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Dr. Gábor Szatmári
Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research

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0 Environmental Statistics
0 Geostatistics
0 GIS
0 Pedometrics
0 Digital Soil Mapping

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Journal article
Published: 05 August 2021 in Geoderma
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Many national and international initiatives rely on spatially explicit information on soil organic carbon (SOC) stock change at multiple scales to support policies aiming at land degradation neutrality and climate change mitigation. In this study, we used regression cokriging with random forest and spatial stochastic cosimulation to predict the SOC stock change between two years (i.e. 1992 and 2010) in Hungary at multiple aggregation levels (i.e. point support, 1 × 1 km, 10 × 10 km square blocks, Hungarian counties and entire Hungary). We also quantified the uncertainty associated with these predictions in order to identify and delimit areas with statistically significant SOC stock change. Our study highlighted that prediction of spatial totals and averages with quantified uncertainty requires a geostatistical approach and cannot be solved by machine learning alone, because it does not account for spatial correlation in prediction errors. The total topsoil SOC stock for Hungary was predicted to increase between 1992 and 2010 with 14.9 Tg, with lower and upper limits of a 90% prediction interval equal to 11.2 Tg and 18.2 Tg, respectively. Results also showed that both the predictions and uncertainties of the average SOC stock change were smaller for larger spatial supports, while spatial aggregation also made it easier to obtain statistically significant SOC stock changes. The latter is important for carbon accounting studies that need to prove in Measurement, Reporting and Verification protocols that observed SOC stock changes are not only practically but also statistically significant.

ACS Style

Gábor Szatmári; László Pásztor; Gerard B.M. Heuvelink. Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics. Geoderma 2021, 403, 115356 .

AMA Style

Gábor Szatmári, László Pásztor, Gerard B.M. Heuvelink. Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics. Geoderma. 2021; 403 ():115356.

Chicago/Turabian Style

Gábor Szatmári; László Pásztor; Gerard B.M. Heuvelink. 2021. "Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics." Geoderma 403, no. : 115356.

Journal article
Published: 26 April 2021 in Science of The Total Environment
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The geographical environment fundamentally influences the transport and deposition of sediments, including microplastics. In addition, the socioeconomic differences inherent in transboundary catchments result in various waste management strategies among the different countries influencing the input of microplastics into rivers. The catchment of the Tisza River in Central Europe is shared by five countries with different economic statuses and wastewater treatment practices. The aim of this research is to study the spatial changes in microplastic debris deposition along the Tisza and its main tributaries. The mean number of microplastic particles in the sediments of the Tisza was 3177 ± 1970 items/kg, while 3808 ± 1605 items/kg were counted in the sediments of the tributaries. Most of the particles were fibres, indicating the dominance of municipal wastewater input; this is especially the case in the upstream sub-catchments, where there are low degrees of wastewater management. The highest amount of microplastics was found in the sediments of the most-upstream section, where a low number of households are connected to wastewater treatment plants. Thus, it is hypothesized that suburban areas where high population densities and improper waste management co-exist may contribute to the direct input of microplastics into river systems and the development of local microplastic contamination hotspots. In addition, a high microplastic concentration was measured at the furthest downstream section, resulting from the decreased flow velocity related to impoundment by a dam. The efficiency of the microplastic trapping of the various depositionary forms varies along the river, suggesting various influencing factors and the complexity of the process. The higher concentration of microplastics observed in the tributaries compared to that observed in sediments of the main stream may reflect the importance of local sources and the complexity of source-to-sink relations for microplastic routes through the fluvial system; these processes do not always reflect progressive downstream increases.

ACS Style

Tímea Kiss; Szilveszter Fórián; Gábor Szatmári; György Sipos. Spatial distribution of microplastics in the fluvial sediments of a transboundary river – A case study of the Tisza River in Central Europe. Science of The Total Environment 2021, 785, 147306 .

AMA Style

Tímea Kiss, Szilveszter Fórián, Gábor Szatmári, György Sipos. Spatial distribution of microplastics in the fluvial sediments of a transboundary river – A case study of the Tisza River in Central Europe. Science of The Total Environment. 2021; 785 ():147306.

Chicago/Turabian Style

Tímea Kiss; Szilveszter Fórián; Gábor Szatmári; György Sipos. 2021. "Spatial distribution of microplastics in the fluvial sediments of a transboundary river – A case study of the Tisza River in Central Europe." Science of The Total Environment 785, no. : 147306.

Preprint content
Published: 04 March 2021
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As Earth observation (EO) data is increasing in volume, fast and reliable data-processing tools are also required especially for analyzing large areas with high spatial resolution. Google Earth Engine (GEE) platform provides wide sets of EO imagery and elevation data in a cloud-based processing environment. This research focused on i) the generation of bare soil map of Hungary and ii) the accuracy assessment of created soil maps representing soil texture (clay, sand, silt) and soil chemical parameters (SOC, pH and CaCO3).

In this study Copernicus Sentinel-1 SAR and Sentinel-2 optical images acquired on a mid-term time period between 2017 April and 2020 December were used to generate a median composite. Optical images were filtered for cloud coverage less than 50% and a cloud mask was also implemented on all remaining images. The threshold values for Normalized Difference Vegetation Index and Normalized Burn Ratio indices were 0.55 and 0.35 respectively to differentiate bare soil pixels.

We tested the prediction accuracy of bare soil composite supplemented by various environmental datasets as additional predictor variables in different scenarios: (i) using solely bare soil composite data (ii) composite data, elevation and its derived parameters (e.g. slope, aspect) (iii) composite data and spectral indices and (iv) all aforementioned data in fusion.

For validation two types of datasets were used: i) the reference points of the Hungarian Soil Information and Monitoring System with a five-fold cross-validation method and ii) the recently compiled national maps for soil texture and soil chemical parameters.

Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820 and K-124290) and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022).

