Artificial lands or islands reclaimed from the sea due to their vast land spaces and air are suitable for the construction of airports, harbors, and industrial parks, which are convenient for human and cargo transportation. However, the settlement process of reclamation foundation is a problem of public concern, including soil consolidation and water recharge. Xiamen New Airport, one of the largest international airports in China, has been under construction on marine reclamation land for three years. At present, the airport has reached the second phase of construction, occupying 15.33 km². The project will last about twenty years. To investigate the temporal and spatial evolution of ground settlement associated with land reclamation, Sentinel-1 synthetic aperture radar (SAR) data, including intensity images and phase measurements, were considered. A total of 82 SAR images acquired by C-band Sentinel-1 satellite covering the time period from August 2015 to October 2018 were collected. First, the spatial evolution process of land reclamation was analyzed by exploring the time series of SAR image intensity maps. Then, the small baseline subset InSAR (SBAS–InSAR) technique was used to retrieve ground deformation information over the past three years for the first time since land reclamation. Results suggest that the reclaimed land experienced remarkable subsidence, especially after the second phase of land reclamation. Furthermore, 26 ground settlement areas (i.e., 0.015% of the whole area) associated with land reclamation were uncovered over an area of more than 1200 km² of the Xiamen coastal area from January 2017 to October 2018. This study offers important guidance for the next phase of land reclamation and the future construction of Xiamen New Airport.

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang’an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang’an Airport and Shuangyu Island to prevent and control land subsidence.

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention.

Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors” step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than −40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed.