Ground motion areas detection (GMA-D): an innovative approach to identify ground deformation areas using the SAR-based displacement time series

Bonì, Roberta; Meisina, Claudia; Poggio, Linda; Fontana, Alessandro; Tessari, Giulia; Riccardi, Paolo; Floris, Mario

doi:10.5194/piahs-382-277-2020

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Exploiting the full displacement time series enables us to better explore the InSAR dataset and gain a much deeper understanding of the deformation kinematics: for example, how landslide velocity responds to rainfall [30]. Additionally, clustering has been extended to the spatial distribution of principal components extracted from InSAR time series datasets [6,31]. However, the Principal Component Analysis used to estimate the principal components assumes linearity in the data which may not be appropriate for analyzing large, complex InSAR datasets since non-linear data relationships would be neglected.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

Rygus,

Novellino,

Hussain

et al. 2023

Remote Sensing

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Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract meaningful information. We propose a novel framework for interpreting the large number of ground displacement measurements derived from InSAR time series techniques using a three-step process: (1) dimensionality reduction of the displacement time series from an InSAR data stack; (2) clustering of the reduced dataset; and (3) detecting and quantifying accelerations and decelerations of deforming areas using a change detection method. The displacement rates, spatial variation, and the spatio-temporal nature of displacement accelerations and decelerations are used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to potential causal and triggering factors. We tested the method over the Bandung Basin in Indonesia using Sentinel-1 data processed with the small baseline subset InSAR time series technique. The results showed widespread subsidence in the central basin with rates up to 18.7 cm/yr. We identified 12 main clusters of subsidence, of which three covering a total area of 22 km2 show accelerating subsidence, four clusters over 52 km2 show a linear trend, and five show decelerating subsidence over an area of 22 km2. This approach provides an objective way to monitor and interpret ground movements, and is a valuable tool for understanding the physical behaviour of large deforming areas.

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Section: Introductionmentioning

confidence: 99%

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

Rygus,

Novellino,

Hussain

et al. 2023

Remote Sensing

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“…Bonì et al [1] and Barra et al [2] established the initial methodologies for the automatic detection of Active Deformation Areas (ADAs) using GIS tools. Bonì et al [3] implemented their methodology using ArcGIS, while Navarro et al [4] implemented Barra's methodology in a software package with a graphical user interface called ADAfinder (V2.0.9 is the last version and it's available free on request), using the C++ programming language. ADAfinder determines active Deformational Time Series (DTS) through standard deviation thresholds, isolation distance, and average velocity.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Active Deformation Areas Based on Synthetic Aperture Radar Data and Environmental Covariates Using Machine Learning—Application in SE Spain

Rivera-Rivera,

Béjar-Pizarro,

Aguilera

et al. 2023

IV Conference on Geomatics Engineering

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Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

Milillo

Sacco

Martire³

et al. 2022

Front. Earth Sci.

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We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time series of displacements. We demonstrate this method with a set of 170 million time series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed Synthetic Aperture Radar satellite constellations. We create a training dataset that has been compared with independently validated data and current state-of-the-art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall, and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data, or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing.

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Ground motion areas detection (GMA-D): an innovative approach to identify ground deformation areas using the SAR-based displacement time series

Cited by 3 publications

References 9 publications

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

Automatic Classification of Active Deformation Areas Based on Synthetic Aperture Radar Data and Environmental Covariates Using Machine Learning—Application in SE Spain

Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

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