ACS Style

János Mészáros; Tünde Takáts; Mátyás Árvai; Annamária Laborczi; Gábor Szatmári; László Pásztor. Accuracy assessment of bare soil map of Hungary based on Sentinel satellite data. 2021, 1 .

AMA Style

János Mészáros, Tünde Takáts, Mátyás Árvai, Annamária Laborczi, Gábor Szatmári, László Pásztor. Accuracy assessment of bare soil map of Hungary based on Sentinel satellite data. . 2021; ():1.

Chicago/Turabian Style

János Mészáros; Tünde Takáts; Mátyás Árvai; Annamária Laborczi; Gábor Szatmári; László Pásztor. 2021. "Accuracy assessment of bare soil map of Hungary based on Sentinel satellite data." , no. : 1.

Preprint content
Published: 03 March 2021
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Due to certain socio-economic processes and technical pressure, the number of potential data sources targeting the Earth’s surface increases rapidly as well as the data generated by them. Soil mapping heavily relied on these changes in the paradigm shift, which took place in the population and interpretation of spatial soil information in the last decade. In digital soil mapping practice, auxiliary, environmental co-variables, which are related to soil forming factors and processes, have been taken into account in spatially exhaustive form. However, the potential hidden in spatially non-exhaustive (most frequently point-like), ancillary information – originating from observations also targeting the soil mantle – is far from being exploited. In their thematic features, accuracy and reliability they are inferior to primary field and/or laboratory measurements collected directly, but they are generated in more facile, cheaper way, in greater volume, with denser temporal and spatial coverage and characteristically they are available in significantly easier form. Data sequences of various installed field sensors, data collections by proximal sensing techniques, information supply by farmers and land managers as well as citizen science are considered as possible information sources. Essentially, the (soft) data supplied by them don’t provide spatially exhaustive coverage, neither direct pedological reference, nevertheless they are hypothesized to be utilized as auxiliary information within DSM framework. In a recently started project we started to investigate, (i) in which way and with what efficiency these ancillary information originating from different secondary sources can be applied, furthermore (ii) in what manner their application influences (hopefully improves) the results, accuracy and reliability of goal-specific spatial predictions. The elaborated digital mapping procedures, which are based on (i) large amount of data with differing quality and (ii) integrated geostatistical and data mining methods can be absorbed in various earth and environmental science applications.

 

Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and Gábor Szatmári by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390).

ACS Style

László Pásztor; Gábor Szatmári; Annamária Laborczi; János Mészáros; Tünde Takáts; Zsófia Kovács; Mátyás Árvai; Péter László; Sándor Koós; András Benő. Application of information originating from spatially non-exhaustive ancillary observations in digital soil mapping. 2021, 1 .

AMA Style

László Pásztor, Gábor Szatmári, Annamária Laborczi, János Mészáros, Tünde Takáts, Zsófia Kovács, Mátyás Árvai, Péter László, Sándor Koós, András Benő. Application of information originating from spatially non-exhaustive ancillary observations in digital soil mapping. . 2021; ():1.

Chicago/Turabian Style

László Pásztor; Gábor Szatmári; Annamária Laborczi; János Mészáros; Tünde Takáts; Zsófia Kovács; Mátyás Árvai; Péter László; Sándor Koós; András Benő. 2021. "Application of information originating from spatially non-exhaustive ancillary observations in digital soil mapping." , no. : 1.

Preprint content
Published: 03 March 2021
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‘Strategic objective 1’ of the United Nations Convention to Combat Desertification (UNCCD) aims to improve conditions of affected ecosystems, combat desertification/land degradation, promote sustainable land management, and contribute to land degradation neutrality. The indicator ‘Proportion of land that is degraded over total land area’ (SO1) is compiled from three sub-indicators: ‘Trends in land cover’ (SO1-1), ‘Trends in land productivity or functioning of the land’ (SO1-2), ‘Trends in carbon stocks above and below ground’ (SO1-3).

Soil organic carbon (SOC) stock can be adopted as the metric of SO1-3, until globally accepted methods for estimating the total terrestrial system carbon stocks will be elaborated. SOC can be considered as one of the most important properties of soil, which shows not just spatial but temporal variability. According to our previous results in the topic, UNCCD default data of SOC stock for Hungary is strongly recommended to be replaced with country specific estimation of SOC stock.

SOC stock maps were compiled in the framework of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) initiative, predicted by proper digital soil mapping (DSM) method. Reference soil data were derived from a countrywide monitoring system. The selection of environmental covariates was based on the SCORPAN model. The elaborated SOC stock mapping methodology have two components: (1) point support modelling, where SOC stock is computed at the level of soil profile, and (2) spatial modelling (quantile regression forest), where spatial prediction and uncertainty quantification are carried out using the computed SOC stock values.

We analyzed how SOC stock changed between 1998 and 2016.  Nationwide SOC stock predictions were compiled for the years 1998, 2010, 2013, and 2016. For the intermediate years, we do not recommend to calculate SOC stock values, because we have no information on the dynamics of change in the intervening years. Based on the 1998 SOC stock prediction, we compiled a SOC stock map for 2018, using only land use conversion factors, according to the default data conversion values.

According to the elaborated scheme during the respective period, significant changes cannot be detected, only tendentious SOC stock changes appear. Based on our results, we recommend to use spatially predicted layers for all years when data are available, rather than calculating SOC stock change based on land use conversion factors.

Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).

ACS Style

Annamária Laborczi; Gábor Szatmári; János Mészáros; Sándor Koós; Béla Pirkó; László Pásztor. Supporting land degradation neutrality assessment by soil organic carbon stock mapping in Hungary. 2021, 1 .

AMA Style

Annamária Laborczi, Gábor Szatmári, János Mészáros, Sándor Koós, Béla Pirkó, László Pásztor. Supporting land degradation neutrality assessment by soil organic carbon stock mapping in Hungary. . 2021; ():1.

Chicago/Turabian Style

Annamária Laborczi; Gábor Szatmári; János Mészáros; Sándor Koós; Béla Pirkó; László Pásztor. 2021. "Supporting land degradation neutrality assessment by soil organic carbon stock mapping in Hungary." , no. : 1.

