Land Subsidence Monitoring Research Articles

Gravity Predictions in Data-Missing Areas Using Machine Learning Methods

Gravity data, comprising a key foundational dataset, are crucial for various research, including land subsidence monitoring, geological exploration, and navigational positioning. However, the collection of gravity data in specific regions is difficult because of environmental, technical, and economic constraints, resulting in a non-uniform distribution of the observational data. Traditionally, interpolation methods such as Kriging have been widely used to deal with data gaps; however, their predictive accuracy in regions with sparse data still needs improvement. In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy.

Spatiotemporal Relationship Between Land Subsidence and Ecological Environmental Quality in Shenfu Mining Area, Loess Plateau, China

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence.

Tunnel-induced land subsidence assessment in a densely populated residential area using Sentinel-1 PS-InSAR

Land subsidence detection in residential areas based on advanced remotely sensed data is an effective technique to mitigate catastrophic disasters in time. Synthetic aperture radar (SAR) in spaceborne platforms is a highly sophisticated technique to estimate ground surface subsidence with millimeter accuracy for wide-area observation. SAR is beneficial for large-scale social infrastructure project planning and monitoring in densely populated residential areas. In this study, we applied a permanent scatterer (PS) interferometric SAR (PS-InSAR)-based technique for monitoring land subsidence in a tunnel construction site in the Greater Tokyo Area to investigate tunnel-induced ground surface subsidence. Sentinel-1 repeat-pass SAR data in ascending and descending orbital directions were used for continuous observation. Consecutively estimated interferograms based on repeat-pass SAR data were generated to monitor and detect tunnel-induced land subsidence as well as confirm long-term stability along the tunnel excavation path. A bidirectional displacement of the ground area parallel to the earth’s surface in the east–west direction and perpendicular to the earth’s surface in the up–down direction was estimated at a high-phase consistency location using a PS interferometric stacking technique. The resultant data were then analyzed to detect anomalies in the ground area in the spatiotemporal domains by performing displacement time-series analysis. An area near Higashi-Tsutsujigaoka’s local administrative region in the metropolitan center of Tokyo was observed to have 15-mm subsidence and 30-mm displacement in the east–west direction during August–October 2020. The conventional level survey results in the same ground area showed 15–20-mm subsidence above the tunnel excavation path. The findings of this study offer insights into understanding land subsidence in residential environments.

Land Subsidence Detection Using SBAS- and Stacking-InSAR with Zonal Statistics and Topographic Correlations in Lakhra Coal Mines, Pakistan

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan’s largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment.

Assessment of Land Subsidence Pattern in Pokhara Valley Using Sentinel-1 InSAR Processing

This research examines the use of Sentinel-1's enhanced remote method and Interferometric Synthetic Aperture Radar (InSAR) processing to monitor ground subsidence in Pokhara Valley City, Nepal. It aims to comprehend the reciprocal effects of land sinking on environmental sustainability, infrastructure stability, and urban growth. The methodology encompasses the acquisition and processing of Sentinel-1 SAR data, along with rigorous interferometric analysis to detect and quantify ground deformation. Spanning from 2019 to 2022 AD and, -analyzing annual displacement trends, initially, a subsiding trend was observed in 2019-20 AD with an average rate of 0.037 m/year followed by 0.072 m/year in 2020-21 AD, and then escalating to 0.165 m/year in 2021-22 AD. These fluctuations in rates illustrate the intricate interplay of urbanization and geological dynamics affecting land stability in the region. The implications of this study extend to urban planning and infrastructure management, emphasizing the necessity of integrating land subsidence monitoring into the developmental policies and strategies of Pokhara Valley. Additionally, the potential of Sentinel-1 InSAR as a valuable tool for geotechnical investigations offers a non-invasive, cost-effective means to assess and monitor ground deformation risks. By leveraging the capabilities of Sentinel-1 InSAR, the study contributes to the body of knowledge in geotechnical and civil engineering fields, particularly within the context of sustainable urban planning and disaster risk reduction.

Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model

Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model

Urban land subsidence monitoring and risk assessment using the point target based SBAS-InSAR method: a case study of Changsha City

ABSTRACT Changsha City is the political, economic and cultural centre of Hunan Province. In recent years, because of large-scale urban construction and special geological structure, land subsidence has become one of the main geological disasters in Changsha City, which seriously threatens the health of buildings and infrastructure. To solve this problem, this paper monitored the land subsidence in Changsha City from 2015 to 2021 using the Point Target (PT) based Small Baseline Subset InSAR (SBAS-InSAR), revealed the spatial distribution characteristics of land subsidence in the study area, studied the causes of subsidence, and carried out geological disaster risk assessment. The results show that the overall stability of Changsha City is dominated by uneven subsidence. The subsidence areas are mainly distributed in the north of Kaifu District, Moon Island, Malan Mountain Chuangzhi Park and the periphery of the main urban area. The maximum settlement rate is about 87 mm/year, and the cumulative settlement reaches 250 mm. The settlement monitoring results are basically consistent with the actual situation at the same time, which is mainly caused by engineering construction.

