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The Jing-Mi Diversion Canal is a large-scale water diversion project in Beijing. Routine monitoring is crucial for the reliability and stability of urban water supply. Compared with traditional monitoring methods, interferometric synthetic aperture radar (InSAR) has the advantages of large scale and high accuracy. Based on the small baseline subset InSAR, 187 ascending and 102 descending SAR images obtained from Sentinel-1 were used to detect the deformation along the diversion canal from 2017 to 2023. The results show that there was a sinking trend along the diversion canal. The subsidence was serious in the first half of the canal, and continued to sink from 2019 to 2020. The subsidence was alleviated in 2023. Combined with leveling measurements, the InSAR deformation monitoring results of important pumping station buildings were verified. The measurement accuracy of InSAR can reach the millimeter level. We extracted the groundwater level time series and subsidence for risky canal segments. Through pixel-by-pixel comparison, it was found that fluctuations in groundwater level would have some impact on surface deformation. Severe local subsidence or uplift deformation occasionally occurred. To ensure the safety of water diversion, the monitoring and maintenance of relevant pump station buildings in risky areas should be increased in the future. [ABSTRACT FROM AUTHOR]

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter's experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation. [ABSTRACT FROM AUTHOR]

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images. [ABSTRACT FROM AUTHOR]

Accurate, rapid, and automatic local climate zone (LCZ) mapping is essential for urban climatology and studies in terms of urban heat islands. Remotely sensed imageries incorporated with machine learning algorithms are widely utilized in LCZ labeling. Nevertheless, large-scale LCZ mapping is still challenging due to the complex vertical structure of underlying urban surfaces. This study proposed a new method of LCZ labeling that uses a random forest classifier and multi-source remotely sensed data, including Sentinel 1A Synthetic Aperture Radar (SAR), Sentinel 2 Multispectral Instrument, and Luojia1-01 night-time light data. In particular, leaf-on and -off imageries and surface thermal dynamics were utilized to enhance LCZ labeling. Additionally, we systematically evaluated how daytime and night-time features influence the performance of the classification procedure. Upon examination, the results for Beijing, China, were confirmed to be robust and refined; the Overall Accuracy (OA) value of the proposed method was 88.86%. The accuracy of LCZs 1–9 was considerably increased when using the land surface temperature feature. Among these, the Producer Accuracy (PA) value of LCZ 3 (compact low-rise) significantly increased by 16.10%. Notably, it was found that NTL largely contributed to the classification concerning LCZ 3 (compact low-rise) and LCZ A/B (dense trees). The performance of integrating leaf-on and -off imageries for LCZ labeling was better than merely uses of leaf-on or -off imageries (the OA value increased by 4.75% compared with the single use of leaf-on imagery and by 3.62% with that of leaf-off imagery). Future studies that use social media big data and Very-High-Resolution imageries are required for LCZ mapping. This study shows that combining multispectral, SAR, and night-time light data can improve the performance of the random forest classifier in general, as these data sources capture significant information about surface roughness, surface thermal feature, and night-time features. Moreover, it is found that incorporating both leaf-on and leaf-off remotely sensed imageries can improve LCZ mapping. [ABSTRACT FROM AUTHOR]

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing's geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing's transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent. [ABSTRACT FROM AUTHOR]

Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%. [ABSTRACT FROM AUTHOR]

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing's subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing's Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention. [ABSTRACT FROM AUTHOR]

Groundwater overexploitation is a critical issue in the North China Plain (NCP), resulting in groundwater level decline and surface subsidence for the last half-century. This problem, however, has been greatly alleviated by the South-to-North Water Diversion (SNWD) Project since 2015. Monitoring of this process has been steadily improved in recent years using water level and geodetic observations. Here, we characterize the water storage change at the Huairou groundwater reserve site (HGRS) in Beijing due to the SNWD by combining Interferometric Synthetic Aperture Radar (InSAR) data of the Sentinel-1 satellites, continuous Global Positioning System (GPS) data, and well water level data observed during the same time. InSAR observations revealed subsidence up to ~400 mm in the Beijing plain but uplift at ~40 mm in the HGRS during 2015–2019, and more than 70% of the uplift occurred from October 2018 to January 2019. By integrating the most significant uplift deformation during October 2018 to January 2019 with water level observations at the same time, we estimated the storativity of the confined aquifer system at HGRS as 1. 68 – 7. 82 × 10 − 3 , weighing in the correction for effective stress and surface deformation for various situations. Based on the estimated aquifer storativity and the observed water level change in the unconfined and confined aquifer, the recharged water storage for the confined and unconfined aquifers was estimated as 1. 20 – 1. 39 × 10 7   m 3 and ~ 2. 86 × 10 8   m 3 from 6 October 2018 to 22 January 2019, respectively, which is about 4% and 91% of the surface water recharge through river channels in the same period due to the SNWD Project. Our study demonstrates that integration of geodetic and hydrological data can provide crucial information for the assessment of groundwater circulation and assistance of groundwater management. [ABSTRACT FROM AUTHOR]

