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Accurate estimation of the timing of intensive spring leaf growth initiation at mid and high latitudes is crucial for improving the predictive capacity of biogeochemical and Earth system models. In this study, we focus on the modeling of climatological onset of spring leaf growth in Central Europe and use three spring phenology models driven by three meteorological datasets. The MODIS-adjusted NDVI3g dataset was used as a reference for the period between 1982 and 2010, enabling us to study the long-term mean leaf onset timing and its interannual variability (IAV). The performance of all phenology model–meteorology database combinations was evaluated with one another, and against the reference dataset. We found that none of the constructed model–database combinations could reproduce the observed start of season (SOS) climatology within the study region. The models typically overestimated IAV of the leaf onset, where spatial median SOS dates were best simulated by the models based on heat accumulation. When aggregated for the whole study area, the complex, bioclimatic index-based model driven by the CarpatClim database could capture the observed overall SOS trend. Our results indicate that the simulated timing of leaf onset primarily depends on the choice of model structure, with a secondary contribution from the choice of the driving meteorological dataset.
Réka Dávid; Zoltán Barcza; Anikó Kern; Erzsébet Kristóf; Roland Hollós; Anna Kis; Martin Lukac; Nándor Fodor. Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data. Atmosphere 2021, 12, 963 .
AMA StyleRéka Dávid, Zoltán Barcza, Anikó Kern, Erzsébet Kristóf, Roland Hollós, Anna Kis, Martin Lukac, Nándor Fodor. Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data. Atmosphere. 2021; 12 (8):963.
Chicago/Turabian StyleRéka Dávid; Zoltán Barcza; Anikó Kern; Erzsébet Kristóf; Roland Hollós; Anna Kis; Martin Lukac; Nándor Fodor. 2021. "Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data." Atmosphere 12, no. 8: 963.
The EU’s climate change mitigation plans of 55% reduction in greenhouse gas emission by 2030 and reaching climate-neutrality by 2050 rely significantly on maintaining and increasing the carbon sink in European forests. In addition to direct consequences of climate change and ageing forests, this sink is becoming threatened by the new invasive forest pests which can decrease forest productivity. The Oak lace bug (Corythucha arcuata, Say 1832), native to North America, is a new invasive species rapidly spreading since 2012 from the east to the west of Europe. The oak lace bug (OLB) after establishment in an area shows no signs of retreating and negatively affects the tree photosynthetic capacity by feeding on leaf sap. The consequences of such new and persistent pest, which are not imminently life-threatening to trees but are long-lasting, have yet to be determined.
In our study, we used remotely sensed MODIS NDVI (MOD09Q1), gridded meteorological data (FORESEE), soil water content (ERA5 Land), available national forest management and land cover data to develop methods for detecting the presence and the assessment of the impact of the OLB. The study was focused on the modelling tools to decouple the effects caused by the environmental variables from the pest damage on the measured NDVI. To this different NDVI models were created based on the Least Absolute Shrinkage and Selection Operator (LASSO) technique and the most influential periods, to support accurate forest pest detection. We investigated forests containing oak trees in the transboundary area of Hungary and Croatia. The results show that the LASSO technique is a promising tool in NDVI modelling using meteorological and environmental data. The performance of the models based on the Most Influential Periods (MIP) of the different variables showed just slightly worse results, although their application is more intuitive. In the case of the OLB, the damage assessment results with the LASSO and MIP methods showed that the pest-caused NDVI decrease in pure oak stands during the late August to early September period can be as much as -14.5% and -15.6%, respectively.
Asknowledgments:
The research has been supported by the Croatian Science Foundation project MODFLUX (HRZZ IP-2019-04-6325), by the Hungarian Scientific Research Fund (OTKA FK-128709) and by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
Hrvoje Marjanovic; Aniko Kern. Decoupling the effect of the forest pest damage from the effects of meteorology using space-borne remote sensing and modelling. 2021, 1 .
AMA StyleHrvoje Marjanovic, Aniko Kern. Decoupling the effect of the forest pest damage from the effects of meteorology using space-borne remote sensing and modelling. . 2021; ():1.
