Towards harmonisation of image velocimetry techniques for river surface velocity observations

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 Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.

How to cite: Perks, M. T., Dal Sasso, S. F., Hauet, A., Jamieson, E., Le Coz, J., Pearce, S., Peña-Haro, S., Pizarro, A., Strelnikova, D., Tauro, F., Bomhof, J., Grimaldi, S., Goulet, A., Hortobágyi, B., Jodeau, M., Käfer, S., Ljubičić, R., Maddock, I., Mayr, P., Paulus, G., Pénard, L., Sinclair, L., and Manfreda, S.: Towards harmonisation of image velocimetry techniques for river surface velocity observations, Earth Syst. Sci. Data, 12, 1545–1559,, 2020. [pdf]

Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers

River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing.

Keywords: river monitoring; image velocimetry; LSPIV; PTV; UAS; surface flow velocity; seeding density

How to cite: Dal Sasso, S.F.; Pizarro, A.; Manfreda, S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in RiversRemote Sens. 202012, 1789. [pdf]

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

Concept Diagram

How to cite: Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Dor, E.B.; Szabó, B.; Vico, G.; Nasta, P.; Zhuang, R.; Francos, N.; Mészáros, J.; Sasso, S.F.D.; Bassiouni, M.; Zhang, L.; Rwasoka, D.T.; Retsios, B.; Yu, L.; Blatchford, M.L.; Mannaerts, C. An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water 202012, 1495. [pdf]

Current Practices in UAS-based Environmental Monitoring

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

Figure 1 – Workflow Suggested by HARMONIOUS WG1

How to Cite: Tmušić, G.; Manfreda, S.; Aasen, H.; James, M.R.; Gonçalves, G.; Ben-Dor, E.; Brook, A.; Polinova, M.; Arranz, J.J.; Mészáros, J.; Zhuang, R.; Johansen, K.; Malbeteau, Y.; de Lima, I.P.; Davids, C.; Herban, S.; McCabe, M.F. Current Practices in UAS-based Environmental Monitoring. Remote Sens.12, 1001, 2020. [pdf]

An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

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.

Figure 1: Comparison of surface flow velocities obtained with different algorithms.

How to cite: Pearce, S.; Ljubičić, R.; Peña-Haro, S.; Perks, M.; Tauro, F.; Pizarro, A.; Dal Sasso, S.F.; Strelnikova, D.; Grimaldi, S.; Maddock, I.; Paulus, G.; Plavšić, J.; Prodanović, D.; Manfreda, S. An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. Remote Sens., 12, 232, 2020. [pdf]

Training Course “Monitoring Natural and Agricultural Ecosystems with Unmanned Aerial Systems (UAS)”

HARMONIOUS Training Course 2020  – Villach, 26.-29.2 2020

COST Action HARMONIOUS is organizing a Training Course on UAS-based moitoring of Natural and Agricultural Ecosystems.


  1. Preprocessing of the UAS data
  • Flight planning and data preprocessing
  • Radiometric Calibration
  • 3D reconstruction with UAS (Photogrammetry)
  • Quality assessment of the UAS-products
  1. UAS Vegetation Monitoring

Explaining the theory and methods, hand on activities on the case studies

  • Monitoring Natural Ecosystems – plant composition/change and forest structure
  • Monitoring Agricultural Ecosystems – plant stress and soil moisture monitoring
  • Practical examples
  • Subgroup Activities on the case studies proposed by trainers (each participant chooses either natural or agricultural topic, students will work in small groups on the sample data provided with the help of material provided and tutors)


Carinthia University of Applied Sciences (CUAS)

Spatial Information Management, Europastrasse 4, A-9524 Villach, Austria

CUAS Campus in Villach/St. Magdalen


Free of charge, accommodation and travel will be supported by the COST Action HARMONIOUS – Harmonization of UAS techniques for agricultural and natural ecosystems monitoring. Selected students will receive an economical support of 800€.


