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技术的围填海区域地面沉降监测

以围填海活动为代表的沿海快速城市化过程,是引起地面沉降的重要影响因素之一。研究聚焦沿海围填海活动热点区域广州市南沙区,使用2015年6月~2018年4月共34景Sentinel-1数据,应用SBAS-InSAR技术,揭示了南沙区在研究时段内地面沉降的时空变化格局及演变特征。结果表明：①南沙区整体呈现持续沉降的趋势,沉降速率分化严重,平均沉降速率达到3.2 mm/a,圈层分析法显示中心圈层平均沉降速率为2.6 mm/a,最外层平均沉降速率为26.8 mm/a;②该区地面沉降在空间上呈现出异质性,主要分布在东部和南部,其中南部万顷沙、龙穴岛地面沉降最为严重,最大年沉降速率达到72.2 mm/a,在2015年6月~9月还出现地面沉降回弹现象,可能是台风天气带来季节性强降水变化影响。③基于不同极化方式的Sentinel-1数据进行交叉验证,VV极化、VH极化监测结果平均值分别为2.09 mm和1.01 mm,均方根误差分别为1.12 mm和2.65 mm。结果表明：SBAS-InSAR技术在提取围填海区域的地面沉降信息方面是有效可靠的,能更好地为监测沿海地区的地面沉降情况提供科学依据。     

基于PS-InSAR的天津地区沉降监测及分析

城市地面沉降监测是保障城市安全建设和健康发展的重要手段之一,而传统的沉降监测方法无法大尺度反映地面形变信息。针对近几年天津地区出现大面积沉降现象,利用Sentinel-1A数据基于永久散射体干涉测量技术开展城区大范围沉降监测研究并分析了地面沉降原因。结果表明：近年来天津地区多处出现地面沉降,严重沉降区集中天津的武清区、北辰区以及郊区乡镇结合区域的王庆坨镇、胜芳镇、左各庄镇、静海镇以及大寺镇,其最大沉降漏斗位于王庆坨镇,沉降速率为-63.2 mm/a。经分析发现天津地面沉降与地下水过度开采、大型工业区的迁移和建设以及活动断裂带地质活动有关。     

基于PS-InSAR的天津地区沉降监测及分析

城市地面沉降监测是保障城市安全建设和健康发展的重要手段之一，而传统的沉降监测方法无法大尺度反映地面形变信息。针对近几年天津地区出现大面积沉降现象，利用Sentinel-1A数据基于永久散射体干涉测量技术开展城区大范围沉降监测研究并分析了地面沉降原因。结果表明：近年来天津地区多处出现地面沉降，严重沉降区集中天津的武清区、北辰区以及郊区乡镇结合区域的王庆坨镇、胜芳镇、左各庄镇、静海镇以及大寺镇,其最大沉降漏斗位于王庆坨镇，沉降速率为-63.2 mm/a。经分析发现天津地面沉降与地下水过度开采、大型工业区的迁移和建设以及活动断裂带地质活动有关。     

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PS-InSAR技术的廊坊市地面沉降监测研究

随着城市化进程的加速发展,地面沉降危害不断加剧。以廊坊市城区为示范研究区,选取近5 a的ENVISat卫星ASAR数据,采用永久散射体干涉测量(PS-InSAR)技术,提取廊坊市城区2003~2007年时间序列地面沉降形变信息,并对廊坊市地面沉降空间分布特征进行初步分析。研究结果表明:廊坊市城区年平均沉降速率在-19.2~18.7mm/a之间变化,沉降中心主要分布在城区北部地区,对城市基础设施及施工建设产生严重影响。     

北京市典型地区地面沉降空间格局分析

针对区域地面沉降空间演化格局分析较少问题,选用小基线雷达干涉测量技术(SBAS-InSAR)获取了北京典型地区的地面沉降数据,并利用全局Moran′s I和局部Moran′s I指数对研究区地面沉降格局特征进行分析。结果表明,研究区地面沉降速率最大值为-110mm/a,中部、东南部沉降严重,其他地区相对轻微;该区域地面沉降全局空间自相关十分显著,全局Moran′s I指数达到了0.8(p=0.001),局部空间格局包括3个亚区,分别是高集聚区、低集聚区以及非相关区;热点分析结果表明地面沉降存在过渡带,过渡带不仅是地面沉降的未来发展方向,同时还存在不均匀地面沉降,是未来地面沉降防治的重点关注区域。     

