8th Galileo Conference in Naples (Italy)

Considering this year’s meeting situation, the organizing team has decided it is necessary to post-pone the event by one year to ensure for all participants a successful, safe on-site meeting and good travel conditions.

The new conference date is 19–22 September 2022.

Looking forward to an exciting exchange in the historical center of sunny Naples!

The organizing committee:
Günter Blöschl (TU Wien, Austria)
Isabelle Braud (Irstea, France)
Gabrielle de Lannoy (KU Leuven, Belgium) 
Karsten Høgh Jensen (University of Copenhagen, Denmark) 
Laurent Pfister (Luxembourg Institute of Science and Technology LIST, Luxembourg)
Nunzio Romano, Salvatore Manfreda and Paolo Nasta (University of Naples Federico II, Italy) 
Sonia Seneviratne (ETH Zurich, Switzerland) 
Ana Maria Tarquis (Universidad Politécnica de Madrid, Spain) 
Ilja van Meerveld (University of Zurich, Switzerland) 
Harry Vereecken, Heye Bogena, Ralf Kunkel and Roland Baatz (Forschungszentrum Jülich, Germany) 
Marc Voltz (INRAE, France) 
Yijian Zeng (Twente University, the Netherlands)


Additional information is at the conference webpage:
https://www.egu-galileo.eu/gc8-hydro/about/general_information.html.

Please, SAVE THE DATE and we look forward to seeing you in Naples next year.

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Trend of Annual Maximum Rainfall in Campania Region (Southern Italy)

Extreme rainfall events are increasing in both number and intensity at global scale; however, it is hard to quantify the impact of climate change at local scale given the strong temporal and spatial heterogeneity of this process. Moreover, limited data availability and its spatial variability requires significant effort to identify specific trends at the regional level. In this study, we attempt to construct a detailed description of rainfall patterns and trends over the Campania Region, southern Italy. For this reason, the dataset of rainfall annual maxima in pre-assigned durations was constructed using all available records and extended using interpolation methods such as Inverse Distance Weighting and Ordinary Kriging methods. The rainfall dataset allowed actual trends over the region to be quantified using the Mann-Kendall trend test and the record-breaking analysis. The trend test reveals that most of the rainfall stations display no statistically significant trend, however, an increasing trend of extreme rainfall for short durations, in specific portions of the region, is observed.

How to cite: Avino, A., S. Manfreda, L. Cimorelli, and D. Pianese, Trend of Annual Maximum Rainfall in Campania Region (Southern Italy), Hydrological Processes, (doi:10.1002/hyp.14447), 2021. [pdf]

In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.

How to cite: Zhang, L.; Zeng, Y.; Zhuang, R.; Szabó, B.; Manfreda, S.; Han, Q.; Su, Z. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sens. 202113, 4893. [pdf]

Call for Abstract EWaS5

University of Naples Federico II and  University of Thessaly are organizing the 5th EWaS (Efficient Water Systems) International Conference on “Water Security and Safety Management: emerging threats or new challenges? Moving from Therapy and Restoration to Prognosis and Prevention”. The meeting will take place in Naples (Italy) from 13th to 16th July, 2022.

Abstract should be submitted by December 15th, 2021 using the following platform: 

Abstract Submission

Home pagewww.ewas5.it

Within the 5th EWaS, we are promoting the special session entitled “Impact of climate change on hydrological extremes

Delineation of flood-prone areas in cliffed coastal regions through a procedure based on the geomorphic flood index

The geomorphic flood index (GFI) method provides a good representation of flood-prone areas. However, the method does not account for floodwater transfers in undefined interbasins (UIBs), which represent intercluded small basins along the coastline likely to be flooded by adjacent major rivers. The present work addresses this shortcoming by complementing the GFI approach with an iterative procedure that considers UIBs and water transfers between basins. The methodology was tested on a coastal basin in southern Italy and the outcome was compared with a flood map obtained by a two-dimensional hydraulic simulation. GFI performance as a morphological descriptor improved from 74% (standard method) to 94% with the addition of the iterative procedure. The proposed methodology, with the same parameterization, was applied on a second adjacent coastal basin obtaining improvements both in terms of true positive (from 56 to 79%) and false negative rates (from 44 to 21%). Finally, a sensitivity analysis to the flood return periods highlighted a strong influence on model parameterization for return periods below 20 years. This achievement represents a new development in the application of the GFI method, which can help stakeholders in a more time- and cost-effective flood risk management in hazard-prone areas.

