Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network
Landslides are a major geohazard that endangers human lives and properties. Recently, efforts have been made to use Synthetic Aperture Radar Interferometry (InSAR) for landslide monitoring. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed ov...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2022.963322/full |
| _version_ | 1817999672709480448 |
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| author | Lv Fu Qi Zhang Teng Wang Weile Li Qiang Xu Daqing Ge |
| author_facet | Lv Fu Qi Zhang Teng Wang Weile Li Qiang Xu Daqing Ge |
| author_sort | Lv Fu |
| collection | DOAJ |
| description | Landslides are a major geohazard that endangers human lives and properties. Recently, efforts have been made to use Synthetic Aperture Radar Interferometry (InSAR) for landslide monitoring. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed over a large area due to phase unwrapping errors, decorrelation, troposphere turbulence and computational requirements. In this study, we develop a new approach combining phase-gradient stacking and a deep-learning network based on YOLOv3 to automatically detect slow-moving landslides from large-scale interferograms. Using Sentinel-1 SAR images acquired from 2014 to 2020, we developed a burst-based, phase-gradient stacking algorithm to sum up phase gradients in short-temporal-baseline interferograms along the azimuth and range directions. The stacked phase gradients clearly reveal the characteristics of localized surface deformation that is mainly caused by slow-moving landslides and avoids the errors due to phase unwrapping in partially decorrelated areas and atmospheric effects. Then, we trained the improved Attention-YOLOv3 network with stacked phase-gradient maps of manually labeled landslides to achieve quick and automatic detection. We applied our method in an ∼180,000 km2 area of southwestern China and identified 3,366 slow-moving landslides. By comparing the results with optical imagery and previously published landslides in this region, the proposed method can achieve automatic detection over a large area precisely and efficiently. From the derived landslide density map, we determined that most landslides are distributed along the three large rivers and their branches. In addition to some counties with known high-density landslides, approximately 10 more counties with high landslide density were exposed, which should attract more attention to their risks for geohazards. This application demonstrates the potential value of our newly developed method for slow-moving landslide detection over a nation-wide area, which can be employed before applying more time-consuming time-series InSAR analysis. |
| first_indexed | 2024-04-14T03:12:05Z |
| format | Article |
| id | doaj.art-f39bbbfcaff440419f63a0c19a5b1f4b |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-14T03:12:05Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-f39bbbfcaff440419f63a0c19a5b1f4b2022-12-22T02:15:34ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2022-08-011010.3389/fenvs.2022.963322963322Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning networkLv Fu0Qi Zhang1Teng Wang2Weile Li3Qiang Xu4Daqing Ge5School of Earth and Space Sciences, Peking University, Beijing, ChinaSchool of Earth and Space Sciences, Peking University, Beijing, ChinaSchool of Earth and Space Sciences, Peking University, Beijing, ChinaState Key Laboratory of Geo-Hazards Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu, ChinaState Key Laboratory of Geo-Hazards Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu, ChinaChina Aero Geophysical Surveying and Remote Sensing Center for Natural Resources (AGRS), Beijing, ChinaLandslides are a major geohazard that endangers human lives and properties. Recently, efforts have been made to use Synthetic Aperture Radar Interferometry (InSAR) for landslide monitoring. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed over a large area due to phase unwrapping errors, decorrelation, troposphere turbulence and computational requirements. In this study, we develop a new approach combining phase-gradient stacking and a deep-learning network based on YOLOv3 to automatically detect slow-moving landslides from large-scale interferograms. Using Sentinel-1 SAR images acquired from 2014 to 2020, we developed a burst-based, phase-gradient stacking algorithm to sum up phase gradients in short-temporal-baseline interferograms along the azimuth and range directions. The stacked phase gradients clearly reveal the characteristics of localized surface deformation that is mainly caused by slow-moving landslides and avoids the errors due to phase unwrapping in partially decorrelated areas and atmospheric effects. Then, we trained the improved Attention-YOLOv3 network with stacked phase-gradient maps of manually labeled landslides to achieve quick and automatic detection. We applied our method in an ∼180,000 km2 area of southwestern China and identified 3,366 slow-moving landslides. By comparing the results with optical imagery and previously published landslides in this region, the proposed method can achieve automatic detection over a large area precisely and efficiently. From the derived landslide density map, we determined that most landslides are distributed along the three large rivers and their branches. In addition to some counties with known high-density landslides, approximately 10 more counties with high landslide density were exposed, which should attract more attention to their risks for geohazards. This application demonstrates the potential value of our newly developed method for slow-moving landslide detection over a nation-wide area, which can be employed before applying more time-consuming time-series InSAR analysis.https://www.frontiersin.org/articles/10.3389/fenvs.2022.963322/fullmulti-temporal InSARphase-gradient stackingattention-YOLOv3landslide detectiongeohazards |
| spellingShingle | Lv Fu Qi Zhang Teng Wang Weile Li Qiang Xu Daqing Ge Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network Frontiers in Environmental Science multi-temporal InSAR phase-gradient stacking attention-YOLOv3 landslide detection geohazards |
| title | Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network |
| title_full | Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network |
| title_fullStr | Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network |
| title_full_unstemmed | Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network |
| title_short | Detecting slow-moving landslides using InSAR phase-gradient stacking and deep-learning network |
| title_sort | detecting slow moving landslides using insar phase gradient stacking and deep learning network |
| topic | multi-temporal InSAR phase-gradient stacking attention-YOLOv3 landslide detection geohazards |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2022.963322/full |
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