Multi-temporal InSAR for Urban Deformation Monitoring:Progress and Challenges

Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.

Land Surface Displacement Geohazards Monitoring Using Multi-temporal InSAR Techniques

China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every year.Geohazards occur not only in remote areas but also in highly populated cities.In the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various geohazards.Therefore,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination studies.Suspicious movements detected in the different study areas were cross validated with different SAR sensors and truth data.

Assessing the potential landslide risk identification in the northern section of CPEC route Pakistan based on Multi-Temporal InSAR approaches

Landslides are the most frequent geoenvironmental natural hazards in hilly regions,owing to broken rock masses and slope instability.Every year,landslides occur along the Karakorum highway in the northern section of the China-Pakistan Economic Corridor,involving complex geological action and causing significant damages and fatalities.To mitigate landslide hazard risks and a better understanding of landslide occurrence in steep mountainous regions,a comprehensive and precise analysis of slow-moving landslides is necessary.To address this challenge,a Multi-Temporal(MT),Interferometry Synthetic Aperture Radar(InSAR)approach using Small Baseline Subsets and Interferometric Point Target Analysis techniques was utilized to extract ground deformation rates.A total of 121 multitrack synthetic aperture radar images from Sentinel-1A were acquired from 2021 to 2023,enabling the detection and monitoring of ground displacement over time.Eight active slope movements were successfully identified by calculating the differences in deformation,indicating a significant deformation rate within the trust fault lines and regional geological formations.The research findings reveal that the regional geological structure,including lithology and fault lines,significantly increase the deformation rate.These identified landslide hazard areas range from 1.47 km2 to 14.88 km2,with an annual average rate of line of sight surface displacement estimated between−4.13 to−16.07 cm/yr.The MT-InSAR analysis demonstrates that fault lines and geology play significant role in surface deformation,providing valuable insights into the deformation induced by regional tectonic activities.

Satellite Multi-Temporal Data and Cropping Pattern Approach for Green Gram Crop Management in the Lower Midland Zone IV and V in Kenya

Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for the vegetative stage of the green gram (Vigna. radiata L.) over 5 years (2020, 2018, 2017, 2015, and 2013) for agroecological zone IV and V in Kenya. The years chosen were those whose satellite resolution data was available for the vegetative stage of crop growth in the short rain season (October, November, December (OND)). We used Landsat 8 OLI satellite imagery in this study. Cropping pattern data for the study area were evaluated by calculating the Top of Atmosphere reflectance. Farms geo-referencing, along with field data collection, was undertaken to extract Top of Atmosphere reflectance for bands 2, 3, 4 and 7. We also carried a spectral similarity assessment on the various cropping patterns. The spectral reflectance ranged from 0.07696 - 0.09632, 0.07466 - 0.09467, 0.0704047 - 0.12188,0.19822 - 0.24387, 0.19269 - 0.26900, and 0.11354 - 0.20815 for bands 2, 3, 4, 5, 6, and 7 for green gram, respectively. The results showed a dissimilarity among the various cropping patterns. The lowest dissimilarity index was 0.027 for the maize (Zea mays L.) bean (Phaseolus vulgaris) versus the maize-pigeon pea (Cajanus cajan) crop, while the highest dissimilarity index was 0.443 for the maize bean versus the maize bean and cowpea cropping patterns. High crop dissimilarities experienced across the cropping pattern through these spectral reflectance values confirm that the green gram was potentially identifiable. The results can be used in crop type identification in agroecological lower midland zone IV and V for mung bean management. This study therefore suggests that use of reflectance data in remote sensing of agricultural ecosystems would aid in planning, management, and crop allocation to different ecozones.

Detecting land subsidence near metro lines in the Baoshan district of Shanghai with multi-temporal interferometric synthetic aperture radar

Land subsidence is a major factor that affects metro line （ML） stability. In this study, an improved multi- temporal interferometric synthetic aperture radar （InSAR） （MTI） method to detect land subsidence near MLs is presented. In particular, our multi-temporal InSAR method provides surface subsidence measurements with high observation density. The MTI method tracks both point-like targets and distributed targets with temporal radar back- scattering steadiness. First, subsidence rates at the point targets with low-amplitude dispersion index （ADI） values are extracted by applying a least-squared estimator on an optimized freely connected network. Second, to reduce error propagation, the pixels with high-ADI values are classified into several groups according to ADI intervals and processed using a Pearson correlation coefficient and hierarchical analysis strategy to obtain the distributed targets. Then, nonlinear subsidence components at all point-like and distributed targets are estimated using phase unwrapping and spatiotemporal filtering on the phase residuals. The proposed MTI method was applied to detect land subsidence near MLs of No. 1 and 3 in the Baoshan district of Shanghai using 18 TerraSAR-X images acquired between April 21, 2008 and October 30, 2010. The results show that the mean subsidence rates of the stations distributed along the two MLs are -12.9 and -14.0 ram/year. Furthermore, three subsidence funnels near the MLs are discovered through the hierarchical analysis. The testing results demonstrate the satisfactory capacity of the proposed MTI method in providing detailed subsidence information near MLs.

