Sentinel-1数据在西南山区水库变形斜坡InSAR监测中的适用性评价:以溪洛渡水库为例

Sentinel卫星凭借其超高的辐射分辨率、稳定的轨道系统、较大的覆盖能力、较短的重返时间、可免费下载的数据,在斜坡灾害识别监测方向上有广泛的应用。自1963年意大利瓦伊昂特大滑坡发生以来,岸坡地质灾害一直是峡谷区水库关注的主要问题之一。以金沙江上游溪洛渡水库区为例,结合PALSAR-2、TerraSAR-X数据,评价Sentinel-1 SAR数据在西南山区水库变形斜坡InSAR监测中的适用性,以理论结合实际结果分析Sentinel-1数据是否可以在一定条件下替代其他商业数据,为今后相关行业应用提供参考。结果显示:Sentinel-1数据在研究区可解译的变形斜坡约200处,类型有滑坡、危岩体和塌岸;经现场核查,Sentinel-1数据解译的最小变形斜坡投影面积约为2400 m2,约35 m(长)×77 m(宽)大小,共16个变形像元聚集。高山峡谷区叠掩、阴影现象严重,通过对雷达常用观测模式下的SAR数据的比较,在SAR数据交集区域,有效观测面积为Sentinel-1升轨70.3%,Sentinel-1降轨68.9%,PALSAR-2升轨70.4%,PALSAR-2降轨67.6%,TerraSAR-X降轨52.5%,在不考虑分辨率的情况下,在库区Sentinel-1数据与其他两种SAR数据观测能力相比持平或更优秀。6月至11月初是溪洛渡水库的水位上升期,周边植被发育较好,造成数据相干性较差,2017年后Sentinel-1A(1B)双星拍摄获取的SAR数据量增加,高频观测可使相干性提高,利用2017年后该卫星数据可有效识别水库蓄—排水周期内的区域性变形斜坡发育变化情况。当长时间缺失SAR数据时,会造成最近一对SAR数据间的某些像元测量的变形超过其InSAR最大量程,解缠时丢失相位周期。Sentinel-1数据由于连续性较好,监测斜坡的变形趋势较为连续,因此更适合连续小变形的趋势识别。      

Testing the potential of Sentinel-1A TOPS interferometry for the detection and monitoring of landslides at local scale (Veneto Region,Italy)

The recent Sentinel-1 mission, started by the European Space Agency in April 2014, provides the scientific community with new capabilities for the monitoring of the Earth surface. In particular, the Terrain Observation by Progressive Scans imaging technique used in the Interferometric Wide swath acquisition mode permits to acquire data over very wide areas (250 km of swath extension) at 20-m spatial resolution, with 12-day revisit time, making it suitable for ground displacement monitoring applications. With more than 1 year of synthetic aperture radar images available, it is now possible to carry out monitoring activities of slow moving phenomena such as landslides at both regional and local scales. In this work, the potential of Sentinel-1A for the monitoring of shallow (from 2 to 6 m of depth) landslides occurring in the North-Eastern Italian Pre-Alps was tested. Two stacks of Sentinel-1A scenes acquired in both ascending and descending orbits were processed using the Permanent Scatterer Interferometry (PSI) technique. The results, analysed in terms of PS density and quality, were compared with the ERS-1/2 and ENVISAT PSI database available from the Italian National Cartographic Portal to assess the capabilities of Sentinel-1A in detecting and monitoring landslides in respect to the previous satellite missions. The results of this work show the great potential of Sentinel-1A in the continuous monitoring of landslide-prone territories even at local scale. The achievable results can provide information that is useful to delineate the spatial and temporal evolution of landslides and precisely assess their rates of deformation.     

