Located in the Halmahera Arc Volcano, Mt Dukono is one of the most active volcanoes in the Maluku islands. There are reports of eruptions that had occurred here in the past, such as 1550, 1719, 1868, 1901, and 1933. Due to its location that is hard to access, the report on monitoring of volcanic activity in this volcano, especially geodetic monitoring, becomes rare. This is where Interferometric Synthetic Aperture Radar (InSAR) come to tackle this problem. In this observation, we were using 118 SAR images from Sentinel 1 satellite from January 2021 towards December 2024. Small baseline subset method was used in this observation to extract time series information of the deformation that occurred in Mt Dukono. To mitigate the atmospheric artifacts that can affect the measurement, we were using GACOS model. From the time series analysis that we got, there was a down trend in the deformation on the edifice of the Mt Dukono with different rates in the north and southern part of the volcano in the period of 2021 to 2023. Since July 2023, we have found some build-up phases that have occurred on all sides of the Mount Dukono edifice. We assume that the role of the influx of magma from the magma chamber played an important role in the deformation of Mt. Dukono. In this case, there was an increase in the magma supply that is more than its previous phase, making Mt. Dukono to be uplifted.

Due to drought and underground water extraction, many plains in Iran are experiencing subsidence. Among these areas, we can mention the southwestern part of Tehran, which has a large resident population and has suffered severe subsidence in the last two decades. In order to study subsidence, various ground and aerial methods are used, and the interferometric synthetic aperture radar (InSAR) system is one of those techniques that measures accurate values of ground surface displacement with high spatial resolution across a large study area. The small baseline subset (SBAS) method is a remote sensing-based technique to analyze the time series of radar interferometry. It is particularly important to examine subsidence patterns over different time frames in a geographical area and their relationship with climatic parameters, such as precipitation, in remote sensing. In this context, this research uses the SBAS method to obtain the average displacement velocity field of southwest Tehran for the period from 2014 to 2017. The maximum amount of subsidence in this area is 174 mm per year along the satellite's line of sight and 227 mm per year in the vertical direction. The time series obtained from InSAR shows the uplift during certain periods. This uplift is attributed to rainfall exceeding 20 mm before the uplift events, particularly in the last six measurements, where heavy rain has resulted in an uplift of up to 50 mm.

We investigated the post-seismic period of the March 2021 Damasi-Tyrnavos (Thessaly, Greece) normal fault earthquakes by applying the multi-temporal interferometric Small Baseline Subset method. We processed 68 ascending Sentinel-1 acquisitions between 2020/03/15 and 2022/09/12. Our results identified three areas on the hanging wall of the ruptured faults showing non-linear deformation trends (systematic motion away from the satellite), and another area, on the footwall (systematic motion towards the satellite), interpreted as due to a post-seismic effect. Inversion of the InSAR data indicated the occurrence of afterslip co-planar to the sequence’s two largest fault planes (M 6.3 and M 6.0, respectively). Most of the afterslip, with a peak of about 0.2 m, occurred on the fault corresponding to the 4 March 2021 event, at a depth of 7.5 km, while the fault corresponding to the M 6.3 event only showed very shallow adjustments and minor features at the border of the coseismic pattern. The transient uplift affected the footwall of the 3 March 2021 event and may indicate that the rupture nearly reached the surface towards the SW of the epicenter. The afterslip showed a fast phase lasting between March and August 2021 (5 months) and a second phase from March 2022 up to September 2022. A correlation between afterslip and relocated hypocenters indicates that most of the afterslip was aseismic. The moment release of the afterslip (fast phase) is about 7% that of the mainshocks.

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability.

The article presents the results of significance analyses of selected mining and geological variables for an area of underground mining activity. The study area was a region of an underground copper ore mine located in southwest Poland. The input data consisted of satellite radar data from the Sentinel 1 mission as well as mining and geological data. The line-of-sight subsidence, calculated with the use of the small baseline subset method and arranged in time series, was decomposed to extract the vertical component. The significance analysis of individual variables for the observed surface subsidence was performed using the SHapley Additive exPlanations method for the XGBoost machine learning model. The results of the analysis showed that the observed ground surface subsidence velocities were most influenced by the thickness of the PZ3 layer, which is located approximately 200 m above the roof of the mined seam, the thickness of the seam, and the timing of mining. It was also found that the proposed model was able to detect a nonlinear relationship between the analyzed excavations. The most significant influence on ground subsidence over mine excavations are mining parameters such as the spatially averaged thickness of the deposit and the time since liquidation of the deposit. The proposed approach can be successfully employed in planning both mining operations and mine closure in such a manner that the environmental impact is minimized.

