The research group “Hydrolab” coordinated by Prof. Salvatore Manfreda participates in the COST project “Harmonious – Harmonisation of UAS techniques for agricultural and natural ecosystems monitoring” with research teams from important European universities (Manfreda et al., 2018). The research activities related to the WG4 “River morphology and streamflow monitoring” aim to provide a scientific contribution to the definition of guidelines for river monitoring using Unmanned Aerial Systems (UASs). The research activities are addressed to the identification of methodologies and procedures to optimise surface flow velocities estimation by field experiences and numerical modelling. Specifically, the research activities concern the analysis of surface velocity field from optical sensors mounted on mobile platforms (UAS).

The research contributions involve the following topics: 1) the construction of a database of case studies with supporting benchmark measurements; 2) the experimentation of image pre-processing techniques to optimise surface velocity estimates; 3-4) the application and comparison of image velocimetry techniques and tools to real case studies; and, 5) the elaboration of guidelines for hydrological monitoring by UAS.

Figure 1. Activities go the WG4 of the Harmonious COSt Action on image velocimetry.

The Hydrolab research group in the last years has actively contributed to:

(2a) the implementation and comparison of image stabilisation techniques aimed at removing the effects of camera movements induced by wind, vibrations or operator inexperience (http://10.5194/hess-2021-112). VISION software is an open-source tool written in Matlab for video stabilisation using the automatic detection of key features without direct operator control. Code is available at the following web address: http://10.17605/OSF.IO/HBRF2.

The synthetic images and the calculation codes relating to the metrics and the SDI index are published at the following link: https://doi.org/10.17605/OSF.IO/8EGQW.

Figure 3. Frame selection based on the SDI evolution in time.

This page is part of the Hydrological Monitoring research line of HydroLAB. See also: Innovation in Hydrological Monitoring · Flow-Area Rating Curve · Soil Moisture Monitoring · Water Quality Monitoring.

Publications on this topic

  • Dal Sasso, S. F., R. Ljubicic, A. Pizarro, S. Pearce, I. Maddock and S. Manfreda, Evaluating SSIMS-flow velocimetry performances under varying seeding densities: A proof-of-concept field study., 2026. [pdf]
  • Manfreda, S., D. Miglino, K. C. Saddi, S. Jomaa, A. Etner, M. Perks, D. Strelnikova, S. Peña-Haro, I. Maddock, F. Tauro, S. Grimaldi and Y. Zeng, Advancing river monitoring using image-based techniques: Challenges and opportunities., Hydrological Sciences Journal, 2024. [pdf] [doi]
  • Pizarro, A., S. F. Dal Sasso and S. Manfreda, VISION: VIdeo StabilisatION using automatic features selection for image velocimetry analysis in rivers., SoftwareX, 2022. [pdf] [doi]
  • Strelnikova, D., M. T. Perks, S. F. Dal Sasso, A. Eltner, S. Peña-Haro, A. Pizarro, P. Vuono, U. Scherling and S. Manfreda, A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations., Hydrology and Earth System Sciences, 2021. [doi]
  • Dal Sasso, S. F., A. Pizarro and S. Manfreda, Recent advancements and perspectives in UAS-based image velocimetry., Drones, 2021. [doi]
  • Dal Sasso, S. F., A. Pizarro, S. Pearce, I. Maddock and S. Manfreda, Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales., Journal of Hydrology, 2021. [doi]
  • 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, 2020. [doi]
  • Pizarro, A., S. F. Dal Sasso and S. Manfreda, Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation., Hydrological Processes, 2020. [doi]
  • Dal Sasso, S. F., A. Pizarro and S. Manfreda, Metrics for the quantification of seeding characteristics to enhance image velocimetry performance in rivers., Remote Sensing, 2020. [doi]
  • Perks, M. T., S. F. Dal Sasso, A. Hauet, E. Jamieson, J. Le Coz, S. Pearce, S. Peña-Haro, A. Pizarro, D. Strelnikova, F. Tauro, J. Bomhof, S. Grimaldi and S. Manfreda, Towards harmonisation of image velocimetry techniques for river surface velocity observations., Earth System Science Data, 2020. [doi]
  • Pearce, S., S. Peña-Haro, M. Perks, F. Tauro, A. Pizarro, S. F. Dal Sasso, D. Strelnikova, S. Grimaldi, I. Maddock, G. Paulus and S. Manfreda, An evaluation of image velocimetry techniques under low flow conditions and high seeding densities using unmanned aerial systems., Remote Sensing, 2020. [doi]
  • Dal Sasso, S. F., A. Pizarro, C. Samela, L. Mita and S. Manfreda, Exploring the optimal experimental setup for surface flow velocity measurements using PTV., Environmental Monitoring and Assessment, 2018. [pdf] [doi]