Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region
Abstract Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited...
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Wiley
2024-09-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036700 |
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| author | Jie Chen Xingchen Lin Tonghua Wu Junming Hao Xiaodong Wu Defu Zou Xiaofan Zhu Guojie Hu Yongping Qiao Dong Wang Sizhong Yang Lina Zhang |
| author_facet | Jie Chen Xingchen Lin Tonghua Wu Junming Hao Xiaodong Wu Defu Zou Xiaofan Zhu Guojie Hu Yongping Qiao Dong Wang Sizhong Yang Lina Zhang |
| author_sort | Jie Chen |
| collection | DOAJ |
| description | Abstract Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. |
| format | Article |
| id | doaj-art-16a6af2a5814432c93479d650691fac8 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-16a6af2a5814432c93479d650691fac82025-08-20T02:36:34ZengWileyWater Resources Research0043-13971944-79732024-09-01609n/an/a10.1029/2023WR036700Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source RegionJie Chen0Xingchen Lin1Tonghua Wu2Junming Hao3Xiaodong Wu4Defu Zou5Xiaofan Zhu6Guojie Hu7Yongping Qiao8Dong Wang9Sizhong Yang10Lina Zhang11Cryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaSchool of Civil Engineering Lanzhou University of Technology Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaCryosphere Research Station on the Qinghai‐Tibet Plateau Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou ChinaSchool of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou ChinaAbstract Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic.https://doi.org/10.1029/2023WR036700seasonal deformationInSARpermafrostmachine learningactive layerQinghai‐Tibet Plateau |
| spellingShingle | Jie Chen Xingchen Lin Tonghua Wu Junming Hao Xiaodong Wu Defu Zou Xiaofan Zhu Guojie Hu Yongping Qiao Dong Wang Sizhong Yang Lina Zhang Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region Water Resources Research seasonal deformation InSAR permafrost machine learning active layer Qinghai‐Tibet Plateau |
| title | Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region |
| title_full | Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region |
| title_fullStr | Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region |
| title_full_unstemmed | Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region |
| title_short | Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region |
| title_sort | predicting seasonal deformation using insar and machine learning in the permafrost regions of the yangtze river source region |
| topic | seasonal deformation InSAR permafrost machine learning active layer Qinghai‐Tibet Plateau |
| url | https://doi.org/10.1029/2023WR036700 |
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