Automatic co-registration of multi-epoch Unmanned Aerial Vehicle (UAV) image sets remains challenging due to the radiometric differences in complex dynamic scenes. Specifically, illumination changes and vegetation variations usually lead to insufficient and spatially unevenly distributed common tie points (CTPs), resulting in under-fitting of co-registration near the areas without CTPs. In this paper, we propose a novel Common-Feature-Track-Matching (CFTM) approach for UAV image sets co-registration, to alleviate the shortage of CTPs in complex dynamic scenes. Instead of matching features between multi-epoch images, we first search correspondences between multi-epoch feature tracks (i.e., groups of features corresponding to the same 3D points), which avoids the removal of matches due to unreliable estimation of the relative pose between inter-epoch image pairs. Then, the CTPs are triangulated from the successfully matched track pairs. Since an even distribution of CTPs is crucial for robust co-registration, a block-based strategy is designed, as well as enabling parallel computation. Finally, an iterative optimization algorithm is developed to gradually select the best CTPs to refine the poses of multi-epoch images. We assess the performance of our method on two challenging datasets. The results show that CFTM can automatically acquire adequate and evenly distributed CTPs in complex dynamic scenes, achieving a high co-registration accuracy approximately four times higher than the state-of-the-art in challenging scenario. Our code is available at https://github.com/lixinlong1998/CoSfM.

Large and shallow strike-slip earthquakes produce striking ground ruptures, damaging roads and infrastructure but providing great opportunities for examining the fault's structure. After the 2022 Mw 6.7 Menyuan earthquake, we observed abundant surface fractures by a combination of optical remote sensing, radar offset and unmanned aerial vehicle measurements. These fractures reveal a complex fault structure including apparent bending geometries and bifurcating branches, which are essential to understanding the mechanisms of faulting. In this paper, we used triangular dislocations to construct the fault geometry that reflected the distribution of measured strike changes but avoided unexcepted discontinuities and overlaps where the fault bent. The modeled fault geometry revealed an extensional releasing bend which was responsible for the southward branching of the fault rupture at its western edge. Our results also demonstrated the potential to explain the occurrence of aftershock clusters and to infer their fault geometries through the correlation analysis of the aftershock distribution and the slip induced stress field. The triangular dislocation model also enabled the calculation of the fault plane roughness and its spatial variation which directly controlled the fault slip magnitude and rupture termination. These analyses reveal an unprecedented level of detail of the fault structure and slip mechanics and, to some extent, offer insights into the physical processes and structural properties of crustal faults in the Earth's shallow crust.

随着光学遥感卫星的增多以及影像质量的提升,利用光学遥感影像监测地表形变呈现出极大的发展潜力.以金沙江流域典型的高位远程滑坡——白格滑坡为例,选取Sentinel-2光学影像,综合利用像素偏移量跟踪(Offset-tracking)技术和时序反演(Time-series Inversion)算法,对2018年"10·10"白格滑坡和"11·3"白格滑坡前后的地表形变进行了反演与分析.结果表明:①"10·10"白格滑坡发生前(2015年11月13日～2018年2月5日)坡体最大水平位移为31.69 m,时序位移呈现出明显的"初始启动→等速变形→加速变形"过程特征;②"11·3"白格滑坡发生前(2018年10月28日～2018年11月2日)东西向(主滑方向)形变高达12.89 m,与雷达影像偏移量跟踪结果一致;③"11·3"白格滑坡发生后(2020年1月16日～2022年2月4日)部分残留堆积体东西向累计形变最高达7.71 m,滑移迹象明显,存在再次成灾风险.通过分析COSI-Corr和CARST两种光学影像偏移量跟踪软件的形变结果、InSAR形变探测结果和无人机三维变化检测结果,验证光学影像偏移量跟踪方法探测地表形变具有较好的精度和可靠性.

我国是世界上受滑坡影响最大的国家之一,也投入了大量的人力物力开展区域性滑坡隐患探测工作.近年的政府工作表明,80％的滑坡发生在已圈定的隐患点范围外,80％的滑坡发生在防灾减灾工作条件相对薄弱的边远农村地区.为了解决这个困境,亟需:(1)厘清不同类型滑坡宜选用的广域探测技术,解决滑坡隐患广域探测的漏检问题;(2)突破社区协同滑坡防灾的难题,助力滑坡隐患探测和风险评估.本文将滑坡隐患分为4类:斜坡变形区、复活历史变形破坏区、稳定历史变形破坏区和潜在斜坡变形区,以便充分发挥多源遥感数据和技术的优势;进而提出一种"滑坡隐患广域探测-单体滑坡隐患风险评估-社区协同防灾"的多源遥感滑坡防灾技术框架.以青藏高原交通工程关键区段约10000 km2区域作为研究区,协同社区(如设计和建设单位)共识别出滑坡隐患263处,其中斜坡变形区249处,复活历史变形破坏区5处,稳定历史变形破坏区9处,并针对3个典型滑坡隐患进行风险定量评估和社区协同防灾.该多源遥感技术框架将有助于提高社区滑坡防灾的能力,也将直接服务于青藏高原交通工程的建设与运维.

Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods-single processing and multiprocessing-are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan'an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.