Using structure from motion for analyzing change detection and flood events in the context of flood preparedness: a case study for the Laufer Muehle area at the Aisch river in Germany for conducting near real-time analyses

IF 2.3 Q2 REMOTE SENSING
Michael Kögel, Dirk Carstensen
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引用次数: 0

Abstract

Recent flood events (FE) in Germany have shown that the extent and impact of extreme flood events cannot be estimated solely based on numerical models. For analyzing the development of such an event and to develop and implement safety measures more efficiently, additional data must be collected during the event. Within the scope of this research, the possibilities of near real-time recording using an unmanned aerial vehicle (UAV) and data processing with the Structure from Motion (SfM) method were tested in a case study. Different recording parameter combinations were tested in the Laufer Muehle area on the Aisch river in Germany. The focus of the investigations was the identification of a parameter combination that allows a short recording interval for aerial imagery. Based on these findings, the identification of changes in the study area by comparing multitemporal photography (flood prevention), as well as the recording of flooded areas during a FE should be possible. The accuracy analysis of the different parameter combinations between two point clouds as well as the process of change detection was done by a Multiscale Model to Model Cloud Comparison (M3C2) and including ground control points. As a result, a parameter combination was identified which led to the desired results in the study area. The processes were transformed into fully automated and scripted workflows. The results serve as a basis for establishing a workflow for near real-time analyses in future studies.

在防洪准备工作中利用运动结构分析变化探测和洪水事件:对德国艾施河劳费尔-穆埃勒地区进行近实时分析的案例研究
德国最近发生的洪水事件(FE)表明,极端洪水事件的范围和影响不能仅靠数值模型来估计。为了分析此类事件的发展,更有效地制定和实施安全措施,必须在事件发生时收集更多数据。在本研究范围内,使用无人飞行器(UAV)进行近实时记录和使用 "运动结构"(SfM)方法进行数据处理的可能性在案例研究中进行了测试。在德国艾施河的劳费尔-穆埃勒地区测试了不同的记录参数组合。研究的重点是确定一种参数组合,以缩短航空图像的记录时间间隔。基于这些发现,通过比较多时摄影(防洪)来识别研究区域的变化以及记录 FE 期间的洪水区域应该是可能的。通过多尺度模型与模型云对比(M3C2)以及地面控制点,对两个点云之间的不同参数组合以及变化检测过程进行了精度分析。结果,确定了一种参数组合,可在研究区域内获得理想的结果。这些过程被转化为完全自动化和脚本化的工作流程。这些结果可作为在未来研究中建立近实时分析工作流程的基础。
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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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