Mediterranean PhD School

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)

UAS-based Environmental Monitoring: Improving data collection through a standardised workflow

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.

UAS can provide visual data across the electromagnetic spectrum.

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:

  1. study design;
  2. pre-flight fieldwork;
  3. flight mission;
  4. processing of aerial data;
  5. quality assurance.

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.

Workflow: Each mission requires a bespoke study design. Then, a pre-flight study takes place before the flight mission. The final stage describes how to best process the imagery and data from the flight.

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:

  1. UAS regulations and legislation;
  2. platform and sensor choice;
  3. camera settings and UAS control software;
  4. target geo-referencing.
There are clear economic, temporal, and qualitative benefits in using UAS over satellites. However, UAS is still an immature technology and limitations exist in how data is collected and analysed.

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.

Orthomosaic obtained by UAS imagery, which highlights the potential to provide high level of details of natural ecosystems.

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.

Personal Response

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.

Special Issue on RS entitled Global Gridded Soil Information Based on Machine Learning

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.


Increasing LSPIV performances by exploiting the seeding distribution index

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]

Interview on SpeCtrum

Can you tell us how you started working on using UASs for environmental monitoring? What was your motivation, and what did you find the most interesting in this research field? What are the knowledge gaps and major challenges in this research field?

I have always been interested in spatial patterns of natural ecosystems. Nature is able to create an incredible diversity of elements that have been inspiring for all of us. The driving processes that produce such patterns are open questions stimulating many of my studies. In this context, UAS offers the opportunity to explore such patterns at a level of detail that was unimaginable a few years ago. Therefore, I envisaged the possibility to use this tool to tackle my research questions in the field of hydrological and ecohydrological science.

Can you share with us any current specific project, activity, or initiative that you are particularly excited about?

I’m particularly proud to be the Chair of the COST Action “Harmonization of UAS techniques for agricultural and natural ecosystems monitoring – HARMONIOUS”, which includes more than 100 scientists from 36 countries. The HARMONIOUS Action is one of the biggest Actions funded by COST Organization ( focusing on the development of guidelines for the use of UAS applied for hydrological monitoring. Members of the HARMONIOUS Action are now focusing on the preparation of a book edited by Elsevier providing more detailed guidelines for UAS applications in hydrology, which will be one of the main deliverables of the project.

More details about the project activities can be found on the web-page

What are some of the areas of research you’d like to see tackled over the next ten years?

UAS offers the opportunity of acquiring high-resolution data for monitoring environmental processes, bridging the gap between traditional field studies and satellite remote sensing [An important paper in this context is]. Their versatility, adaptability, and flexibility may allow the implementation of new strategies to support the validation of satellite products, which are systematically adopted in a series of operational weather and hydrological models. This may help to develop an integrated global monitoring system of higher accuracy and precision.

Can you share with us your perspectives and experiences on how UAS remote sensing has changed the way the world addresses environmental monitoring and conservation agendas? What do you think is the role of remote sensing and geospatial information science in achieving a sustainable environment?

With the evolution of drone technologies over the last decade, UAS became an inexpensive way of mapping environmental processes for forestry planning, tracking landslides, river monitoring and precision agriculture. Environmental agencies and civil protection are increasingly adopting UAS- photogrammetry, but there are an enormous number of additional information that may be retrieved by UAS (e.g., stream flow, morphological evolution, soil moisture, state of vegetation, among others). It is our responsibility to simplify the use of UASs and make their products accessible to anyone.

What are some of the biggest challenges you face (or have you faced) as a scientist in your field? Are there any common misconceptions about this area of research?

It is common to underestimate the complexity associated with the use of these tools. UAS requires a large number of competencies and knowledge that should be implemented in clear protocols in order to transform the huge amount of data acquired to useful information. Therefore, one challenge is represented by the standardization of procedures adopted for UAS surveys in different operating configurations and environmental conditions. In this context, the members of the HARMONIOUS COST Action have published some preliminary studies to support this process [see the manuscript].

Finally, what are you most passionate about? What is your advice to students and young professionals who are pursuing research on UAS remote sensing and environmental protection, and nature conservation? Which areas in this research field remain understudied and should be considered for future research?

I believe that UAS remote sensing will evolve in the coming years, offering new monitoring opportunities. One of the main limitations that we are encountering right now in the description of hydrological processes is represented by the limited extent of UAS imagery. There is a pressing need to extend the limits of surveyed areas in order to have intercomparison between UAS and satellite data. This may help to define downscaling procedures for the estimation of environmental variables at high resolution and over large scales. This will be possible with the use of long range UAS or with swarms of drones which will be fundamental for future advances in remote sensing.

Happy Easter

I would like to wish you all a Happy Easter of Resurrection.

After this long period of difficulties, I hope that this will really be a reborn for a new COVID free life.

A comparison of tools and techniques for stabilising UAS imagery for surface flow observations

This research presents an investigation of different strategies and tools for digital image stabilisation for image velocimetry purposes. Basic aspects of image stabilisation and transformation are presented, and their applicability is discussed in terms of image velocimetry. Seven free-to-use open-source tools (six community-developed and one off-the-shelf) are described and compared according to their stabilisation accuracy, robustness in different flight and ground conditions, computational complexity, ease of use, and other capabilities. A novel approach for fast stabilisation accuracy analysis is also developed, presented, and applied to the stabilised image sequences. Based on the obtained results, some general guidelines for choosing a suitable tool for specific image velocimetry tasks have been obtained. This research also aims to provide a basis for further development or improvement of digital image stabilisation tools, as well as for the analyses of stabilisation impact on image velocimetry results.

How to cite: Ljubičić, R., D. Strelnikova, M. T. Perks, A. Eltner, S. Peña-Haro, A. Pizarro, S. F. Dal Sasso, U. Scherling, P. Vuono, and S. Manfreda, A comparison of tools and techniques for stabilising UAS imagery for surface flow observations, Hydrology and Earth System Sciences, 2021. [pdf]

Impact of Flood Control Systems on the Probability Distribution of Floods

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 to provide 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 distribution is 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 to demonstrate 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 characterised by the presence of artificial detention basins can be used to improve existing flood mitigation practices, support the design of flood control systems and flood risk analyses.

How to cite: Manfreda, S., D. Miglino, and C. Albertini, Impact of Flood Control Systems on the Probability Distribution of FloodsHydrology and Earth System Sciences,, in review, 2021. [pdf]

VISION: VIdeo StabilisatION using automatic features selection

This project presents the codes and example of the use of one of the algorithms (FAST) used in the automatic feature selection part of the manuscript entitled “A comparison of tools and techniques for stabilizing UAS imagery for surface flow observations”. The “StabilisationFunction.m” is a Matlab function aiming at stabilising videos for image velocimetry analyses in rivers. It is a command-line function without GUI at the moment. An example of how to call the stabilisation function is also provided in the file “ExampleScript.m”. All the codes were written in Matlab R2020a.


How to cite: Pizarro, A., S.F. Dal Sasso, S. Manfreda, VISION: VIdeo StabilisatION using automatic features selection, DOI 10.17605/OSF.IO/HBRF2, 2021.