Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

Cigna, Francesca; Tapete, Deodato

doi:10.3390/rs13050885

Cited by 61 publications

(33 citation statements)

References 86 publications

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“…In recent decades, the interferometric synthetic aperture radar (SAR, InSAR) technique has been greatly developed and widely used to serve geohazard monitoring processes, such as those for mining subsidence [1][2][3], landslides [4][5][6], earthquakes [7][8][9], and volcano eruptions [10][11][12]. Especially when integrating multi-temporal SAR images with advanced time series InSAR (TS-InSAR) methods (e.g., persistent scatter (PS), small baseline subset (SBAS), and mixed PS/SBAS methods) [13][14][15][16][17], the inherent errors in a single interferogram (e.g., decorrelation noise and atmospheric delay) can be effectively mitigated, and simultaneously, the deformation time series of the study area can be obtained, which is of great significance for understanding the evolution process and mechanism of geohazards.…”

Section: Introductionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

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Section: Introductionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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“…By using computer program, formulas ( 15)-( 18) were programmed to realize the Markov random process, and the predicted results were obtained for the two models. The predicted results are as shown from figure 7 to figure 10: [4][5][6][7] show the predicted accuracy for subsidence data at each monitoring point after the optimization of the Gray-Markov model. We get the predicted accuracy by calculating the ratio of the true value to the predicted value.…”

Section: Optimization Of the Gray-markov Modelmentioning

confidence: 99%

“…InSAR technology has the advantages of all-weather use, low cost, large coverage, and high spatial resolution. One example is Sentinel-1 TOPS (Terrain Observation with Progressive Scans in azimuth) mode SAR data (open to all users) [6,7]. Ideally, SAR data obtained using the TOPS mode can monitor about 40,000 2 km for 6 days (with two satellites) with a return period of 12 days to produce surface deformation maps with a spatial resolution of about 20 m (including all mining areas in this range) [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Application of Gray-Markov Model to Land Subsidence Monitoring of a Mining Area

et al. 2021

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Land subsidence monitoring in mining areas is one of the main applications of surface deformation monitoring, which is of great significance for safety production. Using the IPTA (Interferometric Point Target Analysis) time-series InSAR (Interferometry Synthetic Aperture Radar) method, land subsidence data from the new exploration area in the Weizhou mining area were analyzed and compared with static GPS (Global Positioning System) monitoring data for 2017-2020. Gray-Markov model was established by combining the gray prediction model with the Markov model to predict the surface subsidence of the mining area. The results show that (1) InSAR data have high accuracy and application potential in prediction of long-term surface deformation in mining areas; (2) The Gray-Markov model can better reflect the volatility and practicality of subsidence data in mining areas; (3) The prediction results have high accuracy, and the Gray-Markov model can serve as an effective guide for long-term surface deformation monitoring and safety management.

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“…A few studies have already shown the capability of this system for efficient MTI analysis using a variety of SAR sensors (Galve et al, 2017, Cigna andTapete, 2021).…”

Section: Geohazard Monitoring For Large Areas (Gep)mentioning

confidence: 99%

Exploring Cloud-Based Platforms for Rapid Insar Time Series Analysis

Piter¹,

Vassileva

Haghighi³

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The idea of near real-time deformation analysis using Synthetic Aperture Radar (SAR) data as a response to natural and anthropogenic disasters has been an interesting topic in the last years. A major limiting factor for this purpose has been the non-availability of both spatially and temporally homogeneous SAR datasets. This has now been resolved thanks to the SAR data provided by the Sentinel-1A/B missions, freely available at a global scale via the Copernicus program of the European Space Agency (ESA). Efficient InSAR analysis in the era of Sentinel demands working with cloud-based platforms to tackle problems posed by large volumes of data. In this study, we explore a variety of existing cloud-based platforms for Multi-Temporal Interferometric SAR (MTI) analysis and discuss their opportunities and limitations.

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Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

Cited by 61 publications

References 86 publications

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Application of Gray-Markov Model to Land Subsidence Monitoring of a Mining Area

Exploring Cloud-Based Platforms for Rapid Insar Time Series Analysis

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