Integrated high-precision monitoring method for surface subsidence in mining areas using D-InSAR, SBAS, and UAV technologies

The use of unmanned operations to monitor mining induced land subsidence is increasing. This study conducts a detailed comparative analysis of accuracy of measured ground deformation provided by Differential Interferometric Synthetic Aperture Radar (D-InSAR), Small Baseline Subset (SBAS), and Unmanned Aerial Vehicle (UAV) tilt photogrammetry with respect to levelling measurements. Based on such analysis we propose an integrated approach that combines multiple remote sensing methods to achieve a better global accuracy in the land subsidence monitoring in mining areas. Conducted at the Banji Coal Mine, this study collected subsidence data from April 10, 2021, to June 28, 2022, through D-InSAR, SBAS, and UAV techniques. After segmenting the subsidence basin into distinct zones, we qualitatively assessed each area with UAV-derived 3D models and quantitatively evaluated the precision of all applied techniques, benchmarking against leveling data. Our findings indicate that integrating D-InSAR, SBAS, and UAV technologies significantly enhances monitoring accuracy over any single method, demonstrating their combined effectiveness in different subsidence areas. Consequently, the synergistic integration of D-InSAR, SBAS, and UAV technologies, capitalizing on their complementary strengths, enables the achievement of intuitive, comprehensive, and high-precision monitoring of subsidence basins in mining areas.

Spatio-Temporal Characteristics of Land Subsidence and Driving Factors Analysis in Shenzhen

Analyzing land subsidence using Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology holds significant importance for the secure development of urban areas. Shenzhen, being a crucial component of the Pearl River Delta, faces the threat of land subsidence, similar to most deltaic cities. Numerous studies have already indicated the presence of severe land subsidence in certain localities of Shenzhen. However, due to limitations in data scope and research methodologies, the comprehensive spatial-temporal distribution of land subsidence across the entire city of Shenzhen remains unclear. This study initially employed MT-InSAR technology to process a total of 534 Sentinel-1A SAR images from three different frames (P11F71, P113F71, P11F65), covering the entire city of Shenzhen. This processing resulted in the generation of subsidence rate maps and subsidence time series. Subsequently, the temporal evolution patterns of the subsidence were analyzed while significant subsidence regions were identified. By integrating information from optical images reflecting human activities on the Earth’s surface, the study deduced the subsidence mechanisms in various significant subsidence areas. Research findings indicate that land subsidence in Shenzhen is primarily caused by construction activities, with a concentration in the western coastal areas of Shenzhen, reaching a maximum rate of 80 mm/yr, located at the estuary of Dongbao River (113.770385, 22.745305). The cumulative subsidence from March 2017 to June 2023 amounts to 500 mm. The expansion of the Qinglinjing Reservoir has led to an increased demand for water, resulting in a significant rise in formation pressure and subsequent land subsidence. InSAR land subsidence monitoring and analysis in urban areas can address the spatial and temporal resolution limitations of traditional subsidence monitoring methods, providing effective recommendations for widespread subsidence prevention and control.

Land subsidence monitoring using InSAR technique in the southwestern region of Bangladesh

Deltas like the Ganges-Brahmaputra Delta (GBD) experience significant land subsidence due to overlaying load, consolidation, sediment compaction, regional tectonics, and human activity. The southwestern region of Bangladesh, marked by its low-lying floodplain with compressible sediments, experiences significant land subsidence. However, accurate assessments for these areas are very scarce, often focusing on the point-based measurements. This study employed Interferometric Synthetic Aperture Radar analysis, spanning from 2014 to 2022, to effectively measure land subsidence, and visualize it spatially. In the study area, subsidence rates were found to be ranging from 3 to 20 mm/year, with most areas experiencing 3-10 mm/year. While some areas show higher rates of 10-13 mm/year, very few exceeded 14 mm/year. The average subsidence rate was 5.98 mm/year, demonstrating significant spatial variability, with higher rates in Gopalganj, Patuakhali, Bhola, Barishal, and other areas. The findings were validated based on existing Global navigation satellite system (GNSS) data points, affirming their reliability. Despite minor discrepancies with other measurements at specific points, the overall results align well with existing data. This comprehensive analysis provides a detailed assessment of land subsidence in the southwestern region of Bangladesh, highlighting significant spatial variability and offering valuable insights for future coastal development and risk mitigation strategies.

