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Cropmarks are a major factor in the effectiveness of traditional aerial archaeology. Identified almost 100 years ago, the positive and negative features shown by cropmarks are now well understood, as are the role of the different cultivated plants and the importance of precipitation and other elements of the physical environment. Generations of aerial archaeologists are in possession of empirical knowledge, allowing them to find as many cropmarks as possible every year. However, the essential analyses belong mostly to the predigital period, while the significant growth of datasets in the last 30 years could open a new chapter. This is especially true in the case of Hungary, as scholars believe it to be one of the most promising cropmark areas in Europe. The characteristics of soil formed of Late Quaternary alluvial sediments are intimately connected to the young geological/geomorphological background. The predictive soil maps elaborated within the framework of renewed data on Hungarian soil spatial infrastructure use legacy, together with recent remote sensing imagery. Based on the results from three study areas investigated, analyses using statistical methods (the Kolmogorov–Smirnov and Random Forest tests) showed a different relative predominance of pedological variables in each study area. The geomorphological differences between the study areas explain these variations satisfactorily.
Zoltán Czajlik; Mátyás Árvai; János Mészáros; Balázs Nagy; László Rupnik; László Pásztor. Cropmarks in Aerial Archaeology: New Lessons from an Old Story. Remote Sensing 2021, 13, 1126 .
AMA StyleZoltán Czajlik, Mátyás Árvai, János Mészáros, Balázs Nagy, László Rupnik, László Pásztor. Cropmarks in Aerial Archaeology: New Lessons from an Old Story. Remote Sensing. 2021; 13 (6):1126.
Chicago/Turabian StyleZoltán Czajlik; Mátyás Árvai; János Mészáros; Balázs Nagy; László Rupnik; László Pásztor. 2021. "Cropmarks in Aerial Archaeology: New Lessons from an Old Story." Remote Sensing 13, no. 6: 1126.
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).
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 StyleJános 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 StyleJános 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.
The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.
The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.
The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.
Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km2 in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.
We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.
For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.
Acknowledgment: Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022). Our project carried out using PRISMA Products, © of the Italian Space Agency (ASI), delivered under an ASI License to use.
Zsófia Adrienn Kovács; János Mészáros; Mátyás Árvai; Annamária Laborczi; Gábor Szatmári; Péter László; László Pásztor. Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data. 2021, 1 .
AMA StyleZsófia Adrienn Kovács, János Mészáros, Mátyás Árvai, Annamária Laborczi, Gábor Szatmári, Péter László, László Pásztor. Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data. . 2021; ():1.
Chicago/Turabian StyleZsófia Adrienn Kovács; János Mészáros; Mátyás Árvai; Annamária Laborczi; Gábor Szatmári; Péter László; László Pásztor. 2021. "Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data." , no. : 1.
Cropmarks are a major factor in the effectiveness of traditional aerial archaeology. The positive and negative features shown up by cropmarks are the role of the different cultivated plants and the importance of precipitation and other elements of the physical environment. In co-operation with the experts of the Eötvös Loránd University a new research was initiated to compare the pedological features of cropmark plots (CMP) and non-cropmark plots (nCMP) in order to identify demonstrable differences between them. For this purpose, the spatial soil information on primary soil properties provided by DOSoReMI.hu was employed. To compensate for the inherent vagueness of spatial predictions, together with the fact that the definition of CMPs and nCMPs is somewhat indefinite, the comparisons were carried out using data-driven, statistical approaches. In the first round three pilot areas were investigated, where Chernozem and Meadow type soils proved to be correlated with the formation of cropmarks. Kolmogorov-Smirnov tests and Random Forest models showed a different relative predominance of pedological variables in each study area. The geomorphological differences between the study areas explain these variations satisfactorily. In the next round, the identified relationships between cropmarking and soil features are planned to be utilized in the spatial inference of soil properties, where crop-marking sites will represent a unique, spatially non-exhaustive auxiliary information.
Mátyás Árvai; Zoltán Czajlik; János Mészáros; Balázs Nagy; László Pásztor. Cropmarks used in aerial archaeology as special spatial indicators of soil features potentially applicable in soil mapping. 2021, 1 .
AMA StyleMátyás Árvai, Zoltán Czajlik, János Mészáros, Balázs Nagy, László Pásztor. Cropmarks used in aerial archaeology as special spatial indicators of soil features potentially applicable in soil mapping. . 2021; ():1.
