Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor
Interferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties wi...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2231 |
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| author | Peng Fan Hong Lin Zhengjia Zhang Heming Deng |
| author_facet | Peng Fan Hong Lin Zhengjia Zhang Heming Deng |
| author_sort | Peng Fan |
| collection | DOAJ |
| description | Interferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties with deformation automatic detection. In this study, a full-coverage deformation rate map of the 10 km buffer of the Qinghai–Tibet Engineering Corridor (QTEC) was generated by combining nine driving factors and the deformation rate of the 5 km buffer along the QTEC based on three machine learning methods. The importance of the factors contributing to ground deformation was explored. The experimental results show that support vector regression (SVR) yielded the best performance (R2 = 0.98, RMSE = 0.76 mm/year, MAE = 0.74 mm/year). The 10 km buffer of deformation data obtained not only preserved the original deformation data well, but it also filled the blank areas in the deformation map. Subsequently, we trained the Faster R-CNN model on the deformation rate map simulated by SVR and used it for the automatic detection of permafrost thaw settlement areas. The results showed that the Faster R-CNN could identify the permafrost thawing slump quickly and accurately. More than 300 deformation areas along the QTEC were detected through our proposed method, with some of these areas located near thaw slump and thermokarst lake regions. This study confirms the significant potential of combining InSAR and deep learning techniques for permafrost degradation monitoring applications. |
| format | Article |
| id | doaj-art-1baf18d690154bd89d14a1c30b3fee48 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-1baf18d690154bd89d14a1c30b3fee482025-08-20T03:16:42ZengMDPI AGRemote Sensing2072-42922025-06-011713223110.3390/rs17132231Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering CorridorPeng Fan0Hong Lin1Zhengjia Zhang2Heming Deng3School of New Energy and Electrical Engineering, Hubei University, 368 Friendship Avenue, Wuchang District, Wuhan 430062, ChinaSchool of Geography and Information Engineering, China University of Geosciences, 388 Lumo Road, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, 388 Lumo Road, Wuhan 430074, ChinaSchool of New Energy and Electrical Engineering, Hubei University, 368 Friendship Avenue, Wuchang District, Wuhan 430062, ChinaInterferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties with deformation automatic detection. In this study, a full-coverage deformation rate map of the 10 km buffer of the Qinghai–Tibet Engineering Corridor (QTEC) was generated by combining nine driving factors and the deformation rate of the 5 km buffer along the QTEC based on three machine learning methods. The importance of the factors contributing to ground deformation was explored. The experimental results show that support vector regression (SVR) yielded the best performance (R2 = 0.98, RMSE = 0.76 mm/year, MAE = 0.74 mm/year). The 10 km buffer of deformation data obtained not only preserved the original deformation data well, but it also filled the blank areas in the deformation map. Subsequently, we trained the Faster R-CNN model on the deformation rate map simulated by SVR and used it for the automatic detection of permafrost thaw settlement areas. The results showed that the Faster R-CNN could identify the permafrost thawing slump quickly and accurately. More than 300 deformation areas along the QTEC were detected through our proposed method, with some of these areas located near thaw slump and thermokarst lake regions. This study confirms the significant potential of combining InSAR and deep learning techniques for permafrost degradation monitoring applications.https://www.mdpi.com/2072-4292/17/13/2231ground deformationpermafrostmachine learningQinghai–Tibet Engineering Corridor |
| spellingShingle | Peng Fan Hong Lin Zhengjia Zhang Heming Deng Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor Remote Sensing ground deformation permafrost machine learning Qinghai–Tibet Engineering Corridor |
| title | Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor |
| title_full | Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor |
| title_fullStr | Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor |
| title_full_unstemmed | Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor |
| title_short | Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor |
| title_sort | enhanced detection of permafrost deformation with machine learning and interferometric sar along the qinghai tibet engineering corridor |
| topic | ground deformation permafrost machine learning Qinghai–Tibet Engineering Corridor |
| url | https://www.mdpi.com/2072-4292/17/13/2231 |
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