VISION: VIdeo StabilisatION using automatic features selection for image velocimetry analysis in rivers

VISION is open-source software written in MATLAB for video stabilisation using automatic features detection. It can be applied for any use, but it has been developed mainly for image velocimetry applications in rivers. It includes a number of options that can be set depending on the user’s needs and intended application: 1) selection of different feature detection algorithms (seven to be selected with the flexibility to choose two simultaneously), 2) definition of the percentual value of the strongest features detected to be considered for stabilisation, 3) geometric transformation type, 4) definition of a region of interest on which the analysis can be performed, and 5) visualisation in real-time of stabilised frames. One case study was deemed to illustrate VISION stabilisation capabilities on an image velocimetry experiment. In particular, the stabilisation impact was quantified in terms of velocity errors with respect to field measurements obtaining a significant error reduction of velocities. VISION is an easy-to-use software that may support research operating in image processing, but it can also be adopted for educational purposes.

How to cite: Pizarro, A., S.F. Dal Sasso, S. Manfreda, VISION: VIdeo StabilisatION using automatic features selection for image velocimetry analysis in rivers, SoftwareX, Volume 19, 101173, 2022. [pdf]

Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions

The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magnitude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleoclimatic reconstructions. We aimed to determine the fractal behaviour and the long-range dependence behaviour of the streamflow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as suggested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal distribution and a generalized Hurst–Kolmogorov (HK) dependence structure.

How to cite: Pizarro A, Dimitriadis P, Iliopoulou T, Manfreda S, Koutsoyiannis D. Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions. Hydrology. 2022; 9(7):126. https://doi.org/10.3390/hydrology9070126

Best Paper Award 2022

I’m proud to announce that our recent paper “Current Practices in UAS-based Environmental Monitoring” has been awarded with the Best Paper Award 2022 by Remote Sensing MDPI. This paper has been the result of a joint effort carried out by several authors collaborating within the framework of COST Action – HARMONIOUS

PAPERS AWARD

The recognition should be shared with the colleagues Goran Tmušić, Salvatore Manfreda, Helge Aasen, Mike R James, Gil Gonçalves, Eyal Ben-Dor, Anna Brook, Maria Polinova, Jose Juan Arranz, János Mészáros, Ruodan Zhuang, Kasper Johansen, Yoann Malbeteau, Isabel Pedroso de Lima, Corine Davids, Sorin Herban, Matthew McCabe. Project was funded by COST Association – European Cooperation in Science and Technology

Mediterranean PhD Short School on “From sustainable to regenerative and resilient design”

DICEA of University Federico II is promoting the 3rd edition of the Mediterranean PhD Short School on “From sustainable to regenerative and resilient design”, which is scheduled from 10th to 15th of October 2022 in Napoli. The lectures and the activities will be held both in presence and online.
More information can be found at the following link:

Mediterranean PhD School 2022

Call for Ph.D. Candidates

We are looking for very motivated and talented candidates for a Ph.D. scholarship on “Monitoring of water quality of natural rivers exploiting image processing techniques” (CU1.14) at Università degli Studi di Napoli Federico II within the program PhD in Sustainable Development and Climate Change | PhD SDC. Description of the Ph.D. program is available on the web page: www.phd-sdc.it

The call is available at the following link: http://old.iusspavia.it/bandi-e-concorsi/-/asset_publisher/IOHGPojrUNnv/content/concorso-pubblico-per-titoli-ed-esami-per-l-ammissione-al-corso-di-dottorato-di-ricerca-in-sustainable-development-and-climate-change-xxxviii-ciclo-a-?redirect=%2Fbandi-e-concorsi%3Fp_auth%3Dd9VjRkpv%26p_p_id%3D101_INSTANCE_IOHGPojrUNnv%26p_p_lifecycle%3D1%26p_p_state%3Dnormal%26p_p_mode%3Dview%26p_p_col_id%3Dcolumn-2%26p_p_col_count%3D1

Deadline for applications is set on 5 August 2022.

#velocimetry #waterquality #imageprocessing #phdposition #water

Appunti di Idrologia Superficiale

The e-book of the book “Appunti di Idrologia Superficiale” published by Editrice Aracne is now available online.

SUMMARY: The text offers insights and insights into surface hydrological processes and with particular reference to the water-soil interaction, also taking into account the technical-practical needs of the reader. For this reason, in addition to proposing general contents on the subject of surface hydrology, useful information is provided for the hydrological characterization in different contexts of the national territory.

Disponibile al link: Aracne Editrice

HARMONIOUS meets StoolTechnologies

We had the great pleasure to meet, during our COST Action – HARMONIOUS General Assembly in Debrecen, the StoolTechnologies that has developed a new UAS with a payload of about 70kg and a fly autonomy of 1 hour. The new drone may offer a new range of potential applications in precision farming and environmental monitoring.

