| publications-2331 |
Peer reviewed articles |
2018 |
Anna Jurado, Alberto V. Borges, Estanislao Pujades, Pierre Briers, Olha Nikolenko, Alain Dassargues, Serge Brouyère |
Dynamics of greenhouse gases in the river–groundwater interface in a gaining river stretch (Triffoy catchment, Belgium) |
Hydrogeology Journal |
10.1007/s10040-018-1834-y |
Simulation & Modeling |
Natural Water Bodies |
|
No abstract available |
675120 |
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| publications-2332 |
Peer reviewed articles |
2020 |
G. Ezzati, O. Fenton, M.G. Healy, L. Christianson, G.W. Feyereisen, S. Thornton, Q. Chen, B. Fan, J. Ding, K. Daly |
Impact of P inputs on source-sink P dynamics of sediment along an agricultural ditch network |
Journal of Environmental Management |
10.1016/j.jenvman.2019.109988 |
Data Management & Analytics |
Natural Water Bodies |
|
No abstract available |
675120 |
|
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| publications-2333 |
Peer reviewed articles |
2020 |
Evgenia Micha, Owen Fenton, Karen Daly, Gabriella Kakonyi, Golnaz Ezzati, Thomas Moloney, Steven Thornton |
The Complex Pathway towards Farm-Level Sustainable Intensification: An Exploratory Network Analysis of Stakeholders’ Knowledge and Perception |
Sustainability |
10.3390/su12072578 |
Control Systems |
Uncategorized |
|
Farm-level sustainable intensification of agriculture (SIA) has become an important concept to ensuring food security while minimising negative externalities. However, progress towards its achievement is often constrained by the different perceptions and goals of various stakeholders that affect farm management decisions. This study examines farm-level SIA as a dynamic system with interactive components that are determined by the interests of the stakeholders involved. A systems thinking approach was used to identify and describe the pathways towards farm-level SIA across the three main pillars of sustainability. An explanatory network analysis of fuzzy cognitive maps (FCMs) that were collectively created by representative groups of farmers, farm advisors and policy makers was performed. The study shows that SIA is a complex dynamic system, affected by cognitive beliefs and particular knowledge within stakeholder groups. The study concludes that, although farm-level SIA is a complex process, common goals can be identified in collective decision making. |
675120 |
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| publications-2334 |
Peer reviewed articles |
2020 |
Collins Amoah-Antwi, Jolanta Kwiatkowska-Malina, Steven F. Thornton, Owen Fenton, Grzegorz Malina, Ewa Szara |
Restoration of soil quality using biochar and brown coal waste: A review |
Science of The Total Environment |
10.1016/j.scitotenv.2020.137852 |
Uncategorized |
Uncategorized |
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No abstract available |
675120 |
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| publications-2335 |
Peer reviewed articles |
2017 |
Hari R. Upadhayay, Samuel Bodé, Marco Griepentrog, Dries Huygens, Roshan M. Bajracharya, William H. Blake, Gerd Dercon, Lionel Mabit, Max Gibbs, Brice X. Semmens, Brian C. Stock, Wim Cornelis, Pascal Boeckx |
Methodological perspectives on the application of compound-specific stable isotope fingerprinting for sediment source apportionment |
Journal of Soils and Sediments |
10.1007/s11368-017-1706-4 |
Control Systems |
Uncategorized |
|
No abstract available |
644320 |
|
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| publications-2336 |
Peer reviewed articles |
2023 |
Savvas Apostolidis; Georgios Vougiatzis; Athanasios Kapoutsis; Savvas Chatzichristofis; Elias Kosmatopoulos |
Systematically Improving the Efficiency of Grid-Based Coverage Path Planning Methodologies in Real-World UAVs’ Operations |
Drones |
10.3390/drones7060399 |
Control Systems |
Uncategorized |
|
