Research progresses of InSAR technology application on landslide identification and monitoring
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摘要:研究目的
滑坡灾害是威胁山区重大工程建设与人类生命财产安全的主要地质灾害类型，滑坡灾害早期识别与监测预警已成为有效防范灾害风险的重要途径。
研究方法根据文献资料，本文介绍了InSAR技术的基本原理及其发展历程，梳理了InSAR技术在滑坡灾害识别与监测中的应用研究现状。
研究结果滑坡InSAR识别主要包括区域性滑坡识别、重点区段滑坡识别和单体滑坡精准识别；滑坡InSAR监测主要针对具有重大险情的大型单体滑坡，着重阐述了监测方法选取和预警模型方面的研究进展。在此基础上，指出了当前InSAR技术在滑坡识别与监测研究中面临的主要挑战：复杂地形条件下侧视成像几何畸变挑战、滑坡大梯度形变探测挑战、大气延迟及植被穿透的挑战等。InSAR技术在滑坡应用中还存在着大范围监测能力不足、处理流程自动化程度较低、数据分析与挖掘程度不够等问题。
结论对InSAR技术未来在滑坡识别与监测中的发展方向进行了展望。伴随着未来InSAR技术应用水平的不断提升，将使滑坡灾害风险防范水平提升至新的高度。
创新点:从区域性滑坡识别、重点区段滑坡识别和单体滑坡识别三个层次梳理了InSAR技术在滑坡识别中的应用，着重阐述了应用InSAR技术开展滑坡监测的方法选取与预警模型方面的研究，探讨了未来的研究趋势与发展方向。
Abstract:This paper is the result of geological survey engineering.
ObjectiveLandslides pose a significant risk to major constructions and human safety in mountainous areas, and the early identification and monitoring of landslide has become an important way to prevent risk.
MethodsThis paper briefly introduces the basic principle of InSAR technology and its development history, and introduces the current research status of its application in landslide identification and monitoring.
ResultsThe three types of landslide InSAR identification are regional, key sections and single landslide. InSAR monitoring of landslides mainly focuses on large landslides with significant risk, and highlights the research progress in monitoring method and early warning modeling. On this basis, the main challenges faced by InSAR technology in landslide identification and monitoring research at this stage are pointed out, including: the challenge of geometrical distortion in side−view imaging under complex terrain conditions, detecting large gradient deformation of landslides, atmospheric delays and vegetation penetration, etc. InSAR technology in landslide application still exists problems such as insufficient capacity of large−scale monitoring, low degree of automation of processing process, and insufficient degree of data analysis and mining.
ConclusionsAccordingly, the future development direction of InSAR technology in landslide identification and monitoring is prospected. With the continuous improvement of the application level of InSAR technology, it will effectively promote the new leap in landslide disaster risk prevention.
Highlights:The application of InSAR technology in landslide identification is summarized at three levels: Regional landslide identification, key section landslide identification and monolithic landslide identification, focusing on the method selection and early warning modeling of landslide monitoring using InSAR technology, and discussing the future research trends and development directions.