https://www.dronexpo.co.uk/news/49027/

Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions

Soil moisture (SM) is a connective hydrological variable between the Earth’s surface and atmosphere and affects various climatological processes. Surface soil moisture (SSM) is a key component for addressing energy and water exchanges and can be estimated using different techniques, such as in situ and remote sensing measurements. Discrete, costly and prolonged, in situ measurements are rarely capable in demonstration of moisture fluctuations. On the other hand, current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative RS applications, which is critical for heterogeneous covers. RS-based unmanned aerial systems (UASs) represent an option to fill the gap between these techniques, providing low-cost approaches to meet the critical requirements of spatial, spectral and temporal resolutions. In the present study, SM was estimated through a UAS equipped with a thermal imaging sensor. To this aim, in October 2018, two airborne campaigns during day and night were carried out with the thermal sensor for the estimation of the apparent thermal inertia (ATI) over an agricultural field in Iran. Simultaneously, SM measurements were obtained in 40 sample points in the different parts of the study area. Results showed a good correlation (R²=0.81) between the estimated and observed SM in the field. This study demonstrates the potential of UASs in providing high-resolution thermal imagery with the aim to monitor SM over bare and scarcely vegetated soils. A case study based in a wide agricultural field in Iran was considered, where SM monitoring is even more critical due to the arid and semi-arid climate, the lack of adequate SM measuring stations, and the poor quality of the available data.

How to cite: 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 Horticulture, 1335, 339-348, (DOI: 10.17660/ActaHortic.2022.1335.42), 2022. [pdf]

Analysis of Imagery – Image Sequences Processing

Measuring object displacement and deformation in image sequences is an important task in remote sensing, photogrammetry and computer vision and a vast number of approaches have been introduced. In the field of environmental sciences, applications are, for instance, in the studies of landslides, tectonic displacements, glaciers, and river flows (Manfreda et al., 2018). Tracking algorithms are vastly utilized for monitoring purposes in terrestrial settings and in satellite remote sensing, which need to be adapted for the application with UAV imagery because resolution, frequency and perspective are different. For instance, geometric and radiometric distortion need to be minimal for successful feature tracking, which can be a large issue for UAV imagery in contrast to satellite imagery with much smaller image scales (Gruen, 2012).

Using UAV systems for multi-temporal data acquisition as well as capturing images with high frequencies during single flights enables lateral change-detection of moving objects. And if the topography is known, a full recovery of the 3D motion vector is possible. The underlying idea is the detection or definition of points or areas of interest, which are tracked through consecutive images or frames considering the similarity measures.

In this chapter, pre-processing steps to successful image tracking and vector scaling are introduced. Afterwards, two possible strategies of tracking, i.e. feature-based and patch-based, are explained. Furthermore, different choices of tracking in image sequences are discussed. And finally, examples are given in different fields.

How to cite: Eltner, A., Manfreda, S., Hortobagyi, B., Image Sequences Processing, Unmanned Aerial Vehicles In Environmental Sciences, edited by Eltner, A.; D. Hoffmeister; A. Kaiser, P. Karrasch, L. Klingbeil, C. Stöcker, A. Rovere, (ISBN 978-3-534-40588-6), 260-272, 2022. [PDF]

HARMONIOUS GENERAL ASSEMBLY

University of Debrecen, Hungary

Detailed program of the Workshop

08:30 – 09:30 Registration

09:30-09:50: László Bertalan, Brigitta Tóth: Welcome from local organizers
09:50-10:05: Salvatore Manfreda – The achievements HARMONIOUS COST Action
10:05-10:25: Plenary talk: Francesco Nex – Towards real-time UAV mapping: example, challenges and opportunities
10:25-10:55: Plenary talk: James Dietrich – Drones for River Monitoring, a ten-year perspective

10:55-11:20 Coffee Break

11:20-11:35: Eyal Ben-Dor – Summary of WG5: Harmonization of methods and results
11:30-11:50: Sorin Herban – Summary of WG1: UAS data processing
11:50-12:05: Jana Müllerová – Summary of WG2: Vegetation status (part 1)
12:05-12:20: Antonino Maltese – Summary of WG2: Vegetation status (part 2)
12:20-12:35: Yijian Zeng – Summary of WG3: Soil moisture content
12:35-12:50: Dariia Strelnikova – Summary of WG4: River monitoring

12:50-14:30: Lunch break

14:30-14:45: Gábor Papp – HungaroControl’s Air-Ground-Air communication concept in order to enable UAVs’ ecosystem
14:45-15:00: Géza Király et al. – UAS and their application in forest monitoring
15:00-15:15: Gábor Bakó et al. – HRAM: High Spatial Resolution Aerial Monitoring Network for Nature Conservation
15:15-15:30: Ferenc Kovács et al. – Application of UAV imagery in environmental research at the University of Szeged
15:30-15:45: Anette Eltner et al. – Hydro-morphological mapping of river reaches using videos captured with UAS
15:45-16:00: Ilyan Kotsev et al. – UAS-aided bedform and habitat mapping of Bolata Cove, Bulgarian Black Sea

16:00-16:30: Coffee Break

16:30-16:50: Lance R. Brady – UAS for Research and Applied Science in the United States Geological Survey
16:50-17:05: Kamal Jain et al. – Crop identification and classification from UAV images using conjugated dense convolutional neural network
17:05-17:20: Nicolas Francos et al. – Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy
17:20-17:35: Martin Jolley et al. – Considerations When Applying UAS-based Large-Scale PIV and PTV for Determining River Flow Velocity
17:35-17:50: Adrian Gracia-Romero et al. – UAS plant phenotyping under abiotic stresses
17:50-18:05: Shawn C. Kefauver et al. – High-resolution UAV Imaging for Forest Productivity Monitoring