While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.
How to cite: Ljubičić, R., Strelnikova, D., Perks, M. T., Eltner, A., Peña-Haro, S., Pizarro, A., Dal Sasso, S. F., Scherling, U., Vuono, P., and Manfreda, S.: A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations, Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, 2021. [pdf]
Ecosystem complexity is among the important drivers of biodiversity and ecosystem functioning, and unmanned aerial systems (UASs) are becoming an important tool for characterizing vegetation patterns and processes. The variety of UASs applications is immense, and so are the procedures to process UASs data described in the literature. Optimizing the workflow is still a matter of discussion. Here, we present a comprehensive synthesis aiming to identify common rules that shape workflows applied in UAS-based studies facing complexity in ecosystems. Analysing the studies, we found similarities irrespective of the ecosystem, according to the character of the property addressed, such as species composition (biodiversity), ecosystem structure (stand volume/complexity), plant status (phenology and stress levels), and dynamics (disturbances and regeneration). We propose a general framework allowing to design UAS-based vegetation surveys according to its purpose and the component of ecosystem complexity addressed. We support the framework by detailed schemes as well as examples of best practices of UAS studies covering each of the vegetation properties (i.e. composition, structure, status and dynamics) and related applications. For an efficient UAS survey, the following points are crucial: knowledge of the phenomenon, choice of platform, sensor, resolution (temporal, spatial and spectral), model and classification algorithm according to the phenomenon, as well as careful interpretation of the results. The simpler the procedure, the more robust, repeatable, applicable and cost effective it is. Therefore, the proper design can minimize the efforts while maximizing the quality of the results.
How to cite: Müllerová J. , X. Gago, M. Bučas,J. Company, J. Estrany, J. Fortesa, S. Manfreda, A. Michez, M. Mokroš, G. Paulus, E. Tiškus, M. A. Tsiafouli, R. Kent, Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis, Ecological Indicators, Volume 131, November 2021, 108156. [pdf]
Videos acquired from Unmanned Aerial Systems (UAS) allow for monitoring river systems at high spatial and temporal resolutions providing unprecedented datasets for hydrological and hydraulic applications. The cost-effectiveness of these measurement methods stimulated the diffusion of image-based frameworks and approaches at scientific and operational levels. Moreover, their application in different environmental contexts gives us the opportunity to explore their reliability, potentialities and limitations, and future perspectives and developments. This paper analyses the recent progress on this topic, with a special focus on the main challenges to foster future research studies.
How to cite: Dal Sasso, S.F.; Pizarro, A.; Manfreda, S. Recent Advancements and Perspectives in UAS-Based Image Velocimetry. Drones, 5, 81, 2021. [pdf]
Detention dams are one of the most effective practices for flood mitigation. Therefore, the impact of these structures on the basin hydrological response is critical for flood management and the design of flood control structures. With the aim of providing a mathematical framework to interpret the effect of flow control systems on river basin dynamics, the functional relationship between inflows and outflows is investigated and derived in a closed form. This allowed the definition of a theoretically derived probability distribution of the peak outflows from in-line detention basins. The model has been derived assuming a rectangular hydrograph shape with a fixed duration and a random flood peak. In the present study, the undisturbed flood peaks are assumed to be Gumbel distributed, but the proposed mathematical formulation can be extended to any other flood-peak probability distribution. A sensitivity analysis of parameters highlighted the influence of detention basin capacity and rainfall event duration on flood mitigation on the probability distribution of the peak outflows. The mathematical framework has been tested using for comparison a Monte Carlo simulation where most of the simplified assumptions used to describe the dam behaviours are removed. This allowed demonstrating that the proposed formulation is reliable for small river basins characterized by an impulsive response. The new approach for the quantification of flood peaks in river basins characterized by the presence of artificial detention basins can be used to improve existing flood mitigation practices and support the design of flood control systems and flood risk analyses.
How to cite: Manfreda, S., D. Miglino, and C. Albertini, Impact of detention dams on the probability distribution of floods, Hydrol. Earth Syst. Sci., 25, 4231–4242, https://doi.org/10.5194/hess-25-4231-2021, 2021 [pdf]
Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.