This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region’s representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs’ coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method’s effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced Coverage Mode, the Better Coverage Mode and the Complete Coverage Mode, each incorporating features suitable for specific types of vehicles and real-world applications. For the design of the coverage trajectories, user-defined percentages of overlap (sidelap and frontlap) are taken into consideration, so that the collected data will be appropriate for applications like orthomosaicing and 3D mapping. The newly introduced modes are evaluated through simulations, using 20 publicly available benchmark regions as testbed, demonstrating their stenghts and weaknesses in terms of coverage and efficiency. The proposed method with its ad-hoc modes can handle even the most complex-shaped, concave regions with obstacles, ensuring complete coverage, no-sharp-turns, non-overlapping trajectories and strict geo-fencing. The achieved results demonstrate that the common issues encountered in grid-based methods can be overcome by considering the appropriate parameters, so that such methods can provide robust solutions in the CPP domain. |
101004152 |
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| publications-2337 |
Peer reviewed articles |
2023 |
Saeed Karami; Farid Saberi-Movahed; Prayag Tiwari; Pekka Marttinen; Sahar Vahdati |
Unsupervised feature selection based on variance–covariance subspace distance |
Neural Networks |
10.1016/j.neunet.2023.06.018 |
Control Systems |
Wastewater Treatment Plants |
|
No abstract available |
101004152 |
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| publications-2338 |
Peer reviewed articles |
2022 |
Mirza Mohtashim Alam; Md Rashad Al Hasan Rony; Mojtaba Nayyeri; Karishma Mohiuddin; M. S. T. Mahfuja Akter; Sahar Vahdati; Jens Lehmann |
Language Model Guided Knowledge Graph Embeddings |
IEEE Access |
10.1109/access.2022.3191666 |
Data Management & Analytics |
Wastewater Treatment Plants |
|
No abstract available |
101004152 |
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| publications-2339 |
Peer reviewed articles |
2022 |
Clémence Goyens; Héloïse Lavigne; Antoine Dille; Han Vervaeren |
Using Hyperspectral Remote Sensing to Monitor Water Quality in Drinking Water Reservoirs |
Remote Sensing; Volume 14; Issue 21; Pages: 5607 |
10.3390/rs14215607 |
Simulation & Modeling |
Uncategorized |
|
At the Blankaart Water Production Center, a reservoir containing 3 million m3 of raw surface water acts as a first biologic treatment step before further processing to drinking water. Over the past decade, severe algal blooms have occurred in the reservoir, hampering the water production. Therefore, strategies (e.g., the injection of algaecide) have been looked at to prevent these from happening or try to control them. In this context, the HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR), installed since early 2021, helps in monitoring the effectiveness of these strategies. Indeed, the HYPSTAR provides, at a very high temporal resolution, bio-optical parameters related to the water quality, i.e., Chlorophyll-a (Chla) concentrations and suspended particulate matter (SPM). The present paper shows how the raw in situ hyperspectral data (a total of 8116 spectra recorded between 2021-02-03 and 2022-08-03, of which 2988 spectra passed the quality check) are processed to find the water-leaving reflectance and how SPM and Chla are derived from it. Based on a limited number of validation data, we also discuss the potential of retrieving phycocyanin (an accessory pigment unique to freshwater cyanobacteria). The results show the benefits of the high temporal resolution of the HYPSTAR to provide near real-time water quality indicators. The study confirms that, in conjunction with a few water sampling data used for validation, the HYPSTAR can be used as a quick and cost-effective method to detect and monitor phytoplankton blooms. |
101004152 |
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| publications-2340 |
Peer reviewed articles |
2023 |
Vasileios Sitokonstantinou,Alkiviadis Koukos,Ilias Tsoumas, Nikolaos S. Bartsotas, Charalampos Kontoes, Vassilia Karathanassi |
Fuzzy clustering for the within-season estimation of cotton phenology |
PLOS ONE |
10.1371/journal.pone.0282364 |
AI & Machine Learning |
Uncategorized |
|
Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication. |
101004152 |
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