Chicago/Turabian StyleMátyás Árvai; Zoltán Czajlik; János Mészáros; Balázs Nagy; László Pásztor. 2021. "Cropmarks used in aerial archaeology as special spatial indicators of soil features potentially applicable in soil mapping." , no. : 1.
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).
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 StyleLászló 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 StyleLászló 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.
Parent material is an essential soil property, whose mapping is a challenging task. Usually, large scale geological maps are used if they are available. However, in many cases, especially in medium and large scale mapping, such source data are too old or not existing at all. In this project have been looking for a solution for this problem. Our aim is to create a new, large scale, lithological map of parent material in an old mining region.
The study area is the Dorogi Basin in northern central Hungary. It is known for coal mining, which ended in 2003 after more than two centuries. The latest large scale (1:10,000) geological map series from this area was made in the 1960’s, in the “golden age” of mining.
Google Earth Engine was selected as main GIS platform, using mainly open source data and programs for mapping. We have used data originating from Earth Observation as ancillary information (e.g. satellite images, SRTM) and machine learning techniques to spatially predict parent material. The satellite images were used to calculate several geological indices, which can be used as indicators of chemical composition. We examined the use of multiple satellite platform (Sentinel-2, Landsat 8, ASTER) as it has different geological indices. The existing geological maps were used for training in the classification concerning the lithological composition.To predict the parent materials we have used random forest, using geomorphometric features and geological indices as predictors. The newly compiled map was validated by comparing it with the old one.
Acknowledgment: Our research was supported by the Hungarian National Research,Development and Innovation Office (NKFIH; K-131820) and from the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.
Tünde Takáts; János Mészáros; Gáspár Albert; László Pásztor. Mapping parent material using data originating from Earth Observation as ancillary information. 2021, 1 .
AMA StyleTünde Takáts, János Mészáros, Gáspár Albert, László Pásztor. Mapping parent material using data originating from Earth Observation as ancillary information. . 2021; ():1.
Chicago/Turabian StyleTünde Takáts; János Mészáros; Gáspár Albert; László Pásztor. 2021. "Mapping parent material using data originating from Earth Observation as ancillary information." , no. : 1.
‘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.
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).
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 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). . 2021; ():1.
Chicago/Turabian StyleGábor 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.
Land refers to the planet’s surface not covered by seas, lakes or rivers, but by different types of vegetation (e
László Pásztor. Advanced GIS and RS Applications for Soil and Land Degradation Assessment and Mapping. ISPRS International Journal of Geo-Information 2021, 10, 128 .
AMA StyleLászló Pásztor. Advanced GIS and RS Applications for Soil and Land Degradation Assessment and Mapping. ISPRS International Journal of Geo-Information. 2021; 10 (3):128.
Chicago/Turabian StyleLászló Pásztor. 2021. "Advanced GIS and RS Applications for Soil and Land Degradation Assessment and Mapping." ISPRS International Journal of Geo-Information 10, no. 3: 128.
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.
The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.
Levente Papp; Boudewijn Van Leeuwen; Péter Szilassi; Zalán Tobak; József Szatmári; Mátyás Árvai; János Mészáros; László Pásztor. Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data. Land 2021, 10, 29 .
AMA StyleLevente Papp, Boudewijn Van Leeuwen, Péter Szilassi, Zalán Tobak, József Szatmári, Mátyás Árvai, János Mészáros, László Pásztor. Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data. Land. 2021; 10 (1):29.
Chicago/Turabian StyleLevente Papp; Boudewijn Van Leeuwen; Péter Szilassi; Zalán Tobak; József Szatmári; Mátyás Árvai; János Mészáros; László Pásztor. 2021. "Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data." Land 10, no. 1: 29.
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.
As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care.
István Waltner; Sahar Saeidi; János Grósz; Csaba Centeri; Annamária Laborczi; László Pásztor. Spatial Assessment of the Effects of Land Cover Change on Soil Erosion in Hungary from 1990 to 2018. ISPRS International Journal of Geo-Information 2020, 9, 667 .
AMA StyleIstván Waltner, Sahar Saeidi, János Grósz, Csaba Centeri, Annamária Laborczi, László Pásztor. Spatial Assessment of the Effects of Land Cover Change on Soil Erosion in Hungary from 1990 to 2018. ISPRS International Journal of Geo-Information. 2020; 9 (11):667.