Measuring precursory movements of the recent Xinmo landslide in Mao County,China with Sentinel-1 and ALOS-2 PALSAR-2 datasets

The Xinmo landslide occurred in the early morning of 24 June 2017 at about 5:38 am local time. This catastrophic event caused enormous casualties and huge economic losses in Xinmo Village, Mao County, Sichuan Province, China. In this study, Synthetic Aperture Radar (SAR) datasets acquired by X-band TerraSAR-X, Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2), and C-band Sentinel-1 over the disaster area were collected and analyzed to characterize this landslide. The high-resolution TerraSAR-X intensity images were used to evaluate the landslide disaster and delineate the sliding area. Afterwards, two ALOS-2 PALSAR-2 image pairs and a stack of 45 Sentinel-1 images were processed to detect precursory movements of the landslide surface, using the conventional differential InSAR (DInSAR) method and advanced time series InSAR analysis. The unstable source area near the ridge was identified from the displacement rate map derived from Sentinel-1 datasets. The maximum displacement rate detected at the source area was ?35mm/year along the radar line of sight (LOS) direction. The time series of LOS displacements over 2 years presents an easily discerned seasonal evolution pattern. In particular, a sudden acceleration of the displacement, dozens of days before the collapse was clearly captured by the Sentinel-1 observations, which might suggest that early warning of landslide disasters is possible given the availability of operational SAR data acquired in frequent repeat-pass mode, such as the Sentinel-1 twin-satellite constellation.     

How to avoid false interpretations of Sentinel-1A TOPSAR interferometric data in landslide mapping? A case study: recent landslides in Transdanubia,Hungary

Natural Hazards - It is a crucial issue to better understand the usability of Sentinel-1 satellites in geomorphologic applications, since Sentinel-1 and the Copernicus Program are considered to be...     

Google Earth Engine for concurrent flood monitoring in the lower basin of Indo-Gangetic-Brahmaputra plains

Natural Hazards - The present study focused on the recent flood inundation (July 2020) that occurred in the lower Indo-Gangetic-Brahmaputra plains (IGBP) using concurrent C-band Sentinel-1A...     

川西高原活动性滑坡识别与空间分布特征研究

成都平原向西至松潘?甘孜褶皱带完成了从平原到高山峡谷区的转变,区域内起伏落差巨大,地势奇峻,河流下切侵蚀严重,构造活动频繁,地震频发,内外动力作用强烈,地质灾害众多。文章利用覆盖全区的Sentinel-1A升降轨数据以及重点区域的ALOS-2数据进行InSAR技术处理,结合GIS空间分析,对研究区活动性滑坡进行早期识别以及空间分布规律的探索,再辅以部分野外调查佐证,获得了以下认识:研究区滑坡集中分布地区按其诱因可分为水库蓄水诱发灾害区（黑水县毛尔盖水库）、震后破碎山体灾害区（茂县岷江与黑水沟交界、汶川至理县一带、九寨沟至石鸡坝镇一线）和重要河流灾害区（舟曲、腊子口镇、小金县和丹巴县）;区域内活动性滑坡主要分布于千枚岩等变质岩和泥页岩等碎屑岩中;主要地形范围为坡向南东、东、北东向,坡度15°~40°,高程区间1000~3000 m,相对高差>1000 m;主要分布断裂有岷江断裂、玛曲?荷叶断裂、光盖山?迭山北麓断裂和茂汶?汶川断裂。Sentinel-1A升降轨数据的结合,使得有效观测区域提高到研究区面积的73.41%。在川西高原区ALOS-2数据相对优于Sentinel-1A数据,ALOS-2和Sentinel-1A数据在九寨沟和茂县重叠区识别的结果重合率为58.7%和44.8%,识别数量前者分别是后者的3.98倍和1.39倍。      