Preprint content
Published: 03 March 2021
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Recently, FAO and Global Soil Partnership (GSP) launched the Global Map of Salt-affected Soils (GSSmap) international initiative, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The objective of our study is to present how Hungary contributed to this international initiative by preparing its own SAS maps according to the GSSmap specifications. For this purpose, we used not just a combination of advanced machine learning and multivariate geostatistical techniques for predicting the spatial distribution of the selected SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and for the subsoil (30–100 cm), but also a number of image indices exploiting a huge amount of relevant information contained in Sentinel-2 satellite images. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling of SAS indicators was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

 

Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820 and K-124290) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).

ACS Style

Gábor Szatmári; Zsófia Bakacsi; Annamária Laborczi; Ottó Petrik; Róbert Pataki; Tibor Tóth; László Pásztor. Elaborating Hungarian segment of the Global Map of Salt-Affected Soils (GSSmap). 2021, 1 .

AMA Style

Gábor Szatmári, Zsófia Bakacsi, Annamária Laborczi, Ottó Petrik, Róbert Pataki, Tibor Tóth, László Pásztor. Elaborating Hungarian segment of the Global Map of Salt-Affected Soils (GSSmap). . 2021; ():1.

Chicago/Turabian Style

Gábor Szatmári; Zsófia Bakacsi; Annamária Laborczi; Ottó Petrik; Róbert Pataki; Tibor Tóth; László Pásztor. 2021. "Elaborating Hungarian segment of the Global Map of Salt-Affected Soils (GSSmap)." , no. : 1.

Journal article
Published: 05 January 2021 in Sustainability
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The Nitrates Directive aims (a) to protect water quality across Europe from nitrates originating from agricultural sources that pollute ground and surface water, and (b) to promote good farming practices. One of the most controversial measures of the directive is the winter prohibition period of fertilization, which has been extended by a month in two steps in recent years. According to the regulation, it is forbidden to apply nitrogen fertilization in Hungary between 31st October and 15th February, even though the winter climate is gradually becoming milder. Using the fertilization data of nearly half a million parcels of land in the Hungarian Nitrate Database, a crop model-based spatial analysis was carried out. Our aim was to test if a shift in the prohibition period starting date from 31st October to 30th November caused any differences in the nitrate amount leached at a 90 cm depth. Detailed nitrate inputs and soil and weather databases were coupled with the 4M crop model. The yield, plant nitrogen uptake, and nitrate leaching under five major crops were simulated, covering a considerable portion of arable land. Shifting the prohibition period starting date did not result in significant changes in the nitrate leaching. Further runs of the 4M model with different weather scenarios are needed to decide whether the modification of the prohibition period significantly affects the amount of nitrate leached.

ACS Style

Sándor Koós; Béla Pirkó; Gábor Szatmári; Péter Csathó; Marianna Magyar; József Szabó; Nándor Fodor; László Pásztor; Annamária Laborczi; Klára Pokovai; Anita Szabó. Influence of the Shortening of the Winter Fertilization Prohibition Period in Hungary Assessed by Spatial Crop Simulation Analysis. Sustainability 2021, 13, 417 .

AMA Style

Sándor Koós, Béla Pirkó, Gábor Szatmári, Péter Csathó, Marianna Magyar, József Szabó, Nándor Fodor, László Pásztor, Annamária Laborczi, Klára Pokovai, Anita Szabó. Influence of the Shortening of the Winter Fertilization Prohibition Period in Hungary Assessed by Spatial Crop Simulation Analysis. Sustainability. 2021; 13 (1):417.

Chicago/Turabian Style

Sándor Koós; Béla Pirkó; Gábor Szatmári; Péter Csathó; Marianna Magyar; József Szabó; Nándor Fodor; László Pásztor; Annamária Laborczi; Klára Pokovai; Anita Szabó. 2021. "Influence of the Shortening of the Winter Fertilization Prohibition Period in Hungary Assessed by Spatial Crop Simulation Analysis." Sustainability 13, no. 1: 417.

Journal article
Published: 12 December 2020 in Remote Sensing
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Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

ACS Style

Gábor Szatmári; Zsófia Bakacsi; Annamária Laborczi; Ottó Petrik; Róbert Pataki; Tibor Tóth; László Pásztor. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative. Remote Sensing 2020, 12, 4073 .

AMA Style

Gábor Szatmári, Zsófia Bakacsi, Annamária Laborczi, Ottó Petrik, Róbert Pataki, Tibor Tóth, László Pásztor. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative. Remote Sensing. 2020; 12 (24):4073.

Chicago/Turabian Style

Gábor Szatmári; Zsófia Bakacsi; Annamária Laborczi; Ottó Petrik; Róbert Pataki; Tibor Tóth; László Pásztor. 2020. "Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative." Remote Sensing 12, no. 24: 4073.

Journal article
Published: 30 November 2020 in Environment International
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A detailed knowledge of the stable isotope signature of precipitation is the basis of investigations in a variety of scientific fields and applications. To obtain robust and reliable results, the representativity of the currently operating (at least, as of 2018) precipitation stable isotope monitoring stations across Slovenia (n = 8) and Hungary (n = 9) was evaluated on the basis of amount-weighted annual averages with the aim of revealing any redundantly (i.e. over-) represented or un(der)represented areas. In the case of the latter, optimal locations for additional sites were suggested in Slovenia and Hungary. The networks of both countries are design-based systems that need to be fine-tuned for long-term optimized operation. The evaluation of the monitoring network was performed taking into consideration the stations operating in Slovenia and Hungary, as well as closely situated ones operating in neighboring countries. The evaluation was carried out in nine different combinations, using spatial simulated annealing, with regression kriging variance as a quality measure. The results showed that (i) there are over- and un(der)represented areas in the network, an issue requiring remedial action, (ii) the mutual information exchange of the precipitation stable isotope monitoring networks of Slovenia and Hungary increases the precision of precipitation δ18O estimation by ~0.3‰ in a 15–30 km wide zone near the borders, and (iii) by an even greater degree in the neighboring countries’ stations. The current research may be termed pioneering in the matter of the detailed geostatistical assessment of spatial representativity of a precipitation stable isotope monitoring network, and as such, can serve as an example for future studies aiming for the spatial optimization of other regional precipitation stable isotope monitoring networks.

