An interesting liquefaction event happened following the 5.6-Mw Pohang earthquake on November 15, 2017. Liquefaction affects soil density as a result of earthquake vibrations, causing water to ascend and combine with solid soil. In general, this effect results from increasing water pressure in the buried lower layer. Remote sensing data, particularly those obtained with differential interferometry SAR (DInSAR), can be utilized to assess surface changes and soil moisture levels. This analysis utilizes Sentinel-1 C-band data from 2017 to 2020. Furthermore, the biased time-series identification presented to analyze the anomaly may be present in this study. In addition, optical satellite data were used to estimate changes in water content and soil moisture following Pohang earthquake. A combination of spectral bands sensitive to changes in water content was used to identify anomalies following an earthquake based on Sentinel-2 and Landsat-8 data. Based on the spatial analysis, optical images may detect changes in water content with a spatial accuracy of 60%–80% when compared to field data. Moreover, the liquefaction susceptibility map has been generated using a hybrid dense convolutional neural network (DCNN) architecture and swarm-based optimization algorithm. As a result, the susceptibility model performance was conducted using k-fold cross correlation with an area under the curve (AUC) value of 0.75–0.86. However, this research was the initial effort to determine the potential of liquefaction in the future based on the 2017 Pohang earthquake, and the results can improve our understanding of this phenomenon compression.

Land subsidence is a global concern that can result in infrastructure damage and property devaluation. We proposed the improved combined scatterers interferometry with optimized point scatterers (ICOPS) method for analyzing land surface deformation using InSAR time-series data. The method combined persistent scatterers (PS) and distributed scatterers (DS) to enhance spatial coverage. The distributed scatterers were generated using a spatial homogeneity evaluation based on a likelihood ratio test with coherence evaluation to maintain the quality of pixels. A key aspect of this research involved post-processing optimization using machine learning and statistical approaches. Support vector regression (SVR) was applied to determine the optimal measurement points based on time-series characteristics. Optimization hotspot analysis minimized interference from uncorrelated points, resulting in a clustered deformation map. The study demonstrated the ICOPS methods in measuring land subsidence, revealing that the coastal reclamation land in the Bugok Industrial Zone, Dangjin, South Korea experienced a significant mean deformation rate of approximately 2-5 cm/year from 2017 to 2019. Overall, the ICOPS method is a valuable tool for analyzing coastal reclamation land in South Korea, providing enhanced accuracy and reliability, and offering valuable insights into dynamic processes for monitoring and managing such areas.

The condition of land subsidence in Pekalongan has worsened the area that prone to coastal inundation during high tide. Monitoring land subsidence in Pekalongan becomes vital to mitigate the other possible land subsidence occurrence area and the possible hazard caused by land subsidence. This study used Synthetic Aperture Radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2020 and processed using Improved Combined Scatterers Interferometry with Optimized Point Scatterers (ICOPS) with the integration of convolutional neural network (CNN) as the optimization algorithm and Optimized Hot Spot Analysis (OHSA) as the statistical clusterization method to identify significance measurement point between each data. The comparison of the time-series Interferometry SAR (InSAR) result with the GPS measurements shows a good correlation. Furthermore, this study uncovered a significant correlation between land subsidence, geological landforms, and land use within the study area.

Floods in Pekalongan, Indonesia, often occur due to river water overflowing during heavy monsoon rain. Simultaneously, the northern coastal area of Pekalongan, located adjacent to the Java Sea, has been affected by coastal floods due to sea level rise. The flood conditions in this area were exacerbated by land subsidence, leading to coastal inundation. Monitoring land subsidence in Pekalongan has become essential in predicting other possible land subsidence occurrence areas and mitigating the possible hazards caused by land subsidence. The analysis of land subsidence has been much easier since the introduction of radar satellites. In this study, 124 synthetic aperture radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2022 in descending tracks were used. The data were processed through a time-series interferometry SAR (InSAR) method based on the improved combined scatterers interferometry with optimized point scatterers (ICOPS) algorithm to provide accurate measurements over large areas by improving the selection of measurement points from persistent scatterer (PS) and distributed scatterer (DS) points using a deep learning algorithm based on a convolutional neural network (CNN), and the resulting optimized measurement points were then spatially clustered using optimized hot spot analysis (OHSA) to estimate significant points statistically and define them as hot spot points. The results of time-series deformation in Pekalongan were compared with the GPS station measurements. From the comparison, a good correlation in terms of deformation patterns between time-series InSAR and GPS measurements was observed. Our study revealed that land subsidence in Pekalongan has occurred mostly in settlement areas under the young alluvium soil, which cannot support many buildings’ maximum compression. Another cause of land subsidence in Pekalongan is excessive groundwater extraction in settlement areas. Thus, compaction in the aquifer areas may occur as a result of the reduced effective stress of the pore pressure. Further analysis of this study would involve monitoring groundwater activity using data from the GRACE satellite and comparing them with weather station data. The analysis of the two datasets aims to understand the relationships between groundwater storage data and the monthly precipitation in Pekalongan. Finally, the potential outcomes of land subsidence in Pekalongan will be assessed using the geographic information system (GIS) method based on susceptibility mapping.

