<p>The handling of natural disasters, especially heavy rainfall and corresponding floods, requires special demands on emergency services. The need to obtain a quick, efficient and real-time estimation of the water level is critical for monitoring a flood event. This is a challenging task and usually requires specially prepared river sections. In addition, in heavy flood events, some classical observation methods may be compromised.</p><p>With the technological advances derived from image-based observation methods and segmentation algorithms based on neural networks (NN), it is possible to generate real-time, low-cost monitoring systems. This new approach makes it possible to densify the observation network, improving flood warning and management. In addition, images can be obtained by remotely positioned cameras, preventing data loss during a major event.</p><p>The workflow we have developed for real-time monitoring consists of the integration of 3 different techniques. The first step consists of a topographic survey using Structure from Motion (SfM) strategies. In this stage, images of the area of interest are obtained using both terrestrial cameras and UAV images. The survey is completed by obtaining ground control point coordinates with multi-band GNSS equipment. The result is a 3D SfM model georeferenced to centimetre accuracy that allows us to reconstruct not only the river environment but also the riverbed.</p><p>The second step consists of segmenting the images obtained with a surveillance camera installed ad hoc to monitor the river. This segmentation is achieved with the use of convolutional neural networks (CNN). The aim is to automatically segment the time-lapse images obtained every 15 minutes. We have carried out this research by testing different CNN to choose the most suitable structure for river segmentation, adapted to each study area and at each time of the day (day and night).</p><p>The third step is based on the integration between the automatically segmented images and the 3D model acquired. The CNN-segmented river boundary is projected into the 3D SfM model to obtain a metric result of the water level based on the point of the 3D model closest to the image ray.</p><p>The possibility of automating the segmentation and reprojection in the 3D model will allow the generation of a robust centimetre-accurate workflow, capable of estimating the water level in near real time both day and night. This strategy represents the basis for a better understanding of river flooding and for the development of early warning systems.</p>

<p>Between June and August 2022, the European Forest Fire Information System (EFFIS) reported more fires in Europe than in any other recent summer season. This is particularly true for Central Europe, where the largest forest fire in recent Czech history occurred in the German-Czech border region. With global warming and resulting longer dry periods, the length and severity of wildfire seasons in central Europe will likely increase. Therefore, easy to implement and cost-effective methods to assess wildfire damage and regeneration of the ecosystems are getting increasingly important. In this study we evaluated how different datasets obtained by uncrewed aerial system (UAS) can be incorporated with datasets obtained from the ground to describe the fire affected landscape. Thereby, multi-spectral 3D point clouds were derived from low-cost UAV laser scanning and using structure from motion (SfM) photogrammetry applied to RGB and multi-spectral imagery. The aerial datasets were combined with ground-based terrestrial and mobile laser scanning. The datasets were acquired in several surveys following the forest fire event in the German part of the National park Saxonian/Bohemian Switzerland.</p><p>Initial results show the potential of UAS-based sensing for efficient mapping of a burned area with a high resolution (600-1000 pts/m²). The combination of point clouds from UAS-based laser scanning and photogrammetry enables a detailed representation of the burned forest with different levels of fire damage (e.g., in still present canopy) when compared to the single datasets. The UAS based laser scanning data reveals higher noise compared to the SfM-based point clouds. However, the accuracy is still sufficient to improve the quality of orthomosaics in densely vegetated areas. In a next step, further investigations on data accuracy are conducted and automated point cloud fusion algorithms based on classified points are considered.</p>

<p>Obtaining real-time water level estimations is crucial for effective monitoring and response during emergencies caused by heavy rainfall and rapid flooding. Typically, this type of monitoring can be a difficult task, requiring river reach preparations and specialized equipment. Moreover, in extreme flood events, standard observation methods may become ineffective. This is why the possibility of developing low-cost, automatic monitoring systems represents a significant advancement in our ability to monitor river courses and allow emergency teams to respond appropriately.</p> <p>Image-based methods for water level estimation facilitate the development of a low-cost river monitoring strategy in a quick and remote approach. These techniques are faster and more convenient regarding the setup than traditional water stage monitoring methods, allowing us to efficiently monitor the river from different locations with a cost-effective approach. By increasing the density of the observation network, we can improve flood warning and management.</p> <p>The approach presented involves placing cameras in secure locations to capture images of the river, for which we have previously modelled the terrain in 3D using Structure from Motion (SfM) algorithms supported by GNSS data. With the images obtained every 15 minutes, we perform a Convolutional Neural Network (CNN) segmentation based on artificial intelligence algorithms that allow us to automatically extract the contours of the water surface area. In this study, two different neural network approaches are presented to segment water in the images.</p> <p>Using a photogrammetric strategy, we reproject the water line extracted by the AI on the 3D model of the scene. This reprojection is also supported by the use of a keypoint detection neural network that allows us to accurately identify the ground control points (GCPs) observed in the images captured by the surveillance camera. This approach allows us to automatically assign to each image the real coordinates of the GCPs and subsequently estimate the camera pose.</p> <p>This AI segmentation and automatic reprojection into the 3D model has allowed us to generate a robust centimetre-accurate workflow, capable of estimating the water level in near-real time for daylight conditions. In addition, the automatic detection of the GCP has permitted to obtain automatic water level measurements over a longer period of time (one year). This approach represents the basis for obtaining other river monitoring parameters, such as velocity or discharge, which allow a better understanding of river floods and represent key steps for the development of early warning systems for flood events.</p>

