Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning
Permafrost stability on the Qinghai-Tibet Plateau (QTP) is vital amid environmental changes and human activities. Interferometric Synthetic Aperture Radar (InSAR) effectively monitors permafrost deformation, capturing seasonal and long-term trends via active layer hydrothermal shifts. While prior st...
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Elsevier
2025-09-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25009070 |
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| author | Xingchen Lin Tonghua Wu Jie Chen Jianjun Chen Ren Li Xiaofan Zhu Peiqing Lou |
| author_facet | Xingchen Lin Tonghua Wu Jie Chen Jianjun Chen Ren Li Xiaofan Zhu Peiqing Lou |
| author_sort | Xingchen Lin |
| collection | DOAJ |
| description | Permafrost stability on the Qinghai-Tibet Plateau (QTP) is vital amid environmental changes and human activities. Interferometric Synthetic Aperture Radar (InSAR) effectively monitors permafrost deformation, capturing seasonal and long-term trends via active layer hydrothermal shifts. While prior studies emphasized large-scale deformation, finer-scale regulatory effects of environmental factors and human activities remain underexplored. We used Small Baseline Subset (SBAS)-InSAR to derive ground deformation time series and developed an XGBoost-based model to assess impacts of soil hydrothermal conditions, vegetation, terrain, and human activities. Our results show that soil moisture plays a dominant role in permafrost deformation, with its interaction with soil temperature exhibiting nonlinear effects on permafrost deformation. Long-term changes in Normalized Difference Vegetation Index (NDVI) were significantly positively correlated with the seasonal deformation amplitude (R2 = 0.3546, p < 0.001). Geomorphons exerted significant control, with valleys and lowlands exhibiting reduced permafrost deformation due to distinct hydrothermal conditions, whereas highlands demonstrated greater stability. Human infrastructure further influenced ground deformation. The Wudaoliang (WDL) Train Station (median subsidence rate: −11.55 mm/yr) and Qinghai-Tibet Railway (median subsidence rate: −8.75 mm/yr) exhibited strong regulatory effects, whereas WDL Town (median subsidence rate: −12.45 mm/yr) and the Qinghai-Tibet Highway (median subsidence rate: −10.32 mm/yr) experienced more pronounced deformation, which highlights the importance of engineering design in mitigating permafrost degradation. The results provide new insights into the regulatory mechanisms of environmental factors and human activities on permafrost deformation across the QTP using interpretable machine learning (ML), which is important for environmental conservation and engineering disaster prevention in permafrost regions. |
| format | Article |
| id | doaj-art-46791c4122f44203ab0b7c76ee66151a |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-46791c4122f44203ab0b7c76ee66151a2025-08-20T03:43:55ZengElsevierEcological Indicators1470-160X2025-09-0117811397710.1016/j.ecolind.2025.113977Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learningXingchen Lin0Tonghua Wu1Jie Chen2Jianjun Chen3Ren Li4Xiaofan Zhu5Peiqing Lou6Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Corresponding authors.Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Corresponding authors.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaPermafrost stability on the Qinghai-Tibet Plateau (QTP) is vital amid environmental changes and human activities. Interferometric Synthetic Aperture Radar (InSAR) effectively monitors permafrost deformation, capturing seasonal and long-term trends via active layer hydrothermal shifts. While prior studies emphasized large-scale deformation, finer-scale regulatory effects of environmental factors and human activities remain underexplored. We used Small Baseline Subset (SBAS)-InSAR to derive ground deformation time series and developed an XGBoost-based model to assess impacts of soil hydrothermal conditions, vegetation, terrain, and human activities. Our results show that soil moisture plays a dominant role in permafrost deformation, with its interaction with soil temperature exhibiting nonlinear effects on permafrost deformation. Long-term changes in Normalized Difference Vegetation Index (NDVI) were significantly positively correlated with the seasonal deformation amplitude (R2 = 0.3546, p < 0.001). Geomorphons exerted significant control, with valleys and lowlands exhibiting reduced permafrost deformation due to distinct hydrothermal conditions, whereas highlands demonstrated greater stability. Human infrastructure further influenced ground deformation. The Wudaoliang (WDL) Train Station (median subsidence rate: −11.55 mm/yr) and Qinghai-Tibet Railway (median subsidence rate: −8.75 mm/yr) exhibited strong regulatory effects, whereas WDL Town (median subsidence rate: −12.45 mm/yr) and the Qinghai-Tibet Highway (median subsidence rate: −10.32 mm/yr) experienced more pronounced deformation, which highlights the importance of engineering design in mitigating permafrost degradation. The results provide new insights into the regulatory mechanisms of environmental factors and human activities on permafrost deformation across the QTP using interpretable machine learning (ML), which is important for environmental conservation and engineering disaster prevention in permafrost regions.http://www.sciencedirect.com/science/article/pii/S1470160X25009070Interpretable machine learningSBAS-InSARPermafrost deformationEnvironmental factorsHuman activities |
| spellingShingle | Xingchen Lin Tonghua Wu Jie Chen Jianjun Chen Ren Li Xiaofan Zhu Peiqing Lou Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning Ecological Indicators Interpretable machine learning SBAS-InSAR Permafrost deformation Environmental factors Human activities |
| title | Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning |
| title_full | Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning |
| title_fullStr | Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning |
| title_full_unstemmed | Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning |
| title_short | Revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central Qinghai-Tibet Plateau using InSAR and interpretable machine learning |
| title_sort | revealing the impacts of environmental and anthropogenic factors on permafrost deformation in the central qinghai tibet plateau using insar and interpretable machine learning |
| topic | Interpretable machine learning SBAS-InSAR Permafrost deformation Environmental factors Human activities |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25009070 |
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