How to cite: Francos, N.; Romano, N.; Nasta, P.; Zeng, Y.; Szabó, B.; Manfreda, S.; Ciraolo, G.; Mészáros, J.; Zhuang, R.; Su, B.; Ben-Dor, E. Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: a Case Study of Alento, Italy. Remote Sensing, 13, 2606, (doi: 10.3390/rs13132606) 2021. [pdf]
Research and innovation driving transformative change.
Becoming the world’s first climate-neutral continent by 2050, Europe needs to modernize the approach to engineering design, to ensure an inclusive ecological transition.
Research and innovation will play a central role in accelerating and navigating the necessary transition to a climate-neutral engineering.
This Phd School aims to spread among young researchers the green transition in the field of civil, architectural and environmental engineering.
DICEA School series
This is the second event of a series of PhD Schools that our Department, DICEA, will organize annually in the framework of the Department of Excellence, project funded by the Italian Ministry of University and Research.
PhD Students in any field are invited to participate free of charge. Awards are available reserved to PhD students in the Civil, Architectural and Environmental Engineering area.
Topic of the school
The PhD school will include a plenary session (yellow), which will focus on Ecological Transition and four parallel thematic sessions (red) on Hydraulic, Transportation, Architectural and Geotechnical Engineering. An important effort will be devoted to applications (blue)
Unmanned Aerial Systems (UAS) play an increasingly important role in collecting data for environmental monitoring. The primary challenges for UAS in environmental studies include creating consistent, standardised guidelines for data collection and establishing practices that apply to a range of environments. Dr Salvatore Manfreda from the University of Naples Federico II, along with the HARMONIOUS team, identified critical steps in planning, acquiring, and processing UAS data to ensure best practices and a streamlined, effective workflow.
As drone technology has improved over the last decade, Unmanned Aerial Systems (UAS) have become a fundamental part of environmental monitoring, bridging the gap between traditional field studies and satellite remote sensing. UAS is an inexpensive way of acquiring visual data on a large temporal scale across the electromagnetic spectrum, making it an invaluable technology for monitoring dynamic environmental processes.
UAS can provide real-time aerial photography or video to map and monitor natural and artificial ecosystems, giving a unique insight into the environment. The versatility, adaptability, and flexibility of UAS make them an essential tool for environmental studies such as forestry planning, tracking glacier geomorphology and precision agriculture, to name but a few applications.
The continual improvements in UAS and sensor technologies, coupled with the variety of environmental settings in which they are deployed, have led to a diversity of methodologies in how data is collected, analysed, and processed. The inconsistencies in the UAS study designs have triggered multiple issues regarding the quality of the final imagery and data collected and have led to overblown budgets. These issues highlighted the necessity for a standardised protocol in UAS environmental mapping and monitoring to be developed.
There are clear economic, temporal and qualitative benefits in using UAS over satellites or manned aircraft.
Dr Manfreda from the University of Naples Federico II, with the international team of researchers of the HARMONIOUS COST Action, explored the primary issues in utilising UAS in environmental studies and produced guidance to improve planning, acquisition, and processing of data and the quality and reproducibility of research. They created a generalised workflow methodology with five interconnected steps:
processing of aerial data;
UAS limitations There are clear economic, temporal, and qualitative benefits in using UAS over satellites or manned aircraft, which are limited by their cost and how often a survey can use them. However, as UAS is still an immature technology, limitations exist in how data is collected and analysed.
Previous studies have indicated that many UAS surveys fail to consider the planning and processing of UAS imagery. When the speed and height of the UAS and the calibration of the sensors are not considered in the planning stage, and the weather is not accounted for on the day of the flight, the UAS imagery will be blurred or of incorrect resolution.
These limitations could be mitigated through a structure of standardisation which can work as a checklist for UAS surveys to ensure accurate collection and analysis of data.
