多模式雷达在矿区沉降监测中的应用研究

合成孔径雷达差分干涉测量(DInSAR)技术在地表形变监测方面已得到广泛应用。介绍了将差分InSAR技术运用于矿区地表沉降监测,获得了河北峰峰煤矿地表Envisat/ASAR和ALOS/PALSAR的雷达形变干涉相位图,并对Envisat C波段和ALOS L波段的形变干涉相位图进行了相干特性和相位特性的分析。通过综合考虑C波段和L波段的优势与不足,将两者联合使用,实验表明利用多模式雷达数据对矿区地表沉降进行检测的可行性。同时,通过对雷达干涉相位图的分析,能够及时提供正在进行地下开采活动的矿区地理位置。     

北京平原区地面沉降PS-InSAR监测

地面沉降是北京平原区的主要地质灾害之一。针对地下水长期超量开采引发的大范围地面沉降,采用雷达遥感的技术方法对其进行监测分析:以2003~2010年间覆盖北京的31景ENVISAT ASAR数据为基础,采用永久散射体干涉测量技术对北京市平原区进行长时间序列的地面沉降监测,并对比地下水水位变化数据,通过GIS空间分析的方法讨论地面沉降的时空演化特征。结果表明:2003~2010年,北京市平原区地表形变速率范围为-52.1~8.2mm/yr,已经形成五大沉降漏斗(朝阳—通州沉降漏斗、天竺—金盏沉降漏斗、来广营沉降漏斗、高丽营沉降漏斗和昌平沉降漏斗),地面沉降发生区域与地下水漏斗形成区域基本一致。     

基于SBAS-InSAR技术的西宁地表形变监测

地表形变引发的地质灾害给自然环境和社会带来了巨大威胁,小基线集合成孔径雷达干涉测量（SBAS-InSAR）技术以其监测精度高、监测范围大和非接触等优势,成为地表形变监测的重要手段,为预防地质灾害发生、降低灾害损失,实现地表形变有效监测具有重要意义。利用SBAS-InSAR技术对青海省西宁市2018年1月7日至11月27日27景Sentinel-1A数据进行处理,得到西宁市地表平均形变速率分布图。与同期8个西宁南山GPS地面观测点比较,除一个点误差较大外,其余7个点均方根误差都在3 mm以内,证明了SBAS-InSAR监测结果的可靠性。SBAS-InSAR监测结果表明：山体滑坡是西宁市地表形变的主要形式,特别是沿互助北山和G6京藏高速公路一带滑坡运动尤为明显。实验首次获取了西宁市火车站东北滑坡灾害点定量形变数据,为分析该灾害点状况、保障西宁火车站安全运行提供有价值的参考。     

基于SBAS-InSAR技术的煤矿开采沉陷监测与分析

地下煤炭资源大量开采导致的地表形变,引发严重的安全和环境隐患,雷达干涉测量技术是高精度、大范围地表形变监测的重要手段之一。以辽宁省沈阳市沈北新区蒲河煤矿为例,采用SBAS-InSAR技术探测2018—2019年矿区地表形变结果,获取了采煤引起地表形变的时空分布特征,结合采场所在区域的地质条件和变形诱发因素,利用数值模拟技术对观测形变结果进行模拟分析,进而讨论了蒲河煤矿地面沉降在时间和空间上的变形规律和机制。InSAR形变监测结果显示,开采区域内存在两处沉降漏斗,且数值模拟结果与InSAR形变观测值分布规律一致,反演结果接近实际情况,可为相关部门制定地面沉降防治措施提供科学依据。     