How to cite: Albertini, C., D. Miglino, V. Iacobellis, F. De Paola, S. Manfreda, Flood-prone areas delineation in coastal regions using the Geomorphic Flood IndexJournal of Flood Risk Management, e12766,(https://doi.org/10.1111/jfr3.12766) 2021.

3D Models of the Cultural Heritage

UAS-based surveys and structure from motion (SfM) can lead to extraordinary and realistic 3D models to preserve our cultural heritage.

In our recent applications, our members are developing new strategies to build extremely detailed point clouds using UAS and portable cameras. In the following, we provided some examples developed within HARMONIOUS partnership cooperation:

Planning the future of Harmonious

The Department of Topography and Cartography of the Technical University of Madrid hosted our work group meeting of COST Action – HARMONIOUS from 27 up to the 30 of October.

During this meeting the WG1 finalized the Glossary of terms used for UAS-based applications considering the three macro categories : platform and equipment, software and outputs.

GLOSSARY

1 Category: Platforms and Equipment 

  • Global Navigation Satellite System (GNSS) is a constellation of satellites used for positioning a receiver on the ground.
  • GALILEO is the GNSS European solution used to determine the ground position of an object.  
  • GPS is the most common GNSS based on the reception of signals from about 24 orbiting satellites by the USA, used to determine the ground position of an object. This global and accurate system allows users to know their exact location, velocity, and time 24 hours per day, anywhere in the world.    
  • Light Detection and Ranging (LiDAR) is based on laser pulses to locate the acquired point cloud in a 3D remote sensing. LiDAR data products are often managed within a gridded or raster data format.
  • Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum.  The used spectral regions are often at least partially outside the visible spectral range, covering parts of the infrared and ultraviolet region. For example, a multi-spectral imager may provide wavelength channels for near-UV, red, green, blue, near-infrared, mid-infrared and far-infrared light – sometimes even thermal radiation.
  • Near Infrared (NIR) is a subset of the infrared band that is just outside the range of what humans can see. Applied to cameras, NIR cameras cover the wavelength range of 900 to 1700 nm, a range that is best suited for absorption and radiation characteristics analyses.
  • Noise    is an irregular fluctuation that accompanies a transmitted electrical signal but is not part of it and tends to obscure it. The main sources of noise can be divided into two main categories: the physical noise, linked to physics constraints like the corpuscular nature of light, and the hardware noise, linked to mechanical issues in the camera.
  • Optical Camera is a photographic device aimed to form and record an image of an object. An optical camera sensor is an imager that collects visible light (400~700nm).
  • Payload is the weight a drone or unmanned aerial vehicle (UAV) can carry on board. It is usually counted outside of the weight of the drone itself and includes anything additional to the drone – such as extra cameras, sensors, or packages for delivery.
  • Pixel size of an image identifies the spatial resolution and it is dependent on the sensor capabilities. It provides a measure of the image resolution, which is higher with finer grids, where the degree of recognizable details increases.
  • RGB Camera is equipped with a standard Complementary Metal Oxide Semiconductor (CMOS) sensor through which the colourful images of persons and objects are acquired. In a CMOS sensor, the charge from the photosensitive pixel is converted to a voltage at the pixel site and the signal is multiplied by row and column to multiple on chip Digital-to-Analog Converters (DACs). In a RGB camera, the acquisition of static photos is commonly expressed in megapixels that define the amount of pixels in a singular photo. While, the acquisition of videos is usually expressed with terms such as Full HD or Ultra HD.        
  • Thermal Camera is a non-contact temperature measurement sensor. All objects (above absolute zero) emit infrared energy as a function of their temperature. The vibration of atoms and molecules generates infrared energy. The higher the temperature of an object, the faster its molecules and atoms move. This movement is emitted as infrared radiation, which our eyes cannot see but our skin can feel (as heat). Thermal imaging uses special infrared camera sensors to illuminate a spectrum of light invisible to the naked eye. Thermal energy is invisible to the naked eye and works in different ways; it can be emitted, absorbed, or reflected. Infrared cannot see through objects but can detect differences in radiated thermal energy between materials. This is known as thermal bridging or heat transfer. 
  • Unmanned Aerial System (UAS) is a remotely controlled professional system integrating several technological components (e.g., navigation system, gyroscope, and sensors) in order to perform spatial observations.
  • Unmanned Aerial Vehicle (UAV) is a remotely controlled vehicle able to perform several operations and observations.

