Abstract
In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes.
Highlights
The capabilities of Persistent Scatterer Interferometry (PSI) and Small baseline subsets (SBAS)-InSAR methods, polarimetric decomposition techniques, and synthetic aperture radar (SAR) backscattering multitemporal series estimation have been demonstrated for the case study of the landslide reactivation on the Bureya River in Russia
It is shown that the spatial variations of the backscattered signal in the surface of the rupture in different years behave in a rather similar way and do not allow make a conclusion about interannual changes
The results of polarimetric analysis over the entire surface of the rupture based on dual polarization C-band data showed that the scattering mechanisms remained practically unchanged
Summary
Study and monitoring of catastrophic natural processes are among the most relevant applications of these methods. These methods are successfully applied, for example, for the monitoring of seismically hazardous areas to identify earthquake precursors by analyzing anomalous variations in ionospheric parameters [3], recorded, including by signals from satellite navigation systems [4,5], by analyzing changes in lineament systems revealed during satellite images processing [6]. The efficiency of dangerous phenomena forecasting increases greatly when integrating satellite data analysis results with the approaches based, for example, on the application of geomechanical models [8,9] or seismic entropy [10]
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