京津城际铁路（北京段）沉降监测及影响因素分析

利用干涉点目标分析技术对37景TerraSAR-X数据进行处理,从而准确地估计沿线区域的地表形变。此外,引入同期二等水准测量数据验证了计算结果可靠且精度较高;采用最大信息系数分析高铁的形变及其与影响因素之间的关系,将变形结果与收集的地下水、降水、可压缩层厚度等资料结合,定量描述其与沉降点之间的关系。结果表明,在观测期间,沿着高铁跨丰台区、东城区段年均沉降率小于10mm/a,至朝阳区前段沉降率增大,至中段达40～60mm/a,通州区年均沉降速率稳定;地面沉降与地下水位的变化有很好的响应,地下水开采量的增加和地下水位下降导致该地区的沉降量增加;地面沉降与地质构造有着一定的关系。确定沉降监测的重点区域,为铁路的安全运行提供决策支持。     

基于SBAS-InSAR的关闭矿井地表形变规律研究

矿井关闭后,煤岩体在应力、地下水、氧气等多种因素的作用下发生风化劣化、强度降低,采动破裂岩体的应力和承载能力发生改变,易导致采空区地表发生二次形变。现有研究多是集中在矿区开采过程中的地表形变监测,而针对矿井关闭后的地表形变监测研究较少。为探明矿井关闭后地表的形变规律,利用2015-12-21—2019-12-24的62景Sentinel-1A影像,采用短基线集干涉测量(SBAS-InSAR)技术获取了徐州西部关闭矿井(包括庞庄矿、夹河矿和张小楼矿)4 a内时间序列的地表形变结果和形变规律。研究结果表明:3个矿区内地表最大沉降速率达-48 mm/a,4 a累计最大下沉178 mm;夹河矿地表呈现先下沉后抬升现象,而庞庄矿、张小楼矿地表持续下沉;4 a内3个矿区地表最大倾斜变形为1.70 mm/m,最大曲率变形为-0.039 mm/m 2;通过插值统计计算得到庞庄矿、夹河矿和张小楼矿地表形变大于10 mm的形变面积分别为10.5,13.7,11.6 km 2,且呈逐年上升趋势。     

基于D-InSAR技术的矿区地表形变监测研究

本文利用时序Envisat单视复数据(SLC),分别采用"2轨法"和"3轨法"雷达差分干涉测量,对河北邯郸峰峰煤矿地区地表沉降检测试验,检测矿区内地表沉降发生的位置及范围,结合实验分析了差分干涉测量技术在地表形变监测中存在的问题及其应用前景.     

基于SBAS-InSAR方法的岷江上游峡谷区地表沉降的坡向分异规律研究

岷江上游峡谷区滑坡等地质灾害频发,认识灾害的分布特征及诱因对于减小灾害损失具有重要意义。基于2015～2019年的60景哨兵一号（Sentinel-1A）卫星雷达影像,采用小基线集合成孔径雷达干涉测量（SBAS-InSAR）方法获取了地表雷达视线向形变的时间序列及多年平均速率,分析了地表沉降的分布规律及其同植被覆盖、降水、人类活动等因子的内在联系。结果表明：研究区地表沉降具有明显的坡向分异规律,即快速沉降区多分布于阳坡,年平均沉降速率（-33.02 mm/a）明显快于阴坡（-9.33 mm/a）。针对该地表沉降的坡向分异规律,研究进一步揭示了其环境控制机制：(1)阳坡水分条件胁迫较强而植被覆盖差,不利于地表稳定;(2)地形雨来向多为偏南方向,阳坡受侵蚀较为强烈;(3)阳坡光热条件好,受垦殖等人类活动影响大。由此建议：在岷江上游峡谷区的偏南向边坡,应特别注意防范滑坡等灾害,加强地表形变监测和滑坡灾害预警。     