Chicago/Turabian StyleHrvoje Marjanovic; Aniko Kern. 2021. "Decoupling the effect of the forest pest damage from the effects of meteorology using space-borne remote sensing and modelling." , no. : 1.
The occurrence frequency of regional atmospheric new aerosol particle formation and consecutive growth events (fNPF) were studied with respect to vegetation activity, aerosol properties, air pollutants and meteorological data in Budapest over the time interval from 2008 to 2018. The data set evaluated contained results of in situ measurements on the land surface that were mostly performed at the Budapest platform for Aerosol Research and Training Laboratory, of satellite-based products recorded by MODIS on Terra and of modelled vegetation emission-related properties from an advanced regional biogeochemical model. The annual mean relative occurrence frequencies were considerable (with an overall mean of 21 %), remained at a constant level (with an overall SD of 5 %) and did not exhibit tendentious change over the years. The shape of the distributions of monthly mean fNPF exhibited large variability from year to year, while the overall average distribution already possessed a characteristic pattern. The structure of the new particle formation (NPF) occurrence distributions was compared to those of environmental variables including concentrations of gas-phase H2SO4, SO2, O3, NO, NO2, CO, PM10 mass and NH3; particle numbers in the size fractions of 6–1000, 6–100 and 100–1000 nm; condensation sink; air temperature (T); relative humidity (RH); wind speed (WS); atmospheric pressure (P); global solar radiation (GRad); gross primary production (GPP) of vegetation; leaf area index (LAI); and stomatal conductance (SCT). There were no evident systematic similarities between fNPF on the one hand and all of the variables studied on the other hand, except for H2SO4 and perhaps NH3. The spring maximum in the NPF occurrence frequency distribution often overlapped with the time intervals of positive T anomaly in vegetated territories. The link between the potential heat stress exerted on plants in sultry summer intervals and the summer fNPF minimum could not be proven. The relevance of environmental variables was assessed by their ratios on NPF event days and on non-event days. The gas-phase H2SO4 concentration showed the largest monthly ratios, followed by O3. The WS, biogenic precursor gases and SO2 can generally favour NPF events, although their influence seemed to be constrained. An association between the fNPF and vegetation growth dynamics was clearly identified.
Imre Salma; Wanda Thén; Pasi Aalto; Veli-Matti Kerminen; Anikó Kern; Zoltán Barcza; Tuukka Petäjä; Markku Kulmala. Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth. Atmospheric Chemistry and Physics 2021, 21, 2861 -2880.
AMA StyleImre Salma, Wanda Thén, Pasi Aalto, Veli-Matti Kerminen, Anikó Kern, Zoltán Barcza, Tuukka Petäjä, Markku Kulmala. Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth. Atmospheric Chemistry and Physics. 2021; 21 (4):2861-2880.
Chicago/Turabian StyleImre Salma; Wanda Thén; Pasi Aalto; Veli-Matti Kerminen; Anikó Kern; Zoltán Barcza; Tuukka Petäjä; Markku Kulmala. 2021. "Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth." Atmospheric Chemistry and Physics 21, no. 4: 2861-2880.
Occurrence frequency (fNPF) of regional atmospheric new aerosol particle formation (NPF) and consecutive growth events were studied with respect to vegetation activity, aerosol properties, air pollutants and meteorological data in Budapest over the time interval of 2008–2018. The data set evaluated contained results of in situ measurements on land surface mostly performed at the Budapest platform for Aerosol Research and Training Laboratory, of satellite-based products recorded by MODIS on Terra and of modelled vegetation emission-related properties from an advanced regional biogeochemical model. Annual mean relative occurrence frequencies were considerable (with an overall mean of 21 %), remained at a constant level (with an overall SD of 5 %) and did not exhibit tendentious change over the years. The shape of the distributions of monthly mean fNPF exhibited large variability from year to year, while the overall distribution already possessed a characteristic pattern. This structure of the NPF occurrence distributions was compared to those of environmental variables including concentrations of gas-phase H2SO4, SO2, O3, NO, NO2, CO, PM10 mass and NH3, of particle numbers in the size fractions of 6–1000, 6–100 and 100–1000 nm, condensation sink, air temperature (T), relative humidity, wind speed (WS), atmospheric pressure, global solar radiation, gross primary production of vegetation, leaf area index and stomatal conductance. There were no evident systematic similarities between fNPF on one hand and all variables on the other hand, except for H2SO4 and perhaps NH3. The spring maximum in the NPF occurrence frequency distribution often overlapped with the time intervals of positive T anomaly on vegetated territories. The link between the potential heat stress exerted on plants in sultry summer intervals and the summer fNPF minimum could not be proved. The relevance of environmental variables was assessed by their ratios on NPF event day and on non-event days. Gas-phase H2SO4 concentration showed the largest monthly ratios, followed by O3. The WS, biogenic precursor gases and SO2 can generally favour NPF events although their influence seemed to be constrained. Association between the fNPF and vegetation growth dynamics was clearly identified.