时序InSAR解译西安-咸阳地区地面沉降时空分布特征

采用2015-06~2023-01覆盖西安-咸阳地区的Sentinel-1A卫星数据,以时序InSAR为主要技术手段,结合其他资料,获得西安-咸阳地区地面沉降分布特征和形变速率等信息,并分析地面沉降的原因和演化规律。结果表明,SBAS-InSAR与StaMPS-InSAR所得结果基本一致,地表形变区主要分布在西安中部和南部,主要影响因素为地下水开采,分布与走向受地裂缝与断层影响;咸阳大部分地区处于稳定状态,仅在局部地区有较为明显的沉降,沉降受地下水开采、城市建设等因素影响。

国产卫星时序InSAR形变监测精度分析

陆地探测一号01组卫星(LT-1)的发射成功实现了我国L波段干涉SAR卫星“从无到有”的发展。LT-1卫星因空间避障等原因,部分空间基线较长;经过调轨后,空间基线长度控制在400 m以内。为了验证LT-1卫星数据的可用性及精度,本文以山西大同矿区为例,获取了2022年12月23日至2023年5月20日共25景LT-1条带模式影像数据,分别进行差分干涉测量短基线集时序分析(SBAS)技术和永久散射体(PS)技术数据处理。对时序InSAR和GPS站点在SAR卫星视线向上的形变监测结果进行对比分析,二者差异标准差分别为5.7 mm/a(SBAS-InSAR)和3.4 mm/a(PS-InSAR),时间序列均方根误差均小于5 mm,具有高度一致性。研究表明国产LT-1卫星具备高精度的形变监测能力,为国内地形测绘及形变监测提供了可靠的数据保障。

MCJ-UNet:一种双/多通道联合InSAR相位解缠网络

干涉合成孔径雷达(InSAR)可实现地表高程的高效获取,在地形测绘中应用广泛。双/多通道InSAR技术可借助不同通道(基线、频点)的高程模糊度差异,解决相位欠采样问题,完成高程陡变区域的干涉相位解缠,实现InSAR技术在测绘困难区域的有效应用。该文即面向高效高精度相位解缠需求,利用深度学习这一有力工具,结合不同通道的相位特征及相互约束关系,提出了一种双/多通道联合干涉相位解缠网络:Multi-Channel-Joint-UNet(MCJ-UNet)。该网络的构建以双通道(双频、双基线)InSAR为基本观测构型,并可实现向多通道构型的扩展,其构建的核心思路主要包括3点:首先,将干涉相位解缠中的模糊数估计问题转化为语义分割问题,并采用UNet网络完成分割处理;其次,引入挤压激励模块(SE)动态调整信息权重,以增强网络不同通道对其所需信息的感知能力;最后,利用多通道联合约束下的相位残差优化损失函数,实现网络调谐。此外,为避免语义分割结果的边缘细节误差对解缠效果的影响,该文还提出了一种基于多通道联合约束的解缠误差自修正方法,以保证解缠质量。模拟地形仿真数据、真实地形仿真数据以及TerraSAR-X实测数据验证了所提方法的有效性。

中尼公路聂拉木至樟木段InSAR广域地灾隐患识别与特征分析

基于2017-04~2019-06共130景Sentinel-1A卫星升降轨影像,采用StaMPS-SBAS InSAR方法,并融合GACOS大气校正,对中尼公路聂拉木至樟木段进行大范围地质灾害隐患点探测和识别。结果表明,研究区内共发现54处隐患点,组成多处滑坡群,升轨最大形变速率达114 mm/a,降轨最大形变速率达122 mm/a。结合研究区降水数据统计分析可知,有35处隐患点形变与降水有关,形变季节性特征明显。本文研究结果有助于了解该路段地表形变趋势及成因,丰富该地区地灾隐患点历史记录,为该地区地质灾害隐患的持续监测提供参考。