融合PS、SBAS、DS InSAR技术的昆明地面沉降研究

时序InSAR技术为城市地面沉降监测和防治提供了有效方法,然而PS-InSAR和SBASInSAR技术自身的缺陷限制了InSAR技术的监测精度,特别是复杂地形产生的低相干性引起的PS点稀疏问题。在PS和SBAS算法基础上,通过引入Stacking技术联合PS-InSAR进行相干目标点选取,提出融合PS、SBAS、DS-InSAR的技术,并对比提出的方法与常规PS+SBAS-InSAR的监测结果。研究结果表明：提出的方法与PS+SBAS-InSAR反演的昆明沉降速率结果存在较好的一致性,其中本实验提出的方法能增强观测区域点目标空间分布的密度,进而得到更多有效的地表形变信息。从整个研究区来看,昆明市城区地表整体存在-22～8 mm/a的沉降速率,严重沉降区集中在官渡区、西山区和五华区,并已经形成多个沉降漏斗。自1989年以来,小板桥和河尾村依然是最严重的两个沉降漏斗中心,而东北方向的蒋家营则是本次研究中发现的新沉降点。结合历史资料的验证分析表明：昆明地面沉降主要受地下水抽汲、建筑荷载与工程施工以及断陷盆地的构造运动的影响。     

基于NPSI方法的西安市地裂缝灾害链地表形变监测与演化态势分析

受构造背景、地下水采掘、活断层等因素的综合作用,西安市地裂缝于近年间不断加剧,引发多处不均匀地表沉降,并演化为对地表及地下建筑物均有强烈破坏作用的城市地质灾害链。为探明西安市地裂缝灾害链区域近年间的地表沉降态势、辨析不均匀沉降与地裂缝发育的联系,引入在城市地表形变监测精度和可靠性方面具有显著优势的网络化永久散射体时序雷达干涉测量（NPSI）方法,以2017年3月至2018年3月间成像的15期Sentinel-1A卫星SAR影像为数据源针对西安地区开展了时序监测分析,结合水准测量数据验证发现NPSI监测结果的精度达到 ±4.75 mm。实验结果表明：西安市地裂缝正向西南郊发育,地下水采掘及地上地下工程的建设在地裂缝发育趋势下加剧了不均匀沉降灾害,在形成地裂缝灾害链的鱼化寨、电子城、曲江新区以及地铁3号线等危害严重区域需要实时监测地裂缝发育趋势,并合理规划地下水开采及工程建设活动。相关研究结果可为路政、城建等部门的业务工作及相关研究提供参考信息。     

Land Subsidence Monitoring in Tianjin with PS-InSAR Technique based on Sentinel -1 Data

Land Subsidence is one of the most important geological hazards in many areas. In order to prevent disasters caused by land subsidence efficiently, 24 Sentinel-1A images covering area of Tianjin are choosed from 2015 to 2018. Based on Persistent Scatterers InSAR technique, the results of land subsidence for three years are extracted using the precise orbit data and TanDEM-X DEM and compared with the monitoring results of SBAS (Small Baseline Subset) method. Combined with land use types, hydrogeological and traffic data, the characteristics and formation reasons of several subsidence areas are analyzed. The experimental results show that: (1) In recent three years, the land subsidence in Tianjin urban area is relatively slow, with an average speed of less than 8 mm/a. However, suburban land subsidence is still serious with an average speed between 50 mm/a~70 mm/a. The most serious land subsidence area was Wangqingtuo Town in Wuqing district, the total land subsidence was over 200 mm. And there is a trend of connectivity in these subsidence areas. (2) Land subsidence and the falling of groundwater levels have a very high spatial correlation and the difference between the cumulative shape variables obtained by the two methods of SBAS and PSInSAR is less than 5 mm. The results of this study can provide data support for the government of Tianjin.     