ACS Style

István Gábor Hatvani; Gábor Szatmári; Zoltán Kern; Dániel Erdélyi; Polona Vreča; Tjaša Kanduč; György Czuppon; Sonja Lojen; Balázs Kohán. Geostatistical evaluation of the design of the precipitation stable isotope monitoring network for Slovenia and Hungary. Environment International 2020, 146, 106263 .

AMA Style

István Gábor Hatvani, Gábor Szatmári, Zoltán Kern, Dániel Erdélyi, Polona Vreča, Tjaša Kanduč, György Czuppon, Sonja Lojen, Balázs Kohán. Geostatistical evaluation of the design of the precipitation stable isotope monitoring network for Slovenia and Hungary. Environment International. 2020; 146 ():106263.

Chicago/Turabian Style

István Gábor Hatvani; Gábor Szatmári; Zoltán Kern; Dániel Erdélyi; Polona Vreča; Tjaša Kanduč; György Czuppon; Sonja Lojen; Balázs Kohán. 2020. "Geostatistical evaluation of the design of the precipitation stable isotope monitoring network for Slovenia and Hungary." Environment International 146, no. : 106263.

Journal article
Published: 20 April 2020 in ISPRS International Journal of Geo-Information
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Inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Inland excess water is an interrelated natural and human induced land degradation phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behavior of the occurrence, frequency of inland excess water. Spatial auxiliary information representing inland excess water forming environmental factors were taken into account to support the spatial inference of the locally experienced inland excess water frequency observations. Two hybrid spatial prediction approaches were tested to construct reliable maps, namely Regression Kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use, and climate. Comparing the results of the two approaches, we did not find significant differences in their accuracy. Although both methods are appropriate for predicting inland excess water hazard, we suggest the usage of RFK, since (i) it is more suitable for revealing non-linear and more complex relations than RK, (ii) it requires less presupposition on and preprocessing of the applied data, (iii) and keeps the range of the reference data, while RK tends more heavily to smooth the estimations, while (iv) it provides a variable rank, providing explicit information on the importance of the used predictors.

ACS Style

Annamária Laborczi; Csaba Bozán; János Körösparti; Gábor Szatmári; Balázs Kajári; Norbert Túri; György Kerezsi; László Pásztor. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard. ISPRS International Journal of Geo-Information 2020, 9, 268 .

AMA Style

Annamária Laborczi, Csaba Bozán, János Körösparti, Gábor Szatmári, Balázs Kajári, Norbert Túri, György Kerezsi, László Pásztor. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard. ISPRS International Journal of Geo-Information. 2020; 9 (4):268.

Chicago/Turabian Style

Annamária Laborczi; Csaba Bozán; János Körösparti; Gábor Szatmári; Balázs Kajári; Norbert Túri; György Kerezsi; László Pásztor. 2020. "Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard." ISPRS International Journal of Geo-Information 9, no. 4: 268.

Preprint content
Published: 23 March 2020
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The main objective of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) initiative has been to broaden the possibilities, how demands on spatial soil related information could be satisfied in Hungary, how the gaps between the available and the expected could be filled with optimized digital soil (related) maps. During our activities we have significantly extended the potential, how goal-oriented, map-based soil information could be created to fulfill the requirements. Primary and specific soil property, soil type and certain tentative functional soil maps were compiled. The set of the applied digital soil mapping techniques has been gradually broadened incorporating and eventually integrating geostatistical, machine learning and GIS tools. Soil property maps have been compiled partly according to GlobalSoilMap.net specifications, partly by slightly or more strictly changing some of their predefined parameters (depth intervals, pixel size, property etc.) according to the specific demands on the final products. The nationwide, thematic digital soil maps compiled in the frame and spin-off of our research have been utilized in a number of ways.

Soil hydraulic properties (saturated hydraulic conductivity, wilting point, field capacity, saturated water content) were mapped applying generalized pedotransfer functions on available, primary soil property maps supplemented with further environmental co-variables, which were also used in the elaboration of the specific PTF.

Spatial assessment of certain provisioning and regulating soil functions and services was carried out by the involvement of soil property maps in digital process/crop models, which properly simulate the soil-plant-water environment conditioned by various factors based on actual, predicted or presumed data. Specific outputs of the modelled processes provided adequate information on functional behavior of soils.

Programs or studies dedicated to the designation of areas suitable for irrigation; risk modelling of inland excess water hazard; mapping of potential habitats; spatial assessment and mapping of ecosystem services were heavily relied on the novel type spatial soil information. The approaches sometimes required certain modifications of the standard GSM products due to various reasons.

The paper will present various national functional applications of primary soil property maps provided by DOSoReMI.hu.

 

Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: KH126725).

ACS Style

László Pásztor; Annamária Laborczi; Brigitta Szabó; Nándor Fodor; Sándor Koós; Gábor Szatmári. Functional applications of primary soil property maps provided by DOSoReMi.hu. 2020, 1 .

AMA Style

László Pásztor, Annamária Laborczi, Brigitta Szabó, Nándor Fodor, Sándor Koós, Gábor Szatmári. Functional applications of primary soil property maps provided by DOSoReMi.hu. . 2020; ():1.

Chicago/Turabian Style

László Pásztor; Annamária Laborczi; Brigitta Szabó; Nándor Fodor; Sándor Koós; Gábor Szatmári. 2020. "Functional applications of primary soil property maps provided by DOSoReMi.hu." , no. : 1.