Standardising UAS data collection Although every UAS survey will be slightly different owing to the wide variety of vegetation, topography, climate, and local legislation in study environments, a standardised workflow, which accounts for every stage of the survey and applies to every environment, will be incredibly beneficial in assuring appropriate planning for high-quality results.
Through creating a generalised workflow in five interrelated steps, HARMONIOUS’s research aims to improve the final quality of data and analysis. The workflow was designed based on harmonising multiple methods collated from recent research and reviews of different UAS surveys.
Workflow design Every UAS study can vary greatly and therefore requires a bespoke study design to set out a detailed mission plan for the study area. Consequently, the initial step in the workflow process is to design the study; this step is essential to set up the parameters of the survey and consider the specifics of the environment and the research question as this will shape where, how, and when the flight can take place and what sensors will be used.
When all factors are considered, the study design can be an incredibly complex problem. The final quality of the model is dependent on all of these interconnected factors being correctly accounted for.
In general, mission plans for environmental studies focus on four primary elements:
UAS regulations and legislation;
platform and sensor choice;
camera settings and UAS control software;
Local UAS regulations and legislation will have to be understood first to ensure the mission will get permission to fly in the study area. The platform, sensor, camera settings, and UAS control software choices are purely dependent on the survey’s requirements and limitations – concerning budget and time limitations, or the image quality, spectral and spatial resolution, and the survey area’s size. Finally, in the study design, target geo-referencing must be conducted to ensure the imagery is taken correctly. The best way to do this is to find ground control points (GCP) for reference.
Once the study design is complete, the next step in the workflow is to conduct a pre-flight study. This section of the workflow entails reconnaissance and a terrestrial survey of the survey area. The area’s reconnaissance will reveal take-off and landing points, any possible visual or flight obstructions, and any GCP’s for the flight to be geo-referenced. The field study will be highly dependent on the environmental medium being studied but will supplement and influence any data collected from the UAS study.
The researchers have created a harmonised workflow that will be an essential element of any UAS survey in the future.
Following the pre-flight, the workflow explains how to safely and most effectively conduct the flight itself. The challenge at this stage is to account for the weather accurately. Wind speed, humidity, light levels, and fog can affect data quality, so it must be compensated for before the flight takes place.
The final stage in the workflow describes how to best process the imagery and data from the flight. When processing, it is essential for the surveyors to account for the distortions, often in UAS imagery. These can misrepresent the radiometrics and geometrics of the study object. However, a series of steps quantify the radiometric or geometric problems, for which there is a corrective method.
A critical aspect of HARMONIOUS’s method is that quality assurance must be evaluated at every step to guarantee a quality survey outcome. One such way alluded to, which can save time and money and ensure quality images, uses a portable resolution test chart. These charts, when used correctly, can give assurances that cameras are calibrated correctly before the flight takes place.
A new standard practice Recent advances in UAS have meant that low-cost and near real-time data collection has become possible in an array of environmental studies. With their essential work, Dr Manfreda and his fellow researchers have created a harmonised workflow and accompanying checklists that will be a vital element of any UAS survey in the future, furthering the efficacy of UAS and making them a more valuable tool in studying the environment.
The researchers have designed the workflow to reduce error in data collection and processing and ensure flights are conducted within budget, safely, and effectively. This research will undoubtedly improve future UAS studies and be a template by which all reviews can be guided, streamlining the study process and making results easily reproducible.
HARMONIOUS’s research assists in furthering UAS procedures and ensuring that UAS studies in the future will have more accurate results if they utilise the workflow checklists referenced in this article.
As new iterations of UAS technologies are developed, could the workflow process become more automated?
We are now focusing on the preparation of a book edited by Elsevier providing more detailed guidelines for UAS applications in environmental monitoring.
This Special Issue is dedicated to machine learning-based methods in:•proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);•computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;•preprocessing Earth observation data to feed into global soil mapping;•data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;•optimizing temporal resolution to globally track the changes of soil properties;•uncertainty assessment of the derived gridded soil information;•other related topics.
A 40% discount can be granted to papers received from this conference/project on the basis that the manuscript is accepted for publication following the peer review process.
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]