Imre Salma; Wanda Thén; Pasi Aalto; Veli-Matti Kerminen; Anikó Kern; Zoltán Barcza; Tuukka Petäjä; Markku Kulmala. Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth. 2020, 2020, 1 -32.
AMA StyleImre Salma, Wanda Thén, Pasi Aalto, Veli-Matti Kerminen, Anikó Kern, Zoltán Barcza, Tuukka Petäjä, Markku Kulmala. Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth. . 2020; 2020 ():1-32.
Chicago/Turabian StyleImre Salma; Wanda Thén; Pasi Aalto; Veli-Matti Kerminen; Anikó Kern; Zoltán Barcza; Tuukka Petäjä; Markku Kulmala. 2020. "Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth." 2020, no. : 1-32.
Imre Salma; Wanda Thén; Pasi Aalto; Veli-Matti Kerminen; Anikó Kern; Zoltán Barcza; Tuukka Petäjä; Markku Kulmala. Supplementary material to "Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth". 2020, 1 .
AMA StyleImre Salma, Wanda Thén, Pasi Aalto, Veli-Matti Kerminen, Anikó Kern, Zoltán Barcza, Tuukka Petäjä, Markku Kulmala. Supplementary material to "Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth". . 2020; ():1.
Chicago/Turabian StyleImre Salma; Wanda Thén; Pasi Aalto; Veli-Matti Kerminen; Anikó Kern; Zoltán Barcza; Tuukka Petäjä; Markku Kulmala. 2020. "Supplementary material to "Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth"." , no. : 1.
We aim to predict Hungarian corn yields for the period of 2020–2100. The purpose of the study was to mutually consider the environmental impact of climate change and the potential human impact indicators towards sustaining corn yield development in the future. Panel data regression methods were elaborated on historic observations (1970–2018) to impose statistical inferences with simulated weather events (2020–2100) and to consider developing human impact for sustainable intensification. The within-between random effect model was performed with three generic specifications to address time constant indicators as well. Our analysis on a gridded Hungarian database confirms that rising temperature and decreasing precipitation will negatively affect corn yields unless human impact dissolves the climate-induced challenges. We addressed the effect of elevated carbon dioxide (CO2) as an important factor of diverse human impact. By superposing the human impact on the projected future yields, we confirm that the negative prospects of climate change can be defeated.
Tibor Marton; Anna Kis; Anna Zubor-Nemes; Anikó Kern; Nándor Fodor. Human Impact Promotes Sustainable Corn Production in Hungary. Sustainability 2020, 12, 6784 .
AMA StyleTibor Marton, Anna Kis, Anna Zubor-Nemes, Anikó Kern, Nándor Fodor. Human Impact Promotes Sustainable Corn Production in Hungary. Sustainability. 2020; 12 (17):6784.
Chicago/Turabian StyleTibor Marton; Anna Kis; Anna Zubor-Nemes; Anikó Kern; Nándor Fodor. 2020. "Human Impact Promotes Sustainable Corn Production in Hungary." Sustainability 12, no. 17: 6784.