基于H/α分解原理的古河道信息提取——以松嫩平原西部地区为例

古河道对于重现古气候、古生态环境变化有着重要的意义。极化合成孔径雷达（SAR）数据以散射矩阵的形式记录了地物的后向散射信息,能有效地识别隐伏的古河道信息。本文以古河道发育的松嫩平原西部作为研究区域,选取Sentinel-1双极化数据（VV-VH）作为数据源,通过VV-VH双极化模式下的H/α分解处理,构建了由散射熵H与散射角α构成的二维H/α平面。依据雷达波在古河道充填沉积物中发生体散射以及在古河床底界面发生二次散射,并且体散射功率大于二次散射功率,确定了古河道散射类型属于H/α平面上的高熵多次散射。结合此特征与Sentinel-2影像,最终对研究区内的古河道信息进行了提取。研究表明,通过VV-VH双极化模式下的H/α分解方式可以提取到在Sentinel-2影像上无明显特征的古河道信息。     

基于MSBAS技术的金沙江上游色拉滑坡形变分析

金沙江缝合带是滑坡灾害的高发区,且具有较大的堵江威胁。以堵江风险较高的色拉滑坡为研究对象,选取高时间分辨率的升降轨Sentinel-1A/B数据,利用MSBAS InSAR技术对该滑坡展开地表形变监测研究。文章在利用不同轨道的Sentinel-1A/B获取色拉滑坡2018—2020年间的二维动态形变时间序列的基础上,分析了典型特征点形变时间序列特征。结果表明,在2018年1月—2020年4月色拉滑坡东西向累积形变最高达到165 mm,垂直向累积形变达?102 mm,滑坡体形变加速的时间点被成功地捕获。最后,分析了该滑坡的形变趋势,通过现场调查结果验证了所获得滑坡监测结果的准确性。     

时序InSAR技术三峡库区藕塘滑坡稳定性监测与状态更新

坡体表面形变是表征坡体稳定性的重要信息,因此,非常有必要对滑坡多发区域进行时序常规变形监测.近年来,星载合成孔径雷达数据由于其覆盖范围大、形变监测精度高的特点,被越来越多的用于山区滑坡识别与探测.首先介绍了联合分布式目标与点目标的时序InSAR方法,并将该方法应用于分析覆盖三峡藕塘滑坡的2007年至2011年的19景ALOS PALSAR数据和2015年至2018年的47景Sentinel-1数据,提取了数据覆盖时间段内的藕塘地区的变形速率.发现相比于2007年至2011年,2015年至2018年新增三处不稳定斜坡.进一步对滑坡的时序变形分析表明,降雨和水位变化是坡体稳定性最大的两个影响因素.实验证明时序InSAR方法可以作为常规形变手段来识别与监测三峡库区等地区潜在的滑坡,为防灾减灾提供支持与依据.      

Heavy rainfall drives slow-moving landslide in Mazhe Village,Enshi to a catastrophic collapse on 21 July 2020

Landslides - Following extremely heavy rainfall, a catastrophic landslide occurred in Mazhe Village, Enshi, China, on 21 July 2020. In this study, we use C-band Sentinel-1A Interferometric...     

D-InSAR与随机森林模型耦合的活动性滑坡识别方法探究

灾害的早期识别是防灾减灾领域的关键技术。文中以甘肃省舟曲县为例,利用2018年1月－2019年1月Sentinel-1A雷达卫星降轨数据和2021年5月Sentinel-2光学遥感影像数据,通过D-InSAR技术获取研究区地表形变信息,利用随机森林模型识别潜在的滑坡体。结果表明:使用已有的滑坡数据集,采用随机森林模型能够较好地识别出潜在滑坡体。潜在滑坡点分布位置均位于地表形变量大的区域。舟曲县整体形变沿东西向发生,主要分布于舟曲县东北和西南方向,与潜在滑坡点高度重合。识别出的潜在滑坡点（立节乡北山滑坡）,年形变量达到0.12?m,于2021年1月18日发生滑坡,该滑坡典型案例也印证了文中方法的有效性。     