Beijing is a major city suffering from land subsidence due to long-term over-exploitation of groundwater. The South-to-North Water Diversion Project (SNWDP), however, has had a significant impact on the structure of water consumption since the end of 2014, and it is changing the status of land subsidence in Beijing. In this study, we employed Persistent Scatterers Synthetic Aperture Radar Interferometric (PS-InSAR) to investigate the decadal evolution of land subsidence in Beijing with 100 TerraSAR-X stripmap images collected from April 2010 to December 2019. The water resources, historic climate and urban construction data were compiled for the years of 2010 to 2019 to analyze changes in groundwater level, human activity, surface geology, active faults and land subsidence patterns. The results show that the changes in the water supply structure are correlated to a rise in groundwater level after 2015. These changes include an increase in the water supply from the SNWDP, a reduction in groundwater exploitation, the optimization of water consumption, replacing recycled water for environmental water and a reduction in the use of water for agriculture. Land subsidence in the study area was concentrated in the eastern regions, trending towards a decreasing velocity starting about two years after the commencement of SNWDP in 2015. Uneven subsidence in the land subsidence area was related to excavations of underground soil, and the construction of Line 6 and Line 7 led to rapid nonlinear subsidence. Our results have scientific significance for reducing subsidence hazards in the context of SNWDP and urban expansion. [ABSTRACT FROM AUTHOR]

Since the 1930s, due to the rapid development of the city and the increase of population, the demand from Beijing residents for water resources has gradually increased. Land deformation in the Beijing Plain is a serious issue. In order to warn of, and mitigate, disasters, it is urgently necessary to obtain the latest rate, extent, and temporal evolution of land subsidence in Beijing. Firstly, the temporal and spatial distribution characteristics of land deformation in Beijing during 2003–2020 were unveiled using the time-series interferometric synthetic aperture radar (InSAR) technique and two different satellite datasets, sentinel-1a/1b and ENVISAT ASAR. By means of combining calibration of InSAR results with the global positioning system (GPS), we studied the evolutionary process of long-term land subsidence in Beijing. The precision of our InSAR annual subsidence results is less than 10 mm. Land subsidence in Beijing is unevenly distributed, and so five main land subsidence zones were monitored. The time-series results showed that the rate of land subsidence rate continued to increase from 2003 to 2015, but has gradually shown a slowing trend from 2015 to 2020. Further, we used the quadratic polynomial fitting method to interpolate the time-series deformation results from 2010 to 2015, and compared these with GPS. The results demonstrated that although the InSAR observation method is not strictly registered with GPS in time, its deformation trend is consistent. In addition, the calibrated long time-series was consistent with the three deformation stages of land subsidence evolution in Beijing. Finally, we analyzed the deformation information obtained by InSAR technology in combination with land use type data, precipitation and groundwater data. The results demonstrated that the central area is mostly stable, and land deformation in the northeast is obvious and uneven. In addition, land use type and precipitation have little influence on land subsidence. Changes in land subsidence were closely related to changes in groundwater level, and seasonal variations in deformation correlated with precipitation. [ABSTRACT FROM AUTHOR]

