Advancing observation systems for a changing world
Introduction
Hydrological monitoring is undergoing a profound transformation driven by technological innovation, interdisciplinary approaches, and the urgent need to understand complex water-related processes. Traditional monitoring systems, while foundational, are increasingly insufficient to capture the spatial and temporal variability of hydrological dynamics in a rapidly changing environment .
Recent advances in remote sensing, unmanned aerial systems (UAS), image-based techniques, and low-cost sensing technologies are redefining how we observe, measure, and interpret hydrological processes. These innovations enable higher-resolution data, improved accessibility, and new opportunities for real-time and large-scale monitoring.
From Traditional Monitoring to Innovative Observation Systems
Conventional hydrological monitoring relies heavily on in situ measurements, which remain the “gold standard” but are often limited by cost, maintenance, and spatial coverage . The decline in monitoring networks worldwide has further emphasized the need for alternative approaches.
Innovative monitoring systems aim to:
- Increase spatial and temporal resolution
- Reduce costs and logistical constraints
- Enable continuous and distributed observations
- Integrate multi-source data streams
The emergence of new Earth Observation platforms—including satellites, drones, and ground-based imaging systems—has significantly expanded observational capabilities.
Role of Unmanned Aerial Systems (UAS)
Unmanned Aerial Systems have become a cornerstone of modern environmental monitoring by bridging the gap between ground observations and satellite remote sensing .
Key Contributions
- High-resolution spatial data acquisition
- Flexible deployment across diverse environments
- Cost-effective monitoring solutions
- Multi-sensor integration (optical, thermal, multispectral)
However, the rapid growth of UAS applications has led to fragmented methodologies, highlighting the need for standardization and harmonization of workflows .
Standardized Workflow (HARMONIOUS framework)
A major contribution of this research is the development of a generalized workflow for UAS-based monitoring, consisting of:
- Study design
- Pre-flight fieldwork
- Flight mission
- Data processing
- Quality assurance
This framework improves data quality, reproducibility, and operational efficiency .
Image-Based Hydrological Monitoring
One of the most transformative innovations is the use of image-based techniques for hydrological observations.
Applications
- Surface flow velocity estimation
- Flood monitoring
- River discharge estimation
- Snow and rainfall detection
Optical sensing and computer vision methods (e.g., image velocimetry) allow non-invasive and spatially distributed measurements, offering new insights into hydrological processes .
Recent Advances
Your recent work highlights how image-based river monitoring can:
- Improve accuracy and data richness
- Enable real-time analysis
- Integrate with artificial intelligence and citizen science
- Support decision-making in water management
These approaches represent a paradigm shift toward scalable, data-driven monitoring systems .
Innovation through Interdisciplinarity: The MOXXI Initiative
The MOXXI (Measurements and Observations in the XXI Century) initiative emphasizes innovation through:
- Low-cost sensing technologies
- Citizen science and participatory monitoring
- Cross-disciplinary collaboration
- Custom-built and opportunistic measurement systems
This initiative promotes a shift from traditional monitoring to adaptive, flexible, and creative observation strategies .
Challenges and Future Directions
Despite significant progress, several challenges remain:
- Lack of standardized protocols across applications
- Data integration and interoperability issues
- Quality assurance and uncertainty quantification
- Regulatory and operational constraints
Future research directions include:
- Integration of AI and machine learning
- Development of real-time monitoring systems
- Fusion of multi-source datasets
- Expansion of citizen science networks
The convergence of these approaches is expected to create a new generation of smart hydrological monitoring systems.
Publications
- 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]
- Marye, A. T., C. Caramiello, D. De Nardi, D. Miglino, G. Proietti, K. C. Saddi, C. Biscarini, S. Manfreda, M. Poggi and F. Tauro, Remote sensing for monitoring macroplastics in rivers: A review., WIREs Water, 2025. [pdf] [doi]
- Miglino, D., S. Jomaa, M. Rode, K. C. Saddi, F. Isgrò and S. Manfreda, Technical note: Image processing for continuous river turbidity monitoring — full-scale tests and potential applications., Hydrology and Earth System Sciences, 2025. [pdf] [doi]
- Mullerova, J., R. Kent, J. Bruna, M. Bruna, J. Estrany, S. Manfreda, A. Michez, M. Mokros, M. A. Tsiafouli and X. Gago, Understanding spatio-temporal complexity of vegetation using drones: What could we improve?., Journal of Environmental Management, 2024. [pdf] [doi]
- 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]
- Paridad, P., S. F. Dal Sasso, A. Pizarro, L. Mita, M. Fiorentino, M. R. Margiotta, F. Faridani, A. Farid and S. Manfreda, Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions., Acta Horticulturae, 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]
- Müllerová, J., X. Gago, J. Company, J. Estrany, J. Fortesa, S. Manfreda, A. Michez, G. Paulus, M. A. Tsiafouli and R. Kent, Characterizing vegetation complexity with unmanned aerial systems (UAS) — A framework and synthesis., Ecological Indicators, 2021. [pdf] [doi]
- Dal Sasso, S. F., A. Pizarro and S. Manfreda, Recent advancements and perspectives in UAS-based image velocimetry., Drones, 2021. [doi]
- Francos, N., N. Romano, P. Nasta, Y. Zeng, B. Szabó, S. Manfreda, G. Ciraolo, J. Mészáros, R. Zhuang, B. Su and E. Ben-Dor, Mapping water infiltration rate using ground and UAV hyperspectral data: A case study of Alento, Italy., Remote Sensing, 2021. [pdf] [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]
- Petropoulos, G. P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli and S. Manfreda, Exploring the use of UAVs with the simplified “triangle” technique for soil water content and evaporative fraction retrievals in a Mediterranean setting., International Journal of Remote Sensing, 2021. [pdf] [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]
- Manfreda, S., H. Aasen, M. James, G. Gonçalves, E. Ben-Dor, A. Brook, M. Polinova, J. J. Arranz, J. Mészáros, R. Zhuang, K. Johansen, Y. Malbeteau, I. P. Lima, C. Davids, S. Herban and M. McCabe, Practical guidance for UAS-based environmental mapping., Remote Sensing, 2020. [doi]
- Manfreda, S., A. Pizarro, T. Moramarco, L. Cimorelli, D. Pianese and S. Barbetta, Potential advantages of flow-area rating curves compared to classic stage-discharge relations., Journal of Hydrology, 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]
- Manfreda, S., P. Dvorak, J. Mullerova, S. Herban, P. Vuono, J. J. Arranz Justel and M. Perks, Assessing the accuracy of digital surface models derived from optical imagery acquired with unmanned aerial systems., Drones, 2019. [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]
- Manfreda, S., On the derivation of flow rating-curves in data-scarce environments., Journal of Hydrology, 2018. [doi]
- Manfreda, S., M. F. McCabe, P. E. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben-Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová and F. Tauro, On the use of unmanned aerial systems for environmental monitoring., Remote Sensing, 2018. [doi]
This page is part of the Hydrological Monitoring research line of HydroLAB. See also: Flow-Area Rating Curve · Soil Moisture Monitoring · Image Velocimetry · Water Quality Monitoring.