基于星载雷达特征参数的黄河冰厚反演及冰储量估算

冰厚是冰凌成因分析及预报的重要基础信息,可为防凌减灾提供重要依据。以黄河内蒙古段包头至头道拐水文站为例,利用Sentinel-1雷达影像结合Sentinel-2光学影像对研究区河冰厚度进行估算,首先对Sentinel-2光学影像进行处理,提取凌汛期前黄河主河道边界;然后对Sentinel-1雷达影像进行处理,提取2个强度信息和4个极化分解参数,分析6个雷达特征参数与河冰厚度的相关性;选择相关性最高的参数,采用统计回归方法建立冰厚反演线性回归模型,模型的调整R²为0.657,验证RMSE为9.82 cm,MRE为13.46%,MAE为8.26 cm;对凌汛期黄河冰厚进行反演,分析冰厚时空变化特征,并估算冰储量,同时讨论了河冰的散射机制。研究证明了主动微波遥感数据在黄河冰厚反演中的可行性,可为黄河内蒙古段防凌减灾提供科学参考。     

基于Sentinel-1A雷达影像的崇明东滩芦苇盐沼植被识别提取

为研究芦苇盐沼植物在一个生长周期不同生长季节的雷达后向散射系数变化特征,对芦苇分布信息进行提取,探究Sentinel-1A卫星数据在长江口湿地植被监测中的应用前景。以对长江河口崇明东滩南部为研究区域,利用2016年11个时相的Sentinel-1A雷达卫星影像VV（vertical transmit/vertical receive）+VH（vertical transmit/horizontal receive）双极化数据,分析潮滩地带芦苇、白茅、海三棱藨草、水体和光滩在植被生长周期内的雷达后向散射强度变化特征,对芦苇盐沼植被进行识别提取。结果表明：相较于VH极化方式,VV极化方式下不同地物的后向散射强度差异更为明显,芦苇的后向散射强度在枯叶期下显著高于其他地物;进行芦苇植被提取时,需要对植被枯萎期不同潮位状况下的雷达影像进行组合运算,芦苇提取精度可达到88.7%;对芦苇植被雷达后向散射强度和临近时相的光学遥感归一化植被指数（normalized difference vegetation index,I_NDV）进行相关性分析,发现两者呈良好的正相关关系,相关系数为0.78。     

基于Worldview-3与Sentinel-1 SAR数据的草原矿区复垦植被生物量反演方法研究

利用多源遥感数据定量反演矿区复垦植被生物量是高效、动态、大面积监测土地复垦和生态恢复效果的必要手段之一。本文以内蒙古草原露天煤矿为研究区,联合遥感光学与雷达数据各自的优势,探索基于Worldview-3(WV-3)与Sentinel-1 SAR数据的矿区复垦植被生物量反演方法,选择主成分-小波变换(W-PCA)算法对WV-3与Sentinel-1 SAR数据进行融合,揭示波段反射率、植被指数、后向散射系数及纹理特征等参数与生物量之间的相关关系,建立多变量的生物量反演模型,并分析不同生物量模型的空间不确定性。结果表明:(1)通过W-PCA算法得到融合后的图像,信息熵的提高反映了融合图像与光学WV-3图像相比具有更多的细节信息,平均梯度的提高反映了融合图像与Sentinel-1 SAR图像相比具有更高的清晰度和更丰富的纹理信息。融合后的第8波段相关系数最高、光谱扭曲度最低、光谱保真度最高。(2)通过相关性分析,发现增强型植被指数(EVI)、归一化植被指数(NDVI)、VH极化、VH均值纹理以及融合后第8波段与生物量显著正相关。WV-3的NDVI与Sentinel-1的VH_ME建模精度(R²=0.834 0,RMSE=16.464 6 g/m²,Ac=81.52%)最高,融合后的第8波段验证精度(R²=0.798 3,RMSE=22.828 3 g/m²,Ac=74.64%)最高。(3)基于不同模型的残差不确定性分析,Sentinel-1 SAR数据变量建立的模型估测结果更容易出现高估及饱和现象,两者联合变量建立的模型可实现优势互补,利用融合数据建立的模型明显改善生物量小于40 g/m²时的高估计现象以及生物量大于100 g/m²时的两者饱和现象,其不确定性降低2.42~9.68 g/m²。因此,利用光学和雷达遥感融合能够有效提高复垦植被生物量的估算精度,为草原矿区复垦植被精细监测提供有效的数据支持。     