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

The increase in ocean temperature causes the expansion of seawater volume, resulting in an increase in sea level rise. The phenomenon of land subsidence also exacerbates the occurrence of tidal floods in coastal areas of Indonesia. This has prompted the need for a study of land subsidence and the distribution of tidal floods in Tangerang as a basis for taking anticipatory steps to reduce the negative impacts. The methods used for estimating land subsidence involved the SAR Sentinel-1A. The research utilized a total of 170 data points, spanning from 2017 until 2022. Data processing was carried out using the Parallel Small Baseline Subset method. The supporting data used in this study included SRTM data, tidal range, rainfall data, wind speed and direction. The results of this study reveal that the city of Tangerang has a maximum deformation value of -10.8 cm per year in the Periuk Sub District. Meanwhile, Tangerang Regency experienced land subsidence at a rate of -8.6 cm per year in Kosambi Sub District. Significant subsidence deformations occurred on the northeast side of Tangerang District and the southeast side of Tangerang City. Based on data analysis, it is evident that the total area inundated by tidal floods in Tangerang covers 33.267 hectares, with the largest affected area being in Pakuhaji District, spanning 9,262 hectares.

Ningdong coal base area located in northwestern China is one of the largest coal-producing bases in China. The aim of this work is to investigate a regional-scale mining subsidence over the Ningdong coal base area, by using both conventional and advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methods. Fifteen L-band SAR images from ALOS-2 satellite and 102 C-band images from Sentinel-1A satellite spanning between November 2014 and July 2019 were used for the analysis. To increase the spatial extent of the displacement signal because of decorrelated effects, we modified the traditional Small Baseline Subset (SBAS) method to incorporate the coherence into the inverse problem, hereafter we call it coherence-based SBAS method. Instead of excluding decorrelated pixels present in the interferograms, we keep all the pixels in the time series analysis and down-weighted the decorrelated pixels with coherence. We performed the coherence-based SBAS method to both the two SAR datasets to obtain the subsidence rate maps and displacement time-series over the mining areas, and compared the results with that from the traditional stacking InSAR method. We evaluated the effectiveness of L-band and C-band DInSAR for monitoring mining subsidence by comparing differential interferograms and displacements derived from SBAS method between ALOS-2 and Sentinel-1A data. Compared to C-band, L-band SAR are less affected by phase aliasing due to large displacement gradients. The most significant subsidence was found at Maliantai mine with −264 mm/year detected by SBAS method from Sentinel-1 data. We validated the InSAR displacement accuracy by comparing both ALOS-2 and Sentinel-1 results with 18 GPS stations above five active mining regions. The average RMSE between InSAR and GPS measurements is 28.4 mm for Sentinel-1 data and 21 mm for ALOS-2 data. Our results demonstrate that the combined exploitation of L-band and C-band SAR data through both conventional and advanced DInSAR methods could be crucial to monitor ground subsidence in mining areas, which provides insights into subsidence dynamics and determine the characteristic surface response to longwall advance.

The 21 July 2020 Shaziba landslide in China: Results from multi-source satellite remote sensing

ABSTRACT In this study, Differential Interferometric Synthetic Aperture Radar Interferometry (DInSAR) of artificial Corner Reflectors (CRs) were validated in the area of fast and nonlinear deformation gradient caused by active coal longwall exploitation. Three Sentinel-1 datasets were processed using conventional DInSAR, Persistent Scatterer Interferometry (PSI), and Small BAseline Subset methods implemented in ENVI SARscape™. For evaluation, leveling and Global Navigation Satellite System (GNSS) measurements were used. Considering the challenge of snow cover, the removal of all winter images was not a successful strategy due to the long temporal baseline and strong movement, which cause phase unwrapping problems and underestimate the real deformation. The results indicate that only conventional DInSAR and SBAS with low network redundancy allow us to capture maximal deformation gradient and the root mean square error calculated between the CRs and the ground truth is on the level of 2–3 cm for the vertical and easting deformation component, respectively. For the small deformation gradient represented by the permanent GNSS station (4 cm/year), all SBAS techniques appeared to be more accurate than DInSAR, which corresponds to higher redundancy and better removal of the atmospheric signal. In contrast, DInSAR results allowed to capture information about two subsidence basins, which was not possible with SBAS and PSI approaches.

