Monitoring the Thaw Slump-Derived Thermokarst in the Qinghai-Tibet Plateau Using Satellite SAR Interferometry

Hu, Bo; Wu, Yang; Zhang, Xingfu; Yang, Bing; Chen, Junyu; Li, Hui; Chen, Xiongle; Chen, Zhiwei

doi:10.1155/2019/1698432

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…Field observation, airborne photogrammetry, satellite remote sensing, and InSAR technology can obtain the geomorphic characteristics of RTSs 48,73,119–122 . Field observations provide valuable insights into RTS characteristics.…”

Section: Evaluating the Susceptibility And Stability Of Rtssmentioning

confidence: 99%

“…Field observation, airborne photogrammetry, satellite remote sensing, and InSAR technology can obtain the geomorphic characteristics of RTSs. 48,73,[119][120][121][122] Field observations provide valuable insights into RTS characteristics. However, local observations limit our ability to obtain the spatial distribution and temporal progression of RTSs over regions to larger scale areas.…”

Section: Dynamic Monitoring Of Rtss By Remote Sensingmentioning

confidence: 99%

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Advances in retrogressive thaw slump research in permafrost regions

Li,

Liu,

Chen

et al. 2024

Permafrost & Periglacial

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A retrogressive thaw slump (RTS) is a slope failure formed by slope thaw settlement and retrogressive slump following the thawing of ice‐rich permafrost or the melting of massive ice. Here, we review recent literature on RTSs, one of the main geomorphological landscapes developed in the process of permafrost degradation. The main topics are as follows: development and temporal evolution, mechanisms and processes, influencing factors, evaluation susceptibility and calculation, and assessment of engineering and environmental impacts. There has been a rapid increase in the number and distribution area of RTSs over permafrost in recent years. Climate warming events, extreme rainfall, forest fires, bank and coast erosion, and anthropogenic activity are the primary factors leading to RTSs in permafrost regions, disrupting the initial hydrothermal equilibrium of permafrost slopes. This causes a rise in ground temperature and the thaw of ice‐rich permafrost. Meltwater seeps down and collects on the ice surface, weakening freeze–thaw interface shear resistance and resulting in soil collapse. The development of RTSs may last several decades or longer. RTSs destabilize infrastructure, destroy vegetation, boost soil erosion and land desertification, alter the environment of nearby waters, and increase emissions of some major greenhouse gases. Numerous methods have been developed and adopted to explore RTSs, including geographic information systems (GIS) and equilibrium, numerical, and reliability analysis methods. However, research on formation mechanisms and processes, quantitative prediction, engineering and environmental influences, and mitigative measures of RTSs under a warming climate are still inadequate. Existing research methods, such as numerical simulations, remote sensing, airborne ground‐based geophysical surveys, investigations and mapping, and hydrothermal and deformation field monitoring, should be systematically integrated. Additionally, equipment for laboratory testing and numerical models for simulating RTSs may need to be timely introduced and better developed.

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Section: Evaluating the Susceptibility And Stability Of Rtssmentioning

confidence: 99%

Section: Dynamic Monitoring Of Rtss By Remote Sensingmentioning

confidence: 99%

Advances in retrogressive thaw slump research in permafrost regions

Li,

Liu,

Chen

et al. 2024

Permafrost & Periglacial

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show abstract

“…InSAR is the method that extracts geophysical features such as surface topography and deformation by comparing the phase offsets of at least two complex-valued SAR images [1]. Comparing SAR images measured at different time instants, InSAR can retrieve the surface displacement at specific time instants, e.g., the surface deformation induced from earthquakes or volcanic activities, landslides [10], thaw-derived slope failure [11], and glacier elevations and changes [12]. However, conventional InSAR has a limitation that it can measure only the line-of-sight (LOS) component of displacement [13].…”

Section: Related Workmentioning

confidence: 99%

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA andk-Means Clustering

Lee

Kim

2020

Journal of Sensors

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The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem.

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“…RTSs are documented throughout the cold regions of the globe: Canada (Lacelle et al 2010;Wang et al 2016;Cassidy et al 2017), Tibet Plateau (China) (Niu et al 2012;Sun et al 2017), Siberia (Russia) (Séjourné et al 2015) and Alaska (USA) (Swanson and Nolan 2018). In recent years, significant progress has been made in improving identification and mapping of RTS with the following remote sensing techniques: photogrammetry (Swanson and Nolan 2018), satellite SAR interferometry (Hu et al 2019) and deep learning (Huang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Preliminary assessment of thaw slump hazard to Arctic cultural heritage in Nordenskiöld Land, Svalbard

2021

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Permafrost-dependent landslides occur in a range of sizes and are among the most dynamic landforms in the Arctic in the warming climate. Retrogressive thaw slumps (RTSs) are enlarging landslides triggered by thawing and release of excess water from permafrost ground ice, causing smaller or larger collapses of ground surface, which in turn exposes new permafrost to rapid thawing and collapse. In this study, a preliminary assessment of previous thaw slump activity in Nordenskiöld Land area of Svalbard is made based on remote sensing digitisation of 400 slump-scar features from aerial images from the Norwegian Polar Institute (NPI). RTS properties and distribution are analysed with an emphasis on their implications for the preservation of the Svalbard’s cultural heritage (CH). Our analysis shows that the areas where RTS scars and CH co-exist in Nordenskiöld Land are, at present, limited and cover mainly areas distributed along north-west (Colesbukta, Grønfjorden, Kapp Starostin), north-east (Sassendalen and Sassenfjorden) and south-west (Van Muydenbukta) coastlines. Taking into consideration the preliminary aspect of this inventory and study, it can be stated that for now, RTS and CH sites do not have a high level of co-existence, except for eight sites which are located at less than 100 m to a RTS and one site that is located inside a currently inactive slump-scar. Further mapping of RTS will be undertaken in order to have a complete picture of these climate triggered landslides potentially threatening the Arctic CH. The results of this study, even if preliminary, can be used by local authorities and stakeholders in prioritising future documentation and mitigation measures and can thus present a powerful tool in disaster risk reduction.

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Monitoring the Thaw Slump-Derived Thermokarst in the Qinghai-Tibet Plateau Using Satellite SAR Interferometry

Cited by 10 publications

References 16 publications

Advances in retrogressive thaw slump research in permafrost regions

Advances in retrogressive thaw slump research in permafrost regions

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA andk-Means Clustering

Preliminary assessment of thaw slump hazard to Arctic cultural heritage in Nordenskiöld Land, Svalbard

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