Preprint content
Published: 23 March 2020
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Inland excess water (IEW), considered to be a typical Carpathian Basin land degradation problem, is an interrelated natural and human induced phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. The term ‘inland excess water’ refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources.
Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behaviour of the occurrence, frequency of IEW. Spatial auxiliary information representing IEW forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency values. Two hybrid spatial prediction approaches, which combine machine learning and geostatistics, were tested to construct reliable maps, namely regression kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. Both methods divides the spatial inference into two parts. 
In Regression Kriging the target variable is modelled at first by multiple linear regression (MLR) of the environmental co-variables. Then ordinary kriging is applied on the difference between the reference and the modelled values (residuals). The prediction result map comes from the sum of the MLR model and the interpolated residuals. Random Forest combined with Kriging is a relatively new method applied in digital environmental mapping. In RFK, the deterministic component of spatial variation is modelled by random forest (RF).  RF algorithm builds lots of regression trees and the final model relies on averaging the result of the trees, which are grown independently from each other. In RFK the stochastic part of variation is modelled by kriging using the derived residuals. The final map is the sum of the two component predictions.
Comparing the results of the two approaches, we did not find significant differences in their accuracy in our pilot. However, both methods are appropriate for predicting inland excess water hazard, RFK is suitable for revealing non-linear and more complex relations than RK. Therefore, we suggest the usage of RFK in further predictions and investigations.

Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

ACS Style

Annamária Laborczi; Csaba Bozán; Gábor Szatmári; János Körösparti; László Pásztor. Spatial assessment of inland excess water hazard using combined machine learning and geostatistical methods. 2020, 1 .

AMA Style

Annamária Laborczi, Csaba Bozán, Gábor Szatmári, János Körösparti, László Pásztor. Spatial assessment of inland excess water hazard using combined machine learning and geostatistical methods. . 2020; ():1.

Chicago/Turabian Style

Annamária Laborczi; Csaba Bozán; Gábor Szatmári; János Körösparti; László Pásztor. 2020. "Spatial assessment of inland excess water hazard using combined machine learning and geostatistical methods." , no. : 1.

Preprint content
Published: 23 March 2020
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There is increasing demand for up‐to‐date spatial information on soil organic carbon (SOC). Meanwhile, Unmanned Aerial Vehicles (UAV) provide flexible technology for monitoring land surface features with high spatial resolution at plot scale. Suitably performed, airborne imagery simultaneously provides spectral and terrain based spatial auxiliary data, which can be used as predictors in DSM-type modelling of topsoil OC.

To test its applicability for spatial prediction of topsoil OC, an aerial survey was carried out on a plot situated on a gently undulating slope by a Cubert UHD-185 hyperspectral snapshot camera mounted on a Pixhawk-based octocopter. The camera is capable to record electromagnetic spectrum between 450-950 nm in 125 spectral bands on 50×50 pixels images and the panchromatic spectrum in 1 Mpx images. Because of the narrow field-of-view of the UHD-185, three consecutive flights were needed to cover the whole area (cca. 10 ha); all were happened in the hours close to noon and flown in automatic flight mode to ensure the right over- and sidelap between images to make possible the photogrammetric processing. Despite the automatic flights a surveying grade GPS unit was also used to survey 12 markers, evenly distributed on the field to orthorectify images later.

The hyperspectral and panchromatic images were pre-processed in Cubert Edelweiss to produce different versions of them depending on the used spectral information to investigate later how built-in pan-sharpening method affects the prediction accuracy. The generated datasets are the native and pan-sharpened hyperspectral mosaics. Later the photogrammetric processing was performed in Agisoft Photoscan for both hyperspectral datasets, resulting in two georeferenced outcomes: a common digital elevation model (DEM) and two hyperspectral orthomosaics of the area, each exported with 1 m spatial resolution. Further data editing steps were carried out in R, generating various versions of exported hyperspectral orthomosaics: mosaic containing all of the 125 spectral bands; filtered (where spectrally overlapping bands with high correlation were removed based on Full Width at Half Minimum information) and Principal Component Analysis transformed versions.

Based on different kind of spectral orthomosaics and DEM combinations, a custom R script using Random Forest algorithm generated 36 predicted layers for topsoil OC, which were validated by Leave-One-Out Cross-Validation, hence independent mean and RMSE errors could be calculated for each dataset combinations. The overall best performing datasets were provided by the FWHM-filtered hyperspectral orthomosaic, hence the lowest mean error is resulted by the filtered, pan-sharpened PCA-transformed combination containing the DEM and its derivatives. However, in the RMSE values there were no significant difference between the six lowest RMSE combinations, but mostly the pan-sharpened and PCA-transformed versions perform better.

ACS Style

János Mészáros; Gergely Jakab; Mátyás Árvai; Judit Szabó; Márton Tóth; Boglárka Keller; Gábor Szatmári; Zoltán Szalai; László Pásztor. Predicting topsoil organic carbon using UAV-based hyperspectral sensor. 2020, 1 .

AMA Style

János Mészáros, Gergely Jakab, Mátyás Árvai, Judit Szabó, Márton Tóth, Boglárka Keller, Gábor Szatmári, Zoltán Szalai, László Pásztor. Predicting topsoil organic carbon using UAV-based hyperspectral sensor. . 2020; ():1.

Chicago/Turabian Style

János Mészáros; Gergely Jakab; Mátyás Árvai; Judit Szabó; Márton Tóth; Boglárka Keller; Gábor Szatmári; Zoltán Szalai; László Pásztor. 2020. "Predicting topsoil organic carbon using UAV-based hyperspectral sensor." , no. : 1.