<p>The measuring of flood events is associated with many challenges. Among them is the determination of flow velocities for the derivation of discharge. Most of the applied methods for velocity determination the disadvantage that they work in direct contact with water. This often makes measuring under critical flow conditions dangerous. Optical measurement methods have a great advantage because they can work remotely, i.e., without water contact.</p> <p>For representative discharge measurements, flow velocity measurements over the entire width of the river cross-section are required. This is a major challenge in the application of PTV, because visible particles must be present across the entire cross-section, which is not always the case. The potential measurement gaps in the surface velocity distribution have a negative effect on the quality of the discharge determination. Because optical measurement methods are relatively new in hydrology, there is not yet a standardised procedure with which the discharge can be determined. </p> <p>The "OptiQ" method presented here is an approach for determining discharge using PTV. This method is based on the continuity equation, which is dependent on two variables, the flow area and the mean flow velocity. The challenge here is to determine the depth-averaged flow velocity, because PTV is used to determine the surface velocity. To get the depth-averaged flow velocities, the PTV results are averaged over a transect and converted using a velocity coefficient. The arithmetic mean, the velocity area method (DIN EN ISO 748:2008-02) and the moving average are considered as averaging methods. A statistical approach was chosen for closing measurement gaps that occurred in the velocity distribution. In this approach, the measurement results with similar discharge conditions in the entire time series, i.e. PTV results for the same water levels, are statistically analysed, filtered and summarised in a lookup table. The gaps in the measurements due to missing particles are filled with the data from the lookup table.</p> <p>For the data collection, three camera gauges were installed at regular gauging stations of the Saxon State Agency for Environmental and Agricultural Monitoring (BfUL). The camera gauges recorded short video sequences at regular time intervals, which were used to determine the velocity distributions using the FlowVelo tool (Eltner et al., 2020). This resulted in three time series covering a period of 10-15 months. For the validation of the optical discharge time series, the regular water level and discharge measurements of the BfUL are used. </p> <p>The application of "OptiQ" shows a significant adjustment of the optically determined discharge data to the reference measurement at all three gauging stations. While acceptable results were determined with the arithmetic mean only at higher discharge, the results with the velocity area method and the moving average are similarly good at all discharges. At the gauging station in Elbersdorf, the average difference from the reference value could be reduced from 29% to 15% with "OptiQ". In the next step, it is planned to further develop the statistical model "OptiQ" by using Deep Learning.</p>

<p>The importance of optical measurement methods in hydrology is increasing in the last years. In contrast to conventional gauging techniques, they can be applied remotely, making the measurement safe for humans and equipment, even under difficult measurement conditions. One important hydrological parameter to measure is discharge. Deriving discharge with remote sensing can be done by applying particle tracking velocimetry (PTV) in combination with the velocity area method (VAM). VAM is a standardized and established method in hydrology. For reliable discharge results with the VAM, surface flow velocity measurements and thus trackable particles in the case of PTV usage are required across the entire width of the river cross section, which is not always the case in natural observation conditions. To fill these data gaps several statistical methods were investigated that incorporate information provided at different measurement times but with similar discharge conditions.</p><p>In this study, data were collected over longer time periods with different cameras at a gauging station of a medium scale river in Saxony, Germany. Stationary cameras recorded short videos, which are used to estimate the velocity distribution at the water surface using PTV incorporated in the FlowVelo tool (Eltner, 2020), and afterwards, to estimate the discharge using VAM. The obtained discharge time series from different cameras and camera positions were used to analyse the performance of different gap filling approaches. The results were compared to discharge and water level measurements of the official gauging station maintained by the federal measuring agency. They show, that the adjustment to the data of the reference measurements increases significantly by application of the gap filling methods. Next steps are to enhance the presented methods by using targeted data filtering and deep learning.</p><p><strong>Keywords</strong>: <em>velocity area method, particle tracking velocimetry, camera based discharge estimation</em></p>