Subsidence Time Series Research Articles

Study on the Relationship between Groundwater and Land Subsidence in Bangladesh Combining GRACE and InSAR

Due to a heavy reliance on groundwater, Bangladesh is experiencing a severe decline in groundwater storage, with some areas even facing land subsidence. This study aims to investigate the relationship between groundwater storage changes and land subsidence in Bangladesh, utilizing a combination of GRACE and InSAR technologies. To clarify this relationship from a macro perspective, the study employs GRACE data merged with GLDAS to analyze changes in groundwater storage and SBAS-InSAR technology to assess land subsidence. The Dynamic Time Warping (DTW) method calculates the similarity between groundwater storage and land subsidence time series, incorporating precipitation and land cover types into the data analysis. The findings reveal the following: (1) Groundwater storage in Bangladesh is declining at an average rate of −5.55 mm/year, with the most significant declines occurring in Rangpur, Mymensingh, and Rajshahi. Notably, subsidence areas closely match regions with deeper groundwater levels; (2) The similarity coefficient between the time series of groundwater storage and land subsidence changes exceeds 0.85. Additionally, land subsidence in different regions shows an average lagged response of 2 to 6 months to changes in groundwater storage. This study confirms a connection between groundwater dynamics and land subsidence in Bangladesh, providing essential knowledge and theoretical support for further research.

A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

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.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm, which seriously threatens the safety of people's production and life in the mining area. Therefore, it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining. Compared with traditional level survey and InSAR (Interferometric Synthetic Aperture Radar) technology, GNSS (Global Navigation Satellite System) online monitoring technology has the advantages of long-term monitoring, high precision and more flexible monitoring methods. The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation, which can not only describe the residual subsidence process from the mechanism, but also predict the residual subsidence. Therefore, based on GNSS online monitoring technology, combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics, this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area. Through the example, the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence. The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground, and the model prediction effect is good, which provides a new method for the prediction of residual subsidence in mountain mining.

Land Subsidence Time Series Prediction Method Based on LSTM-AMSGD

Accurate prediction of geological subsidence is of great importance for geological hazard risk assessment. Various existing prediction models do not take into account the time correlation between geological subsidence, and the prediction effect lacks practical significance. In this paper, an LSTM-AMSGD-based land subsidence prediction method is proposed. Firstly, the high-precision time series inversion results of large-area land surface deformation are obtained by the small baseline interference technique with multiple principal image coherent targets. Secondly, a recurrent neural network (LSTM-AMSGD) is used as the network architecture. The final cumulative subsidence prediction error is within 0.3 mm, and the single-step prediction of more than 400,000 observation points can be completed in 126s. Therefore, the LSTM-AMSGD model in this paper is effective for the prediction of geological subsidence.

Spatiotemporal characteristics analysis of ground subsidence in Wuhan based on SBAS-InSAR

Aiming at the problem of uneven ground subsidence in Wuhan, using the Sentinel-1A SAR image data of 36 scenes from July 2017 to June 2020, the SBAS-InSAR was used to extract the ground subsidence information in Wuhan. Influence of factors such as urban construction, industrial development, rainfall, and carbonate karstification on subsidence. The results show that SBAS-InSAR can effectively monitor the ground subsidence in Wuhan. There is a high correlation between ground subsidence and seasonal changes, and the subsidence time series shows a nonlinear downward trend with seasonal changes. Human activities have a serious impact on ground subsidence, and the spatial distribution of the subsidence funnel area is closely related to the geographical location of the industrial area. Among natural factors, rainfall plays a dominant role in the seasonal component of ground subsidence, and karstification has a greater impact on the area along the river. The two influencing factors jointly caused the uneven subsidence of the ground in Wuhan. The experimental results can provide a certain reference for the follow-up subsidence early warning and urban construction planning in Wuhan.

Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise

Considering that it is easily disturbed by various engineering factors such as weather, hydrology, and construction during engineering monitoring, the collected subsidence data contain various noises. In order to reduce the influence of engineering noise on the accuracy of subsidence prediction, it is proposed to use the Daubechies (DB) wavelet to decompose the original subsidence time series; the items with the low-frequency trend, after decomposition, are predicted using long short-term memory (LSTM) model, items with high-frequency noise used the autoregressive (AR) time series model to make predictions, and the prediction results of the low-frequency trend term and the high-frequency noise term are summed to obtain the total time series predicted value. Combining the actual engineering subsidence monitoring data of the old goaf, compared with the prediction results of the LSTM and RNN models without DB wavelet decomposition and the gray model GM (1,1), the results show that the DB wavelet has an obvious improvement effect in reducing the influence of measurement data noise on prediction error. Compared with the single prediction model LSTM, RNN, and GM (1,1), the proposed prediction model has higher prediction accuracy, smaller error, and better trend. It can be used as a calculation method to improve the prediction accuracy of surface subsidence in old goaf.

Coal fire identification and state assessment by integrating multitemporal thermal infrared and InSAR remote sensing data: A case study of Midong District, Urumqi, China

Coal fire disasters generally occur in major coal-producing countries. They destroyed many coal resources and triggered various environmental problems. The identification and state assessment of coal fire areas are paramount to coal fire management. In this paper, a method for identifying coal fires by integrating land surface temperatures (LSTs) from multitemporal thermal infrared remote sensing and subsidence information from multitemporal interferometric synthetic aperture radar (MT-InSAR) is proposed. The method primarily consists of estimating the LSTs and subsidence values, normalizing and equidistantly partitioning the LSTs, estimating the thermal anomaly frequency (TAF), extracting high-subsidence areas, determining the thermal anomaly frequency threshold (TAFT), and identifying coal fire areas according to the TAFT. The coal fire ratio index (CRI) was developed to quantitatively evaluate the severity of coal fires and its variations. Midong District, Urumqi, Xinjiang, China, was selected as the study area. Forty-five Landsat 8 images and sixty-one Sentinel-1 SAR images were used to retrieve LST and subsidence time series, respectively. The proposed method and CRI were applied to identify and evaluate coal fires in the study area. The results demonstrate the satisfactory reliability of these methods compared with field surveys. The maximum CRI was 6.145 × 10-4 (July 28, 2019), and the minimum CRI was 2.685 × 10-5 (January 13, 2020). Coal fires in summer were more severe than those in winter. The CRI profile presented seasonal and annual periodicities and was affected by the local climate. The soil cohesion and humidity are higher in winter due to possible snowmelt, which weakens the interaction between oxygen and coal seams and the subsequent combustion of coal. The LST, thermal anomaly, and deformation information were combined in a time series overlay analysis to reveal the differences in the LSTs and subsidence values among mining areas, coal fire areas, and areas with both mining and coal fires. The results verify the applicability and reliability of the proposed method.

Assessment of long-term ground subsidence and groundwater depletion in Hanoi, Vietnam

Rapid urbanization and population growth in Hanoi city raise the demand for groundwater and the risk of land subsidence. This study is the first to conduct a long-term interferometric synthetic aperture radar (InSAR) analysis with different SAR sensors including ALOS, COSMO-SkyMed and Sentinel-1 to evaluate the spatiotemporal evolution of subsidence in Hanoi between 2007 and 2018. The results show that subsidence in Central Hanoi had been diminishing during these 12 years, while the southern and western outskirts (Ha Dong and Hoai Duc district) had become hot spots with peak rates of ~50 mm/yr. We further model the subsidence time-series with 22 years of piezometric records to estimate hydrogeological properties in the area. The modeling yields a mean consolidation coefficient of 0.56 m2/yr, a value typical for aquitards composed of >90% fine-grain materials. The model also predicts another 0.5–1.3 m of subsidence in Ha Dong by the end of this century. Based on these results, we propose zoning for subsidence susceptibility as the basis for future groundwater management in Hanoi. From a regional perspective, Hanoi is relatively less affected by land subsidence compared to other South East Asian cities due to geotechnical factors including small consolidation coefficients, as well as social factors such as lower population density.