基于PySide的时序InSAR处理可视化及形变监测实验

基于PySide等Python开源扩展包设计了时序InSAR处理可视化软件,用于实验教学。软件集成了影像搜索、像对组合、干涉解缠、时序处理、结果展示等功能。以山东省菏泽市郓城县城区地表沉降监测为例,进行实验处理,并与PSDS-InSAR方法进行精度对比。相较于传统命令行操作方式,该可视化软件极大地简化数据处理的复杂性,软件处理结果与PSDS-InSAR所得结果的相关性为0.9866。软件提高实验效率,降低实验难度,改善了实验效果,结果精度表明该软件也可直接应用于工程实践。

基于InSAR-COMSOL的露天矿边坡稳定性分析及形变预测

露天矿地表形变特征的快速、准确分析及形变趋势精准预测是推进矿山绿色安全生产的重要保障。针对当前形变监测技术存在的时空采样率低、成本高,预测模型参数难确定等问题,以东鞍山露天铁矿为工程背景,提出了一种融合短基线子集干涉测量(SBAS-InSAR)技术和COMSOL有限元模拟的边坡稳定性分析和形变预测一体化方法。首先,利用SBAS-InSAR技术处理2018年5月—2020年6月获取的62景Sentinel-1A升轨SAR数据,获取了该区域2 a内地表形变时间序列,分析了其形变时空演化特征。然后,采用COMSOL Multiphysics软件模拟外界强降雨影响下的典型沉降区域边坡稳定性状况,探讨了坡体损伤裂化规律及形变机理。基于此,利用粒子群算法(PSO)优化长短期时间记忆(LSTM)网络,搭建了形变时序预测最优模型,开展典型沉降区的形变时序预测,并引入平均绝对误差和均方根误差作为预测精度评价指标。结果表明:矿区西部沉降相对严重,年均沉降速率高达47.8 mm/a,形变速率与区域降雨量存在显著相关性。相较于传统形变预测模型,PSO-LSTM模型的2种误差至少降低了14%和36%,且能够有效反映采区地表形变波动趋势,为滑坡灾前预警提供了新思路。

加权信息量支持下融合InSAR形变特征的滑坡易发性评价

使用SBAS-InSAR处理六盘水市水城区2018-07~2019-08共69景Sentinel-1A卫星影像,获取地表形变作为动态评价因子,用于完善传统滑坡易发性评价研究缺乏动态特征数据应用的问题。结果表明,使用10种静态评价因子融合InSAR形变特征数据作为动态评价因子,在耦合层次分析法与信息量法的加权信息量模型下对比仅使用静态特征数据,ROC曲线下面积分别为0.756 02和0.888 68,模型性能提升约13.3%;再将历史灾害点叠加于2种分区图下检验分区精度,相比于未融入形变特征,融入形变特征可纠正约12.44%的误分类区域,能较好地提升分区的可靠性。

基于时序InSAR的老挝桑片-桑南内水库大坝溃坝回溯研究

InSAR是一种用于水库大坝表面位移监测的新技术,具有周期性、低延迟的特点,且能回溯历史数据。利用老挝桑片-桑南内水库大坝溃坝前期及溃坝期间的卫星影像,收集溃坝前后的大坝表面位移监测数据和降雨数据,对大坝自身和管理方面的溃坝原因进行了分析,结果表明大坝溃坝前已发生较明显的异常变形趋势。本研究将InSAR技术引入大坝风险管理中,能有效减少溃坝事故的发生。

基于SBAS-InSAR技术的港口地表二维形变监测与分析

为了大范围监测果园港港口地表形变情况,预防地面沉降和塌陷所带来的危害,采集2014年11月—2017年11月果园港升轨和降轨的Sentinel-1A号数据,应用SBAS-InSAR技术进行地表形变数据提取与分析。结果表明:得到的垂直方向地表形变变化趋势与水准测量结果相一致;果园港地表垂直方向形变速率为-33.49~13.49 mm·a^(-1),东西方向形变速率为-16.16~29.44 mm·a^(-1);大幅度沉降主要发生在港口的堆场区和交通枢纽处,且沉降速率与距交通线路的距离呈现显著相关。

基于创新能力培养的3S技术及应用综合实验设计与实践——以地表沉陷InSAR数据处理为例

为提升实验课程效果、培养学生的创新能力,结合新时期防灾减灾专业人才需求,开展了3S技术及应用综合实验设计与实践研究。首先,明确了3S技术及应用综合实验课的目标,提出了相应的实验内容及实践步骤。其次,以地表沉陷专题为例,设计了以InSAR数据处理为主线的实验方案,包括具体的实验环节和实验过程。该实验充分融合3S技术及应用课程内容,有效结合课堂理论、学生科创、科研实践等,有助于提高学生的学习热情、增强学生的综合能力和创新意识。

