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.