Eddy-covariance based carbon flux datasets spanning decades are becoming available worldwide due to the effort of associated scientists. Tall tower based monitoring stations are relatively rare, but provide important information about the carbon balance of a larger region surrounding the tower. In this study we report and analyze the 21-year-long dataset provided by the Hungarian tall tower site, Hegyhátsál. The daily and annual cycles of net ecosystem exchange (NEE), gross primary production, and total ecosystem respiration are presented. Footprint analysis reveals that the fluxes mostly originate from the surrounding arable lands; the main source region is located within 1 km around the tower. Long-term, mean NEE was -0.467 gC m-2 day-1, or -170 gC m-2 year-1, revealing that the complex region was a net carbon sink from the atmospheric perspective. Trend analysis indicates that overall, NEE decreased (i.e. became more negative, which means stronger sink) by 12 gC m-2 year-1, a trend that is explained by improved agrotechnology and climate change. Net biome production (NBP) was estimated using crop census data and assumptions about the management practices that affect lateral carbon flux. Long term mean NBP was -30 gC m-2 year-1, which indicates that the soils may be losing carbon, though this loss is within the range of uncertainty of the measurements (~50 gC m-2 year-1). Analysis of county-scale yield statistics suggested that the results can be representative to at least county scale (1651 km2). Interannual variability was analyzed by using environmental variables such as maximum and minimum temperature, precipitation, vapor pressure deficit, soil water content, and satellite based vegetation index aggregated at monthly and longer time intervals. The results indicate that environmental conditions in spring have a major role in the annual carbon balance. Moreover, water availability represented by soil water content rather than by precipitation is a major driver of the interannual variability of NEE. The statistical analysis suggested that the large positive NEE anomaly during 2001–2003 was caused by interplay among the environmental drivers, in particular maximum temperature, vapor pressure deficit and radiation in April, followed by a soil water content deficit during the growing season. This novel statistical analysis provides insight into the drivers of the carbon balance of a mixed agricultural region.
Z. Barcza; A. Kern; K.J. Davis; L. Haszpra. Analysis of the 21-years long carbon dioxide flux dataset from a Central European tall tower site. Agricultural and Forest Meteorology 2020, 290, 108027 .
AMA StyleZ. Barcza, A. Kern, K.J. Davis, L. Haszpra. Analysis of the 21-years long carbon dioxide flux dataset from a Central European tall tower site. Agricultural and Forest Meteorology. 2020; 290 ():108027.
Chicago/Turabian StyleZ. Barcza; A. Kern; K.J. Davis; L. Haszpra. 2020. "Analysis of the 21-years long carbon dioxide flux dataset from a Central European tall tower site." Agricultural and Forest Meteorology 290, no. : 108027.
Plant phenology focuses on the annual repetitive development phases of the terrestrial vegetation. Since the date of the onset and the cessation of vegetation growth define the possible time period for photosynthesis, plant phenology strongly affects the carbon cycle of the ecosystems. Phenology has a serious impact on the climate system through the carbon-, water- and energy cycle. Observations indicate changes in the phenological cycle of the vegetation worldwide that are clear indicators of climate change. Warming climate can be associated with more intense carbon uptake, but it can also negatively affect production. Current studies clearly indicated that the phenological cycle is not properly represented in the Earth System Models which means that further research is needed.
Meteorological variables affecting the state of the environment, such as temperature and precipitation, also play a key role in the development of vegetation. Phenology models of different complexity were developed to quantify the timing of the onset of vegetation growth based on meteorological data. The sensitivity of the models to the source meteorological datasets is rarely studied. The aim of the present study is to quantify the sensitivity of widely used phenology models to the selection of the driving meteorological dataset.
Two phenology models were used to evaluate the different databases. One is the so-called Growing Degree Day (GDD) method, which calculates the onset date based on the degree day logic. The GDD model is further divided into simple thermal forcing model and thermal model, where the latter includes chilling requirement as well. The second method uses minimum temperature, photoperiod and vapor pressure deficit and calculates a so-called Growing Season Index (GSI) which is used to estimate onset date
Considering the meteorological data, three different datasets were used. The ERA5 is a reanalysis database, which is the product of the European Centre for Medium-Range Weather Forecasts (ECMWF). The CarpatClim and the FORESEE (Open Database FOR ClimatE Change-Related Impact Sudies in CEntral Europe) are observation based, gridded datasets for the larger Carpathian Region (Central Europe).
In any modelling exercise aiming at simulating the stages of phenology, observations are essential. In the present study the phenological observation data is originating from satellite data and field observations. The first means the third generation Normalized Vegetation Index (NDVI3g) disseminated by GIMMS (Global Inventory Modeling and Mapping Studies), and the latter means the PEP725 phenology dataset and field observations from the botanical garden of Eötvös Loránd University, located in Budapest.
