Landslide evolution assessment based on InSAR and real-time monitoring of a large reactivated landslide, Wenchuan, China

Xie, Mingli; Zhao, Weihua; Ju, Nengpan; He, Chuan; Huang, Hongdong; Cui, Qinghong

doi:10.1016/j.enggeo.2020.105781

Cited by 58 publications

(20 citation statements)

References 23 publications

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“…The fully comprehension of this phenomenon requires a detailed geotechnical investigation and modeling. Integrating remote sensing methods with geotechnical surveys and in situ monitoring of subsurface movements and pore pressure variations, would help in defining the relationships between rainfall and displacements and implementing effective mitigation measures [54]. However, as observed in this study and in numerous previous surveys [55][56][57] MT-InSAR represents a useful tool for the preliminary analysis of rainfallinduced landslides.…”

Section: Discussionmentioning

confidence: 73%

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

et al. 2021

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Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

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Section: Discussionmentioning

confidence: 73%

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

et al. 2021

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“…The quality of the results still depends on source data quality (Broeckx et al, 2018). With the rapid development of Synthetic Aperture Radar, 3D deformation information of earthquake-affected area can be obtained quickly after an earthquake, which can be effectively used to identify the hidden landslide area (Xie et al, 2020). More reliable negative data may be obtained by integrating the stable area in 3D deformation and the non-landslide area in LIM.…”

Section: Discussionmentioning

confidence: 99%

A One-Class-Classifier-Based Negative Data Generation Method for Rapid Earthquake-Induced Landslide Susceptibility Mapping

Chen

Zhang

et al. 2021

Front. Earth Sci.

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Machine learning with extensively labeled training samples (e.g., positive and negative data) has received much attention in terms of addressing earthquake-induced landslide susceptibility mapping (LSM). However, the extensive amount of labeled training data required by machine learning, particularly the precise negative data (i.e., non-landslide area), cannot be easily and efficiently collected. To address this issue, this study presents a one-class-classifier-based negative data generation method for rapid earthquake-induced LSM. First, an incomplete landslide inventory (i.e., positive data) was produced with the aid of change detection using before-and-after satellite images and the Geographic Information System (GIS). Second, a one-class classifier was utilized to compute the probability of landslide occurrence based on the incomplete landslide inventory followed by the negative data generation from the low landslide susceptibility areas. Third, the positive data as well as the generated negative data (i.e., non-landslide) were compounded to train a traditional binary classifier to produce the final LSM. Experimental results suggest that the proposed method is capable of achieving a result that is comparable to methods using the complete landslide inventory, and it displays good correspondence with recent landslide events, making it a suitable method for rapid earthquake-induced LSM. The findings in this study would be useful in regional disaster planning and risk reduction.

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“…This method can issue early warning information before the actual occurrence of geological hazards and evacuate the residents in the danger zone to protect their lives and properties. Xie et al [71] revealed the spatial and temporal distribution and deformation patterns of significant geological hazards in the upper reaches of the Minjiang River by using a combination of InSAR monitoring and in situ monitoring in 2020. This method can further reduce the loss of life and property to landslides within the Minjiang River Gorge.…”

Section: Potential Uncertainty Of Geological Body Characteristics And...mentioning

confidence: 99%

Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau

Liu

Yao

et al. 2022

Remote Sensing

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The development of landslide hazards is spatially scattered, temporally random, and poorly characterized. Given the advantages of the large spatial scale and high sensitivity of InSAR observations, InSAR is becoming one of the main techniques for active landslide identification. The difficult problem is how to quickly extract landslide information from extensive InSAR image data. Since the instance segmentation model (Mask R-CNN) in deep learning can provide highly robust target recognition, we select the landslide-prone eastern edge of the Tibetan Plateau as a specific test area. Introducing and optimizing this model achieves high-speed and accurate recognition of InSAR observations. First, the InSAR patch landslide instance segmentation dataset (SLD) is established by developing a common object in context (COCO) annotation format conversion code based on InSAR observations. The Mask R-CNN+++ is found by adding three functions of the ResNext module to increase the fineness of the network segmentation results and enhance the noise resistance of the model, the DCB (deformable convolutional block) to improve the feature extraction ability of the network for geometric morphological changes of landslide patches, and an attention mechanism to selectively enhance usefully and suppress features less valuable to the native Mask R-CNN network. The model achieves 92.94% accuracy on the test set, and the active landslide recognition speed based on this model under ordinary computer hardware conditions is 72.3 km2/s. The overall characteristics of the results of this study show that the optimized model effectively enhances the perceptibility of image morphological changes, thereby resulting in smoother recognition boundaries and further improvement of the generalization ability of segmentation detection. This result is expected to serve to identify and monitor active landslides in complex surface conditions on a large spatial scale. Moreover, active landslides of different geometric features, motion patterns, and intensities are expected to be further segmented.

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Landslide evolution assessment based on InSAR and real-time monitoring of a large reactivated landslide, Wenchuan, China

Cited by 58 publications

References 23 publications

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

A One-Class-Classifier-Based Negative Data Generation Method for Rapid Earthquake-Induced Landslide Susceptibility Mapping

Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau

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