A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data

For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.

Sequence Analysis of Several Ancient River Blocking Events in the Suwalong Reach of the Upper Jinsha River Based on Multidisciplinary Approaches, SE Tibetan Plateau

The temporary or permanent river blocking event caused by mass movement usually occurs on steep terrain. With the increase of mountain population and land use pressure and the construction of water conservancy and hydropower projects, river blocking event has gradually attracted people’s attention and understanding. The study area (Wangdalong-Gangda reach) is located in the upper reaches of the Jinsha River and the southeast edge of the Qinghai-Tibet Plateau. Affected by strong tectonic activity in the Jinsha River suture zone and the rapid uplift of the Tibetan Plateau, in the past 6000 years, there have been at least five obvious river blocking events in the reach of about 30 km in the study area. The number and density are very rare. Combined with the field investigation, indoor interpretation, laboratory tests, optically stimulated luminescence (OSL) dating, SBAS-InSAR and previous studies, multidisciplinary approaches are used to systematically summarize the analysis methods and further the understanding of one river blocking event and multiple river blocking events from difference perspectives. Especially in multiple river blocking events, we could get the wrong results, even the opposite conclusion if interaction is not considered. Through this study, the general method of analyzing the river blocking event and the problems that should be paid attention to in sampling are given, and relatively reliable historical results of river blocking events are obtained. This method has extensive applicability to the identification and analysis of river blocking events in other areas.

Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

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.

Contribution of ground ice melting to the expansion of Serling Co lake on the Tibetan Plateau

Serling Co lake, surrounded by permafrost and glacier-occupied regions, has exhibited the greatest increase in water storage over the last 50 years among all the lakes on the Tibetan Plateau. However, increases in precipitation and glacial melting are not enough to explain the increased water volume of lake expansion. The magnitude of the contribution of thawing permafrost to this increase under climate warming remains unknown. This study made the first attempt to quantify the water contribution of ground ice melting to the expansion of Serling Co lake by evaluating the ground surface deformation. We monitored the spatial distribution of surface deformation in the Serling Co basin using the SBAS-InSAR technique and compared it with the findings of field surveys. Then, the ground ice meltwater volume in the watershed was calculated based on the long-term deformation rate. Finally, this volume was compared with the lake volume change during the same period, and the contribution ratio was derived. SBAS-InSAR monitoring during 2017–2020 illustrated widespread and large subsidence in the upstream section of the Zhajiazangbu subbasin, where widespread continuous permafrost is present. The terrain subsidence was normally between 5 and 20 mm/a, indicating rapid ground ice loss in the region. The ground ice meltwater reached 56.0 × 106 m3/a, and the rate of increase in lake water storage was 496.3 × 106 m3/a during the same period, with ground ice meltwater contributing 11.3 % of the lake volume increase. This study is especially helpful in explaining the rapid expansion of Serling Co lake and equilibrating the water balance at the watershed scale. More importantly, the proposed method can be easily extended to other watersheds underlain by permafrost and to help understand the hydrologic changes in these watersheds.

Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore， the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.

Accuracy Verification and Correction of D-InSAR and SBAS-InSAR in Monitoring Mining Surface Subsidence

The accuracy of InSAR in monitoring mining surface subsidence is always a matter of concern for surveyors. Taking a mining area in Shandong Province, China, as the study area, D-InSAR and SBAS-InSAR were used to obtain the cumulative subsidence of a mining area over a multi-period, which was compared with the mining progress of working faces. Then dividing the mining area into regions with different magnitudes of subsidence according to the actual mining situation, the D-InSAR-, SBAS-InSAR- and leveling-monitored results of different subsidence magnitudes were compared and the Pearson correlation coefficients between them were calculated. The results show that InSAR can accurately detect the location, range, spatial change trend, and basin edge information of the mining subsidence. However, InSAR has insufficient capability to detect the subsidence center, having high displacement rates, and its monitored results are quite different from those of leveling. To solve this problem, the distance from each leveling point to the subsidence center was calculated according to the layout of the rock movement observation line. Besides, the InSAR-monitored error at each leveling point was also calculated. Then, according to the internal relationship between these distances and corresponding InSAR-monitored errors, a correction model of InSAR-monitored results was established. Using this relationship to correct the InSAR-monitored results, results consistent with the actual situation were obtained. This method effectively makes up for the deficiency of InSAR in monitoring the subsidence center of a mining area.