Réka Ágnes Dávid; Anikó Kern; Zoltán Barcza. Sensitivity of phenology models to the selection of driving meteorological datasets. 2020, 1 .
AMA StyleRéka Ágnes Dávid, Anikó Kern, Zoltán Barcza. Sensitivity of phenology models to the selection of driving meteorological datasets. . 2020; ():1.
Chicago/Turabian StyleRéka Ágnes Dávid; Anikó Kern; Zoltán Barcza. 2020. "Sensitivity of phenology models to the selection of driving meteorological datasets." , no. : 1.
Wildfires can inflict serious damage to forest ecosystems, agricultural areas and often endanger human settlements and lives. Rising global temperatures and changes in precipitation pattern increase the risk of severe fires. In Croatia, the areas currently most affected with high risk of forest fires are located in the Mediterranean region. Due to climate change the risk will likely increase and further strain the available fire-fighting resources. The situation could be even more alarming in Continental parts of the country where forest fires were not common in the past, but may become increasingly likely in the near future. Therefore, accurately assessing the wildfire risk is increasingly important in implementing fire-avoidance activities and optimizing the management of country’s fire-fighting resources.
The aim of our study is to assess the change in the spatio-temporal distribution of the fire Daily Severity Rating (DSR) and the Seasonal Severity Rating (SSR) in the last two decades, with respect to the reference period 1961–1990. We present a spatial analysis of SSR for the period 1989–2018 in Croatia based on the Croatian Meteorological and Hydrological Service (DHMZ) data and compare it with the one of European Forest Fire Information System (EFFIS). The relation between the SSR and the burned area, estimated from MODIS MCD64A1 Version 6 Burned Area data product, during 2001–2018 is investigated with the aim to facilitate locally optimized model for the assessment of the expected burned area associated with a given SSR. The results should contribute to improved understanding of the near-future risk of severe fires in Croatia related to possible future climate scenarios.
Hrvoje Marjanovic; Anikó Kern; Masa Zorana Ostrogovic Sever; Visnja Vucetic; Mislav Anic. Relations and trends of Fire Weather Severity and MODIS Burned Area in Croatia. 2020, 1 .
AMA StyleHrvoje Marjanovic, Anikó Kern, Masa Zorana Ostrogovic Sever, Visnja Vucetic, Mislav Anic. Relations and trends of Fire Weather Severity and MODIS Burned Area in Croatia. . 2020; ():1.
Chicago/Turabian StyleHrvoje Marjanovic; Anikó Kern; Masa Zorana Ostrogovic Sever; Visnja Vucetic; Mislav Anic. 2020. "Relations and trends of Fire Weather Severity and MODIS Burned Area in Croatia." , no. : 1.
Spring leaf unfolding is a spectacular recurring event at the mid- and high latitudes that is associated with deciduous vegetation. Several lines of evidence indicate that the timing of spring green-up (i.e. the start of the season, SOS) changed in the past decades resulting in an earlier leaf unfolding - a phenomenon which is considered to be a major indicator of the effects of global warming. Contrary to the timing of the SOS, considerably less attention was paid to studying the dynamics of vegetation green-up, characterized by the leaf unfolding speed or the duration of spring green-up. The importance of studying the spring green-up dynamics lies in the fact that the duration of leaf development and timing of the onset of growth jointly determine the annual cycle of vegetation activity including carbon and energy balance, canopy conductance and evapotranspiration.
The aim of our research was to characterize the dynamics of leaf unfolding of deciduous broadleaf forests in the wider Carpathian Basin, located in Central Europe, using satellite remote sensing. The study was based on the Normalized Difference Vegetation Index (NDVI) time-series derived from the MOD09A1 official MODIS products during 2000–2019, the IGBP land cover classification dataset of the MCD12Q1 products, the CORINE 2012 (CLC2012) land cover dataset, the SRTM elevation dataset, and the FORESEE meteorological database. Our results clearly show that there is considerable interannual variability in the green-up duration of the deciduous broadleaf forest during 2000–2019. The last three years had, on average, the shortest (2018) and the two longest (2017 and 2019) recorded green-up durations in the region. Observed variability was partially attributed to the meteorological conditions, namely the extreme weather events occurring during the spring. We demonstrate that the meteorological conditions during the green-up period have a strong effect on the duration. The relationship between the SOS and the green-up duration reveals that the SOS also played an important role as a driver. Our results also reveal considerable elevation dependency both in the green-up duration and also in its correlation with SOS. Multiple linear regression models based on the SOS and the meteorological variables were also created to explain and predict the green-up duration.
