Series Interferometric Synthetic Aperture Radar Research Articles

Multi-Scale and Multi-Dimensional Time Series InSAR Characterizing of Surface Deformation over Shandong Peninsula, China

Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.

Forecasting the magnitude of potential landslides based on InSAR techniques

A new method, combining empirical modeling with time series Interferometric Synthetic Aperture Radar (InSAR) data, is proposed to provide an assessment of potential landslide volume and area. The method was developed to evaluate potential landslides in the Heitai river terrace of the Yellow River in central Gansu Province, China. The elevated terrace has a substantial loess cover and along the terrace edges many landslides have been triggered by gradually rising groundwater levels following continuous irrigation since 1968. These landslides can have significant impact on communities, affecting lives and livelihoods. Developing effective landslide risk management requires better understanding of potential landslide magnitude. Fifty mapped landslides were used to construct an empirical power-law relationship linking landslide area (AL) to volume (VL) (VL = 0.333 × AL1.399). InSAR-derived ground displacement ranges from −64 mm/y to 24 mm/y along line of sight (LOS). Further interpretation of patterns based on remote sensing (InSAR & optical image) and field survey enabled the identification of an additional 54 potential landslides (1.9 × 102 m2 ≤ AL ≤ 8.1 × 104 m2). In turn this enabled construction of a map that shows the magnitude of potential landslide activity. This research provides significant further scientific insights to inform landslide hazard and risk management, in a context of ongoing landscape evolution. It also provides further evidence that this methodology can be used to quantify the magnitude of potential landslides and thus contribute essential information towards landslide risk management.

THREE-DIMENSIONAL SUBSIDENCE MONITORING OF RECLAMATION AREA BASED ON TS-INSAR TECHNOLOGY

Abstract. This study used the Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) technology and the ENVISAT ASAR dataset obtained between 2007 and 2010 and the Sentinel-1A dataset from 2017 to 2019 to determine the subsidence in the Shenzhen reclamation area, and used ASTER GDEM V2 to remove the phase jump caused by spectral decorrelation between bursts. Based on the time series of land subsidence of the reclamation area, a three-dimensional subsidence model of the reclamation area was established. By comparing and analyzing the subsidence results of two sets of satellite time series in the reclamation area, the temporal and spatial evolution of land subsidence and the cause of land subsidence in the reclamation area were analyzed. The results show that the test results of time series ENVISAT ASAR and Sentinel-1A have good continuity in subsidence spatialtemporal variation: From 2007 to 2010, the land subsidence in most areas of Shenzhen reclamation area is relatively stable, and the area where subsidence occurs is mainly distributed in Baoan District, the maximum subsidence rate in Baoan District is −23.12 mm/year. From 2017 to 2019, land subsidence in the reclamation area showed a tendency to spread and spread from west to east. The subsidence characteristics of Nanshan District are the most obvious, and the maximum subsidence rate is −17.52 mm/year.

Locating and monitoring of landslides based on small baseline subset interferometric synthetic aperture radar

Due to the effects of rugged terrain and vegetation cover, improving the locating and monitoring accuracy of time series interferometric synthetic aperture radar (TS-InSAR) in detecting the development of landslides in mountainous and valley areas has become a problem that needs to be solved. Our study uses the ground local incident angle (GLIA) to locate noneffective monitoring areas that are affected by steep terrain as well as to improve the locating accuracy of TS-InSAR technology in the effective coverage area. At the same time, the improved small baseline subset InSAR (SBAS-InSAR) method is used to improve the density of interferometer targets as well as to solve the problem of insufficient permanent scatterers due to the effects of vegetation coverage. A geometric model of GLIA was established. Ascending and descending synthetic aperture radar (SAR) data from the Advanced Land Observation Satellite (ALOS) were used for calculating the value of GLIA. The relationships of the GLIA and the interference characteristics of InSAR data were analyzed, and the locations of noneffective areas were determined. Finally, 19 ascending pass SAR data scenes acquired by the ALOS-1 satellite were processed through using the improved SBAS-InSAR method, and the detailed time series moving displacement information from 2007 to 2011 was mapped in Wudongde hydropower reservoir. Furthermore, the potentially moving landslide areas and landslide hazard areas were located in the effective interference areas. The active Jinpingzi landslide was investigated in detail, and the single-point monitoring results of SBAS-InSAR were compared with the electronic total station (ETS) measurement results. The conclusion shows that the monitoring results of SBAS-InSAR were in agreement with the ground field survey and the single-point ETS monitoring, and the applicability of the model and algorithm proposed was proved. Our study provides a reference for the early identification and high-precision monitoring of landslides in mountainous and valley areas.

Land subsidence in Beijing and its relationship with geological faults revealed by Sentinel-1 InSAR observations

Beijing, the capital city of China, has been affected by land subsidence due to intensive groundwater extraction since 1935. Recent studies reported that the maximum subsidence occurred in the east of Beijing, reaching more than 11 cm/year till 2017. To investigate the subsidence (2015˜2017) in Beijing, in this paper, time series interferometric synthetic aperture radar (TS-InSAR) analysis was performed with 22 Sentinel-1 Terrain Observation by Progressive Scans (TOPS) mode SAR data. Results show that wide areas in the east of Beijing are subsiding with a maximum rate of 14 cm/year, which is consistent with GPS data. Detailed analysis of the obtained subsidence map reveals the existence of several characteristic near-linear boundaries within the subsiding areas. As a result of our previous three-year project, we generated the exact location, direction, and late Quaternary activity of the main potential active faults in the Beijing area. The relationship between the trace of these existing geological faults and the mentioned subsidence boundaries was investigated in detail. It is suggested that land subsidence in Beijing was mainly caused by the over-extraction of groundwater, with its spatial pattern being controlled by geological faults.