基于PSI技术的芜湖市地面沉降时空特征分析

城市的沉降监测有利于了解区域实时高程,可为地质灾害与防护部门提供数据依据,避免因高程损失而带来的地质灾害。基于2016年1月至2017年12月共22景Sentinel-1A干涉宽幅模式影像数据,利用永久散射体合成孔径雷达干涉测量技术以及合成孔径雷达差分干涉测量技术进行芜湖市地表形变监测,并分析研究区地面沉降的时空分布特征。空间上,阐述芜湖市地面沉降的整体格局,再以道路为专题,分析了道路的沉降分布格局。时间上,以时间为基线,逐月分析地面沉降部分在年内的具体变化。结果表明：空间上,芜湖市地面沉降主要集中在长江以东的范围,呈现出由西向东逐渐增加的趋势,长江以西呈现零星漏斗式沉降分布,其中,沉降累积量也与道路的密度与建设相关,道路汇集区与修建区域的沉降累积量较大;时间上,研究区整体沉降量各月变化较均匀,其中,沉降量变化范围在6月最大,10月与11月最小。     

基于PSI技术的芜湖市地面沉降时空特征分析

城市的沉降监测有利于了解区域实时高程,可为地质灾害与防护部门提供数据依据,避免因高程损失而带来的地质灾害。基于2016年1月至2017年12月共22景Sentinel-1A干涉宽幅模式影像数据,利用永久散射体合成孔径雷达干涉测量技术以及合成孔径雷达差分干涉测量技术进行芜湖市地表形变监测,并分析研究区地面沉降的时空分布特征。空间上,阐述芜湖市地面沉降的整体格局,再以道路为专题,分析了道路的沉降分布格局。时间上,以时间为基线,逐月分析地面沉降部分在年内的具体变化。结果表明:空间上,芜湖市地面沉降主要集中在长江以东的范围,呈现出由西向东逐渐增加的趋势,长江以西呈现零星漏斗式沉降分布,其中,沉降累积量也与道路的密度与建设相关,道路汇集区与修建区域的沉降累积量较大;时间上,研究区整体沉降量各月变化较均匀,其中,沉降量变化范围在6月最大,10月与11月最小。     

基于PSI技术的芜湖市地面沉降时空特征分析

城市的沉降监测有利于了解区域实时高程，可为地质灾害与防护部门提供数据依据，避免因高程损失而带来的地质灾害。基于2016年1月至2017年12月共22景Sentinel-1A干涉宽幅模式影像数据，利用永久散射体合成孔径雷达干涉测量技术以及合成孔径雷达差分干涉测量技术进行芜湖市地表形变监测，并分析研究区地面沉降的时空分布特征。空间上，阐述芜湖市地面沉降的整体格局，再以道路为专题，分析了道路的沉降分布格局。时间上，以时间为基线，逐月分析地面沉降部分在年内的具体变化。结果表明：空间上，芜湖市地面沉降主要集中在长江以东的范围，呈现出由西向东逐渐增加的趋势，长江以西呈现零星漏斗式沉降分布，其中，沉降累积量也与道路的密度与建设相关，道路汇集区与修建区域的沉降累积量较大；时间上，研究区整体沉降量各月变化较均匀，其中，沉降量变化范围在6月最大，10月与11月最小。     

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.     

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.     