2 Software 

  • Aero-triangulation is the method most frequently applied to the photogrammetry to determine the X, Y, and Z ground coordinates of individual points based on photo coordinate measurements. The purpose of aero-triangulation is to increase the density of a geodetic network in order to provide images with an exhaustive number of control points for topographic mapping. Deliverables from aero-triangulation may be three-dimensional or planimetric, depending on the number of point coordinates determined.
  • Checkpoints are Ground Control Points (GCPs) used to validate the relative and absolute accuracy of the geo-localization of maps. The checkpoints are not used for processing. Instead, they are used to calculate the error of the map by comparing the known measured locations of the checkpoints to the coordinates of the checkpoints shown on the map.
  • Flight Type refers to the flight mission mode (manual or autonomous). In the manual mode, a pilot manages the UAS during the flight. The autonomous mission is programmed to react to various types of events, in a preset and direct way by means of special sensors. This makes UAS flight predictable and subject to intervention by a remote pilot, only if necessary.
  • Flight Time is a measurement of the total time needed to complete a mission, from the first to the last image taken during a flight. Flight time can be used to characterize the wind impacts on flight performance of UAS.    
  • Ground Control Points (GCPs) are user defined and priorly determined tie points within the mapping polygon used in the process of indirectly georeferencing UAS images. Such tie points can be permanent or portable markers with or without georeferenced data.
  • Masking is the procedure of excluding some part of the scene from image analysis. For instance, clouds, trees, bushes and their shadows should not be considered in further processing, such as in vegetation studies for the evaluation of crop vegetation indices.        
  • Orthorectification is a process of linearly scaling the image pixel size to real-world distances. This is achieved by accounting for the impacts of camera perspective and relative height above the sensed object. The objective is the reprojection of the original image, which could be captured from oblique viewing angles looking at unlevelled terrain, into an image plane to generate a distortion-free photo. 
  • Point Cloud is a collection of data points in a three-dimensional plane. Each point contains several measurements, including its coordinates along the X, Y, and Z-axes, and sometimes additional data such as a color value, which is stored in RGB format, and luminance value, which determines how bright the point is.
  • Radiometric Calibration is a process that allows the transformation of the intensities or digital numbers (DN) of multiple images in order to describe an area and detect relative changes of the landscape, removing anomalies due to atmospheric factors or illumination conditions. 
  • Structure from Motion (SfM) is the process of reconstructing a three-dimensional model from the projections derived from a series of images taken from different viewpoints. Camera orientation and scene geometry are reconstructed simultaneously through the automatic identification of matching features in multiple images.        
  • Tie Point is a point in a digital image or aerial photograph that can be found in the same location in an adjacent image or aerial photograph. A tie point is a feature that can be clearly identified in two or more images and selected as a reference point and whose ground coordinates are not known. The ground coordinates of Tie Points are computed during block triangulation. So, Tie points represent matches between key points detected on two (or more) different images and represent the link between images to get 3D relative positioning.
  • Precision is a description of random errors in the 2D/3D representations.
  • Quality Assessment is an estimation of the statistical geometric and radiometric errors of the final products obtained using ground true data.           