Landslide detection in La Paz City (Bolivia) based on time series analysis of InSAR data

ABSTRACT Geologically, La Paz City is located in an unstable area. During the history of La Paz city, many landslides have destroyed houses and valuable infrastructures. In the last decades, time series Interferometric Synthetic Aperture Radar (InSAR) technologies have demonstrated a great capacity for detecting slow ground displacement, achieving an accuracy of millimetre-level. In order to have a better landslide monitoring of La Paz city, in this study, the Sentinel-1 SAR images have been processed by Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS) techniques. The time span of the datasets is from March 2015 to August 2016. Both ascending and descending Synthetic Aperture Radar (SAR) images have been processed to obtain the line of sight (LOS) ground velocity, and then the results have been combined to estimate the up-down and east-west displacement. Several active movement areas have been identified, showing a surface velocity up to 158 mm year−1 westward and 49 mm year−1 eastward. Furthermore, two important findings have been discovered. First, the InSAR result has detected movement in Auquisamaa hill before the area collapsed (15 February 2017), where five houses are buried. Second, the InSAR result has identified that there are still some unstable sites in Callapa area, where a mega-landslide has destroyed more than a thousand of houses in February 2011. In conclusion, we have verified that the InSAR technology could be a very useful tool to help La Paz public institutions for a better management of urban planning, landslide areas delimitation and landslide risk mitigation.

Applying time series interferometric synthetic aperture radar and the unscented Kalman filter to predict deformations in Maoxian landslide

On June 24, 2017, a catastrophic landslide occurred in Maoxian County, Sichuan, China, burying an entire village and more than 100 people. We focused on mapping and predicting the deformation in Maoxian before the landslide based on 39 C-band synthetic aperture radar images acquired by the Sentinel-1 satellite. In order to identify sufficient measurement points in rural areas, the images were first filtered with joint scatterer interferometric synthetic aperture radar to increase the number of measurement points 20 times. Then, the cumulative time series deformation in the landslide area from November 7, 2014, to June 12, 2017, was obtained by employing a time series InSAR technique combined with an unscented Kalman filter (UKF) to predict the deformation by separating the prediction and update steps. Approximately, 30 suspected landslide points were found in the area. Qualitative and quantitative analyses of the experimental results suggested that combining time series InSAR with UKF can be used to predict deformation trends with millimetric accuracy at a large scale before landslides occur.

Method Combining Probability Integration Model and a Small Baseline Subset for Time Series Monitoring of Mining Subsidence

Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) has high accuracy for monitoring slow surface subsidence. However, in the case of a large-scale mining subsidence areas, the monitoring capabilities of TS-InSAR are poor, owing to temporal and spatial decorrelation. To monitor mining subsidence effectively, a method known as Probability Integration Model Small Baseline Set (PIM-SBAS) was applied. In this method, mining subsidence with a large deformation gradient was simulated by a PIM. After simulated deformation was transformed into a wrapped phase, the residual wrapped phase was obtained by subtracting the simulated wrapped phase from the actual wrapped phase. SBAS was used to calculate the residual subsidence. Finally, the mining subsidence was determined by adding the simulated deformation to the residual subsidence. The time series subsidence of the Nantun mining area was derived from 10 TerraSAR-X (TSX) images for the period 25 December 2011 to 2 April 2012. The Zouji highway above the 9308 workface was the target for study. The calculated maximum mining subsidence was 860 mm. The maximum subsidence for the Zouji highway was about 145 mm. Compared with the SBAS method, PIM-SBAS alleviates the difficulty of phase unwrapping, and may be used to monitor large-scale mining subsidence.

Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance–covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance–covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

ADAPTIVE SPATIAL FILTERING OF INTERFEROMETRIC DATA STACK ORIENTED TO DISTRIBUTED SCATTERERS

Abstract. Standard interferometry poses a challenge in non-urban areas due to temporal and spatial decorrelation of the radar signal, where there is high signal noise. Techniques such as Small Baseline Subset Algorithm (SBAS) have been proposed to make use of multiple interferometric combinations to alleviate the problem. However, the interferograms used in SBAS are multilooked with a boxcar (rectangle) filter to reduce phase noise, resulting in a loss of resolution and signal superstition from different objects. In this paper, we proposed a modified adaptive spatial filtering algorithm for accurate estimation of interferogram and coherence without resolution loss even in rural areas, to better support the deformation monitoring with time series interferometric synthetic aperture radar (InSAR) technique. The implemented method identifies the statistically homogenous pixels in a neighbourhood based on the goodness-of-fit test, and then applies an adaptive spatial filtering of interferograms. Three statistical tests for the identification of distributed targets will be presented, applied to real data. PALSAR data of the yellow river delta in China is used for demonstrating the effectiveness of this algorithm in rural areas.