Preprint content
Published: 23 March 2020
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Soil physical properties and soil water regime have been in the focus of soil surveys and mapping in Hungary due to their importance in various environmental processes and hazards, like waterlogging and drought, which endanger extended areas. 
In the late ‘70s a category system was elaborated for the planning of water management, which was used as the legend of a nationwide map prepared at a scale of 1:500.000. Soils were characterized qualitatively (e.g.: soil with unfavorable water management was defined with low infiltration rate, very low permeability and hydraulic conductivity, and high water retention), without quantification of these features. The category system was also used for creating large-scale (1:10.000) water management maps, which are contained legally by expert’s reports prepared on the subject of drainage, irrigation, liquid manure, sewage or sewage-sludge disposal. These maps were prepared eventually, essentially for individual plots and are not managed centrally and are not available for further applications.
Recently a 3D Soil Hydraulic Database was elaborated for Europe at 250 m resolution based on specific pedotransfer functions and soil property maps of SoilGrids. The database includes spatial information on the soil water content at the most frequently used matric potential values, saturated hydraulic conductivity, Mualem-van Genuchten parameters of the moisture retention and hydraulic conductivity curves. Based on similar idea, the work has been continued to produce more accurate and spatially more detailed hydrophysical maps in Hungary by generalizing the applied pedotransfer functions and using national soil reference data and high resolution, novel, digital soil property maps.
We initiated a study in order to formalize the built-in soil-landscape model(s) of the national legacy map on water management, together with the quantification of its categories and its potential disaggregation. The relation of the legacy map with the newly elaborated 3D estimations were evaluated at two scales: nationwide with 250 m resolution and at catchment scale with 100 m resolution. Hydrological and primary soil property maps were used as predictor variables. Unsupervised classifications were performed for spatial-thematic aggregation of the soil hydraulic datasets to identify their intrinsic characteristics, which were used for the elaboration of a renewed water management classification. Hydrological interpretation of the categories provided by the optimum classifications has been carried out (i) by their spatial cross-tabulation with the categories of the legacy map and (ii) using the interval estimation of the applied soil hydraulic properties provided for the individual water management categories. Machine learning approaches were used to analyze the information content of the legacy maps’s category system, whose results were used for its disaggregation. Conditionally located random points were sequentially generated for virtual sampling of the legacy map to produce reference information. The disaggregated maps with the legend of the traditional water management classes were produced both on national and catchment level.

Acknowledgment: The research has been supported by the Hungarian National Research, Development and Innovation Office (NRDI) under grants KH124765, KH126725, the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and the MTA Cloud infrastructure (https://cloud.mta.hu/).

ACS Style

Brigitta Szabó; Annamária Laborczi; Gábor Szatmári; Zsófia Bakacsi; András Makó; Péter Braun; László Pásztor. Renewal of a national soil water management category system and legacy map by data mining methods, digital primary and hydrological soil property maps. 2020, 1 .

AMA Style

Brigitta Szabó, Annamária Laborczi, Gábor Szatmári, Zsófia Bakacsi, András Makó, Péter Braun, László Pásztor. Renewal of a national soil water management category system and legacy map by data mining methods, digital primary and hydrological soil property maps. . 2020; ():1.

Chicago/Turabian Style

Brigitta Szabó; Annamária Laborczi; Gábor Szatmári; Zsófia Bakacsi; András Makó; Péter Braun; László Pásztor. 2020. "Renewal of a national soil water management category system and legacy map by data mining methods, digital primary and hydrological soil property maps." , no. : 1.

Review article
Published: 12 March 2020 in Geoderma Regional
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The GlobalSoilMap initiative significantly inspired the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project, which was started intentionally for the renewal of the national spatial soil data infrastructure in Hungary. The main objectives of our work has been to broaden the possibilities, how demands on spatial soil related information could be satisfied in Hungary, how the gaps between the available and the expected could be filled with optimized digital soil (related) maps. During our activities, we have significantly extended the potential, how goal-oriented, map-based soil information could be created to fulfill the requirements. Primary and specific soil property, soil type and certain tentative functional soil maps were compiled. The set of the applied digital soil mapping techniques has been gradually broadened incorporating and eventually integrating geostatistical, machine learning and GIS tools. Soil property maps have been compiled partly according to GlobalSoilMap.net specifications, partly by slightly or more strictly changing some of their predefined parameters (depth intervals, pixel size, property etc.) according to the specific demands on the final products. The set of primary and derived soil properties specified by GSM for the required layers have been almost entirely prepared. The web publishing of the results was also elaborated creating a specific WMS environment. The map products are published on the www.dosoremi.hu website. The maps are serviced in two different ways. In the atlas version, map layouts are collected and published for application as graphical elements. Interactive maps are produced for browsing over alternative base map background. Most relevant information on the renewed Hungarian Spatial Soil Data Infrastructure, on its compilation and applicability are also communicated on the site. The nationwide, thematic digital soil maps compiled in the frame and spin-off of our research have been utilized in a number of ways. Programs or studies dedicated to the designation of areas suitable for irrigation; risk modelling of inland excess water hazard; mapping of potential habitats; spatial assessment and mapping of ecosystem services were heavily relied on the novel type spatial soil information. These programs however frequently required certain modifications of the standard GSM products due to various reasons. The paper presents the finalized GSM conform results of DOSoReMI.hu, together with their various national applications. Some reasons behind the application of modified GSM products are also presented.

ACS Style

László Pásztor; Annamária Laborczi; Katalin Takács; Gábor Illés; József Szabó; Gábor Szatmári. Progress in the elaboration of GSM conform DSM products and their functional utilization in Hungary. Geoderma Regional 2020, 21, e00269 .

AMA Style

László Pásztor, Annamária Laborczi, Katalin Takács, Gábor Illés, József Szabó, Gábor Szatmári. Progress in the elaboration of GSM conform DSM products and their functional utilization in Hungary. Geoderma Regional. 2020; 21 ():e00269.

Chicago/Turabian Style

László Pásztor; Annamária Laborczi; Katalin Takács; Gábor Illés; József Szabó; Gábor Szatmári. 2020. "Progress in the elaboration of GSM conform DSM products and their functional utilization in Hungary." Geoderma Regional 21, no. : e00269.