Anikó Kern; Hrvoje Marjanović; Zoltán Barcza. Variability of green-up duration of deciduous broadleaf forests in Central Europe during 2000-2019 based on MODIS NDVI. 2020, 1 .
AMA StyleAnikó Kern, Hrvoje Marjanović, Zoltán Barcza. Variability of green-up duration of deciduous broadleaf forests in Central Europe during 2000-2019 based on MODIS NDVI. . 2020; ():1.
Chicago/Turabian StyleAnikó Kern; Hrvoje Marjanović; Zoltán Barcza. 2020. "Variability of green-up duration of deciduous broadleaf forests in Central Europe during 2000-2019 based on MODIS NDVI." , no. : 1.
The present study focuses on the leaf unfolding dynamics of deciduous broadleaf forests in Central Europe. MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) was used to quantify green-up duration (GUD) for the wider Carpathian Basin located in Central Europe, covering the time period 2000–2019. GUD was calculated for ~170 000 pixels with deciduous broadleaf forest cover at 500 m spatial resolution. The GUD exhibited large interannual and elevation-dependent variability where the latter likely indicates the distribution of the different species. The longest mean GUD occurred in 2017 (32.7 days), while the shortest (14.5 days) was associated with 2018. The relationship between the start of leaf unfolding (SOS) and the GUD (R = -0.62) reveals that the timing of the bud break plays an important role in the leaf unfolding process. Multiple linear regression models were constructed to explain and forecast the GUD based on the date of SOS, the elevation and the meteorological variables. The main explanatory variable was the SOS date, explaining 38.3% of the GUD variability (RMSE=8 days), while the addition of the elevation and its square to the model increased the explained variance to 47.8% (RMSE=7.34 days). Further addition of meteorological variables covering periods prior to and after the SOS increased the explained variance (the best R2 was 0.65). The results indicate the complexity of processes that drive the leaf unfolding. We propose that future studies should consider SOS, elevation and meteorological variables together to interpret GUD dynamics. Earlier SOS implies longer GUD, while delayed SOS is associated with short GUD, which means that the benefits of climate change possibly realized as a longer growing season could be smaller than anticipated. Accurate estimation of the SOS is a prerequisite for the successful modeling of the GUD.
Anikó Kern; Hrvoje Marjanović; Zoltán Barcza. Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data. Agricultural and Forest Meteorology 2020, 287, 107969 .
AMA StyleAnikó Kern, Hrvoje Marjanović, Zoltán Barcza. Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data. Agricultural and Forest Meteorology. 2020; 287 ():107969.
Chicago/Turabian StyleAnikó Kern; Hrvoje Marjanović; Zoltán Barcza. 2020. "Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data." Agricultural and Forest Meteorology 287, no. : 107969.
Réka Ágnes Dávid; Zoltán Barcza; Anikó Kern; Erzsébet Kristóf; Roland Hollós; Anna Kis. Növényfenológiai vizsgálatok Magyarországon a különböző meteorológiai adatbázisok segítségével. Jelenlegi PhD kutatások a 75 éves Meteorológiai Tanszéken 2020, 25 -31.
AMA StyleRéka Ágnes Dávid, Zoltán Barcza, Anikó Kern, Erzsébet Kristóf, Roland Hollós, Anna Kis. Növényfenológiai vizsgálatok Magyarországon a különböző meteorológiai adatbázisok segítségével. Jelenlegi PhD kutatások a 75 éves Meteorológiai Tanszéken. 2020; ():25-31.