Interseismic deformation rate of the Haiyuan fault system based on the modified SBAS method

Kinematics of the ∼1000 km Haiyuan fault system in northeastern Tibet from high-resolution Sentinel-1 InSAR velocities: Fault architecture, slip rates, and partitioning

Monitoring deformation of architectural heritage sites is important for the quantitative evaluation of their stability. However, deformation monitoring of sites in mountainous areas remains challenging whether utilizing global navigation satellite system (GNSS) or interferometric synthetic aperture radar (InSAR) techniques. In this study, we improved the small baseline subset (SBAS) approach by introducing the pseudo-baseline combination strategy to avoid the errors caused by inaccurate external DEM, resulting in robust deformation estimations in mountainous areas where the architectural heritage site of the Great Wall is located. First, a simulated dataset and a real dataset were used to verify the reliability and effectiveness of the algorithm, respectively. Subsequently, the algorithm was applied in the landscape deformation monitoring of the Shanhaiguan section of the Great Wall using 51 Sentinel-1 scenes acquired from 2016 to 2018. A thematic stability map of the Shanhaiguan Great Wall corridor was generated, revealing that the landscape was generally stable save for local instabilities due to to unstable rocks and wall monuments. This study demonstrated the capabilities of adaptive multitemporal InSAR (MTInSAR) approaches in the preventive landscape deformation monitoring of large-scale architectural heritage sites in complex terrain.

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

As one of the largest hypersaline lakes, Lake Tuz, located in the middle of Turkey, is a key waterbird habitat and is classified as a Special Environmental Protection Area in the country. It is a dynamic lake, highly affected by evaporation due to its wide expanse and shallowness (water depth <40 cm), in addition to being externally exploited by salt companies. Monitoring the dynamics of its changes in volume, which cause ecological problems, is required to protect its saline lake functions. In this context, a spatially homogeneous distributed gauge could be critical for monitoring and rapid response; however, the number of gauge stations and their vicinity is insufficient for the entire lake. The present study focuses on assessing the feasibility of a time-series interferometric technique, namely the small baseline subset (SBAS), for monitoring volume dynamics, based on freely available Sentinel-1 data. A levelling observation was also performed to quantify the accuracy of the SBAS results. Regression analysis between water levels, which is one of the most important volume dynamics, derived by SBAS and levelling in February, April, July and October was 67%, 80%, 84%, and 95% respectively, for correlation in the range of 10–40 cm in water level, and was in line with levelling. Salt lake components such as water, vegetation, moist soil, dry soil, and salt, were also classified with Sentinel-2 multispectral images over time to understand the reliability of the SBAS measurements based on interferometric coherence over different surface types. The findings indicate that the SBAS method with Sentinel-1 is a good alternative for measuring lake volume dynamics, including the monitoring of water level and salt movement, especially for the dry season. Even though the number of coherent, measurable, samples (excluding water) decrease during the wet season, there are always sufficient coherent samples (>0.45) over the lake.

A quarry failure along the slopes of the Wai Khar open-pit jade mine in Hpakant, Myanmar has led to the deaths of at least 172 jade miners on 2 July 2020. This paper conducts a systematic investigation of the incident by integrating data from multiple sensors, including high-resolution optical imagery, Sentinel-1 synthetic aperture radar (SAR) images, unmanned aerial system (UAS) footage, SRTM and ALOS digital elevation models (DEMs), soil moisture product from multi-spectral Landsat-8 satellite and precipitation records from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Optical imagery, UAS footage and DEMs allow us to build a comprehensive mapping of tailing areas and quarry scarps from 2010 and reconstruct the 2D pit geometry prior to failure. Deformation signals from multi-temporal SAR interferometry (MTInSAR), soil moisture variations and precipitation trends further allow us to identify possible failure causes. To evaluate the quality of deformation obtained from different distributed-scatterer phase estimators, we develop an empirical mapping function based on areal fraction values to facilitate the comparison of temporal coherence values that are differently formulated in each phase estimator. The comparison shows that phase linking algorithm outperforms the small baseline subset method in terms of signal recovery and phase reliability. Our investigation points out that the mining site is under aggressive mining cycles that are exacerbated by frequent, uncontrolled landslides. Seepage failure, which involves the expulsion of water from rapidly compacting tailings, may be a critical factor in the 2020 incident. Instead of extreme weather, the failure had occurred under normal to drier conditions. This means that the sliding planes were already in a critical state, which is evident from the accelerated deformation around the collapse area since the beginning of 2020. Based on these findings, we provide recommendations to improve mining site regulations and management practices for safer open-pit mining in Myanmar and probably in similar contexts outside Myanmar.

