@article{MTMT:34749769,
	title = {Budapest nagyméretarányú kerületi térképsorozata (1944–1986) és georeferálásuk},
	url = {https://m2.mtmt.hu/api/publication/34749769},
	author = {Timár, Gábor and Sipos, András and Kovács, Viktória and Kozma, László},
	journal-iso = {CATASTRUM},
	journal = {CATASTRUM: ÉVNEGYEDES KATASZTERTÖRTÉNETI FOLYÓIRAT},
	volume = {11},
	unique-id = {34749769},
	issn = {2064-5805},
	year = {2024},
	pages = {41-48},
	orcid-numbers = {Timár, Gábor/0000-0001-9675-6192}
}

@article{MTMT:34536076,
	title = {A Step from Vulnerability to Resilience: Restoring the Landscape Water-Storage Capacity of the Great Hungarian Plain—An Assessment and a Proposal},
	url = {https://m2.mtmt.hu/api/publication/34536076},
	author = {Timár, Gábor and Jakab, Gusztáv and Székely, Balázs},
	doi = {10.3390/land13020146},
	journal-iso = {LAND-BASEL},
	journal = {LAND (BASEL)},
	volume = {13},
	unique-id = {34536076},
	abstract = {The extreme drought in Europe in 2022 also hit hard the Great Hungarian Plain. In this short overview article, we summarize the natural environmental conditions of the region and the impact of river control works on the water-retention capacity of the landscape. In this respect, we also review the impact of intensive agricultural cultivation on soil structure and on soil moisture in light of the meteorological elements of the 2022 drought. The most important change is that the soil stores much less moisture than in the natural state; therefore, under the meteorological conditions of summer 2022, the evapotranspiration capacity was reduced. As a result, the low humidity in the air layers above the ground is not sufficient to trigger summer showers and thunderstorms associated with weather fronts and local heat convection anymore. Our proposed solution is to restore about one-fifth of the area to the original land types and usage before large-field agriculture. Low-lying areas should be transformed into a mosaic-like landscape with good water supply and evapotranspiration capacity to humidify the lower air layers. Furthermore, the unfavorable soil structure that has resulted from intensive agriculture should also be converted into more permeable soil to enhance infiltration.},
	year = {2024},
	eissn = {2073-445X},
	orcid-numbers = {Timár, Gábor/0000-0001-9675-6192; Jakab, Gusztáv/0000-0002-2569-5967; Székely, Balázs/0000-0002-6552-4329}
}

@article{MTMT:34747774,
	title = {A 2023. február 6-i tragikus törökországi–szíriai földrengések és a történeti szeizmológiai események kutatásának fontossága • The Tragic TURKISH–SYRIAN Earthquakes of 6 February 2023 and the Importance of Research of Historical Seismological Events},
	url = {https://m2.mtmt.hu/api/publication/34747774},
	author = {Varga, Péter and Győri, Erzsébet and Fodor, Csilla and Timár, Gábor},
	doi = {10.1556/2065.184.2023.11.10},
	journal-iso = {MAGYAR TUDOMÁNY},
	journal = {MAGYAR TUDOMÁNY},
	volume = {184},
	unique-id = {34747774},
	issn = {0025-0325},
	year = {2023},
	eissn = {1588-1245},
	pages = {1445-1456},
	orcid-numbers = {Fodor, Csilla/0000-0001-9134-4017; Timár, Gábor/0000-0001-9675-6192}
}

@article{MTMT:34589157,
	title = {The 1879 geological map of Switzerland – Base map, georeference and evolution of geological signs},
	url = {https://m2.mtmt.hu/api/publication/34589157},
	author = {Galambos, Csilla and Timár, Gábor},
	journal-iso = {E-PERIMETRON},
	journal = {E-PERIMETRON},
	volume = {18},
	unique-id = {34589157},
	year = {2023},
	eissn = {1790-3769},
	pages = {224-233},
	orcid-numbers = {Galambos, Csilla/0000-0002-6041-1800; Timár, Gábor/0000-0001-9675-6192}
}

@article{MTMT:34190861,
	title = {Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal},
	url = {https://m2.mtmt.hu/api/publication/34190861},
	author = {Sahbeni, Ghada and Székely, Balázs and Musyimi, Peter Kinyae and Timár, Gábor and Sahajpal, Ritvik},
	doi = {10.3390/agriengineering5040109},
	journal-iso = {AgriEngineering},
	journal = {AgriEngineering},
	volume = {5},
	unique-id = {34190861},
	abstract = {Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level.},
	year = {2023},
	eissn = {2624-7402},
	pages = {1766-1788},
	orcid-numbers = {Székely, Balázs/0000-0002-6552-4329; Musyimi, Peter Kinyae/0000-0003-4165-8565; Timár, Gábor/0000-0001-9675-6192; Sahajpal, Ritvik/0000-0002-6418-289X}
}