基于Sentinel-1A数据的天津地区PS-InSAR地面沉降监测与分析

为有效预防地面沉降带来的灾害,利用2015年4月~2018年2月天津地区的24景Sentinel-1A数据,进行了永久散射体干涉测量处理,并使用高精度轨道数据和TanDEM-X DEM修正残差相位,提取了3 a的地面沉降结果,结合土地利用类型、水文、地质和交通等数据,分析了多处沉降地区的特征和形成原因,最后和小基线集方法的监测结果进行对比分析。结果表明:近3 a来天津城区沉降治理效果显著,平均沉降速率在8 mm/a以内,郊区沉降仍然严重,沉降速率在50~70 mm/a,沉降最为严重的区域为武清区王庆坨镇,3 a累计沉降量超过200 mm,并且有和其他沉降漏斗连成片的趋势。地面沉降发生的区域与地下水漏斗形成的区域基本一致,且两种方法得到的累积形变量差值95%在5 mm以内,说明本研究结果可以为天津市地质灾害防治提供数据支撑和决策依据。     

Recent subsidence in Tianjin,China: observations from multi-looking TerraSAR-X InSAR from 2009 to 2013

Tianjin, China, has been suggested to have serious ground subsidence due to excessive extraction of groundwater. It is essential to monitor this subsidence, which has potential hazards and risks. Time series InSAR (TS-InSAR), such as small baselines subset (SBAS), is a powerful tool that can monitor ground deformation with high accuracy and at high spatial resolution over a long time interval. However, the high computational complexity may exceed computer memory limit when high-spatial resolution SAR (such as TerraSAR-X, TSX) images are used. In this article, the multi-look approach is introduced to the SBAS tool from StaMPS/MTI (Stanford method for persistent scatter/multi-temporal InSAR) in order to balance the spatial resolution and subsidence information in detection. The looks used for multi-looking are first fixed in terms of the accuracy of deformation and the density of coherent points. Then, the recent subsidence in Tianjin is extracted using multi-looking SBAS based on 48 TSX images acquired from 2009 to 2013. The results are validated by levelling measurements with a root mean square error (RMSE) of 4.7 mm year^–1, which demonstrates that SBAS analysis can effectively monitor deformation based on multi-looking TSX acquisitions in the area under investigation. Besides, the results also show that Tianjin has been suffering from subsidence during this period, and there were two separate large subsidence basins located in this study area with more than 500 mm cumulative subsidence. Moreover, the subsidence rate increased after December 2010 in Tianjin.     

Monitoring land deformation in Changzhou city (China) with multi-band InSAR data sets from 2006 to 2012

Over exploitation of groundwater in Changzhou city, China can cause land deformation, which in turn proves detrimental to the urban infrastructure. In this study, multi-band synthetic aperture radar (SAR) data sets (C-band Envisat ASAR, L-band ALOS PALSAR, and X-band COSMO-SkyMed) acquired from 2006 to 2012 were analysed using the synthetic aperture radar (SAR) interferometry (InSAR) time-series method to investigate the relationship between spatial–temporal distribution of land deformation and groundwater exploitation. Annual deformation rate inferred from multi-band interferograms ranges from ?58 to 24 mm year^?1. Levelling-survey data were used to validate the multi-band InSAR measurements. The results showed that these two types of measurements were generally in agreement. Correlating groundwater-table and multi-band InSAR measurements at six groundwater-well stations showed that with the rise of the water table, the land rebounded. But in some areas with larger subsidence, continual subsidence was observed even though the water table rose after the prohibition of groundwater exploitation. This may have been caused by the hysteresis effect due to the consolidation of strata (especially for the creep deformation). Our study provides scientific evidence on the management of groundwater extraction and the assessment of land-subsidence hazards.     