Changes in surface soil mositure during freeze-thaw process under different vegetated status using Sentinel-1A in Beiluhe北大核心CSCD

Beiluhe basin lies in a permafrost region where is located in the interior of Tibetan Plateau. Ecosystem in the area is subjected to the freeze-thaw process of the active tjaele,and there is conspicuous correlation between soil moisture（SM）and vegetation coverage. To retrieve the soil moisture content of Beiluhe basin with a total area of 2 037. 94 km2,a synergistic method,which combined improved water cloud model,Oh,Dubois and Topp model,was presented in this paper base on Sentinel-1A multi-polarization SAR and Landsat-8 time series images data. The accuracy was validated with the in-situ point SM data：Adjusted-R2 of the regression equation is 0. 6848,and RMSE is 0. 039 cm3·cm-3. The analysis of correlation among freeze-thaw process,SM and vegetation cover from macro watershed scale manifests：Vegetation coverage has a significant delayed effect on the freeze-thaw process of the active tjaele,that is,the higher vegetation coverage,the more lagging freeze-thaw time;These study results are basically consistent with predecessors in-situ observation data,verifying the feasibility of studying correlation among soil freeze-thaw process,SM,and vegetation coverage from the macro watershed scale based on Sentinel-1A annual time series data. © 2022 Science Press (China).     

Co-seismic landslide mapping using Sentinel-2 10-m fused NIR narrow,red-edge,and SWIR bands

Landslides - An accurate and timely inventory for major landslides triggered by seismic events is essential for hazard assessment and risk governance. Sentinel-2 MultiSpectral Instrument (MSI) data...     

Rapid flash flood calamity in Chamoli,Uttarakhand region during Feb 2021: an analysis based on satellite data

Natural Hazards - The present study investigates the accelerating factors for extreme flash flood at Chamoli district of Uttarakhand on 7 February 2021. The Sentinel-2A and 2B satellite data have...     

The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data

Post-event Interferometric Synthetic Aperture Radar (InSAR) analysis on a stack of 45 C-band SAR images acquired by the ESA Sentinel-1 satellites from 9 October 2014 to 19 June 2017 allowed the identification of a clear precursory deformation signal for the Maoxian landslide (Mao County, Sichuan Province, China). The landslide occurred in the early morning of 24 June 2017 and killed more than 100 people in the village of Xinmo. Sentinel-1 images have been processed through an advanced multi-interferogram analysis capable of maximising the density of measurement points, generating ground deformation maps and displacement time series for an area of 460 km² straddling the Minjiang River and the Songping Gully. InSAR data clearly show the precursors of the slope failure in the source area of the Maoxian landslide, with a maximum displacement rate detected of 27 mm/year along the line of sight of the satellite. Deformation time series of measurement points identified within the main scarp of the landslide exhibit an acceleration starting from April 2017. A detailed time series analysis leads to the classification of different deformation behaviours. The Fukuzono method for forecasting the time of failure appear to be applicable to the displacement data exhibiting progressive acceleration. Results suggest that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.     

基于SBAS-InSAR的辽宁义县中东部区域地表沉降监测

以辽宁义县中东部区域为例,选2017—2018年间18景Sentinel1数据,采用短基线差分干涉测量技术(SBAS-InSAR)获取了该地区的形变时间序列和平均沉降速率。结果显示义县存在不均匀的沉降,最大平均沉降速率为98 mm/a。结合野外实地调查成果,验证了SBAS-InSAR技术的可靠性,并分析了地表沉降的主要原因是矿石的开采。