Image-based approaches for surface velocity estimations are becoming increasingly popular because of the increasing need for low-cost river flow monitoring methods. In this context, seeding characteristics and dynamics along the video footage represent one of the key variables influencing image velocimetry results. Recent studies highlight the need to identify parameter settings based on local flow conditions and environmental factors apriori, making the use of image velocimetry approaches hard to automatise for continuous monitoring. The seeding distribution index (SDI) – recently introduced by the authors – identifies the best frame window length of a video to analyse, reducing the computational loads and improving image velocimetry performance. In this work, we propose a method based on an average SDI time series threshold with noise filtering. This method was tested on three case studies in Italy and validated on one in UK, where a relatively high number of measurements is available. Following this method, we observed an error reduction of 20-39% with respect to the analysis of the full video. This beneficial effect appears even more evident when the optimisation is applied at sub-sector scales, in cases where SDI shows a marked variability along the cross-section. Finally, an empirical parameter t was proposed, calibrated, and validated for practical uses to define the SDI threshold. tshowed relatively stable values in the different contexts where it has been applied. Application of the seeding index to image-based velocimetry for surface flow velocity estimates is likely to enhance measurement accuracy in future studies.
Keywords: Image Velocimetry, UAS, river flow monitoring, LSPIV, seeding metrics, Seeding Distribution Index, frame footage.
How to cite: Dal Sasso, S.F., A. Pizarro, S. Pearce, I. Maddock, S. Manfreda, Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales, Journal of Hydrology, 2021. [pdf]
This research presents an investigation of different strategies and tools for digital image stabilisation for image velocimetry purposes. Basic aspects of image stabilisation and transformation are presented, and their applicability is discussed in terms of image velocimetry. Seven free-to-use open-source tools (six community-developed and one off-the-shelf) are described and compared according to their stabilisation accuracy, robustness in different flight and ground conditions, computational complexity, ease of use, and other capabilities. A novel approach for fast stabilisation accuracy analysis is also developed, presented, and applied to the stabilised image sequences. Based on the obtained results, some general guidelines for choosing a suitable tool for specific image velocimetry tasks have been obtained. This research also aims to provide a basis for further development or improvement of digital image stabilisation tools, as well as for the analyses of stabilisation impact on image velocimetry results.
How to cite: Ljubičić, R., D. Strelnikova, M. T. Perks, A. Eltner, S. Peña-Haro, A. Pizarro, S. F. Dal Sasso, U. Scherling, P. Vuono, and S. Manfreda, A comparison of tools and techniques for stabilising UAS imagery for surface flow observations, Hydrology and Earth System Sciences, 2021. [pdf]
River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI=ν0.1/(ρ/ρcν1), where ν, ρ, and ρcν1 are the spatial-clustering level, the seeding density, and the reference seeding density at ν=1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 % and 16.1 % were calculated – using PTV and PIV, respectively – by employing the optimal frame window.
How to cite: Pizarro, A., S.F. Dal Sasso, M. Perks and S. Manfreda, Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow,Hydrology and Earth System Sciences, 24, 5173–5185, (10.5194/hess-24-5173-2020) 2020. [pdf]