Despite the development of point-based interferometric synthetic aperture radar (InSAR) time-series methods, such as persistent scatterer interferometry (PSI), it remains difficult to identify reliable measurement points (MPs) in areas under vegetated cover. In this study, we developed a new algorithm, improved combined scatterers interferometry with optimized point scatterers (ICOPS), to monitor changes in surface deformation by combining persistent scatterers (PSs) and distributed scatterers (DSs). The algorithm was subsequently improved through the application of a machine learning process. A generalized likelihood ratio test (GLRT) was applied to identify statistically homogeneous pixels suitable for a small SAR dataset. A machine learning-based optimization process was used to overcome the increase in MPs after the combined scatterers interferometry (CSI) process. The MP optimization process used the support vector regression (SVR) algorithm to find the optimal point from all MPs in the dataset. To improve the reliability of the MP surface deformation mapping after optimization, the optimized hot-spot analysis (OHSA) method was applied to obtain spatially clustered MPs; this was possible despite a low deformation identification rate. To demonstrate the effectiveness of the ICOPS method, we applied this approach to Yellowstone Lake in the USA using 95 SAR images from the Sentinel-1 satellite that was taken during the period of 2017–2020. The CSI method produced an increase in MP density, especially in areas not covered by PSI, which indicated its ability to detect deformations in mountainous areas. Comparisons with GPS data and traditional methods produced promising results with an accuracy of 1 cm/year in terms of the root mean square error (RMSE). The optimization process in CSI also had the advantage of retrieving helpful information in the MP dataset and increased the accuracy of CSI by 12%. Deformation mapping using the optimized results provided new insights regarding the spatial clustering of surface deformation in MPs. This could provide the foundation for developing a posttime-series optimization process based on point scatter as a surface change detection tool.

In Semarang, Indonesia, land subsidence had exacerbated the prone area of river floods due to heavy monsoon rain and coastal floods due to sea-level rise during high tide. Monitoring land subsidence in Semarang becomes essential to prevent the coastal inundation which leads the city to be submerged by seawater. In this study, land subsidence in Semarang was mapped using time-series analysis based on Stanford Methods for Persistent Scatterer (StaMPS) on the Sentinel-1 SAR datasets from March 2017 to May 2020 in both ascending and descending tracks. Optimized Hot Spot Analysis (OHSA) was conducted on the persistent scatterer points to spatially clustered the points with a high significance level statistically. The comparison of mean vertical deformation maps between two tracks shows a good correlation between displacement patterns. The land subsidence in Semarang was mainly due to groundwater extraction for industrial use and the compaction from the young alluvium soil.

On 28 September 2018, an Mw 7.4 earthquake was hit Donggala County, Central Sulawesi Province, Indonesia, triggering tsunami and liquefaction in Palu City and Donggala. Due to this destructive event, a post-earthquake damage map is needed for the first step of evacuation and mitigation plan. This study used Landsat-8 satellite images to classified using an artificial neural network (ANN) and support vector machine (SVM) classifiers. The method used to generate the post-earthquake damage map was decorrelation method which then the result of the damage map will be compared to the field data. The conformity between ANN and SVM result was analyzed and shows the conformity of 85.83%. The result of post-earthquake damage is useful for assessing the distribution of seismic damage and mitigate damage in the future earthquake occurrence.

Sinabung volcano in Indonesia is a part of the Pacific Ring of Fire, formed due to the subduction between the Eurasian and the Indo-Australian plate. We study the deformation of Sinabung volcano using ALOS/PALSAR interferometric synthetic aperture radar (InSAR) images acquired from Feb. 2007 to Jan. 2011. Based on multi-temporal InSAR processing, we have mapped the ground surface deformation before, during, and after the 2010 eruption. During the 3 years before the 2010 eruption, the volcano inflated at an average rate ∼1.7 cm/yr with marked higher rate of 6.6 cm/year during the 6 months prior to the 2010 eruption. The inflation is constrained to the top of the volcano. Since the 2010 eruption to Jan. 2011, the volcano has subsided for about 3 cm. The observed inflation and deflation are modeled with a Mogi and Prolate spheroid source. The source of inflation is located about 0.3–1.3 km below sea level directly underneath the crater. On the other hand, deflation source is modeled about 0.6–1.0 km depth with coeruption period. The average volumetric change was about from −2.7×10−5to 1.9×10−6km3/yr during the deformation event. Modified Laharz model compare to Landsat-7 ETM+ image through supervised classification method. We interpret the inflation was due to magma accumulation at a shallow reservoir beneath Sinabung. Pyroclastic flow's inundation area is highly matched between two different methods with about 86 % common region inserting for deflation pattern of volume by Mogi model.

Augustine volcano is an active stratovolcano located at the southwest of Anchorage, Alaska. Augustine volcano had experienced seven significantly explosive eruptions in 1812, 1883, 1908, 1935, 1963, 1976, and 1986, and a minor eruption in January 2006. We measured the surface displacements of the volcano by radar interferometry and GPS before and after the eruption in 2006. ERS-1/2, RADARSAT-1 and ENVISAT SAR data were used for the study. Multiple interferograms were stacked to reduce artifacts caused by different atmospheric conditions. Least square (LS) method was used to reduce atmospheric artifacts. Singular value decomposition (SVD) method was applied for retrieval of time sequential deformations. Satellite radar interferometry helps to understand the surface displacements system of Augustine volcano.