This is the questionnaire developed by HARMONIOUS aimed at identifying the most urgent and important challenges drone-based science will probably face in the near future. All UAS users are encouraged to participate on this short survey.
River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating 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 level of aggregation. Therefore, a requirement 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 particle aggregation, 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) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρcν1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.
How to cite: Pizarro, A., Dal Sasso, S. F., Perks, M., and Manfreda, S.: Spatial distribution of tracers for optical sensing of stream surface flow, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-188, in review, 2020. [pdf]
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.
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]
River monitoring is a critical issue for hydrological modelling that strongly relies on the use of Flow Rating Curves (FRCs). In most of the cases, FRCs are approximated by least-squares fitting, whose performance may be influenced by measurements variability, which is often limited in high values. In this context, a new formulation has been recently introduced to exploit available knowledge on cross-sectional geometry for a more robust derivation of FRCs. This method combines the wetted-area/stage and the cross-sectionally averaged velocity/stage functions in the FRCs derivation limiting, at least partially, the uncertainty in the extrapolation of higher discharge values. The methodology is tested on four gauged cross-sections of the Tiber River basin, where a relatively high number of measurements are available. This dataset is used to test the reliability of the new approach with respect to the classic method in relatively stable river cross-sections. A jackknifing approach is used to understand the role played by the number of gaugings and range of observations on the applicability of the new formulation highlighting its advantages in data-scarce environments. In particular, we observed that the new approach becomes advantageous when the observations are limited both in terms of the range of observations or in terms of sample size (i.e., <10 samples).
How to cite: Manfreda, S., A. Pizarro, T. Moramarco, L. Cimorelli, D. Pianese, S. Barbetta, Potential advantages of flow-area rating curves compared to classic stage-discharge-relations, Journal of Hydrology, Volume 585, 124752, 2020. [pdf]
It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.
How to cite: Zhuang, R.; Zeng, Y.; Manfreda, S.; Su, Z. Quantifying Long-term Land Surface and Root Zone Soil Moisture over Tibetan Plateau. Remote Sens. 2020, 12, 509. [pdf]
Foundation scour is among the main causes of bridge collapse worldwide, resulting in significant direct and indirect losses. A vast amount of research has been carried out during the last decades on the physics and modelling of this phenomenon. The purpose of this paper is, therefore, to provide an up-to-date, comprehensive, and holistic literature review of the problem of scour at bridge foundations, with a focus on the following topics: (i) sediment particle motion; (ii) physical modelling and controlling dimensionless scour parameters; (iii) scour estimates encompassing empirical models, numerical frameworks, data-driven methods, and non-deterministic approaches; (iv) bridge scour monitoring including successful examples of case studies; (v) current approach for assessment and design of bridges against scour; and, (vi) research needs and future avenues.
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.
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]
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.
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]
Workshop “Standardization of procedures in using UAS for environmental monitoring”
Date: 6 November, 2019
Venue: Department of Civil Engineering, University of Coimbra (Coimbra, Portugal)
We are pleased to inform that the next Workshop of the COST Action HARMONIOUS will be held at the University of Coimbra, in Coimbra, Portugal, on November 6th, 2019. The Workshop will focus on the Standardization of procedures in using UAS for environmental monitoring, and will be opened to all interested (scientific and technical communities, stakeholders). Come to Coimbra to find out about our activities!
Contributions from the scientific and technical communities are welcome and will be presented in oral and poster sessions.
Submit an Abstract (Deadline 30/07/2019)
In the following days after this workshop, there will be other activities reserved for COST HARMONIOUS members only: WG meetings (7/11/2019) and MC meeting (8/11/2019).
More info are available here:
Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, post-processing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.
How to cite: 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, F. Tauro, M. I. de Lima, J. L. M. P. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, On the Use of Unmanned Aerial Systems for Environmental Monitoring, Remote Sensing, 10(4), 641; (doi:10.3390/rs10040641) 2018. [pdf]