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001-es BibID:	BIBFORM102347
035-os BibID:	(Scopus)85131694715
Első szerző:	Getu, Legese Abebaw (agrármérnök)
Cím:	Soil loss estimation and severity mapping using the RUSLE model and GIS in Megech watershed, Ethiopia / Legese Abebaw Getu, Attila Nagy, Hailu Kendie Addis
Dátum:	2022
ISSN:	2667-0100
Megjegyzések:	Soil erosion is the most serious problem that affects economic development, food security, and ecosystem services, which is the main concern in Ethiopia. This study focused on quantifying soil erosion rate and severity mapping of the Megech watershed for effective planning and decision-making processes to implement protection measures. The RUSLE model integrated with ArcGIS software was used to accomplish the objectives. The six RUSLE model parameters: erosivity, erodibility, slope length and steepness, cover management, and erosion control practices were used as input parameters to compute the average annual soil loss and identify erosion hotspots in the watershed. The RUSLE estimated a total soil loss of 1,399,210 t yr?1 from the watershed with a mean annual soil loss of 32.84 t ha?1yr?1. The soil erosion rate varied from 0.08 to greater than 500 t ha?1yr?1. A severity map with seven severity classes was created for 27 sub-watersheds: low (below 10), moderate (10?20), high (20?30), very high (30?35), severe (35?40), very severe (40?45) and extremely severe (above 45) in which the values are in ton ha?1yr?1. The area coverage was 6.5%, 11.1%, 8.7%, 22%, 30.9%, 13.4%, and 7.4% for low, moderate, high, very high, severe, very severe, and extremely severe erosion classes, respectively. About 82% of the watershed was found in more than the high-risk category which reflects the need for immediate land management action. This paper could be important for decision-makers to prioritize critical erosion hotspots for comprehensive and sustainable management of the watershed.
Tárgyszavak:	Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Environmental Challenges. - 8 (2022), p. 1-14. -
További szerzők:	Nagy Attila (1982-) (környezetgazdálkodási agrármérnök) Addis, Hailu Kendie
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM128632
Első szerző:	Guizani, Douraied (okleveles mezőgazdasági vízgazdálkodási mérnök)
Cím:	Refining land cover classification and change detection for urban water management using comparative machine learning approach / Douraied Guizani, János Tamás, Dávid Pásztor, Attila Nagy
Dátum:	2025
ISSN:	2667-0100
Megjegyzések:	Accurate land cover (LC) maps are essential for urban water balance modeling, particularly in rapidly urbanizing cities like Debrecen, Hungary, where industrial expansion has intensified since 2019. However, LC classification remains challenging due to limited studies evaluating the optimal combination of classifiers and satellite data. This study builds upon previous research by introducing a comparative analysis of three machine learning classifiers-Support Vector Machine (SVM), Maximum Likelihood Classification (MLC), and Random Forest (RF)-in LC classification using Sentinel-2 and Landsat 8 imagery from 2018, 2020, and 2022. Results show that SVM on Sentinel-2 achieved the highest accuracy (F1 score: 0.84 ± 0.11, overall accuracy: 88 ± 2.1 %, kappa: 0.84 ± 0.03) with the lowest total disagreement values (D% = 12.6 in 2020, 13.1 in 2022). Consequently, SVM with Sentinel-2 was selected for LC change detection, employing trajectory analysis to assess urban development dynamics. The quantity gain component accounted for 5 % of the study area, representing net urban expansion, while the exchange component (10 %) indicated bidirectional shifts between developed and non-developed classes. Given Debrecen`s rapid industrialization and the lack of a robust LC classification strategy for hydrological applications, this research refines LC change detection methods. It improves water balance calculations by LC type, strengthening the hydrological framework. By demonstrating the value of satellite imagery and GIS in monitoring urbanization, the findings support future urban water balance assess- ments, sustainable planning, and resource management, providing local authorities with a robust tool to adapt spatial strategies to an evolving landscape.
Tárgyszavak:	Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Environmental Challenges. - 19 (2025), p. 1-19. -
További szerzők:	Tamás János (1959-) (környezetgazdálkodási agrármérnök) Pásztor Dávid (1996-) (Okleveles Mezőgazdasági vízgazdálkodási mérnök) Nagy Attila (1982-) (környezetgazdálkodási agrármérnök)
Pályázati támogatás:	RRF 2.3.1 21 2022 00008 Egyéb
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001-es BibID:	BIBFORM120148
035-os BibID:	(Scopus)85189884659
Első szerző:	Guizani, Douraied (okleveles mezőgazdasági vízgazdálkodási mérnök)
Cím:	Enhancing water balance assessment in urban areas through high-resolution land cover mapping: Case study of Debrecen, Hungary / Douraied Guizani, Erika Buday-Bódi, János Tamás, Attila Nagy
Dátum:	2024
ISSN:	2667-0100
Megjegyzések:	Land cover (LC) mapping in urban areas is one of the core applications in remote sensing (RS), and it plays an important role in modern urban planning and management. In order to support up-to-date simplified water balance calculation, LC modelling needs to be developed for precise and high-resolution operations to monitor rapidly expanding urban areas. This study provides a spatial decision support tool for urban water balance calculations considering hydrological parameters at pixel scale. The study is aimed to create a high-resolution LC map for more precise water balance calculations to identify and link the most important parameters, such as crop coefficient, for estimating crop evapotranspiration, runoff coefficients, and infiltration rate. Meteorological data from 2016 to 2019 were involved in evapotranspiration estimation. The study site is Debrecen, a city in northeast Hungary with a population of about 200,000. By integrating Landsat 8 imagery, Google Earth (GE), and Corine Land Cover (CLC), a LC map of 30 m resolution was created with 81.2 % overall accuracy (OA) and a coefficient of kappa greater than 0.78. For the classification of Landsat 8, seven classes were assigned (forests, sealed surfaces, areas with crop cover, grassland, semi-sealed surfaces, bare ground and surface water bodies). Classification results were validated by 101 ground truth samples using other satellite images and aerial photographs. Water balance parameters (i.e. surface runoff, infiltration and evapotranspiration) were then defined and calculated at a pixel scale and for larger areas. The technique developed in this study can also be utilized in other urban areas.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Water balance calculation Landsat 8 Landcover modelling Remote sensing Debrecen
Megjelenés:	Environmental Challenges. - 15 (2024), p. 1-17. -
További szerzők:	Bódi Erika (1989-) (geológus, geográfus) Tamás János (1959-) (környezetgazdálkodási agrármérnök) Nagy Attila (1982-) (környezetgazdálkodási agrármérnök)
Pályázati támogatás:	RRF2.3.121202200008 Egyéb
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