UAS-based Outputs

  • 2D Model is a bidimensional representation of the earth that contains 2 coordinates X and Y.
  • 3D Model is a mathematical or virtual representation of a three dimensional object.
  • 2.5D Model (Pseudo 3D Model) is a three-dimensional representation that uses X, Y coordinates, which are associated to a single elevation value in order to relate different points.
  • Digital Elevation Model (DEM) or Digital Height Model (DHM) is a gridded image describing the altitude of the earth excluding all other objects artificial or natural.    
  • Digital Surface Model (DSM) is a gridded image describing the altitude of the earth including all other objects artificial or natural. For instance, the DSM provides information about dimensions of buildings and forests.    
  • Digital Terrain Model (DTM) is a vector or raster dataset consisting of a virtual representation of the land environment in the mapping polygon. In a DTM the height of the point belongs to the bare ground.
  • Orthophoto is an aerial or terrestrial photograph that has been geometrically corrected to make the scale of the photograph uniform and use it as a map. Since each pixel of the orthophoto has a X and Y, it can be overlapped to other orthophotos, and it can be used to measure true distances of features within the photograph.        
  • Orthomosaic    is a high resolution image made by the combination of many orthophotos. It is a single, radiometrically corrected image that offers a photorealistic representation of an area that can produce surveyor-grade measurements of topography, infrastructure, and buildings.    
  • Feature Identification is a vector information computed from images using artificial intelligence algorithms in order to identify objects (roads, buildings, bridges, etc.) automatically. 
  • Point Cloud is a set of data points in space representing a three-dimensional object. Each point position has its set of Cartesian coordinates (X, Y, Z). It can be generated from overlapping images or LiDAR sensors.
  • Point Cloud Classification is the output of an algorithm that classifies the points of a cloud by computing a set of geometric and radiometric attributes.
  • Image Segmentation is a process that detects the features of an image clearly distinguishable based on the image texture and color.
  • Triangulated Irregular Network (TIN) is a pseudo three-dimensional representation obtained from the  relations in a point cloud using triangles.   
  • Vegetation Indices (VIs) are combinations of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation. VIs are designed to maximize sensitivity to the vegetation characteristics while minimizing confounding factors such as soil background reflectance, directional, or atmospheric effects. VIs can be found in the scientific literature under different forms such as NDVI, EVI, SAVI, etc.                
  • Aerial photograph is an image taken from an air-borne (i.e., UAS) platform using a precision camera. From aerial photographs, it is possible to derive qualitative information of the depicted areas, such as land use/land cover, topographical forms, soil types, etc. 
  • Terrestrial photograph is an image taken from the earth surface using a camera with an orientation that in most cases is not Nadiral.               

A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations

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]

Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis

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]

Remote Sensing of the Environment using UAS

The next Generally Assembly entitled Remote Sensing of the Environment using Unmanned Aerial Systems (UAS) of the Harmonious Cost Action will be on live streaming https://auth.gr/video/29478

Program

9:00-9:30  Registration and welcome

9:30- 10:00 Welcome from local organization

10:00-10:30  Harmonization of UAS techniques for agricultural and natural ecosystems monitoring.

Salvatore Manfreda

10:30-11:00  Mapping water infiltration rate using ground and UAV hyperspectral data: a case study of Alento, Italy.

Nicolas Francos, N. Romano, P. Nasta, Y. Zeng, B. Szabó, S. Manfreda, G. Ciarolo, J. Mészáros, R. Zhuang, B. Su, E. Ben-Dor

11:00 -11:30 Coffee Break

11:30-12:00 UAS optical sensing of plant ecophysiology and structural traits

Jonathan Atherton

12:00-12:30 Soil Moisture Monitoring Using UAS.

Ruodan Zhuang, S. Manfreda, Y. Zeng, Z. Su, E. Ben Dor, G. P. Petropoulos

12:30-13:00 Upscaled UAV photogrammetry workflow for disaster recovery purposes.

Petr Dvorak, I. Jebacek, M. Cervenka, T. Hajek, R. Popela, J. Juracka

13:00-13:20 – Discussion

13:20-15:00 Lunch break

15:00-15:30 Monitoring of river channel dynamics by UAS. 

László Bertalan, A. Eltner, I. Maddock, A. Pizarro

15:30-16:00 Stage-Cams For Headwater Streams. 

Salvatore Grimaldi, S. Noto, F. Tauro

16:00-16:30 Combining the use of Uncrewed Aerial Systems (UAS) and Pole-Mounted Photography (PMP) for monitoring leaky dams for Natural Flood Management

Ian Maddock, J. Lynch, J. Atkins 

16:3 17:00:    methological approach to test the sensitivity of Rillstats for the monitoring the effects of conservation tillage on soil erosion using Uncrewed Aerial Vehicles (UAVs). 

Josie Lynch

17:00-17:30  Assessment of natural vegetation by UAS – current state and perspectives. 

Jana Müllerová and members of the WG2  

17:30-18:00 UAS-Based Mapping of Depositional Landforms Along the North Bulgarian Black Sea Coast for Habitats Identification and Classification.

Bogdan Prodanov, I. Kotsev, R. Bekova, T. Lambev, L. Dimitrov

18:00-18:30 Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

Dimitrios KatsantonisDimitrios StavrakoudisArgyrios Kalaitzidis

Topic: Harmonious Workshop – September 2021
Join Zoom Meeting
https://authgr.zoom.us/j/94267194820?pwd=bjlTLzFKTzFSMTJiSFdXUlE3N3JFQT09
Meeting ID: 942 6719 4820
Passcode: 028670

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