Chicago/Turabian StyleRéka Ágnes Dávid; Zoltán Barcza; Anikó Kern; Erzsébet Kristóf; Roland Hollós; Anna Kis. 2020. "Növényfenológiai vizsgálatok Magyarországon a különböző meteorológiai adatbázisok segítségével." Jelenlegi PhD kutatások a 75 éves Meteorológiai Tanszéken , no. : 25-31.
Anikó Kern; Zoltán Barcza; Hrvoje Marjanović; Tamás Árendás; Nándor Fodor; Péter Bónis; Péter Bognár; János Lichtenberger. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology 2018, 260-261, 300 -320.
AMA StyleAnikó Kern, Zoltán Barcza, Hrvoje Marjanović, Tamás Árendás, Nándor Fodor, Péter Bónis, Péter Bognár, János Lichtenberger. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology. 2018; 260-261 ():300-320.
Chicago/Turabian StyleAnikó Kern; Zoltán Barcza; Hrvoje Marjanović; Tamás Árendás; Nándor Fodor; Péter Bónis; Péter Bognár; János Lichtenberger. 2018. "Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices." Agricultural and Forest Meteorology 260-261, no. : 300-320.
Anikó Kern; János Lichtenberger. Muholdas távérzékelés a fenológiai ciklusban bekövetkezo változások szolgálatában. Jelenlegi PhD kutatások a 75 éves Meteorológiai Tanszéken 2018, 95 -102.
AMA StyleAnikó Kern, János Lichtenberger. Muholdas távérzékelés a fenológiai ciklusban bekövetkezo változások szolgálatában. Jelenlegi PhD kutatások a 75 éves Meteorológiai Tanszéken. 2018; ():95-102.
Chicago/Turabian StyleAnikó Kern; János Lichtenberger. 2018. "Muholdas távérzékelés a fenológiai ciklusban bekövetkezo változások szolgálatában." Jelenlegi PhD kutatások a 75 éves Meteorológiai Tanszéken , no. : 95-102.
Maša Zorana Ostrogović Sever; Elvis Paladinić; Zoltan Barcza; Dóra Hidy; Anikó Kern; Mislav Anić; Hrvoje Marjanović. Biogeochemical Modelling vs. Tree-Ring Measurements - Comparison of Growth Dynamic Estimates at Two Distinct Oak Forests in Croatia. South-east European forestry 2017, 8, 71 -84.
AMA StyleMaša Zorana Ostrogović Sever, Elvis Paladinić, Zoltan Barcza, Dóra Hidy, Anikó Kern, Mislav Anić, Hrvoje Marjanović. Biogeochemical Modelling vs. Tree-Ring Measurements - Comparison of Growth Dynamic Estimates at Two Distinct Oak Forests in Croatia. South-east European forestry. 2017; 8 (2):71-84.
Chicago/Turabian StyleMaša Zorana Ostrogović Sever; Elvis Paladinić; Zoltan Barcza; Dóra Hidy; Anikó Kern; Mislav Anić; Hrvoje Marjanović. 2017. "Biogeochemical Modelling vs. Tree-Ring Measurements - Comparison of Growth Dynamic Estimates at Two Distinct Oak Forests in Croatia." South-east European forestry 8, no. 2: 71-84.
Anikó Kern; Hrvoje Marjanović; Laura Dobor; Mislav Anić; Tomáš Hlásny; Zoltán Barcza. Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data. South-east European forestry 2017, 8, 1 .
AMA StyleAnikó Kern, Hrvoje Marjanović, Laura Dobor, Mislav Anić, Tomáš Hlásny, Zoltán Barcza. Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data. South-east European forestry. 2017; 8 (1):1.
Chicago/Turabian StyleAnikó Kern; Hrvoje Marjanović; Laura Dobor; Mislav Anić; Tomáš Hlásny; Zoltán Barcza. 2017. "Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data." South-east European forestry 8, no. 1: 1.