The long-term over-exploitation of groundwater has not only caused the compaction of aquifer thickness and surface deformation but has also further aggravated the loss of groundwater storage (GWS) in Beijing plain. The South-to-North Water Diversion Project (SNWDP) furnishes a new source of water for Beijing. By reviewing related studies, it was found that there are few studies on the realization of GWS estimation based on InSAR technology considering the lag effect. Therefore, in this study, firstly, the long-time series deformation characteristics of Beijing plain were obtained from 46 RadarSat-2 images using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology. Secondly, the seasonal components of surface deformation and hydraulic head change were extracted by means of multichannel singular spectrum analysis (MSSA), verifying the separation accuracy by means of Monto Carlo-SSA (MC-SSA). Finally, for the hydrodynamic delay (aquifer water supply/drainage) of the complex aquifer system, we introduced the time lag cross-correlation (TLCC) approach to correct the hysteresis response of seasonal deformation relative to the variation of the aquifer system head, so as to realize the estimation of aquifer storage properties and GWS loss, even unrecoverable GWS (UGWS). The results showed that the average annual variation of total GWS (TGWS) in Beijing plain was −6.702 × 107 m3, of which the depletion volume of UGWS was −6.168 × 107 m3, accounting for 92.03% of the TGWS. On a temporal scale, the depletion of UGWS lagged behind the total head change, with about one year of lag time. On a spatial scale, in contrast to the north of Beijing plain, the depletion of UGWS in the south only recovered briefly after 2015 and then continued to decline. This further indicated that the process of the decline of middle-deep confined head and long-term GWS loss caused by over-exploitation of groundwater was irreversible. These findings are of great significance to optimize the allocation of groundwater resources, reduce the harm of land subsidence and protect groundwater resources. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei (BTH) has been suffering from severe groundwater storage (GWS) consumption and land subsidence (LS) for a long period. The overexploitation of groundwater brings about severe land subsidence, which affects the safety and development of BTH. In this paper, we utilized multi-frame synthetic aperture radar datasets obtained by the Rardarsat-2 satellite to monitor land subsidence's temporal and spatial distribution in the BTH from 2012 to 2016 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). In addition, we also employed the Gravity Recovery and Climate Experiment (GRACE) mascon datasets acquired by the Center for Space Research (CSR) and Jet Propulsion Laboratory (JPL) to obtain the GWS anomalies (GWSA) of BTH from 2003 to 2016. Then we evaluate the accuracy of the results obtained. Furthermore, we explored the relationship between the regional GWSA and the average cumulative subsidence in the BTH. The total volume change of subsidence is 59.46% of the total volume change of groundwater storage. Moreover, the long-term decreasing trend of the GWSA (14.221 mm/year) and average cumulative subsidence (17.382 mm/year) show a relatively high consistency. Finally, we analyze the heterogeneity of GWS change (GWSC) and LS change (LSC) in the four typical areas by the Lorenz curve model. The implementation of the South-to-North Water Diversion Project (MSWDP) affects the heterogeneity of GWSC and LSC. It can be seen that the largest heterogeneity of LSC lags behind the GWSC in the Tianjin-Langfang-Hengshui-Baoding area. The largest uneven subsidence in Beijing and Tianjin occurred in 2015, and the largest uneven subsidence in Hengshui-Baoding occurred in 2014. After that, the heterogeneity of subsidence gradually tends to stable. [ABSTRACT FROM AUTHOR]

Excessive exploitation of the groundwater has resulted in obvious three-dimensional (3D) deformation features on the surface of the Beijing Plain. This paper, by combining Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) technologies, has obtained time-series information of the 3D surface deformation in the Beijing Plain, analyzing its spatial distribution characteristics. On this basis, the relationship between different controlling factors with the 3D deformation of the surface has been analyzed as well. The following results are obtained: (1) From 2013 to 2018, the land subsidence, which generally showed the trend of slowing down, was mainly concentrated in the eastern, northern, and southern regions of Beijing Plain, with multiple subsidence centers. (2) Under the International Terrestrial Reference Frame 2005 (ITRF2005), the horizontal direction of all GPS points in the plain is basically the same, with the dominant movement direction being NE112.5°~NE113.8°. Under the Eurasian reference frame, the horizontal movement rate of GPS points significantly decreases. The movement rate and direction of each point are not characteristic of overall trend activity. (3) The distribution and extent of the 3D surface deformation in the Beijing Plain are controlled by the basement structure. Part of the subsided area corresponds to a Quaternary depression formed at the junction of active faults disrupting the area. Similarly, the distribution of horizontal deformation in the E-W and N-S directions of the plain is controlled by the regional basement structure comprising major faults bounding horizontal deformation. (4) Groundwater exploitation is the main cause of the 3D surface deformation in the Beijing Plain. The groundwater funnels of the second and third confined aquifer are in suitable agreement with the land subsidence. The horizontal movement in the Beijing Plain is either directed toward the center of the groundwater or the land subsidence funnel, and the deformation is directed from areas with higher to areas with lower groundwater levels. [ABSTRACT FROM AUTHOR]