Preprint content
Published: 10 March 2020
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The minimum set of indicators recommended for tracking progress towards LDN against a baseline are: land cover, land productivity and carbon stocks above and below ground. While land cover and its change can be and actually is operatively monitored by Earth Observation in a relatively straightforward manner, spatio-temporal assessment of the two other, soil related indicators poses challenges.

Soil organic carbon (SOC) stock in Hungary was first mapped in the frame of Global Soil Organic Carbon Map initiative. The Hungarian Soil Information and Monitoring System was used to create the GSOC product with quantile regression forest, which made the assessment of local uncertainty possible.  The map was produced with 500 meter spatial resolution and aggregated for the predefined 1 km grid. Since it used data collected in the first field campaign, in 1994, consequently its estimates represent that year’s state.

In 2018 a national report was expected by UNCCD on LDN firstly quantifying trends in carbon stocks above and below the ground. Based on global databases (ESA Climate Change Initiative Land Cover Dataset, SoilGrids250) default values were assigned to countries, which were asked about its acceptance or providing more accurate estimations based on national datasets. Similarly to the global initiative, SOC change estimation was not based on soil reference data dating from two distinct dates, but on the only available spatial prediction and changes of SOC were exclusively attributed to changes in land cover. Corine Land Cover Change maps were used to derive the GSOC estimations for the base year (2000) as well as for the target year (2012) from the original SOC map (representing 1994) according to Trends.Earth tool guidelines. SOC change between 2000 and 2012 was estimated by the difference of the two predictions.

In the next step, the SOM measurements on the samples collected in 2010 in the frame of Hungarian Soil Information and Monitoring System became available to map soil organic carbon stock in the topsoils (0-30 cm) of Hungary for the year 2010. New modelling was carried out based on the experiences of GSOC estimations, the map was produced with 100 m resolution using quantile regression forest for both years. 10-fold cross-validation was used for checking the accuracy of the spatial predictions and uncertainty quantifications. The performance of the spatial predictions and uncertainty quantifications was appropriate, which was verified by the computed biases, the root mean square errors, accuracy plots and the G statistics. Based on the compiled SOC stock maps, we assessed the spatial and temporal changes of SOC stocks on the whole area of Hungary except artificial surfaces and water bodies. The total SOC stock in the topsoil increased by 27.18 Tg over the respective period. We compared our estimate with others provided by global and continental SOC stock inventories. The comparison pointed out that a SOC stock map compiled by a given country can provide more accurate estimates at national level. We recommend applying the SOC stock map of 1992 as baseline to track and assess SOC stock change in Hungary.

ACS Style

László Pásztor; Annamária Laborczi; Gábor Szatmári. Spatio-temporal modelling of soil organic carbon stock for the support of national level assessment of land degradation neutrality in Hungary. 2020, 1 .

AMA Style

László Pásztor, Annamária Laborczi, Gábor Szatmári. Spatio-temporal modelling of soil organic carbon stock for the support of national level assessment of land degradation neutrality in Hungary. . 2020; ():1.

Chicago/Turabian Style

László Pásztor; Annamária Laborczi; Gábor Szatmári. 2020. "Spatio-temporal modelling of soil organic carbon stock for the support of national level assessment of land degradation neutrality in Hungary." , no. : 1.

Journal article
Published: 12 December 2019 in Plants
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For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

ACS Style

Péter Szilassi; Gábor Szatmári; László Pásztor; Mátyás Árvai; József Szatmári; Katalin Szitár; Levente Papp. Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods. Plants 2019, 8, 593 .

AMA Style

Péter Szilassi, Gábor Szatmári, László Pásztor, Mátyás Árvai, József Szatmári, Katalin Szitár, Levente Papp. Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods. Plants. 2019; 8 (12):593.

Chicago/Turabian Style

Péter Szilassi; Gábor Szatmári; László Pásztor; Mátyás Árvai; József Szatmári; Katalin Szitár; Levente Papp. 2019. "Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods." Plants 8, no. 12: 593.

Journal article
Published: 01 October 2019 in Geoderma
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Over the last decades extensive work has been done on sampling optimization. Many of the related papers focused on the optimization of sampling for only one soil property. However, there is a necessity to prepare a sampling strategy which is optimized for multivariate digital soil mapping (DSM) purposes. The aim of our work was to elaborate a sampling optimization methodology for multivariate DSM considering the demands on economic efficiency. We presented and tested it through a real-time survey at Tokaj Wine Region, Hungary. The soil properties of interest were pH, soil organic matter (SOM), and calcium carbonate (CaCO3) content. The end-users defined the minimal requested precision for the DSM products (in terms of the average range of the 90% prediction interval), and priority areas on which more detailed survey was requested. We planned a two-phase soil survey based on regression kriging (RK). The results from the first-phase sampling were used to parameterize the second-phase sampling in which spatial simulated annealing (SSA) was applied. The spatially averaged range of the 90% prediction interval was the pre-survey quality measure which can be readily derived from the RK variance. The workflow can be summarized as follows: (1) rank the soil properties considering their spatial variabilities, and precision requests, (2) optimize the sampling design by SSA for the dominant soil property, (3) optimize the sampling by the invers application of SSA for the next soil property using the optimized design from the previous step, and (4) repeat the previous step until all soil property are being selected. In our case, SOM was the dominant property. According to the plot of the sample size vs. quality measure, the optimized design with 500 samples will ensure the minimal requested precision for the SOM map (i.e. 0.5%). In the next step, the optimal removal of those sampling points was targeted which have less information content. In the cases of pH and CaCO3, 100 and 175 could be removed from the 500 samples, and the remaining 400 and 325 samples will ensure the requested precision for the pH (i.e. 1.2) and CaCO3 (i.e. 5%) maps. We computed the relative sampling density on priority and non-priority areas for each sampling designs which showed that densities on priority areas were at least 1.5 times higher than on non-priority areas. We could conclude that the methodology is able to optimize the sampling design for multivariate DSM purposes considering numerous sampling constraints such as the predefined precision, priority areas, and economic efficiency.

