In contrast to river floods, the enormous erosion potential in catchments contributes significantly to the extent of damage to infrastructure in valleys. This paper investigates the impact of the heavy precipitation event of 14–15 July 2021 on the railroad in the Ahr valley in Rhineland-Palatinate, Germany. In a first step, a detailed overview of the climatological and hydrological drivers using spatially high-resolved precipitation distribution and peak discharge modeling is provided, and the event is placed in a broader context by comparing it to past flash flood events from 1910 and 2016. In a second step, a detailed mapping of damages along the railroad line is performed using aerial photographs. The mapping revealed that bridges are the weakest point during a flood event and that they contribute to an increase and modification of the flood wave through backwater effects. Since flood events are expected to increase in the future, there is an urgent need to increase the resilience of transportation to this hazard and to answer the question of what magnitudes and return periods of events should be used in future sizing of rail infrastructure.

Riparian zones fulfill diverse ecological and economic functions. Sustainable management requires detailed spatial information about vegetation and hydromorphological properties. In this study, we propose a machine learning classification workflow to map classes of the thematic levels Basic surface types (BA), Vegetation units (VE), Dominant stands (DO) and Substrate types (SU) based on multispectral imagery from an unmanned aerial system (UAS). A case study was carried out in Emmericher Ward on the river Rhine, Germany. The results showed that: (I) In terms of overall accuracy, classification results decreased with increasing detail of classes from BA (88.9%) and VE (88.4%) to DO (74.8%) or SU (62%), respectively. (II) The use of Support Vector Machines and Extreme Gradient Boost algorithms did not increase classification performance in comparison to Random Forest. (III) Based on probability maps, classification performance was lower in areas of shaded vegetation and in the transition zones. (IV) In order to cover larger areas, a gyrocopter can be used applying the same workflow and achieving comparable results as by UAS for thematic levels BA, VE and homogeneous classes covering larger areas. The generated classification maps are a valuable tool for ecologically integrated water management.

Over the Hahnöfer Nebenelbe, a part of the Elbe estuary near Hamburg, Germany, a combined aerial survey with an unmanned aerial system (UAV) and a gyrocopter was conducted to acquire information about the water surface temperatures. The water temperature in the estuary is important for biological processes and living conditions of riverine organisms. This study aimed to develop a workflow that allows for comparing and analysing surface temperatures acquired by two different remote sensing systems. The thermal infrared (TIR) datasets were compared with in situ measurements gathered during the data acquisition, where both TIR datasets showed a varying bias. Potential error sources regarding the absolute and relative accuracy were investigated and modelled based on the available measurements, including emissivity, atmosphere, skin effect at the water surface, camera flat field correction and calibration. The largest effects on the observed TIR water temperature had the camera calibration and the modelled atmospheric effects. After the correction steps, both datasets could be combined to create a multitemporal representation of the temperature pattern and profiles over the survey area’s wadden flats.

Up-to-date information about vegetation types and hydromorphological structures and features are essential for the management of waterways. They are e.g. used for the monitoring and reporting of riparian statuses and their changes e.g. after river restoration and consequently, numerous man-days are spent on field surveys. To allow for an effective survey of vegetation and hydromorphology in large or even inaccessible areas, a data acquisition and processing workflow is being developed complementing in-situ methods with remote sensing techniques. This is part of the joint research project “mDRONES4rivers” funded by the German Federal Ministry of Transport and Digital Infrastructure (19F2054A). Aerial surveys by unmanned aerial systems (UAS) and a gyrocopter are combined with ground measurements of hyperspectral reflectance signatures as well as with field mapping of vegetation types and hydromorphological structures and features. The remote sensing data is classified with an object based image analysis and classification algorithm. The mobile and (at selected sites) permanent measurements of hyperspectral field data and the typical field surveys provide data for calibration. Contrary to other approaches that focus on what can be detected and classified with certain sensor systems and datasets, the project addresses equally the user needs to obtain certain classes for monitoring and reporting. The intended results are (i) data acquisition, correction and classification workflow combining remote sensing and field data, identification and change detection (ii) of important vegetation and biotope types and (iii) of hydromorphological structures and substrate as well as indicators necessary for the evaluation of the hydromorphological quality. The preliminary results to be presented include datasets from UAS, gyrocopter, and field surveys, an outline of processing workflow and classification algorithm based on Python scripts and eCognition software and first vegetation and hydromorphological classification results from spring and summer datasets. In conclusion, procedures and algorithms are developed to use remote sensing in combination with and for the reduction of time-consuming traditional field surveys as a future operational tool for monitoring riparian vegetation and structures.