Intelligent Early Warning and Decision Platform for Long-Term Ground Subsidence in High-Density Areas for Sustainable Urban Development

Long-term ground subsidence (LTGS) is a relatively slow process. However, the accumulation of long-term subsidence has an adverse impact on the normal operation and safety of a subway, hindering sustainable urban development. A wide gap exists between early warning theory and its application in the control of LTGS during subway operation due to time span limitation. Providing decision support for LTGS in high-density urban areas during subway operation is difficult, and a collaborative decision system for real-time early warning and intelligent control is currently lacking. This study establishes the functional components of an intelligent early warning and decision platform, proposes a software system module, constructs an overall software framework structure, and develops a mobile intelligent early warning and decision platform. Moreover, this study introduces an early warning method for LTGS in high-density urban areas during subway operation. This method integrates an intelligent early warning decision-making platform, namely Differential Synthetic Aperture Radar Interferometry (DInSAR), land subsidence monitoring, operation tunnel subsidence monitoring, and other multisource data coupling. The method is applied to sections of the Hangzhou Metro Line 4 Phase I Project (Chengxing Road Station (CRS)–Civic Center Station (CCS)–Jiangjin Road Station (JRS) and Xinfeng Station (XS)–East Railway Station (ERS)–Pengbu Station (PS)). This work can serve as a reference for ensuring urban safety and promoting sustainable development.

Reliability assessment of land subsidence monitoring results using PSI technique in Ho Chi Minh City, Vietnam

ABSTRACT Groundwater exploitation, soft ground, and urban development are the main causes of land subsidence in Vietnam. This study focuses on identifying, zoning, monitoring, and evaluating land subsidence using PSI technique with IBI method applied to Ho Chi Minh City. The results show diverse land subsidence trends from 2014 to 2021 with different levels in varying locations 98,278 km2 had subsided by over 10 cm, 214,593 km2 had subsided by 5 to 10 cm, and 1.377,897 km2 had subsided by less than 5 cm. Notably, certain areas have higher subsidence funnel centres. To ensure accuracy when using PSI techniques, both natural and human-induced factors must be considered during data collection, which influences the actual subsidence rate. Though difficult, this technique provides reliable insights into this complex phenomenon, e.g. in the geodetic precise levelling method to detect millimetre-level deformations in urban areas. The average PS density in urban areas is between 0,5% and 2,5% of the original number of pixels. The positioning accuracy of a PS is within 1 m in all three directions if a large number of SAR scenes are used. Therefore, the results have been documented with 1 mm precision.

Estimating urban land subsidence with satellite data using a spatially multiscale geographically weighted regression approach

Land subsidence has attracted widespread attention because of its potential hazards to sustainable urban development. The traditional methods for estimating land subsidence disregard the crucial aspect of altitude by analyzing spatial data solely on the 2D plane (longitude and latitude geographic coordinates). This research incorporates the altitude feature into the construction of the spatial matrix to develop a new method named spatially multiscale geographically weighted regression (SM-GWR) for a better estimation of land subsidence, enabling to determine the optimal bandwidth at 3D spatial scales. By adopting a 3D perspective, the new algorithm accurately fits settlement points based on spatial distances, addressing previous limitations of projecting points on a two-dimensional surface and ignoring height differences, thereby providing a more realistic depiction of spatial relationships. The satellite data is first used to monitor land subsidence through the small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). Then, the SM-GWR model is developed to construct the 3D spatial weight matrix and update estimate parameters through back-fitting calibration. The proposed method's feasibility is confirmed through examples of land subsidence in China and Singapore. The case study results demonstrate that: (1) The SM-GWR model shows significant improvement in fitting land subsidence compared to the traditional method (R2 value of 0.908 and 0.934 in predicting annual subsidence and cumulative subsidence, respectively); (2) The contribution of the influencing factors is highly related to the nature properties and urban development of the city. The novelty of this study lies in the development of an SM-GWR model combined with satellite monitoring to achieve high-precision estimation of land subsidence, considering the 3D structure of spatial geographic data.

Monitoring of land subsidence by combining small baseline subset interferometric synthetic aperture radar and generic atmospheric correction online service in Qingdao City, China

Monitoring of land subsidence by combining small baseline subset interferometric synthetic aperture radar and generic atmospheric correction online service in Qingdao City, China

Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique.

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from -59 to 17 mm/yr. The results show that subsidence is -28.48 mm/yr in 2018, the subsidence reached -49.02 mm/yr in 2019, while in 2020, the subsidence reached -49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence.