In this article, we present a possible approach to use satellite radar data for a complete description of the formation process of a subsidence trough resulting from an induced seismic event—a mining tremor. Our main goal was to verify whether SAR data allow for the calculation of the basic indicators for the trough (w—subsidence, T—trough slope, K—curvature, u—horizontal displacements, ε—horizontal deformations). We verified the extent to which the Mogi and Yang models can be fitted to match the actual displacements recorded after an induced seismic tremor. The calculations were performed for the Legnica-Glogow Copper Belt (LGCB) area in southwest Poland. Due to intensive mining operations and specific geological and tectonic conditions, the area shows a high level of induced seismic activity. Our detailed analysis focused on four powerful mining tremors: the first tremor occurred on 29 November 2016 (MW3.4), the second on 7 December 2017 (MW3.3), the next on 26 December 2017 (MW3.6) and the last tremor on 29 January 2019 (MW3.7). For each analyzed event, we determined the displacements based on the Differential Interferometric Synthetic Aperture Radar (DInSAR) method and Sentinel 1 synthetic aperture radar (SAR) data from two paths (22 and 73). Additionally, for the period from November 2014 to October 2020, we calculated the displacements using the Small Baseline Subset method (SBAS) time series method. In all cases, the tremor was followed by the development of long-lasting surface deformations. The obtained results allowed us to conclude that it is possible to calculate indicators that result from a specific induced mining event. Considering the full moment tensor and nature of the tremor source, we demonstrated that the Mogi and Yang models can be employed to describe the influence of an induced tremor on the surface in an area of mining activity. We also confirmed the global character of the influence of the reduced troposphere on SAR data calculations. Our conclusions indicate that accounting for the tropospheric correction does not distort horizontal and vertical displacement values in regions influenced by mining activity/tremors.

As an advanced form of differential interferometric synthetic aperture radar (DInSAR), small baseline subset (SBAS)-interferometric synthetic aperture radar (InSAR) boasts large space coverage and high precision and provides an effective monitoring technique for ground deformations. Maintaining high coherence through the monitoring process is a common method for SBAS-InSAR to select ground stable points. Usually, such points are obtained with small temporal and spatial baselines. However, it is unlikely to maintain high coherence in non-urban and non-rock landscapes, due to the change of surface cover or vegetation growth. This change is common in long-term surface deformation. Even with a high average coherence, an interferogram still contains many low coherence areas, where it is difficult to implement phase unwrapping or collect sufficient deformation information. This often results in an incomplete spatial image of the deformation pattern, which hinders the interpretation and research of surface deformation. What is worse, surface deformation might not be detected at all in incoherence areas. To solve these problems, this paper proposes a low-rank matrix completion method to complete the low coherence areas, making the interferograms complete. Then, the general SBAS method was followed to extract the deformed time series. The proposed strategy was applied to the time series monitoring of surface deformation of a 250-km2 area in southern Zibo, China, from January 2, 2016, to March 22, 2018. Compared with the traditional SBAS method, the proposed method can obtain more information of surface deformation. The complete deformation information of the area is obtained.

Monitoring active open-pit mine stability in the Rhenish coalfields of Germany using a coherence-based SBAS method

Multi-temporal InSAR (MT-InSAR) methods have been widely used in remote sensing monitoring of ground subsidence, which occurs at many places around the world. Land subsidence, caused by excessive extraction of groundwater, has always been a problem to be solved in Tianjin, China. Although the subsidence in the urban area has been controlled at a low rate, the subsidence issue has not been effectively solved in the suburban area recently, which should be paid much attention. This paper aims to present two multi-temporal differential interferometry techniques, persistent scatterer (PS) and small baseline subset (SBAS), for monitoring the latest surface subsidence in a Tianjin study area on the basis of 20 Sentinel-1A images obtained from March 2017 to March 2019. Our research showed that the average velocity map obtained from the SBAS method closely followed the outcomes of the PS technique from the perspective of identifying similar subsidence patterns. Subsidence rate gradually increased from the urban area of Tianjin to the suburbs and high subsidence zones were mainly distributed at the junction of the Wuqing, Xiqing and Beichen districts. In the past two years, the annual average subsidence rate in the high settlement area mostly exceeded −50 mm/year, which caused serious damage to local infrastructures. Besides, high-resolution remote sensing images combined with field investigations further verified the successful application of MT-InSAR technology in Tianjin’s subsidence monitoring. Effective ground subsidence control measures need to be taken as soon as possible to prevent the situation from getting worse.