基于Sentinel-1和D-InSAR的地表沉降地理国情监测技术研究

为推进合成孔径雷达差分干涉测量(D-InSAR,Differential Interferometric Synthetic Aperture Radar)技术在地表沉降地理国情监测中的应用,本文利用D-InSAR技术,以某矿区为研究对象,基于5景哨兵-1卫星(Sentinel-1)雷达影像,采用SARscape与Arc GIS软件相结合处理的方式得到了精准的成果数据,并结合实地水准观测结果对D-InSAR地表沉降监测的精度进行了对比分析。结果表明:利用D-InSAR技术进行地表沉降地理国情监测,具有较高的测量精度,且该技术具有大尺度连续覆盖能力、受天气干扰小、低成本等特点,在地理国情监测等相关领域有非常好的应用前景。     

青海赛什腾地区遥感影像岩石信息提取研究

赛什腾地区隶属柴北缘构造带,岩性复杂多变,前人对研究区岩性界线的划分较为笼统,笔者等选择Landsat- 8 OLI、ASTER和Sentinel- 2A为数据源,采用最佳波段指数确定各影像的波段组合,凸显不同岩石的边界;将ASTER短波红外波段与Landsat- 8 OLI、Sentinel- 2A可见光—近红外波段协同处理,构成Landsat- 8+ASTER（LA）数据和Sentinel- 2A+ASTER（SA）数据,分析重采样岩石标准光谱信息,拟定不同岩石波段运算公式,基于多重分形理论选定不同岩石类型的阈值范围,获取主要岩性的分布;根据重采样的黑云母标准光谱曲线,选取SA数据2262 nm波段和2336 nm波段进行定向主成分分析,采用Crosta法阈值分割第二主成分,划分黑云母异常等级,将其与岩性分布相关联,识别出研究区主要岩性分布。通过岩石实测光谱分析、薄片镜下鉴定与野外地质调查相结合的方法完善解译结果。岩性提取结果显示,小赛什腾山东侧新发现“U”型条带,为辉长岩—英云闪长岩—二长花岗岩,重新圈定达肯大坂群第三岩组和第四岩组,滩间山群一组,花岗岩,二长花岗岩,黑云母花岗岩,流纹岩,似斑状石英闪长岩,石英闪长岩,英云闪长岩,辉长闪长岩和辉长岩等岩石的边界。此次基于多源遥感数据岩性识别方法研究对青海赛什腾地区野外地质调查工作具有指导意义,可为高山峡谷区地质填图提供技术参考。     

格陵兰岛 Nioghalvfjerdsfjorden 冰川表面运动特征研究

格陵兰冰盖是世界第二大陆地冰盖,其边缘分布的溢出冰川作为快速传输冰带,是造成冰盖消融的重要因素。本文 以位于格陵兰岛东北部的 Nioghalvfjerdsfjorden 冰川为研究对象, 探索开展基于 sentinel- 1A 和偏移量跟踪技术的溢出冰川表 面运动特征研究, 并利用非冰川区流速和 CP0M NRT IV 数据验证了结果的可靠性。对比分析 2017—2019 年三个时期的春季 冰流速, 发现该冰川在 2019 年春季出现了最大流速上移现象, 推断可能是由于 2018—2019 年间冰川融水渗透引起的底部润 滑加剧所致。     

基于Sentinel-1数据监测临汾盆地地表形

基于以往研究,使用12景影像形成67个干涉图,并利用stacking技术获取临汾盆地2015~2016年形变速率。结果显示,沉降区域主要分布在罗云山断裂带和峨眉-紫金山断裂带之间,中心区域沉降速率超过40 mm/a,与地下水的等高线分布较为相似,地表形变是地下水抽取和断裂带的联合作用。     

南水北调中线区域地面沉降SBAS-InSAR监测研究

基于1 009景Sentinel-1A影像,利用SBAS-InSAR技术对南水北调中线区域地面沉降进行长时间序列监测。结果显示,整个中线沿线地面沉降主要分布于河北省东南部,最大形变速率为-139 mm/a,由于与渠道间有一定距离,因此对输水影响较小。北京市的最大形变速率为-133 mm/a,天津市西南部最大形变速率为-81 mm/a,但天津支线经过了2个沉降区,应当引起相关部门的重视。本文重点分析了南水进京后北京市地面沉降的时序形变特征,结合相关资料分析得知,南水北调工程有效补充了北京地区地下水储量,显著遏制了北京市地面沉降的发展态势。     

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.     

