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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).
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 StyleAnnamária 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 StyleAnnamária 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.
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.
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 StyleSándor 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 StyleSándor 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.
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.
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 StyleGábor 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 StyleGábor 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.
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.
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 StyleAnnamária 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 StyleAnnamária 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.
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).
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 StyleAnnamária 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 StyleAnnamária 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.
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.
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 StyleLászló 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 StyleLászló 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.
There are increasing demands nowadays on spatial soil information in order to support environmental related and land use management decisions. Physical soil properties, especially particle size distribution play important role in this context. A few of the requirements can be satisfied by the sand-, silt-, and clay content maps compiled according to global standards such as GlobalSoilMap (GSM) or Soil Grids. Soil texture classes (e. g. according to USDA classification) can be derived from these three fraction data, in this way texture map can be compiled based on the proper separate maps. Soil texture class as well as fraction information represent direct input of crop-, meteorological- and hydrological models. The model inputs frequently require maps representing topsoil features, which refer most commonly to 0–30 cm depth. This is covered by three consecutive depth intervals according to standard specifications: 0–5 cm, 5–15 cm, 15–30 cm. Becoming GSM and SoilGrids the most detailed freely available spatial soil data sources, the common model users (e. g. meteorologists, agronomists, or hydrologists) would produce input map from (the thickness-weighted mean of) these three layers. However, if the basic soil data and proper knowledge is obtainable, a soil texture map targeting directly the 0–30 cm layer could be independently compiled. In our work we compared Hungary's soil texture maps compiled using the same reference and auxiliary data and inference methods but for differing layer distribution. We produced the 0–30 cm clay, silt and sand map as well as the maps for the three standard layers (0–5 cm, 5–15 cm, 15–30 cm). Maps of sand-, silt-, and clay content were computed through composite regression kriging applying Additive Log-Ratio (alr) transformation. In addition to the Hungarian Soil Information and Monitoring System as reference soil data, digital elevation model and its derived components, soil physical property maps, remotely sensed images, land use-, geological-, as well as meteorological data were applied as auxiliary variables. We compared the directly compiled and the synthetized clay-, sand content, and texture class maps by different tools. In addition to pairwise comparison of basic statistical features (histograms, scatter plots), we examined the spatial distribution of the differences. We quantified the taxonomical distances of the textural classes, in order to investigate the differences of the map-pairs. We concluded that the directly computed and the synthetized maps show various differences. In the case of clay-, and sand content maps, the map-pairs have to be considered statistically different. On the other hand, the differences of the texture class maps are not significant. However, in all cases, the differences rather concern the extreme ranges and categories. Using of synthetized maps can intensify extremities by error propagation in models and scenarios. Based on our results, we suggest the usage of the directly composed maps.
Annamária Laborczi; Gábor Szatmári; András Dezső Kaposi; László Pásztor. Comparison of soil texture maps synthetized from standard depth layers with directly compiled products. Geoderma 2018, 352, 360 -372.
AMA StyleAnnamária Laborczi, Gábor Szatmári, András Dezső Kaposi, László Pásztor. Comparison of soil texture maps synthetized from standard depth layers with directly compiled products. Geoderma. 2018; 352 ():360-372.
Chicago/Turabian StyleAnnamária Laborczi; Gábor Szatmári; András Dezső Kaposi; László Pásztor. 2018. "Comparison of soil texture maps synthetized from standard depth layers with directly compiled products." Geoderma 352, no. : 360-372.
Questions Multiple potential natural vegetation (MPNV) is a framework for the probabilistic and multilayer representation of potential vegetation in an area. How can an MPNV model be implemented and synthesized for the full range of vegetation types across a large spatial domain such as a country? What additional ecological and practical information can be gained compared to traditional potential natural vegetation (PNV) estimates? Location Hungary. Methods MPNV was estimated by modelling the occurrence probabilities of individual vegetation types using gradient boosting models (GBM). Vegetation data from the Hungarian Actual Habitat Database (MÉTA) and information on the abiotic background (climatic data, soil characteristics, hydrology) were used as inputs to the models. To facilitate MPNV interpretation a new technique for model synthesis (re‐scaling) enabling comprehensive visual presentation (synthetic maps) was developed which allows for a comparative view of the potential distribution of individual vegetation types. Results The main result of MPNV modelling is a series of raw and re‐scaled probability maps of individual vegetation types for Hungary. Raw probabilities best suit within‐type analyses, while re‐scaled estimations can also be compared across vegetation types. The latter create a synthetic overview of a location's PNV as a ranked list of vegetation types, and make the comparison of actual and potential landscape composition possible. For example, a representation of forest vs grasslands in MPNV revealed a high level of overlap of the potential range of the two formations in Hungary. Conclusion The MPNV approach allows viewing the potential vegetation composition of locations in far more detail than the PNV approach. Re‐scaling the probabilities estimated by the models allows easy access to the results by making potential presence of vegetation types with different data structure comparable for queries and synthetic maps. The wide range of applications identified for MPNV (conservation and restoration prioritization, landscape evaluation) suggests that the PNV concept with the extension towards vegetation distributions is useful both for research and application.
Imelda Somodi; Zsolt Molnár; Bálint Czúcz; Ákos Bede‐Fazekas; János Bölöni; László Pásztor; Annamária Laborczi; Niklaus E. Zimmermann. Implementation and application of multiple potential natural vegetation models – a case study of Hungary. Journal of Vegetation Science 2017, 28, 1260 -1269.
AMA StyleImelda Somodi, Zsolt Molnár, Bálint Czúcz, Ákos Bede‐Fazekas, János Bölöni, László Pásztor, Annamária Laborczi, Niklaus E. Zimmermann. Implementation and application of multiple potential natural vegetation models – a case study of Hungary. Journal of Vegetation Science. 2017; 28 (6):1260-1269.
Chicago/Turabian StyleImelda Somodi; Zsolt Molnár; Bálint Czúcz; Ákos Bede‐Fazekas; János Bölöni; László Pásztor; Annamária Laborczi; Niklaus E. Zimmermann. 2017. "Implementation and application of multiple potential natural vegetation models – a case study of Hungary." Journal of Vegetation Science 28, no. 6: 1260-1269.
Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The ‘Classification and Regression Trees’ method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.
Annamária Laborczi; Gábor Szatmári; Katalin Takács; László Pásztor. Mapping of topsoil texture in Hungary using classification trees. Journal of Maps 2015, 12, 999 -1009.
AMA StyleAnnamária Laborczi, Gábor Szatmári, Katalin Takács, László Pásztor. Mapping of topsoil texture in Hungary using classification trees. Journal of Maps. 2015; 12 (5):999-1009.
Chicago/Turabian StyleAnnamária Laborczi; Gábor Szatmári; Katalin Takács; László Pásztor. 2015. "Mapping of topsoil texture in Hungary using classification trees." Journal of Maps 12, no. 5: 999-1009.