The land reclaimed from the seaside may have a long-term subsidence trend, which poses a potential geohazard in the future land use. Xiamen Xiang’an New Airport (XXNA) is built on reclaimed land since 2016. Based on the spaceborne Sentinel-1 data between January 2018 to April 2019 and the time series interferometric synthetic aperture radar (InSAR) technique, this paper analyzed the reclaimed land subsidence evolution at XXNA in this period. InSAR measurements show that XXNA is suffering from severe subsidence, mainly in three regions because of the earth and sand compacting. By analyzing the spatial subsidence characterizations of the main subsiding areas combined with historical land reclamation and future land use planning, we find the potential threat of subsidence to future land use. Correlation between subsidence and the period of reclamation was found, indicating that the consolidation and compression in dredger fill is the main cause of subsidence. By combining subsidence monitoring results with different land use types and adopting the Expectation (Ex) and Entropy (En) methods, we analyzed the key area with potential subsidence geo-hazard. This work shows that with SAR interferometry, it is possible to find the large area ground subsidence in the airport reclaimed area. The areas with potential subsidence geo-hazards are consistent with the deep reclaimed earth, which means high subsidence risk in the future.

Dalian Jinzhou Bay International Airport (DJBIA) is an offshore artificial island airport, where the reclaimed land is prone to uneven land subsidence due to filling consolidation and construction. Monitoring and predicting the subsidence are essential to assist the subsequent subsidence control and ensure the operational safety of DJBIA. However, the accurate monitoring and prediction of reclaimed subsidence for such a wide area under construction are hard and challenging. This paper utilized the Small Baseline Subset Synthetic Aperture Radar (SBAS-InSAR) technology based on Sentinel-1 images from 2017 to 2021 to obtain the subsidence over the land reclamation area of the DJBIA, in which the results from ascending and descending orbit data were compared to verify the reliability of the results. The SBAS-InSAR results reveal that uneven subsidence is continuously occurring, especially on the runway, terminal, and building area of the airport, with the maximum subsidence rate exceeding 100 mm/year. It was found that there is a strong correlation between the subsidence rate and backfilling time. This study provides important information on the reclaimed subsidence for DJBIA and demonstrates a novel method for reclaimed subsidence monitoring and prediction by integrating the advanced InSAR technology and Terzaghi Consolidation Theory modeling. Moreover, based on the Terzaghi consolidation theory and the corresponding geological parameters of the airport, predicted subsidence curves in this area are derived. The comparison between predicted curves and the actual subsidence revealed by InSAR in 2017–2021 is highly consistent, with a similar trend and falling in a range of ±25 mm/year, which verifies that the subsidence in this area conforms to Terzaghi Consolidation Theory. Therefore, it can be predicted that in the future, the subsidence rate of the new reclamation area in this region will reach about 80 mm/year ± 25 mm/year, and the subsidence rate will gradually slow down with the accumulation of reclamation time. The subsidence rate will slow down to about 30 mm/year ± 25 mm/year after 10 years.

Beijing Capital International Airport (BCIA) has suffered from uneven land subsidence since 1935, which affects the smoothness of airport runways and seriously threatens the safety of aircrafts. In this paper, a spaceborne interferometric synthetic aperture radar (InSAR) with high-resolution Cosmo-SkyMed SAR data was utilized at BCIA for the first time to diagnose the subsidence hazard. The results show that subsidence is progressing at BCIA at a maximum rate of 50 mm/year, which is mainly distributed in the northwest side of the airport. It was found that the Shunyi-Liangxiang fault directly traverses Runway2 and Runway3 and causes uneven subsidence, controlling the spatial subsidence pattern to some degree. Four driving factors of subsidence were investigated, namely: the over-exploitation of groundwater, active faults, compressible soil thickness, and aquifer types. For the future sustainable development of BCIA, the influence of Beijing new airport and Beijing Daxing International Airport (BDIA), was analyzed and predicted. It is necessary to take relevant measures to control the uneven subsidence during the initial operation of BDIA and conduct long-term monitoring to ensure the regular safe operation of BCIA. This case demonstrates a remote sensing method of diagnosing the subsidence hazard with high accuracy and non-contact, providing a reliable alternative for the geohazard diagnosis of key infrastructures in the future.

The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.

Atmospheric effects are among the primary error sources affecting the accuracy of interferometric synthetic aperture radar (InSAR). The topography-dependent atmospheric effect is particularly noteworthy in reservoir areas for landslide monitoring utilizing InSAR, which must be effectively corrected to complete the InSAR high-accuracy measurement. This paper proposed a topography-dependent atmospheric correction method based on the Multi-Layer Perceptron (MLP) neural network model combined with topography and spatial data information. We used this proposed approach for the atmospheric correction of the interferometric pairs of Sentinel-1 images in the Baihetan dam. We contrasted the outcomes with those obtained using the generic atmospheric correction online service for InSAR (GACOS) correction and the traditional linear model correction. The results indicated that the MLP neural network model correction reduced the phase standard deviation of the Sentinel-1 interferogram by an average of 64% and nearly eliminated the phase-elevation correlation. Both comparisons outperformed the GACOS correction and the linear model correction. Through two real-world examples, we demonstrated how slopes with displacements, which were previously obscured by a significant topography-dependent atmospheric delay, could be successfully and clearly identified in the interferograms following the correction by the MLP neural network. The topography-dependent atmosphere can be better corrected using the MLP neural network model suggested in this paper. Unlike the previous model, this proposed approach could be adjusted to fit each interferogram, regardless of how much of the topography-dependent atmosphere was present. In order to improve the effectiveness of DInSAR and time-series InSAR solutions, it can be applied immediately to the interferogram to retrieve the effective displacement information that cannot be identified before the correction.

A series of small-magnitude earthquakes (M_w 2.9~M_w 4.9) occurred in Rong County, Sichuan Province, China between 30 March 2018 and December 2020, which threatened the safety of local residents. Determining the surface displacement and estimating the damage caused by these earthquakes are significant for earthquake relief, post-earthquake disaster assessment and hazard elimination. This paper integrates the Generic Atmospheric Correction Online Service (GACOS) with interferometry synthetic aperture radar (InSAR) to accurately detect the displacement of the series of small-magnitude earthquakes in Rong County based on 45 Sentinel-1 ascending/descending images acquired from January 2018 to December 2020. We analyze the influence of some factors involved in surface displacement, including earthquake magnitude, focal depth and the distance from the epicenter to the fault. The above measurement for small-magnitude earthquakes and statistics analysis for the displacement have not been performed before, so this can help better understand the displacement features of small-magnitude earthquakes, which are important for post-earthquake hazard assessment and disaster prevention.