机载X-波段双天线InSAR数据森林树高估测方法

短波长的干涉合成孔径雷达(InSAR)适用于数字表面模型(DSM)提取,但难以提取准确的林下地相位,在缺乏高精度数字高程模型(DEM)的森林区域,短波长InSAR数据估测树高的能力受到限制。针对这一问题,采用机载X-波段单极化(HH)双天线InSAR数据开展了森林树高估测方法研究。双天线InSAR可以忽略时间去相干的影响,并且X-波段波长较短,入射角较大(中心入射角45.77°),地表对干涉去相干的贡献可以忽略,因此可将干涉复相干作为体去相干,对体去相干模型中的结构函数进行勒让德展开,截取第0阶展开式得到了基于相干幅度的森林树高估测模型,利用均匀选取的LiDAR冠层高度模型(CHM)检验样本对估测结果进行严格的精度评价,并与差分法的树高估测结果进行对比。精度评价结果显示:相干幅度法与差分法都得到了较高的估测精度,两者的R~2、RMSE、总精度分别为0.81、0.86;1.20m、0.97m;86.4%、88.7%。研究结果表明:相干幅度与森林树高具有负相关关系,适用于估测树高,基于单极化相干幅度的估测模型也可以得到较高的估测精度,与差分法的估测结果相比,虽然估测精度略有降低,但此方法具有两方面的优势:一方面,估测结果不需要实测样地数据标定,对于没有实测样地数据的森林区域亦能进行高精度的树高估测;另一方面,相干幅度法不需要高精度的DEM,具有更强的实用性。     

Characterization and causes of land subsidence in Beijing,China

Long-term overexploitation of groundwater is the primary factor causing regional land subsidence in the Beijing plain area, China. Currently, large subsidence funnels exist, one each in southern and northern Beijing. We adopted the multi-temporal interferometric synthetic aperture radar (MT-InSAR) method, incorporating both persistent scatterer (PS) and small baseline (SB) approaches on 47 Envisat Advanced Synthetic Aperture Radar (ASAR) single look complex (SLC) images to map land subsidence in the Beijing plain area. The temporal and spatial variations of land subsidence and its seasonal variation were explained by the MT-InSAR results. Then, the InSAR results were combined with the dynamic monitoring of groundwater level, extensometer measurements, and hydrogeological data; the characterization and causes of land subsidence were analysed with Geographic Information System (GIS) spatial analysis methods. The results show the following. 1) Land subsidence developed rapidly in the Beijing plain area from 2003 to 2010, with obviously uneven settlement; settlement rates exceeded 100 mm year^?1 in some areas. Seasonal variation in settlement rates may be affected by changes in the precipitation rates and the exploitation of groundwater. 2) The contribution of different aquifer systems to land subsidence varies. The variation in the groundwater level in the second confined aquifer, at a depth of 100–180 m, has the greatest impact on land subsidence. 3) The settlement is centred in the lower part of the Wenyu–Chaobai and Yongding alluvial fan areas, where the compressible layer is more than 100 m thick. Meanwhile, land subsidence forms a structural feature with larger differences in the deformation gradient on both sides of faults.     

Extracting Built\|up Areas from TerraSAR-X Data Using Object-oriented Classification Method

Urban sprawl stands for one of the most dynamic process in the context of global land use changes.Currently developing countries are going through the tide of urban expansion,represented by China and India.The constantly increasing loss of land resources due to growing settlements comes along with various ecological and socioeconomic challenges such as air pollutant,water contamination,urban heat island effect and urban waterlog disaster.In order to prevent these negative consequences,effective methods and strategies for a sustainable development of urban planning is the availability of accurate and up\|to\|date geo\|data on the location,shape,and dynamics of built\|up areas.Based on single\|polarized TerraSAR\|X,the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region\|growing algorithm,which takes the intensity of backscatter and shape\|related properties into account.The object\|oriented procedure consists of three main steps:firstly,the analysis of the local speckle behavior in the SAR intensity data,leading to the generation of a texture image;secondly,a segmentation based on the intensity image;thirdly,the classification of each segment using the derived texture file and intensity information in order to identify and extract build\|up areas.In our research,the distribution of BAs in Dongying City is derived from single\|polarized TSX SM image (acquired on 17th June 2013)with average ground resolution of 3m using our proposed approach.By cross\|validating the random selected validation points with geo\|referenced field sites,Google Earth high\|resolution imagery,confusion matrices with statistical indicators are calculated and used for assessing the classification results.The result of kappa coefficient is 0.85,OA coefficient is 92.89%.We have shown that connect texture information with the analysis of the local speckle divergence,combining texture and intensity of construction extraction is feasible,efficient and rapid.