To register, fill the form at

Places are limited and registration works on first come, first serve bases. For more information see

Posted on Categories NewsTags

Applications of Small Unmanned Aircraft Systems: Best Practices and Case Studies

Small Unmanned Aircraft Systems can access hazardous or inaccessible areas during disaster events and provide rapid response. This is the first book that brings together the best practices of sUAS applied to a broad range of issues in high spatial resolution mapping projects. The case studies included in this book are sUAS based projects.


• Focuses on small UAS based data acquisition and processing into high spatial resolution map products;

• Introduces practical guidance on choosing small UAS hardware, sensors, and software utilized for geospatial mapping;

• Includes a broad range of recently developed case studies lead by highly experienced academics;

• Provides a holistic overview of scientific data acquisition and processing issues and approaches for applications in natural resources, urban environment, disaster response, socio-economic and socio-cultural domains;

• Explains FAA regulations and highlights the different approaches required for mission planning and data analysis.

ORDER NOW AND GET 20% Discount

Il ruolo dei droni nella tutela del territorio ed il monitoraggio ambientale

Il monitoraggio rappresenta la principale fonte di conoscenza nelle scienze ambientali. I recenti progressi nelle tecnologie nell’ambito dell’Osservazione della Terra offrono molteplici possibilità per il monitoring e la tutela ambientale. I Sistemi Aeromobili a Pilotaggio Remoto (SAPR), noti anche con la denominazione di droni, consentono di effettuare una nuova gamma di rilievi ambientali ad altissima risoluzione. Una delle caratteristiche chiave dei sistemi SAPR deriva dalla possibilità di operare come piattaforma multi-sensore, offrendo una visuale estesa dallo spettro del visibile a quello dell’infrarosso termico.

In questo articolo, vengono presentate le principali attività di monitoraggio ambientale svolte dal gruppo di ricerca HydroLAB dell’Università della Basilicata.

Mappatura termica dei vigneti delle Cantine del Notaio a Maschito (PZ)

Manfreda, S., S.F. Dal Sasso, L. Mita, Il ruolo dei droni nella tutela del territorio ed il monitoraggio ambientale, Knowledge Transfer Review, n. 5, 110-115, 2019. [link]

Special issue on Remote Sensing

It is with great pleasure that we invite you to contribute to a Special Issue in the journal Remote Sensing  is dedicated to UAS-based studies focusing on environmental monitoring. Particularly, we welcome contributions with:

  • Added value of UAS data in environmental monitoring;
  • Methods and procedures for UAS data processing;
  • Use of UAS in precision farming;
  • Innovative applications of UAS data for rapid environmental mapping and change detection;
  • Advanced applications of UAS data for monitoring vegetation state, crop production, soil water content, river evolution, and stream flow;
  • Potential of different sensors and algorithms for environmental variables.

The deadline for manuscript submission is December 31, 2019.

Detailed information about the special issue as well as information and guidelines for abstract submission can be found here

Contents of the Special Issue

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees by J. M. JuradoL. OrtegaJ. J. Cubillas and F. R. Feito Remote Sens. 202012(7), 1106; – 31 Mar 2020 Cited by 2 Abstract 

Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams by Dariia StrelnikovaGernot PaulusSabine KäferKarl-Heinrich AndersPeter MayrHelmut MaderUlf Scherling and Rudi Schneeberger Remote Sens. 202012(3), 384; – 25 Jan 2020 Cited by 1 Abstract 

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features by Qi JiangShenghui FangYi PengYan GongRenshan ZhuXianting WuYi MaBo Duan and Jian Liu Remote Sens. 201911(7), 890; – 11 Apr 2019Cited by 5Abstract 

Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System by Adrien MichezPhilippe LejeuneSébastien BauwensAndriamandroso Andriamasinoro Lalaina HerinainaYannick BlaiseEloy Castro MuñozFrédéric Lebeau and Jérôme Bindelle, Remote Sens. 201911(5), 473; – 26 Feb 2019Cited by 5 – Abstract

Posted on Categories NewsTags