Automatic Identification of Slope Active Deformation Areas in the Zhouqu Region of China With DS-InSAR Results

Wang, Yuanjian; Cui, Ximin; Che, Yuhang; Li, Peixian; Jiang, Yue; Peng, Xiaozhan

doi:10.3389/fenvs.2022.883427

Cited by 8 publications

(7 citation statements)

References 37 publications

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“…Some larger landslides are also identified in the study, including the Suoertou, Daxiaowan, Xieliupo, Nanyumentouping, and Zhongpai landslides, all of which occurred along the Bailong River or the Min River. These landslides make up a pre-existing landslide inventory in the previous research [47][48][49][50][51]. The maximum deformation rates were detected to be greater than 136 mm/year.…”

Section: Identifying the Active Deformation Area Of The Unknown Regionmentioning

confidence: 84%

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Wu,

Ge,

et al. 2024

Remote Sensing

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Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities.

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Section: Identifying the Active Deformation Area Of The Unknown Regionmentioning

confidence: 84%

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Wu,

Ge,

et al. 2024

Remote Sensing

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“…In the past 40 years, it reactivated several times and transferred into debris flows during heavy rains in 1978, 1992, 2010. In 1978, reactivation of the landslide damaged many cottages, and the local authorities displaced more than 100 threatened families (Wang et al, 2022). Since August 2020, the landslide has partly reactivated and caused some road collapses in Beishan village near the landslide head.…”

Section: Study Areamentioning

confidence: 99%

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Liu,

Yang,

et al. 2024

Front. Earth Sci.

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Investigating the response of landslide activity to climate change is crucial for understanding the disastrous effects of climate change on high mountains. However, the lack of long-term, spatial–temporal consistent measurement of landslide activity prohibits the study of this relationship. In this work, we used two methods to derive the time series of a landslide’s deformation and study its relationship with precipitation in the northeastern Tibetan Plateau. The small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) method with Sentinel-1A images is first applied to derive time series of the landslide’s deformation from 2020 to 2021. A recently developed method to derive cumulative deformations of optical images was used with Landsat 5 and Sentinel-2 images to derive the long-term deformation time series from 1986 to 2023. Centimeter-scale deformations detected by using the InSAR method are mainly located in the upper and eastern parts of the landslide, whereas meter-scale deformations detected by using the optical method are in the middle of the landslide. Time-series results from both methods show that intra-annual initiations of the landslide’s deformation occurred in rainy months (from July to October). Although there seems to be no direct relations between inter-annual deformations and precipitation, significant displacements since 2020 occurred after exceptionally wet years from 2018 (with a record-breaking precipitation year in 2020). With optical images, we found that the maximum cumulative deformation of the landslide has been >35 m since 1986 with major deformations (>20 m) found after 2020, which may indicate an imminent risk to the Lijie town near the toe of the landslide. With climate change, increased precipitation is expected in future, which may trigger more similar landslides in the vicinity of this region. This work demonstrates an executable framework to assess landslide hazard risk under climate change.

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“…The caused geometric distortions, including layover, foreshortening, and shadow, can be improved using ascending and descending data monitoring [110]. Many advanced InSAR processing methods and other improved methods continue to emerge, such as distributed scatterer (DS-InSAR) technology [111], stacking InSAR [112], and phase-decomposition-based persistent scatterer (PS) InSAR (PD-PSInSAR) technology [113], which enabled a higher density of measurement points in areas with complex topographic environment conditions [114]. In case of very fast displacements, the temporal coherence decreases leading to the loss of measurement points [19].…”

Section: Limitations and Further Directions Of The Approachmentioning

confidence: 99%

Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry

Zhang,

Li,

Meng

et al. 2023

Remote Sensing

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Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions.

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Automatic Identification of Slope Active Deformation Areas in the Zhouqu Region of China With DS-InSAR Results

Cited by 8 publications

References 37 publications

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry

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