Monitoring land subsidence through improved CAESAR algorithm in time-series InSAR processing

Time-series SAR interferometry (InSAR) combining permanent scatterer and distributed scatterer (DS), has been strongly developed in subsidence monitoring. It is known that the Component extrAction and sElection SAR (CAESAR) is a recently presented approach of selecting and filtering scattering mechanisms for DS. This article proposes an improved CAESAR algorithm in InSAR processing. Phase optimization is performed by eigenvalue decomposition and principal component analysis of the coherence matrix that constructed based on the identified homogeneous pixels. In addition, only the interferometric phases with low noise are used to calculate the goodness-of-fit value. The improved method has been tested for subsidence monitoring over a nonurban area located in Xiongxian, China using 25 Sentinel-1A images. The results show that the improved method can provide the high spatial density of deformation measurements with accuracy ensured. Noticeable land subsidence is revealed widely within the north of Xiongxian county, particularly in Daying, Mijiawun and Beishakou towns.

Urbanization and land subsidence: multi-decadal investigation combined SBAS-InSAR and multi-factors in Shanghai, China

Situated in the Yangtze River Delta’s alluvial plain, Shanghai has faced intensified land subsidence problems since the economic reforms of the 1990s. However, previous studies have lacked long time-series land subsidence monitoring and an analysis of multifactorial interactions in Shanghai over the past years. This study analyzed Shanghai’s land subsidence from 1992 to 2020 using SAR data from ERS-1/2, ENVISAT ASAR, and Sentinel-1A, applying the SBAS-InSAR technique. The geo-detector method was used to identify key factors affecting land subsidence, including groundwater, urban structure, metro development, and population dynamics. The results showed that (1) the utilization of multi-sensor SAR data enables the comprehensive analysis of urban land subsidence over an extended temporal series, facilitating the acquisition of its spatiotemporal distribution; (2) the extent of subsidence in Shanghai was gradually spreading from the core to the outskirts, and the rate of subsidence was gradually decreasing during 1992-2020 period; (3) groundwater was the most influential factor on land subsidence, and the interaction between groundwater and metro had a stronger influence on land subsidence.

Detection and Monitoring of Land Subsidence By the PSInSAR Method

The InSAR technique is a powerful tool for monitoring land subsidence, which has the potential to provide millimeter-accurate displacement determinations. By using the free Sentinel-1 images, which have a very large spatial coverage and a high frequency of repeated passes over the same area, the InSAR technique can offer good spatial and temporal displacement analysis with high accuracy. Considering the existence of a significant number of free research software, and the need to engage a smaller number of professional staff, this technique is significantly more financially favorable than other methods.

Influence of South-to-North Water Diversion on Land Subsidence in North China Plain Revealed by Using Geodetic Measurements

As a major grain-producing region in China, the North China Plain (NCP) faces serious challenges such as water shortage and land subsidence. In late 2014, the Central Route of the South-to-North Water Diversion Project (SNWD-C) began to provide NCP with water resources. However, the effectiveness of this supply in mitigating land subsidence remains a pivotal and yet unassessed aspect. In this paper, we utilized various geodetic datasets, including the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO), Global Navigation Satellite System (GNSS) and leveling data, to conduct a spatial-temporal analysis of the equivalent water height (EWH) and vertical ground movement in the NCP. The results reveal a noteworthy decline in EWH from 2011 to 2015, followed by a slight increase with minor fluctuations from 2015 to 2020, demonstrating a strong correlation with the water resources supplied by the SNWD-C. The GRACE-derived surface deformation rate induced by hydrological loading is estimated to be <1 mm/yr. In comparison, GNSS-derived vertical ground movements exhibit considerable regional differences during the 2011–2020 period. Substantial surface subsidence is evident in the central and eastern NCP, contrasting with a gradual uplift in the front plain of the Taihang Mountains. Three-stage leveling results indicate that the rate of subsidence in the central and eastern plains is gradually increasing with the depression area expanding from 1960 to 2010. Based on these geodetic results, it can be inferred that the SNWD-C’s operation since 2014 has effectively mitigated the reduction in terrestrial water storage in the NCP. However, land subsidence in the NCP persists, as the subsidence rate does not turn around in sync with the change in EWH following the operation of SNWD-C. Consequently, it’s necessary to maintain and enforce existing policies, including controlling groundwater exploitation and water resources supply (e.g., SNWD-C) to curtail the exacerbation of land subsidence in the NCP. Additionally, continuous monitoring of land subsidence by GRACE, GNSS, leveling and other geodetic techniques is crucial to enable timely policy adjustments based on monitoring results.

Land Subsidence Monitoring and Analysis in Heifei Based on Sentinel-1A Data

Land Subsidence Monitoring and Analysis in Heifei Based on Sentinel-1A Data