@article{MTMT:34037183,
	title = {Parameters of the best fitting lunar ellipsoid based on GRAIL’s selenoid model},
	url = {https://m2.mtmt.hu/api/publication/34037183},
	author = {Cziráki, Kamilla and Timár, Gábor},
	doi = {10.1007/s40328-023-00415-w},
	journal-iso = {ACTA GEOD GEOPHYS},
	journal = {ACTA GEODAETICA ET GEOPHYSICA},
	volume = {58},
	unique-id = {34037183},
	issn = {2213-5812},
	abstract = {Since the Moon is less flattened than the Earth, most lunar GIS applications use a spherical datum. However, with the renaissance of lunar missions, it seems worthwhile to define an ellipsoid of revolution that better fits the selenoid. The main long-term benefit of this might be to make the lunar adaptation of methods already implemented in terrestrial GNSS and gravimetry easier and somewhat more accurate. In our work, we used the GRGM 1200A Lunar Geoid (Goossens et al. in A global degree and order 1200 model of the lunar gravity field using GRAIL mission data. In: Lunar and planetary science conference, Houston, TX, Abstract #1484, 2016; Lemoine et al. in Geophys Res Lett 41:3382–3389. http://dx.doi.org/10.1002/2014GL060027 , 2014), a 660th degree and order potential surface, developed in the frame of the GRAIL project. Samples were taken from the potential surface along a mesh that represents equal area pieces of the surface, using a Fibonacci sphere. We tried Fibonacci spheres with several numbers of points and also separately examined the effect of rotating the network for a given number of points on the estimated parameters. We estimated the best-fitting rotation ellipsoid’s semi-major axis and flatness data by minimizing the selenoid undulation values at the network points, which were obtained for a = 1,737,576.6 m and f = 0.000305. This parameter pair is already obtained for a 10,000 point grid, while the case of reducing the points of the mesh to 3000 does not cause a deviation in the axis data of more than 10 cm. As expected, the absolute value of the selenoid undulations have decreased compared to the values taken with respect to the spherical basal surface, but significant extreme values still remained as well.},
	year = {2023},
	eissn = {2213-5820},
	pages = {139-147},
	orcid-numbers = {Timár, Gábor/0000-0001-9675-6192}
}

@article{MTMT:34031366,
	title = {Kataszteri területet magyar lakosságért. Meghiúsult csereügylet a magyar-jugoszláv határmegállapítás során},
	url = {https://m2.mtmt.hu/api/publication/34031366},
	author = {Timár, Gábor},
	journal-iso = {CATASTRUM},
	journal = {CATASTRUM: ÉVNEGYEDES KATASZTERTÖRTÉNETI FOLYÓIRAT},
	volume = {10},
	unique-id = {34031366},
	issn = {2064-5805},
	year = {2023},
	pages = {21-28},
	orcid-numbers = {Timár, Gábor/0000-0001-9675-6192}
}

@{MTMT:34016288,
	title = {A budapesti Duna-szakasz szabályozása 1870 után},
	url = {https://m2.mtmt.hu/api/publication/34016288},
	author = {Timár, Gábor},
	booktitle = {Budapest 150},
	unique-id = {34016288},
	year = {2023},
	pages = {111-123},
	orcid-numbers = {Timár, Gábor/0000-0001-9675-6192}
}

@article{MTMT:34010451,
	title = {Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya},
	url = {https://m2.mtmt.hu/api/publication/34010451},
	author = {Musyimi, Peter Kinyae and Sahbeni, Ghada and Timár, Gábor and Weidinger, Tamás and Székely, Balázs},
	doi = {10.3390/rs15123041},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {15},
	unique-id = {34010451},
	abstract = {This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions.},
	year = {2023},
	eissn = {2072-4292},
	orcid-numbers = {Sahbeni, Ghada/0000-0001-8595-3043; Timár, Gábor/0000-0001-9675-6192; Weidinger, Tamás/0000-0001-7500-6579; Székely, Balázs/0000-0002-6552-4329}
}

@article{MTMT:33907937,
	title = {Projection analysis and georeference of the 1:2M Africa map by Régnauld de Lannoy de Bissy (1891-1902)},
	url = {https://m2.mtmt.hu/api/publication/33907937},
	author = {Timár, Gábor and Musyimi, Peter Kinyae and Appel, Stephen},
	journal-iso = {AUTH CARTOGEOLAB},
	journal = {AUTH CARTOGEOLAB},
	volume = {17},
	unique-id = {33907937},
	issn = {2459-3893},
	year = {2023},
	pages = {187-193},
	orcid-numbers = {Timár, Gábor/0000-0001-9675-6192}
}