TY - JOUR AU - Timár, Gábor AU - Sipos, András AU - Kovács, Viktória AU - Kozma, László TI - Budapest nagyméretarányú kerületi térképsorozata (1944–1986) és georeferálásuk JF - CATASTRUM: ÉVNEGYEDES KATASZTERTÖRTÉNETI FOLYÓIRAT J2 - CATASTRUM VL - 11 PY - 2024 IS - 1 SP - 41 EP - 48 PG - 8 SN - 2064-5805 UR - https://m2.mtmt.hu/api/publication/34749769 ID - 34749769 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Timár, Gábor AU - Jakab, Gusztáv AU - Székely, Balázs TI - A Step from Vulnerability to Resilience: Restoring the Landscape Water-Storage Capacity of the Great Hungarian Plain—An Assessment and a Proposal JF - LAND (BASEL) J2 - LAND-BASEL VL - 13 PY - 2024 IS - 2 SN - 2073-445X DO - 10.3390/land13020146 UR - https://m2.mtmt.hu/api/publication/34536076 ID - 34536076 N1 - Department of Geophysics and Space Science, Institute of Geography and Earth Science, ELTE Eötvös Loránd University, Budapest, H-1117, Hungary Department of Environmental and Landscape Geography, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Budapest, H-1117, Hungary Export Date: 28 March 2024 Correspondence Address: Timár, G.; Department of Geophysics and Space Science, Hungary; email: timar.gabor@ttk.elte.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Varga, Péter AU - Győri, Erzsébet AU - Fodor, Csilla AU - Timár, Gábor TI - 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 JF - MAGYAR TUDOMÁNY J2 - MAGYAR TUDOMÁNY VL - 184 PY - 2023 IS - 11 SP - 1445 EP - 1456 PG - 12 SN - 0025-0325 DO - 10.1556/2065.184.2023.11.10 UR - https://m2.mtmt.hu/api/publication/34747774 ID - 34747774 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Galambos, Csilla AU - Timár, Gábor TI - The 1879 geological map of Switzerland – Base map, georeference and evolution of geological signs JF - E-PERIMETRON J2 - E-PERIMETRON VL - 18 PY - 2023 IS - 4 SP - 224 EP - 233 PG - 10 SN - 1790-3769 UR - https://m2.mtmt.hu/api/publication/34589157 ID - 34589157 LA - English DB - MTMT ER - TY - JOUR AU - Sahbeni, Ghada AU - Székely, Balázs AU - Musyimi, Peter Kinyae AU - Timár, Gábor AU - Sahajpal, Ritvik TI - Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal JF - AgriEngineering J2 - AgriEngineering VL - 5 PY - 2023 IS - 4 SP - 1766 EP - 1788 PG - 23 SN - 2624-7402 DO - 10.3390/agriengineering5040109 UR - https://m2.mtmt.hu/api/publication/34190861 ID - 34190861 N1 - Department of Geophysics and Space Science, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Pázmány Péter stny. 1/C, Budapest, H-1117, Hungary Department of Meteorology, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Pázmány Péter stny. 1/A, Budapest, H-1117, Hungary Department of Humanities and Languages, Karatina University, P.O. Box 1957-10101, Karatina, Kenya NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States Export Date: 4 January 2024 Correspondence Address: Sahbeni, G.; Department of Geophysics and Space Science, Pázmány Péter stny. 1/C, Hungary; email: gsahbeni@caesar.elte.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Cziráki, Kamilla AU - Timár, Gábor TI - Parameters of the best fitting lunar ellipsoid based on GRAIL’s selenoid model JF - ACTA GEODAETICA ET GEOPHYSICA J2 - ACTA GEOD GEOPHYS VL - 58 PY - 2023 IS - 2 SP - 139 EP - 147 PG - 9 SN - 2213-5812 DO - 10.1007/s40328-023-00415-w UR - https://m2.mtmt.hu/api/publication/34037183 ID - 34037183 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Timár, Gábor TI - Kataszteri területet magyar lakosságért. Meghiúsult csereügylet a magyar-jugoszláv határmegállapítás során TS - Meghiúsult csereügylet a magyar-jugoszláv határmegállapítás során JF - CATASTRUM: ÉVNEGYEDES KATASZTERTÖRTÉNETI FOLYÓIRAT J2 - CATASTRUM VL - 10 PY - 2023 IS - 2 SP - 21 EP - 28 PG - 8 SN - 2064-5805 UR - https://m2.mtmt.hu/api/publication/34031366 ID - 34031366 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Timár, Gábor ED - Izsák, Éva ED - Szabó, Pál TI - A budapesti Duna-szakasz szabályozása 1870 után T2 - Budapest 150 PB - ELTE TTK Regionális Tudományi Tanszék CY - Budapest SN - 9789634895916 PY - 2023 SP - 111 EP - 123 PG - 13 UR - https://m2.mtmt.hu/api/publication/34016288 ID - 34016288 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Musyimi, Peter Kinyae AU - Sahbeni, Ghada AU - Timár, Gábor AU - Weidinger, Tamás AU - Székely, Balázs TI - Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 15 PY - 2023 IS - 12 SN - 2072-4292 DO - 10.3390/rs15123041 UR - https://m2.mtmt.hu/api/publication/34010451 ID - 34010451 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Timár, Gábor AU - Musyimi, Peter Kinyae AU - Appel, Stephen TI - Projection analysis and georeference of the 1:2M Africa map by Régnauld de Lannoy de Bissy (1891-1902) JF - AUTH CARTOGEOLAB J2 - AUTH CARTOGEOLAB VL - 17 PY - 2023 SP - 187 EP - 193 PG - 7 SN - 2459-3893 UR - https://m2.mtmt.hu/api/publication/33907937 ID - 33907937 LA - English DB - MTMT ER -