InSAR-Based Surface Deformation Analysis and Trend Prediction in Permafrost Areas Along the Qinghai-Tibet Railway Using Sentinel-1A and Environmental Factors

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-03-26 DOI:10.1109/JSTARS.2025.3554388

Tianbao Huo;Yi He;Yaoxiang Liu;Wang Yang;Lifeng Zhang;Hesheng Chen;Yuming Fang;Binghai Gao;Xiyin Zhang

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引用次数: 0

Abstract

Global warming is accelerating the permafrost degradation along the Qinghai-Tibet railway (QTR), causing the surface deformation (SD) of the railway subgrade. Especially in the Salt Lake to Wuli section of the QTR, the permafrost is widely distributed, and the SD has been the most serious. However, the spatiotemporal characteristics and mechanism of SD are still unclear. In addition, it is very important to predict the future trend of SD. Therefore, we acquired time series SD results from 2019 to 2022 based on small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and analyzed the spatiotemporal characteristics and mechanism of SD in the Salt Lake to Wuli section. Subsequently, the EnvCA-GRU model for SD prediction was developed, integrating the multihead cross-attention mechanism and gated recurrent unit (GRU) to account for changes in environmental factors. The model was then employed to forecast SD trends over the next two years. Our results showed that the SD was uneven in the Salt Lake to Wuli section of the QTR from 2019 to 2022, there were six typical deformation areas, and the maximum cumulative ground subsidence reached 126.79 mm. The SD velocity of the sunny slope was higher than that of the shady slope, and the closer to the QTR, the greater the ground subsidence. Land surface temperature (LST), normalized difference vegetation index (NDVI), and precipitation are the main factors affecting SD. Our proposed EnvCA-GRU prediction model fusing NDVI, LST, and precipitation showed a root mean square error of 0.153 and an R² of 0.991, the proposed model was reliable. The maximum cumulative ground subsidence of six typical areas by July 2024 reached 177.52, 268.08, 287.73, 270.99, 190.70, and 211.89 mm, respectively. The results of this study can play a guiding role in the early warning and mitigation of ground subsidence disasters along the QTR.

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基于Sentinel-1A和环境因子的青藏铁路沿线多年冻土区insar地表变形分析及趋势预测

全球变暖加速了青藏铁路沿线多年冻土的退化，造成了铁路路基的表面变形。其中盐湖至五里段冻土分布较广，SD最为严重。然而，SD的时空特征和机制尚不清楚。此外，预测SD的未来趋势也非常重要。为此，基于小基线亚子集干涉合成孔径雷达（SBAS-InSAR）获取2019 - 2022年时间序列SD结果，分析盐湖至五里剖面SD的时空特征及机制。随后，建立了用于SD预测的EnvCA-GRU模型，该模型整合了多头交叉注意机制和门控循环单元（GRU），以考虑环境因素的变化。然后利用该模型预测未来两年的标准差趋势。结果表明：2019 - 2022年，青藏高原盐湖至五里段地表沉降不均匀，存在6个典型变形区，最大累计地面沉降达126.79 mm；阳坡的SD速度高于阴坡，且越靠近QTR，地面沉降越大。地表温度（LST）、归一化植被指数（NDVI）和降水是影响SD的主要因素。我们提出的融合NDVI、LST和降水的EnvCA-GRU预测模型的均方根误差为0.153，R2为0.991，该模型是可靠的。截止2024年7月，6个典型区最大累计地面沉降分别为177.52、268.08、287.73、270.99、190.70和211.89 mm。研究结果对青藏铁路沿线地面沉降灾害的预警和减灾具有一定的指导作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.