Investigation of Sentinel-1-derived land subsidence using wavelet tools and triple exponential smoothing algorithm in Lagos, Nigeria

Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate siting of private borehole and water well. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series was computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between − 94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater-level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm, which is approximately a 23% increase.

Land Subsidence in Beijing’s Sub-Administrative Center and Its Relationship with Urban Expansion Inferred from Sentinel-1/2 Observations

Beijing’s Sub-Administrative Center (BSAC) is located in the South-eastern Beijing Plain, which exhibits severe subsidence. The rapid urban expansion in recent years has aggravated land subsidence and threatens the safe operation of Beijing. First, this study applied the persistent scatterer-interferometric synthetic aperture radar (PS-InSAR) to extract BSAC subsidence time series data. Second, combined with the index-based built-up index (IBI), expansion intensity index (EII), and expansion gradient index (EGI), the spatiotemporal characteristics of urban expansion were retrieved from optical data. Finally, we examined the urban expansion effects on land subsidence at the regional and single-building scales. The results showed that the maximum subsidence velocity in the BSAC reached 121 mm/year from 2015 to 2018, and the urban construction land area increased by 22%. At the regional scale, there existed a positive correlation between land subsidence and EGI or EII. This indicated that urban expansion had a certain impact on land subsidence. Therefore, we further explored the relationship between construction and land subsidence at the single-building scale. The engineering construction effects on land subsidence were divided into three periods, namely, rapid settlement, rebound, and stable periods. Although construction had a significant influence on land subsidence, it did not cause subsidence mutation.

Land subsidence due to groundwater pumping: hazard probability assessment through the combination of Bayesian model and fuzzy set theory

Abstract. Land subsidence caused by groundwater overpumping threatens the sustainable development in Beijing. Hazard assessments of land subsidence can provide early warning information to improve prevention measures. However, uncertainty and fuzziness are the major issues during hazard assessments of land subsidence. We propose a method that integrates fuzzy set theory and weighted Bayesian model (FWBM) to evaluate the hazard probability of land subsidence measured by Interferometric Synthetic Aperture Radar (InSAR) technology. The model is structured as a directed acyclic graph. The hazard probability distribution of each factor triggering land subsidence is determined using Bayes' theorem. Fuzzification of the factor significance reduces the ambiguity of the relationship between the factors and subsidence. The probability of land subsidence hazard under multiple factors is then calculated with the FWBM. The subsidence time series obtained by InSAR is used to infer the updated posterior probability. The upper and middle parts of the Chaobai River alluvial fan are taken as a case-study site, which locates the first large-scale emergency groundwater resource region in the Beijing plain. The results show that rates of groundwater level decrease more than 1 m yr−1 in the confined and unconfined aquifers, with cumulative thicknesses of the compressible sediments between 160 and 170 m and Quaternary thicknesses between 400 and 500 m, yielding maximum hazard probabilities of 0.65, 0.68, 0.32, and 0.35, respectively. The overall hazard probability of land subsidence in the study area decreased from 51.3 % to 28.3 % between 2003 and 2017 due to lower rates of groundwater level decrease. This study provides useful insights for decision makers to select different approaches for land subsidence prevention.

Investigating Ground Subsidence and the Causes over the Whole Jiangsu Province, China Using Sentinel-1 SAR Data