基于PS-InSAR技术的晋城矿区地表形变监测及地质灾害风险预警

地表形变是一种严重的地质灾害现象,不仅严重影响灾害区居民的日常生活,而且会造成巨大的社会经济危害,尤其在采煤区。针对传统地表沉陷监测方法费时费力、无法获取地表沉降面状信息、难以进行地表沉陷灾害评估的不足,基于高分辨率SAR卫星影像,利用永久散射体合成孔径雷达干涉测量(PS-InSAR)技术对山西省晋城市晋城矿区2018年1月至2018年12月期间地表沉陷进行监测,分析获取了该地区地表连续形变情况,并利用该技术获取的海量PS点建立支持向量机(SVM)地质灾害风险评估预警模型,对晋城矿区周边居民点地质灾害风险进行了识别和预测。结果表明:晋城矿区10个煤矿及其周边区域存在较大的地表形变;晋城矿区平均LOS向年平均地表形变速率范围为-37~30.3 mm/a;PS-InSAR技术在晋城矿区地表形变监测中具有可行性,且可以实现矿区地质灾害风险综合识别和预警。

融合SBAS-InSAR技术与GRO-BiLSTM模型的开采沉陷边界划定方法

确定矿山开采沉陷边界有助于评估矿区生产活动对周围环境和基础设施的潜在影响,为制定有效的灾害防控措施提供技术手段。充分考虑到概率积分法等传统方法在划定矿山开采沉陷边界时的不足,采用在获取矿区大范围高精度地表沉降数据方面具有优势的SBAS-InSAR技术,并结合淘金算法(Gold Rush Optimizer,GRO)优化双向长短期记忆(Bidirectional Long Short Term Memory,BiLSTM)模型的预测方法,实现矿区开采沉陷边界划定。以红会煤矿为研究对象,依据SBAS-InSAR技术提取矿区沉降边缘高相干点在2018-11-29—2020-02-04时间段内共37期沉降数据,以下沉10 mm等值线划定沉陷边界,利用GRO-BiLSTM优化模型预测高相干点的地表沉降值,并将预测结果与LSTM和BiLSTM模型预测结果进行了对比分析。结果表明:GRO-BiLSTM模型在整体测试集中均方根误差为3.204mm,比LSTM和BiLSTM模型分别降低了22.16%和8.21%;平均绝对误差为2.062 mm,比LSTM和BiLSTM模型分别降低了23.96%和5.43%,表明该方法可以有效监测和预测矿区边界地区的沉陷状况。

基于insar和人工监测新疆BEJ水库变形体稳定性分析

以新疆BEJ水库为研究对象,通过变形观测分析和理论计算,BX1体垂河向水平位移量为3.78 cm,平均速率0.038 mm/d;垂直沉降总量4.61 cm,平均速率0.046 mm/d;各阶段监测水平位移速率0.038 mm/d→0.023 mm/d→0.034 mm/d→0.015 mm/d→0.010 mm/d;水库蓄水后,BX1变形体发生明显变形,处于蠕动变形状态。建议加强监测预警工作,做好安全保障措施。

InSAR技术在普光气田地质灾害调查中的应用

普光气田高含硫化氢的集输管道经常需穿越一些地形地质条件复杂区域,这些地区通常又是地质灾害的高发区,不同类型的地质灾害威胁着管道的安全运行。通过雷达卫星数据对普光气田集输站场和集输管道周边滑坡进行了地表形变探测,共探测出32处地质灾害点;结合光学遥感影像目视解释和现场勘查^([1]),对普光气田103集气站南侧、胡家阀室、毛坝1井—1#阀室管线西侧共3处典型滑坡形变特征进行了详细分析。结果显示,该区域地质灾害变形速率受降雨的影响较大。调查结果能够对普光气田地质灾害防控工作提供重要的决策依据。

基于PS-InSAR技术的地表形变监测与驱动力分析

以昆明市官渡区为研究区,基于PS-InSAR技术,结合2019年1月—2021年12月覆盖官渡区的36幅Sentinel-1A升轨数据和36幅Sentinel-1A降轨数据,提取研究区内沿雷达视线向地表形变速率,并结合地理探测器对坡度、坡向、高程、降雨量、土地覆被类型、道路密度、距铁路的距离以及距地铁的距离进行单因子和多因子交互的驱动力分析。结果表明,升轨形变速率范围为-30.67~18.27 mm/a,降轨形变速率范围为-24.58~24.36 mm/a,共识别出4个沉降漏斗;通过升降轨数据的交叉验证,证明了利用PS-InSAR技术监测地表形变的可行性;距铁路的距离对官渡区地表形变空间分异的影响最强,同时多因子交互作用增强了地表形变的空间分异。