Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data

Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data
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利用超高分辨率干涉合成孔径雷达数据进行八达岭长城变形监测和专题测绘

DOI:
10.1016/j.jag.2021.102630
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发表时间:
2021-12
影响因子:
7.5
通讯作者:
Parcharidis Issaak
Parcharidis Issaak
中科院分区:
地球科学1区
文献类型:
--
作者:
Chen Fulong;Liu Hanwei;Xu Hang;Zhou Wei;Balz Timo;Chen Pinxiang;Zhu Xiaokun;Lin Hui;Fang Chaoyang;Parcharidis Issaak

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大型文化遗产地的预防性监测和可持续保护需要基于卫星的地球观测。在这项研究中,我们使用根据超高分辨率 (VHR) 多时相聚光 TerraSAR-X 数据计算的变形指标,首次展示了八达岭长城(中国北京)的监测和专题测绘结果。所提出的粗精搜索算法在计算两层网络持久散射体合成孔径雷达(SAR)干涉测量(PSInSAR)方法的未知参数时实现了较高的计算效率。时空变形异常以绝对速度、变形偏差和加速度为特征,可为识别可疑热点以优先考虑监测活动提供信息。通过协同利用自然退化和旅游业的影响,我们为主题测绘和随后的遗产地可持续保护提供了一种易于理解的方法。根据比较新冠疫情前后的数据(2019-2020 年),我们确定该地点的最佳游客容量可能为每月 100 万人次。本研究通过将 InSAR 变形产品与环境和社会数据相结合,展示了星载 PSInSAR 工具在大型建筑遗产地智能管理方面的潜力和性能。
The preventive monitoring and sustainable conservation of large-scale cultural heritage sites require satellite-based Earth observations. In this study, we present the first monitoring and thematic mapping results of the Badaling Great Wall (Beijing, China) using deformation indicators calculated from very high-resolution (VHR) multi-temporal spotlight TerraSAR-X data. The proposed coarse–fine search algorithm achieved high computational efficiency for calculating the unknown parameters of the two-tier network persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) approach. The spatiotemporal deformation anomalies, characterized by the absolute velocity, deformation deviation and acceleration, are informative to identify suspected hotspots for prioritizing monitoring activities. We provide an understandable method for thematic mapping and subsequent sustainable conservation of heritage sites by synergistically exploiting impacts from natural degradation and the tourism industry. We determine that the optimum tourist capacity of the site could be 1.0 million per month based on comparing pre- and post-COVID data (2019–2020). This study demonstrates the potential and performance of spaceborne PSInSAR tools for the intelligent management of large-scale architectural heritage sites by integrating InSAR deformation products with environmental and social data.
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