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图 1 InSAR滑坡隐患识别（据刘筱怡等, 2019修改）
a—广域滑坡识别；b—重点流域滑坡识别；c—重要活动断裂带滑坡识别；d—单体滑坡识别图c、d中黄色滑坡边界为识别出的沿近西北方向（沿雷达视线（LOS）方向沉降）发生形变的滑坡边界，蓝色滑坡边界为识别出的沿近东南方向（沿雷达视线（LOS）方向隆升）发生形变的滑坡边界
Figure 1. InSAR landslide hazard identification (modified from Liu Xiaoyi et al., 2019)
a–Wide-area landslide identification; b–Key river basin landslide identification; c–Active fracture zone landslide identification; d–Monolithic landslide identification The yellow landslide boundaries in Fig. c and d represent the identified deformation zones along the approximately northwest direction (subsidence in the radar line of sight (LOS) direction), while the blue landslide boundaries denote the identified deformation zones along the approximately southeast direction (uplift in the radar line of sight (LOS) direction)
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图 2 基于InSAR技术的滑坡监测曲线（据张永双等, 2020）
a—滑坡形变速率图；b—沿主滑方向剖面的监测曲线；c—关键点监测曲线
Figure 2. Landslide monitoring curve based on InSAR technology (after Zhang Yongshuang et al., 2020)
a–Landslide deformation rate map; b–Profile monitoring curve along the main slide direction; c–Key point monitoring curve
表 1 常见InSAR技术对比（据李晓恩等, 2021; 李振洪等, 2022）
Table 1 Comparison of common InSAR technologies (after Li Xiaoen et al., 2021; Li Zhenhong et al., 2022)
技术方法 诞生年份 核心原理 核心优势 局限性 适用范围 D-InSAR 1989 通过分离干涉相位中除地表形变相位以外的其他相位贡献，得到差分干涉数据 数据量少、计算效率高，厘米级形变探测 易受失相干、大气延迟影响 广域范围滑坡早期识别 InSAR-Stacking 1998 对一段时间内的解缠相位进行加权平均，估计大区域的平均形变速率场 削弱大气延迟误差，提高干涉影像信噪比 只能获取形变速率，无法获取时间序列形变 广域范围滑坡早期识别 PS-InSAR 2000 通过覆盖同一地区的多景SAR影像的永久散射体（PS点）减少失相干，获取高精度的地面时间序列形变信息 精确估计和消除大气效应带来的相位误差 PS点较少的效果较差 永久散射体密度较高区 SBAS-InSAR 2004 选取较短时间和空间基线阈值内的干涉影像用于时间序列分析，最终得到稳定可靠的时间序列形变 无需考虑时空基线和主影像选择问题，运算效率高 高相干点选取难，易损失细节信息 植被覆盖较低且形变梯度较小 R-SSI 2005 通过距离向带通滤波将一景SAR影像划分为上频带和下频带两景影像分别进行干涉处理，获取大梯度形变量 探测大梯度形变 小梯度形变探测精度低 滑坡大变形探测，强震形变监测 MAI 2006 对主辅影像的前、后视影像分别进行干涉处理，获得前后视的差分相位，进而转化为轨道飞行方向的形变量 抑制大气相位延迟、提取地表三维形变 低相干区适用性低，精度受限于相干性 识别具有大形变梯度的滑坡 SqueeSAR 2011 通过统计方法保留相位稳定的DS点并与PS点联合求解时间序列形变 无需考虑时空基线，所有干涉图均参与计算、数据利用率高 计算效率低，不适用于广域范围监测 单体滑坡精细监测 
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表 2 SAR数据各频带的应用领域（据Moreira et al., 2013）
Table 2 Application areas of SAR data in various frequency bands (after Moreira et al., 2013)
频带名称 X C S L 波长/cm 2.5~4.0 4.0~8.0 8.0~15.0 15.0~30.0 代表SAR卫星 TerraSAR、COSMO-SkyMed Sentinel-1、高分三号 HJ-1C ALOS 2、陆地探测一号 特点 高分辨率成像 普适性较强 较强穿透性 强穿透性
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表 3 滑坡速度等级分类
Table 3 Classification of landslide velocity classes
等级分类 形变速率 可应用的代表性InSAR技术 极其缓慢 <16 mm/a D-InSAR、PS-InSAR、SBAS-InSAR、SqueeSAR 非常缓慢 16 mm/a~1.6 m/a D-InSAR、InSAR-stacking 缓慢 1.6 m/a~13 m/月 MAI、R-SSI 中等 13 m/月~1.8 m/h R-SSI 快速 1.8 m/h~3 m/min 地基SAR 非常快速 3 m/mim~5 m/s 极其快速 >5 m/s
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表 4 多时相InSAR技术与地基SAR适用范围对比（Casagli et al., 2023）
Table 4 Comparison of the scope of application of MT-InSAR and GB-SAR (after Casagli et al., 2023)
技术类型 滑坡识别 滑坡监测 滑坡预警 多时相InSAR ES, RS, SLa ES, RS, SLa ESa, RSa 地基干涉测量 ES, RS ES, RS, SLa ES, RS, 注：土质滑坡（ES：从每年1.6 m/yr到1.8 m/h）、岩质滑坡（RS：<1.6 m/yr到>1.8 m/h）、浅层滑坡（SL：<1.6 m/yr到>1.8 m/h），a表示应用能力有限。
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