Interferometric synthetic aperture radar (InSAR) mapping of localized ground surface deformation has become an important tool to manage subsidence-related geohazards. However, monitoring land surface deformation using InSAR at high spatial resolution over a large region is still a formidable task. In this paper, we report a research on investigating ground subsidence and the causes over the entire 107, 200 km2 province of Jiangsu, China, using time-series InSAR. The Sentinel-1 Interferometric Wide-swath (IW) images of 6 frames are used to map ground subsidence over the whole province for the period 2016–2018. We present processing methodology in detail, with emphasis on the three-level co-registration scheme of S-1 data, retrieval of mean subsidence velocity (MSV) and subsidence time series, and mosaicking of multiple frames of results. The MSV and subsidence time series are generated for 9,276,214 selected coherent pixels (CPs) over the Jiangsu territory. Using 688 leveling measurements in evaluation, the derived MSV map of Jiangsu province shows an accuracy of 3.9 mm/year. Moreover, subsidence causes of the province are analyzed based on InSAR-derived subsidence characteristics, historical optical images, and field-work findings. Main factors accounting for the observed subsidence include: underground mining, groundwater withdrawal, soil consolidations of marine reclamation, and land-use transition from agricultural (paddy) to industrial land. This research demonstrates not only the capability of S-1 data in mapping ground deformation over wide areas in coastal and heavily vegetated region of China, but also the potential of inferring valuable knowledge from InSAR-derived results.

HLSTM: Heterogeneous Long Short-Term Memory Network for Large-Scale InSAR Ground Subsidence Prediction

Accurate prediction of ground subsidence is of great significance for the prevention and mitigation of this type of geological disaster. It is still a challenge when wide area is concerned. In this article, a heterogeneous long short-term memory (HLSTM) network is proposed for large-scale ground subsidence prediction based on interferometric synthetic aperture radar (InSAR) data. First, the study area is divided into homogeneous subregions through spatial clustering of InSAR-derived subsidence velocity. Second, a specific LSTM model is constructed to capture complex nonlinear temporal correlations embedded in InSAR-derived subsidence time series for each subregion. Essentially both spatial heterogeneity and temporal correlation are incorporated into the HLSTM prediction. In the experiment part, the HLSTM predictor is validated using a subsidence monitoring result from 80 Sentinel-1 images acquired over Cangzhou, China, from 2017 to 2019. The HLSTM result shows the highest prediction accuracy through comparisons with the results from other seven methods.

Investigating land subsidence and its causes along Beijing high-speed railway using multi-platform InSAR and a maximum entropy model

Beijing-Tianjin High Speed Railway is the first high-speed railway in China. The Beijing section of it runs through areas affected by subsidence which threaten its safe operation. This study develops a new time series fusion method based on the minimum gradient difference of a fitting curve to produce time series subsidence along this section. Through blending Envisat ASAR and TerrSAR-X time series, the InSAR-derived subsidence and its spatial–temporal development was analyzed along the railway. The relationship between subsidence and its causes was then explored using a maximum entropy model. The study reveals that: (1) The subsidence dynamics identified using the new fusion method agrees with the ground deformation measurements; (2) The sections of most severe subsidence occur between kilometer point KP 11 and KP 21; and (3) The main hydrogeological factors affecting subsidence are the compressible deposit thickness and the groundwater level in the second confined aquifer. The new fusion method proposed improves the accuracy and reliability of subsidence time series. It extends the time span of subsidence monitoring. The approach is mainly applicable to areas with significant vertical deformation, and is particularly suitable for integrating multi-platform data with overlapping in time or with a short time gap.

Study on Monitoring, Statistics, Movement and Deformation Law of Mine Subsidence

The Xinli Gold Mine refers to the only undersea metal mine in China. The movement and deformation of rock mass attributed to metal ore mining is considered a sophisticated mechanical problem. Thus, grasping mining-induced movement rules of overburden above goaf and achieving valid assessments for overburden movement refer to the problems to be solved for Xinli seabed gold mine. Based on the long-term monitoring of the vertical displacement of the roof of the mining area, the time series of subsidence is determined. The monitor result shows: (1) the monitoring points should be considered a probability event, i.e., the vertical displacement of each period fluctuates randomly between −20 mm and 12.3 mm; (2) the monitoring points may uplift and subside, in addition, the mean value of vertical displacement interval-probability density (frequency–size) distributions for subsidence and uplift events can be expressed by complying with power laws; (3) The value of the power-law exponent α of subsidence events ranges between 0.9552 and 1.627 and the correlation coefficient (R2) value can reach 95%. The value of the power-law exponent α of uplift events ranges between 0.9891 and 2.025, the correlation coefficient (R2) value can reach 95%. Lastly, complying with the deformation pattern of the roof of respective level, the movement and deformation law of overburden rock are analyzed and summarized. The profile function method is adopted to fit the monitored results of roof of each level, and the accuracy of the fitting is significantly high, which are overall above 81%.

