocities, texture, and topographic characteristics. With the support of high-resolution images acquired by remote sensing and unmanned aerial vehicle (UAV), 14 translational slides, seven rotational slides, and 10 loess flows were recognized in the study area. The derived results may provide solid support for stakeholders to comprehend the hazard of unstable slopes and to undertake specific precautions for moderate and slow slope movements.

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Section: Discussionsupporting

confidence: 58%

Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data

et al. 2020

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“…In order to extract the lineaments in the study area, directional edge detection filters of 0 • , 45 • , 90 • , and 135 • were applied on the 10-m resolution Sentinel 2 satellite images and also to the 30-m resolution DEM of the study area. The employed directional derivative filter is stated in Equation (1).…”

Section: The Density Of Lineamentsmentioning

confidence: 99%

“…Landslides as the most occurring geo-hazard lead to severe landscape damage all over the world [1]. The international databases on the casualties of the landslides reported more than 3876 landslides during the period 1995-2014 in 128 countries around the world, which caused 11,689 injuries and 163,658 deaths [2].…”

Section: Introductionmentioning

confidence: 99%

A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

et al. 2022

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Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.

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“…Surface deformation is an apparent indicator for identifying active potential landslides [18]. Although the traditional methods including the global positioning system (GPS), total stations, and inclinometers can detect surface deformation accurately, they are not suitable for large-scale detection [19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Liang,

Zhang,

et al. 2023

Remote Sensing

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Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management.

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Potential loess landslide deformation monitoring using L-band SAR interferometry

Cited by 9 publications

References 7 publications

Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data

Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data

A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

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