COSMO-SkyMed数据在常州市地表形变监测中的应用

小基线集合成孔径雷达干涉测量技术(SBAS-InSAR)已成功应用于城市地表形变监测,并表现出极大的潜力和优势。X波段高分辨率雷达卫星在地表微小形变探测方面较C波段和L波段更为敏感。选取覆盖常州地区COSMO-SkyMed高分辨率SAR影像,采用SBAS-InSAR方法获得了地表形变时间序列,对比水准观测数据,分析了干涉测量结果的精度,根据历史地下水位监测数据,分析了地下水水位变化对地表形变的影响。结果表明:干涉测量结果与水准观测数据具有很好的一致性,沉降区域主要发生在武进区,最大沉降量超过-40mm,主城区出现了轻微的回弹现象,回弹达到+5mm;地下水水位持续上升与地面沉降减缓、地面回弹趋势一致,地下水水位变化仍然是常州市地表形变的主要影响因素。     

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.     

InSAR time-series analysis of land subsidence in Bangkok,Thailand

Land subsidence poses a serious risk to the low-lying coastal city of Bangkok, Thailand; major flooding occurred there in 1983 and again in 2011. Extreme water pumping in the past led to subsidence rates of up to 120 mm year^?1. Although water extraction is now controlled, maximum rates measured by levelling today are still up to 20 mm year^?1. In this study, we apply interferometric synthetic aperture radar (InSAR) time-series analysis to study subsidence in Bangkok between October 2005 and March 2010. We validate the InSAR results, by comparing levelling rates and find good agreement between the two techniques. We detect approximately 300,000 coherent pixels overall, with an average density of 120 observations per km². This is two orders of magnitude greater than the density of levelling benchmarks and reveals subsiding areas that are missed by the levelling network.     

The present status of subsiding land vulnerable to roof collapse in the Jharia Coalfield,India, as obtained from shorter temporal baseline C-band DInSAR by smaller spatial subset unwrapped phase profiling

In this paper, we identified recently subsiding areas in Jharia Coalfield, Jharkhand, India from the shorter temporal baseline Radarsat-2 C-band interferometric synthetic aperture radar (InSAR) data pairs of 2012. Although shorter wavelength C-band differential InSAR (DInSAR) is more sensitive to slow deformation and better suited for higher precision land subsidence measurement, the dynamic and adverse land cover in mining areas and resulting temporal decorrelation problem poses a serious problem for DInSAR observation in mining areas. We used smaller temporal baseline data pairs and adopted InSAR coherence-guided incremental filtering with smaller moving windows to highlight the deformation fringes over temporal decorrelation noise. We identified the deformation fringes and validated them based on ground information to prepare the land subsidence map of the coalfield in 2012. Several new, previously unreported subsidence areas were detected in the present study with a total subsiding area of 6.9 km². The recent incidence of roof collapse on 15 November 2014 at Angar Patra village in Katras region of the coalfield where 45 houses collapsed and 10 people were injured is situated in a highly subsiding vulnerable area as obtained from the present study. Due to spatial discontinuities of InSAR coherence, DInSAR phase unwrapping for the entire study area in one go did not appear feasible. To avoid this problem, we performed DInSAR processing in smaller spatial subsets and unwrapping of the subset interferograms by a ‘minimum cost flow’ algorithm. Subsequently, we plotted unwrapped phase profiles across the deformation fringes and retrieved the maximum deformation phase with respect to background phase and translated them into radar line of sight (LOS) displacement rates. For obtaining the average subsidence rates, we adopted InSAR coherence-weighted LOS displacement rates taking into account the contribution of each data pair as a function of DInSAR phase quality of the fringe areas. Ground-based subsidence measurements by precision levelling were conducted in four test sites that had been undergoing active underground mining during the observation period. We compared space-borne DInSAR-based subsidence rates obtained by the adopted technique with precision levelling measurements. Overall, the results are found to agree well. In the four test sites with gentle to flat topography, land subsidence occurs at slow to moderate rates due to compression of in-filled material (resulting from sand stowing in underground mining), without any evidence of roof collapse. In such cases, the horizontal displacement component is less significant, and overall surface displacement occurs essentially in the vertical direction. However, we assessed the nature of subtle horizontal strain to infer relative shrinkage or dilation of the land surface which could be additive or subtractive to vertical displacement in DInSAR-based LOS displacement.