Wavelet analysis of land subsidence time-series: Madrid Tertiary aquifer case study

Abstract. Interpretation of land subsidence time-series to understand the evolution of the phenomenon and the existing relationships between triggers and measured displacements is a great challenge. Continuous wavelet transform (CWT) is a powerful signal processing method mainly suitable for the analysis of individual nonstationary time-series. CWT expands time-series into the time-frequency space allowing identification of localized nonstationary periodicities. Complementarily, Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) methods allow the comparison of two time-series that may be expected to be related in order to identify regions in the time-frequency domain that exhibit large common cross-power and wavelet coherence, respectively, and therefore are evocative of causality. In this work we use CWT, XWT and WTC to analyze piezometric and InSAR (interferometric synthetic aperture radar) time-series from the Tertiary aquifer of Madrid (Spain) to illustrate their capabilities for interpreting land subsidence and piezometric time-series information.

MONITORING AND ANALYZING THE SURFACE SUBSIDENCE IN LANGFANG BY TIME-SERIES INTERFEROMETRIC RADAR TECHNIQUE

Abstract. In this paper, the subsidence change maps in Langfang were obtained using ASAR images covering Langfang area acquired from August 2007 to September 2010 with the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique. Surface subsidence spatial-temporal characteristics in Langfang was investigated. The analysis of experimental results show that 1) prominent uneven subsidence patterns were identified in Langfang. Specifically, surface subsidence rates ranged from −77 mm/year to 4 mm/year, and maximum subsidence rate was detected in Nanjianta Town; 2) the standard deviation of the target point subsidence rates in the study area ranges from 0.8 mm/year to 9.3 mm/year, 70.4% of the monitoring point standard deviations in study area are less than 3 mm/year; 3) surface subsidence time series presented nonlinear variation trend. The surface subsidence has a tendency to gradually expand around the center of Nanjianta Town, and the scope and trend of expansion to the north and west of Langfang are more serious.

Deformation Monitoring of Landslide Based on Adapted Distributed Scatter Interferometric Synthetic Aperture Radar

Abstract. Landslide is a typical geological disaster, which may be caused by long-term heavy rainfall, human activities, and earthquakes. Landslides are extremely destructive so as to result in a serious threat to local public security. The deformation monitoring of landslide can provide an early warning and disaster prevention and mitigation measures. At present, the traditional landslide monitoring methods include absolute deformation monitoring, e.g. GPS surveying, total station and levelling， and relative deformation monitoring, e.g. displacement meter, crack meter and fiber optical sensors. Although these methods by ground measuring have the advantage of high precision, they can monitor only a limited number of points on the landslide body and need a large amount of field work and the high cost.In order to improve the accuracy of potential hazard of landslide detection and achieve deformation information for landslide areas, we proposed an adaptive distributed scatterer interferometric Radar method (ADS-InSAR), which can automatically adjust the detection threshold according to the spatiotemporal coherence of different distributed scatterer (DS) so as to improve the density and reliability of spatial distribution of DS. After the time series network modelling and deformation solution of the ADS are achieved, the displacement deformation details of the landslide area are extracted. In this paper, the experimental area of Shuibuya Town, Enshi Prefecture, Hubei Province, China was selected and the Sentinal-1A radar images data were used from March 5, 2016 to April 5, 2017. The ADS-InSAR method was utilized to obtain the subsidence time series of the region. The deformation time series is finally analysed combined with hydro meteorological and related data. Experimental results indicated that the method proposed in this paper can effectively improve the density of DS distribution and can successfully detect the existing ancient landslide group and find multiple suspected landslide areas for disaster warning, and has high reliability and accuracy in landslide disaster prevention and mitigation.