Wheat is one of the most important crops in Hungary, which represents approximately 20% of the entire agricultural area of the country, and about 40% of cereals. A robust yield method has been improved for estimating and forecasting wheat yield in Hungary in the period of 2003–2015 using normalized difference vegetation index (NDVI) derived from the data of the Moderate Resolution Imaging Spectroradiometer. Estimation was made at the end of June – it is generally the beginning of harvest of winter wheat in Hungary – while the forecasts were performed 1–7 weeks earlier. General yield unified robust reference index (GYURRI) vegetation index was calculated each year using different curve-fitting methods to the NDVI time series. The correlation between GYURRI and country level yield data gave correlation coefficient (r) of 0.985 for the examined 13 years in the case of estimation. Simulating a quasi-operative yield estimation process, 10 years’ (2006–2015) yield data was estimated. The differences between the estimated and actual yield data provided by the Hungarian Central Statistical Office were less than 5%, the average difference was 2.5%. In the case of forecasting, these average differences calculated approximately 2 and 4 weeks before the beginning of harvest season were 4.5% and 6.8%, respectively. We also tested the yield estimation procedure for smaller areas, for the 19 counties (Nomenclature of Territorial Units for Statistics-3 level) of Hungary. We found that, the relationship between GYURRI and the county level yield data had r of 0.894 for the years 2003–2014, and by simulating the quasi-operative forecast for 2015, the resulting 19 county average yield values differed from the actual yield as much as 8.7% in average.
Péter Bognár; Anikó Kern; Szilárd Pásztor; János Lichtenberger; David Koronczay; Csaba Ferencz. Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data. International Journal of Remote Sensing 2017, 38, 3394 -3414.
AMA StylePéter Bognár, Anikó Kern, Szilárd Pásztor, János Lichtenberger, David Koronczay, Csaba Ferencz. Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data. International Journal of Remote Sensing. 2017; 38 (11):3394-3414.
Chicago/Turabian StylePéter Bognár; Anikó Kern; Szilárd Pásztor; János Lichtenberger; David Koronczay; Csaba Ferencz. 2017. "Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data." International Journal of Remote Sensing 38, no. 11: 3394-3414.
Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate–NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.
Anikó Kern; Hrvoje Marjanović; Zoltán Barcza. Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013. Remote Sensing 2016, 8, 955 .
AMA StyleAnikó Kern, Hrvoje Marjanović, Zoltán Barcza. Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013. Remote Sensing. 2016; 8 (11):955.
Chicago/Turabian StyleAnikó Kern; Hrvoje Marjanović; Zoltán Barcza. 2016. "Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013." Remote Sensing 8, no. 11: 955.
Tomáš Hlásny; Zoltán Barcza; Ivan Barka; Katarína Merganičová; Róbert Sedmák; Anikó Kern; Jozef Pajtík; Borbála Balázs; Marek Fabrika; Galina Churkina. Future carbon cycle in mountain spruce forests of Central Europe: Modelling framework and ecological inferences. Forest Ecology and Management 2014, 328, 55 -68.
AMA StyleTomáš Hlásny, Zoltán Barcza, Ivan Barka, Katarína Merganičová, Róbert Sedmák, Anikó Kern, Jozef Pajtík, Borbála Balázs, Marek Fabrika, Galina Churkina. Future carbon cycle in mountain spruce forests of Central Europe: Modelling framework and ecological inferences. Forest Ecology and Management. 2014; 328 ():55-68.
Chicago/Turabian StyleTomáš Hlásny; Zoltán Barcza; Ivan Barka; Katarína Merganičová; Róbert Sedmák; Anikó Kern; Jozef Pajtík; Borbála Balázs; Marek Fabrika; Galina Churkina. 2014. "Future carbon cycle in mountain spruce forests of Central Europe: Modelling framework and ecological inferences." Forest Ecology and Management 328, no. : 55-68.
Gy. Gelybó; Zoltán Barcza; Anikó Kern; Natascha Kljun. Effect of spatial heterogeneity on the validation of remote sensing based GPP estimations. Agricultural and Forest Meteorology 2013, 174-175, 43 -53.
AMA StyleGy. Gelybó, Zoltán Barcza, Anikó Kern, Natascha Kljun. Effect of spatial heterogeneity on the validation of remote sensing based GPP estimations. Agricultural and Forest Meteorology. 2013; 174-175 ():43-53.
Chicago/Turabian StyleGy. Gelybó; Zoltán Barcza; Anikó Kern; Natascha Kljun. 2013. "Effect of spatial heterogeneity on the validation of remote sensing based GPP estimations." Agricultural and Forest Meteorology 174-175, no. : 43-53.