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Combining photogrammetric reconstruction (close-range photogrammetry, CRP) and airborne photogrammetry through the structure from motion method (SFM) with terrestrial three-dimensional (3D) laser scanning (TLS), Maelstrom Cave on Big Island, Hawaii (USA), was mapped in three dimensions. The complementary properties of the two methods generated an overall model that depicted significant features of the cave both spatially and visually. Through various processes, the complex geometric quantities were derived from the model that can be used to answer microbiological and climatological questions. In this report, the procedure for the three-dimensional acquisition of the terrain surface above Maelstrom Cave as well as the interior of the cave with TLS and SFM is described. It is shown how the different data sets were combined and contrasted, including a comparison of geometries from the different survey operations. Finally, the editing processes used to quantify and simplify the cave geometry are presented, as well as the analysis of the ellipses generated accordingly to determine the geometric quantities. Through the analysis of the cave geometry, important geometric properties of the Maelstrom Cave could be quantified and categorized. In this way, an effective tool was developed to directly correlate the structure of the cave system with climatological and microbiological parameters in order to answer the corresponding questions.
Aufgrund veränderter Hochwasserabflüsse am Hochwasserrückhaltebecken Watzdorf waren die Überfallcharakteristik der Seitenentlastung sowie die Größe des potenziell abführbaren Abflusses zu quantifizieren. Die Bestimmung erfolgte durch dreidimensionale hydrodynamisch-numerische Simulationen. Die numerischen Berechnungsergebnisse wurden analytischen Berechnungen gegenübergestellt, wodurch deutliche Unterschiede zwischen den jeweiligen Wasserstand-Abfluss-Beziehungen identifiziert werden konnten.
Basierend auf 3-D-HN-Untersuchungen des Ist-Zustandes am Hochwasserrückhaltebecken Watzdorf konnte festgestellt werden, dass im Falle extremer Hochwasserereignisse der entsprechende Zufluss voraussichtlich nicht schadlos abgeführt werden kann. Aufgrund dieser Erkenntnis wurde durch konstruktive Anpassungen im Bereich der Hochwasserentlastungsanlage ein Plan-Zustand entwickelt und numerisch untersucht, durch welchen eine Erhöhung des schadlos abführbaren Zuflusses erfolgen sollte.
The assessment and mapping of riverine flood hazards and risks is recognized by many countries as an important tool for characterizing floods and developing flood management plans. Often, however, these management plans give attention primarily to open-water floods, with ice-jam floods being mostly an afterthought once these plans have been drafted. In some Nordic regions, ice-jam floods can be more severe than open-water floods, with floodwater levels of ice-jam floods often exceeding levels of open-water floods for the same return periods. Hence, it is imperative that flooding due to river ice processes be considered in flood management plans. This also pertains to European member states who are required to submit renewed flood management plans every six years to the European governance authorities. On 19 and 20 October 2022, a workshop entitled “Assessing and mitigating ice-jam flood hazard and risk” was hosted in Poznań, Poland to explore the necessity of incorporating ice-jam flood hazard and risk assessments in the European Union’s Flood Directive. The presentations given at the workshop provided a good overview of flood risk assessments in Europe and how they may change due to the climate in the future. Perspectives from Norway, Sweden, Finland, Germany, and Poland were presented. Mitigation measures, particularly the artificial breakage of river ice covers and ice-jam flood forecasting, were shared. Advances in ice processes were also presented at the workshop, including state-of-the-art developments in tracking ice-floe velocities using particle tracking velocimetry, characterizing hanging dam ice, designing new ice-control structures, detecting, and monitoring river ice covers using composite imagery from both radar and optical satellite sensors, and calculating ice-jam flood hazards using a stochastic modelling approach.
In dieser Studie wird die Umsetzbarkeit verschiedener Methoden zur Analyse von Eisbildung auf der Oder erläutert. Die Untersuchungen schließen Auswertungen von Sentinel-1-Satelliten-Aufnahmen, die Simulation von Eisbildung mit dem Programm RIVICE und den Abgleich der gewonnenen Daten mit einem digitalen Geländemodell (DGM) ein. Auf Basis dieser Studie soll ein neuer Leitfaden für die Prognose von Eisstauereignissen auf der Oder entwickelt werden, um notwendige Schritte zur Vermeidung von Überflutungen infolge von Eisstau einleiten zu können.
Through the mobilization of movable objects due to the extreme hydraulic conditions during a flood event, blockages, damage to infrastructure, and endangerment of human lives can occur. To identify potential hazards from aerial imagery and take appropriate precautions, a change detection tool (CDT) was developed and tested using a study area along the Aisch River in Germany. The focus of the CDT development was on near real-time analysis of point cloud data generated by structure from motion from aerial images of temporally separated surveys, enabling rapid and targeted implementation of measures. The differences identified in the study area using distance comparison (M3C2) were segmented into individual components and categorized. Subsequently, the data was compared to existing two-dimensional hydrodynamic numerical calculation results (HQ100). The implementation of the CDT is feasible for a variety of RGB camera-equipped aerial vehicles due to the point cloud-based analysis and postprocessing. By overlaying and visualizing the detected changes with numerical simulation results, a quick assessment of the hazard potential in the event of a possible flood can be made. In the case of the study area along the Aisch River, the localization of construction materials, a steel container with debris pile, and a motor vehicle in the flood hazard zone of a potential HQ100 event could be confirmed, although no mobilization of the materials was to be expected due to the expected hydraulic conditions of a flood event.
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.
Effective response to flood events requires high-resolution, frequently updated data on flooded areas for comprehensive flood risk assessments. Unmanned aerial vehicles (UAVs) equipped with conventional camera systems and classification based on orthophotos from photogrammetric postprocessing and artificial intelligence are widely used to detect flooded areas. However, these methods often involve time-intensive pre- and postprocessing steps and fail to incorporate geometric factors such as elevation data and water depths. This study introduces SSegRef2Surf, a novel tool that integrates classified flood raster data with terrain information. SSegRef2Surf refines and optimizes coarse raster classifications by filling shadowed areas and correcting misclassified regions. This tool reduces data requirements for AI training and minimizes postprocessing time, enabling near real-time flood monitoring. All processes necessary for SSegRef2Surf were optimized through sensitivity and accuracy analyses to reduce postprocessing duration to a minimum. A comparison of the SSegRef2Surf results with two-dimensional (2D) numerical model results for a flood event revealed discrepancies in the 2D model, caused by inaccuracies in the underlying terrain data. This comparison showed that 30% of the flooded areas identified in the 2D numerical results were incorrect, while missing areas (11%) were added. This highlights the significant potential of SSegRef2Surf for near real-time flood monitoring and traceability of flood events, as combining UAVs’ high-frequency surveying capabilities with SSegRef2Surf allows for more effective validation and optimization of 2D models.
