Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s ${}^{-1}$ , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s ${}^{-1}$ image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s ${}^{-1}$ of the ADCP measurements, on average.

Since the turn of the 21st century, image-based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed that are equipped with differing image pre-processing and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis (along with reference data) is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate and describe datasets acquired from seven countries across Europe and North America, consisting of videos that have been subjected to a range of pre-processing and image velocimetry analyses (Perks et al., 2020, https://doi.org/10.4121/uuid:014d56f7-06dd-49ad-a48c-2282ab10428e). Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.

In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible.

While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.

Comparison of routes recommended by online trip planners, such as Google Maps and MapQuest, with routes traveled by cyclists allows estimation of whether the routing criteria used in such trip planners are the same as those used by cyclists. This study uses GPS tracking data from the Endomondo fitness tracker app from trips in Miami-Dade (Florida) and North Holland and compares their characteristics to cycling routes suggested by Google Maps, MapQuest and the shortest path. Results highlight which trip attributes differ between Endomondo and its alternative trips, and how consistent those differences are across the two study areas. For example, Endomondo cyclists in both regions go through fewer traffic signals, and use more cycleways, footways, or bike lanes than suggested routes from Google and MapQuest. A multinomial logit model on observed Endomondo trips provides insight into the routing preferences of Endomondo commute and sport cyclists in both study regions.