ACS Style

Gábor Szatmári; Péter László; Katalin Takács; József Szabó; Zsófia Bakacsi; Sándor Koós; László Pásztor. Optimization of second-phase sampling for multivariate soil mapping purposes: Case study from a wine region, Hungary. Geoderma 2019, 352, 373 -384.

AMA Style

Gábor Szatmári, Péter László, Katalin Takács, József Szabó, Zsófia Bakacsi, Sándor Koós, László Pásztor. Optimization of second-phase sampling for multivariate soil mapping purposes: Case study from a wine region, Hungary. Geoderma. 2019; 352 ():373-384.

Chicago/Turabian Style

Gábor Szatmári; Péter László; Katalin Takács; József Szabó; Zsófia Bakacsi; Sándor Koós; László Pásztor. 2019. "Optimization of second-phase sampling for multivariate soil mapping purposes: Case study from a wine region, Hungary." Geoderma 352, no. : 373-384.

Journal article
Published: 18 September 2019 in Soil and Tillage Research
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We compiled maps for the topsoil (0–30 cm) organic carbon (SOC) stock and its prediction uncertainty in Hungary at 100 m resolution for the years 1992 and 2010 using a machine learning algorithm, namely, quantile regression forest. 10-fold cross-validation was used for checking the accuracy of the spatial predictions and uncertainty quantifications for both years. The performance of the spatial predictions and uncertainty quantifications was appropriate, which was verified by the computed biases (0.15 and 0.30 for 1992 and 2010), root mean square errors (21.99 and 21.39 for 1992 and 2010), accuracy plots and the G statistics (0.96 for both years) as well. Based on the compiled SOC stock maps, we assessed the spatio-temporal change of SOC stocks on the territory of Hungary. A scheme was elaborated based on the quantified prediction uncertainties for identifying and delimiting significant and tendentious changes of SOC stock during the respective period. The total SOC stock in the topsoil was found to be 424.41 Tg (1 teragram = 1012 grams) in 1992 and 451.59 Tg in 2010. Thus SOC stock increased by 27.18 Tg over the respective period. On those areas where the land use types did not change, we observed that the SOC stock increased under forests (by 16.29 Tg) and pastures (by 2.48 Tg), decreased under wetlands (by 0.49 Tg) and did not change under agricultural areas. On those areas where the land use has been changed during the 18-year period, we found that afforestation has increased the SOC stock, whereas cultivation of pastures has decreased it. Due to soil sealing 34,000 ha of soil have been lost resulting in approximately 1.7 Tg carbon loss. We compared our own total SOC stock estimate and map referring to 1992 with other estimates and maps provided by global and continental initiatives. The comparisons have pointed out that the SOC stock map of 1992 outperformed these maps. We recommend applying the SOC stock map of 1992 as a baseline for Hungary.

ACS Style

Gábor Szatmári; Béla Pirkó; Sándor Koós; Annamária Laborczi; Zsófia Bakacsi; József Szabó; László Pásztor. Spatio-temporal assessment of topsoil organic carbon stock change in Hungary. Soil and Tillage Research 2019, 195, 104410 .

AMA Style

Gábor Szatmári, Béla Pirkó, Sándor Koós, Annamária Laborczi, Zsófia Bakacsi, József Szabó, László Pásztor. Spatio-temporal assessment of topsoil organic carbon stock change in Hungary. Soil and Tillage Research. 2019; 195 ():104410.

Chicago/Turabian Style

Gábor Szatmári; Béla Pirkó; Sándor Koós; Annamária Laborczi; Zsófia Bakacsi; József Szabó; László Pásztor. 2019. "Spatio-temporal assessment of topsoil organic carbon stock change in Hungary." Soil and Tillage Research 195, no. : 104410.

Journal article
Published: 01 July 2019 in Hungarian Geographical Bulletin
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Optimal water supply of plants is key to high yields. However, irrigation in drier regions must be accompanied by soil conservation. Nationwide planning of irrigation needs spatially exhaustive, functional soil maps, which may support proper recommendations for the different areas. For supporting the Hungarian national irrigation strategy, a series of countrywide functional soil maps was created, which reveal the pedological constraints, conditions and circumstances of irrigation by the spatial modelling of the relevant functional features of the soil mantle. Irrigation can improve productivity, while its negative effects may lead to soil degradation. This paper focuses on threats, the spatial identification of potentially affected areas. The thematic maps spatially model the irrigability and vulnerability of soils. Estimation of salt accumulation hazard, and soil structure degradation risks were targeted. The salinization hazard assessment was carried out by two ways. We applied the steady state concept of critical water-table depth and a more dynamic, process-based method. To estimate soil structural degradation hazard, class-based relationships were developed based on soil profile data of MARTHA 1.0 (Hungarian Detailed Soil Hydraulic Database). Soil type, organic matter content, carbonate content, soil reaction and texture class (USDA) were taken into consideration to develop pedotransfer functions for modelling the correlations between primary soil properties and threats indicators. The new maps can help decision makers to improve land use management, and sustainable agronomy.

ACS Style

Zsófia Bakacsi; Tibor Tóth; András Makó; Gyöngyi Barna; Annamária Laborczi; József Szabó; Gábor Szatmári; László Pásztor. National level assessment of soil salinization and structural degradation risks under irrigation. Hungarian Geographical Bulletin 2019, 68, 141 -156.

AMA Style

Zsófia Bakacsi, Tibor Tóth, András Makó, Gyöngyi Barna, Annamária Laborczi, József Szabó, Gábor Szatmári, László Pásztor. National level assessment of soil salinization and structural degradation risks under irrigation. Hungarian Geographical Bulletin. 2019; 68 (2):141-156.

Chicago/Turabian Style

Zsófia Bakacsi; Tibor Tóth; András Makó; Gyöngyi Barna; Annamária Laborczi; József Szabó; Gábor Szatmári; László Pásztor. 2019. "National level assessment of soil salinization and structural degradation risks under irrigation." Hungarian Geographical Bulletin 68, no. 2: 141-156.