TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kovács, Ferenc AU - Kiss, Tímea TI - Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy JF - SENSORS J2 - SENSORS-BASEL VL - 23 PY - 2023 IS - 23 PG - 26 SN - 1424-8220 DO - 10.3390/s23239505 UR - https://m2.mtmt.hu/api/publication/34407342 ID - 34407342 AB - Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy. LA - English DB - MTMT ER - TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kiss, Tímea AU - Kovács, Ferenc TI - Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery JF - ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH J2 - ENVIRON SCI POLLUT R VL - 30 PY - 2023 IS - 25 SP - 67742 EP - 67757 PG - 16 SN - 0944-1344 DO - 10.1007/s11356-023-27068-0 UR - https://m2.mtmt.hu/api/publication/33785558 ID - 33785558 AB - Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary. LA - English DB - MTMT ER - TY - GEN AU - Tobak, Zalán AU - Kovács, Ferenc AU - Van Leeuwen, Boudewijn TI - A műholdas belvíztérképezés alapjai, lehetőségek és korlátok. PY - 2023 UR - https://m2.mtmt.hu/api/publication/33677694 ID - 33677694 N1 - Magyar Hidrológiai Társaság, Szakmai előadói nap: A belvízi távérzékelés múltja, jelene és jövője LA - Hungarian DB - MTMT ER - TY - JOUR AU - Kovács, Ferenc AU - Ladányi, Zsuzsanna TI - Plot-level field monitoring with Sentinel-2 and PlanetScope data for examination of sewage sludge disposal impact JF - GEOGRAPHICA PANNONICA J2 - GEOGRAPHICA PANNONICA VL - 26 PY - 2022 IS - 3 SP - 241 EP - 257 PG - 17 SN - 0354-8724 DO - 10.5937/gp26-37964 UR - https://m2.mtmt.hu/api/publication/33186517 ID - 33186517 AB - Agricultural use of sewage sludge is one of the means of sustainable environmental management. In order to monitor the short-term effects of sludge disposal a multi-year, high-resolution data collection was planned on arable land in south-eastern Hungary. Data acquisition was applied at the highest temporal and spatial resolution using Sentinel-2 and PlanetScope satellite imagery observing the vegetation period based on vegetation indices (EVI, NDVI) from 2016 to 2021. There were statistical differences in the case of sunflower and maize biomass productions but the spatial and statistical deviations between the affected and non-affected areas of sludge disposal were generally not significant. The sensitivity of EVI in the dense vegetation period and its applicability might be emphasized in a comparative analysis. LA - English DB - MTMT ER - TY - CHAP AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kovács, Ferenc AU - Kiss, Tímea ED - Břežný, Michal TI - Sediment discharge estimation of lowland rivers using Sentinel-2 images and machine learning algorithms T2 - State of geomorphological research in 2022 PB - University of Ostrava, Faculty of Science CY - Ostrava SN - 9788075993137 PY - 2022 SP - 54 EP - 55 PG - 2 UR - https://m2.mtmt.hu/api/publication/32852509 ID - 32852509 LA - English DB - MTMT ER - TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kovács, Ferenc AU - Kiss, Tímea TI - Remote Sensing of Sediment Discharge in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms JF - HYDROLOGY J2 - HYDROLOGY-BASEL VL - 9 PY - 2022 IS - 5 PG - 30 SN - 2306-5338 DO - 10.3390/hydrology9050088 UR - https://m2.mtmt.hu/api/publication/32851694 ID - 32851694 LA - English DB - MTMT ER - TY - GEN AU - Babcsányi, Izabella AU - Fehér, Zsolt Zoltán AU - Kovács, Ferenc AU - Lemarchand, Damien AU - Tobak, Zalán AU - Barta, Károly AU - Pham, Thi Ha Nhung AU - Manaljav, Samdandorj AU - Juhász, Szabolcs AU - Balling, Péter AU - Farsang, Andrea TI - A szőlőtermő talajok vizsgálata a talajerózió és az agrokemikáliák használatának összefüggésében CY - 2022. január 27. PY - 2022 UR - https://m2.mtmt.hu/api/publication/32623390 ID - 32623390 N1 - [Előadás] LA - Hungarian DB - MTMT ER - TY - JOUR AU - Barta, Károly AU - Van Leeuwen, Boudewijn AU - Szatmári, József AU - Blanka, Viktória AU - Kovács, Ferenc AU - Ladányi, Zsuzsanna AU - Mezősi, Gábor AU - Rakonczai, János AU - Sipos, György AU - Szilassi, Péter AU - Tobak, Zalán AU - Fiala, Károly AU - Benyhe, Balázs AU - Fehérváry, István TI - A felszínközeli vízkészletek monitoringjának lehetőségei a szélsőséges vízháztartási helyzetek (aszály, belvíz) értékelésének szolgálatában JF - LÉGKÖR: AZ ORSZÁGOS METEOROLÓGIAI INTÉZET SZAKMAI TÁJÉKOZTATÓJA J2 - LÉGKÖR VL - 66 PY - 2021 IS - 4 SP - 4 EP - 12 PG - 9 SN - 0133-3666 UR - https://m2.mtmt.hu/api/publication/32777835 ID - 32777835 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kovács, Ferenc AU - Mezősi, Gábor AU - Kiss, Tímea TI - Sediment Transport Dynamism in the Confluence Area of Two Rivers Transporting Mainly Suspended Sediment Based on Sentinel-2 Satellite Images JF - WATER J2 - WATER-SUI VL - 13 PY - 2021 IS - 21 SP - 3132 PG - 29 SN - 2073-4441 DO - 10.3390/w13213132 UR - https://m2.mtmt.hu/api/publication/32487825 ID - 32487825 LA - English DB - MTMT ER - TY - GEN AU - Mohsen Abdelsadek Metwaly, Ahmed AU - Kovács, Ferenc AU - Kiss, Tímea TI - Sediment Dynamism in the Confluence Zone of the Tisza and Maros Rivers Based on Sentinel-2 Satellite Imageries PY - 2021 UR - https://m2.mtmt.hu/api/publication/32288723 ID - 32288723 N1 - Konferenciaelőadás LA - English DB - MTMT ER -