Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR
Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/23/4738 |
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| author | Xuefei Zhang Min Feng Hong Zhang Chao Wang Yixian Tang Jinhao Xu Dezhao Yan Chunling Wang |
| author_facet | Xuefei Zhang Min Feng Hong Zhang Chao Wang Yixian Tang Jinhao Xu Dezhao Yan Chunling Wang |
| author_sort | Xuefei Zhang |
| collection | DOAJ |
| description | Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity. |
| first_indexed | 2024-03-10T04:46:02Z |
| format | Article |
| id | doaj.art-0855640c8ebb43d890d43b9f9026686e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T04:46:02Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0855640c8ebb43d890d43b9f9026686e2023-11-23T02:55:36ZengMDPI AGRemote Sensing2072-42922021-11-011323473810.3390/rs13234738Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSARXuefei Zhang0Min Feng1Hong Zhang2Chao Wang3Yixian Tang4Jinhao Xu5Dezhao Yan6Chunling Wang7Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Tibetan Environmental Changes and Land Surface Processes, National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Resources and Environment, University of Chinese Academy Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy Sciences, Beijing 100049, ChinaKey Laboratory of Tibetan Environmental Changes and Land Surface Processes, National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Tibetan Environmental Changes and Land Surface Processes, National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Tibetan Environmental Changes and Land Surface Processes, National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaRock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.https://www.mdpi.com/2072-4292/13/23/4738HimalayasMT-InSARrock glaciersdeformationactive deformation areasactivity |
| spellingShingle | Xuefei Zhang Min Feng Hong Zhang Chao Wang Yixian Tang Jinhao Xu Dezhao Yan Chunling Wang Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR Remote Sensing Himalayas MT-InSAR rock glaciers deformation active deformation areas activity |
| title | Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR |
| title_full | Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR |
| title_fullStr | Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR |
| title_full_unstemmed | Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR |
| title_short | Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR |
| title_sort | detecting rock glacier displacement in the central himalayas using multi temporal insar |
| topic | Himalayas MT-InSAR rock glaciers deformation active deformation areas activity |
| url | https://www.mdpi.com/2072-4292/13/23/4738 |
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