A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)
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 sign...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/15/3776 |
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| author | Michelle Rygus Alessandro Novellino Ekbal Hussain Fifik Syafiudin Heri Andreas Claudia Meisina |
| author_facet | Michelle Rygus Alessandro Novellino Ekbal Hussain Fifik Syafiudin Heri Andreas Claudia Meisina |
| author_sort | Michelle Rygus |
| collection | DOAJ |
| description | 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 km<sup>2</sup> show accelerating subsidence, four clusters over 52 km<sup>2</sup> show a linear trend, and five show decelerating subsidence over an area of 22 km<sup>2</sup>. 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. |
| first_indexed | 2024-03-11T00:17:46Z |
| format | Article |
| id | doaj.art-bf436f07740d4789bbb7f671c84359b1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:46Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bf436f07740d4789bbb7f671c84359b12023-11-18T23:30:40ZengMDPI AGRemote Sensing2072-42922023-07-011515377610.3390/rs15153776A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)Michelle Rygus0Alessandro Novellino1Ekbal Hussain2Fifik Syafiudin3Heri Andreas4Claudia Meisina5Department of Earth and Environmental Sciences, University of Pavia, Via Adolfo Ferrata 1, 27100 Pavia, ItalyBritish Geological Survey, Keyworth, Nottingham NG12 5GG, UKBritish Geological Survey, Keyworth, Nottingham NG12 5GG, UKGeospatial Information Agency of Indonesia (Badan Informasi Geospasial), Jl. Ir. H. Juanda No. 193, Dago, Kecamatan Coblong, Kota Bandung 40135, IndonesiaDepartment of Geodesy and Geomatics Engineering, Institute of Technology Bandung, Jalan Ganesha 10, Bandung 40132, IndonesiaDepartment of Earth and Environmental Sciences, University of Pavia, Via Adolfo Ferrata 1, 27100 Pavia, ItalyInterferometric 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 km<sup>2</sup> show accelerating subsidence, four clusters over 52 km<sup>2</sup> show a linear trend, and five show decelerating subsidence over an area of 22 km<sup>2</sup>. 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.https://www.mdpi.com/2072-4292/15/15/3776land subsidenceInSARtime series analysisclusteringBandung |
| spellingShingle | Michelle Rygus Alessandro Novellino Ekbal Hussain Fifik Syafiudin Heri Andreas Claudia Meisina A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) Remote Sensing land subsidence InSAR time series analysis clustering Bandung |
| title | A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) |
| title_full | A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) |
| title_fullStr | A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) |
| title_full_unstemmed | A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) |
| title_short | A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) |
| title_sort | clustering approach for the analysis of insar time series application to the bandung basin indonesia |
| topic | land subsidence InSAR time series analysis clustering Bandung |
| url | https://www.mdpi.com/2072-4292/15/15/3776 |
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