青海赛什腾地区遥感影像岩石信息提取研究

赛什腾地区隶属柴北缘构造带,岩性复杂多变,前人对研究区岩性界线的划分较为笼统,笔者等选择Landsat- 8 OLI、ASTER和Sentinel- 2A为数据源,采用最佳波段指数确定各影像的波段组合,凸显不同岩石的边界;将ASTER短波红外波段与Landsat- 8 OLI、Sentinel- 2A可见光—近红外波段协同处理,构成Landsat- 8+ASTER（LA）数据和Sentinel- 2A+ASTER（SA）数据,分析重采样岩石标准光谱信息,拟定不同岩石波段运算公式,基于多重分形理论选定不同岩石类型的阈值范围,获取主要岩性的分布;根据重采样的黑云母标准光谱曲线,选取SA数据2262 nm波段和2336 nm波段进行定向主成分分析,采用Crosta法阈值分割第二主成分,划分黑云母异常等级,将其与岩性分布相关联,识别出研究区主要岩性分布。通过岩石实测光谱分析、薄片镜下鉴定与野外地质调查相结合的方法完善解译结果。岩性提取结果显示,小赛什腾山东侧新发现“U”型条带,为辉长岩—英云闪长岩—二长花岗岩,重新圈定达肯大坂群第三岩组和第四岩组,滩间山群一组,花岗岩,二长花岗岩,黑云母花岗岩,流纹岩,似斑状石英闪长岩,石英闪长岩,英云闪长岩,辉长闪长岩和辉长岩等岩石的边界。此次基于多源遥感数据岩性识别方法研究对青海赛什腾地区野外地质调查工作具有指导意义,可为高山峡谷区地质填图提供技术参考。     

Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite radar interferometry

ABSTRACT

Over-exploitation of groundwater has caused severe land subsidence in Beijing during the past two decades. Since the middle route of South-to-North Water Diversion Project (SNWDP), the biggest water diversion project in China, started to deliver water to Beijing in December 2014, the groundwater shortage has been greatly alleviated. This study aims to analyze the impact of SNWDP on the spatiotemporal evolution of land subsidence in Beijing. Change in surface displacement in Beijing after SNWDP was retrieved and the spatiotemporal patterns of the change were analyzed based on long time-series Envisat Advanced Synthetic Aperture Radar (ASAR) (2004–2010), Radarsat-2 (2011–2014), and Sentinel-1 (2015–2017) satellite datasets using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques. Land subsidence unevenness index (LSUI) was proposed to represent the spatial unevenness of surface displacement. PS-Time approach was then adapted to examine the time series evolution of LSUI. The results showed that the InSAR measurements agree well with leveling measurements with R² over 0.96. Although the maximum annual displacement rate reached ?159.7 mm/year by 2017, over 57% of the area within 25 mm/year contour line showed decreasing or unchanged displacement rate after the south-north water delivered to Beijing. The settlement rate in Chaoyang-Dongbalizhuang (CD) subsidence center has decreased for 26 mm/year from 2011–2014 to 2015–2017. Only around 15% of the area experienced continued accelerating settlement rate through the three time periods, which was mainly located in the area with the compressible layer thickness over 190 m, while the magnitude of velocity increment considerably decreased after SNWDP. Land subsidence unevenness, represented by LSUI, developed more slowly after SNWDP than that during 2011–2014. However, LSUI at the edge of settlement funnel has kept developing and reached 1.7‰ in 2017. Decreasing groundwater level decline after SNWDP and the positive relationship (R² > 0.74) between land subsidence and groundwater level clearly showed impacts of SNWDP on the alleviating land subsidence. Other reasons include geological background, increasing precipitation, and strict water management policies implemented during these years.