Chicago/Turabian StyleIstván Waltner; Sahar Saeidi; János Grósz; Csaba Centeri; Annamária Laborczi; László Pásztor. 2020. "Spatial Assessment of the Effects of Land Cover Change on Soil Erosion in Hungary from 1990 to 2018." ISPRS International Journal of Geo-Information 9, no. 11: 667.
We propose a method based on multilayered mapping for investigating the current problems of people who live in drylands and we urge decision-makers to support such studies to establish the foundations for future decisive and preventive actions. This paper contains an expandable compilation of the environmental indicators (mostly mappable) that may influence the human geography of a certain region. We believe that this geospatial approach may help to resolve convoluted physical, chemical, and social relationships and, at the same time, generate a valuable database for further research. The application of the concept, if successful, will give directions to tackle certain contemporary problems in drylands and predict future ones caused by global climate change.
Matyas Arvai; Karoly Fekete; Laszlo Pasztor; Tamas Komives. Human geography of drylands. I. Planning the database: Physical, built-up, chemical, biological (ecological), and social indicators. Ecocycles 2020, 6, 19 -24.
AMA StyleMatyas Arvai, Karoly Fekete, Laszlo Pasztor, Tamas Komives. Human geography of drylands. I. Planning the database: Physical, built-up, chemical, biological (ecological), and social indicators. Ecocycles. 2020; 6 (2):19-24.
Chicago/Turabian StyleMatyas Arvai; Karoly Fekete; Laszlo Pasztor; Tamas Komives. 2020. "Human geography of drylands. I. Planning the database: Physical, built-up, chemical, biological (ecological), and social indicators." Ecocycles 6, no. 2: 19-24.
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.
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.
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 StyleJános 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 StyleJános 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.
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.
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/).
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 StyleBrigitta 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 StyleBrigitta 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.
European ground squirrels (EGS) are members of the soil megafauna and part of the ecosystem engineers that shape physical, chemical, and biological characteristics of soil ecosystems in European grasslands. Thanks to their strict protection their abundance and distribution have been surveyed systematically and annually in Hungary. The results of their 20 year monitoring indicate that their population is declining, there are sudden extinctions of local populations, and a desynchronized variation of the abundance of local populations occur either spatially or temporally.
The monitoring protocol involves the estimation of their abundance in each colony by a strip-transect method and the habitat-colony area by visual observations or digital maps. Both approaches use animal burrows as proxies for either their presence (colony area) or density (colony size). These estimation methods, however, consist of systematical errors: first, they consider the animals’ density to be even over the entire habitat-area, second, they conjecture that animals occupy the habitable area completely, and third, evenly. If we were able to survey distribution and abundance of EGS more accurately, frequently, and efficiently, we could better intervene in time when local populations begin to decline or before they disappear. In addition, we could better estimate the effects (+ or -) of management strategies in real time.
The primary aims of our study were to develop a non-invasive, semi-automated method for (1) estimating abundance of EGS in the area of occupation of a colony, and (2) delineating their occupancy within the habitable area. We have defined burrow openings and mounds as quantitative proxies for the presence of animals. We have started to develop a monitoring technique to identify, locate, and count objects of interest in images automatically and to delineate the area of occupancy by identifying those objects of interest from the surroundings. To survey EGS colonies and habitats we have used a multirotor platform UAV equipped with either an RGB visible-range or a hyperspectral sensor.
To test our method several pilot areas with different vegetation and relief were surveyed. Acquired aerial images have been processed by photogrammetric software and resulting high spatial resolution orthomosaics are classified by machine-learning algorithms (randomforest, CART, C5.0) implemented in a custom R script. As detection of mounds and openings are visually restricted by vegetation height (e.g. grass, shrubs, weeds, herbs), we have studied the effect of grass height on detection success. Preliminary results suggest that successful classification can be performed either on RGB visible-range and hyperspectral images. However, the appropriate spatial resolution (below cm range) and the presence of high grass are more important key factors than number of spectral bands.
Detecting EGS burrow openings and mounds is based on surface characteristics of EGS burrow openings and mounds consequently the method is being developed for EGS specifically but can be modified to the characteristics of other burrowing mammals of this size (e.g. mole-rats, moles).
Mátyás Árvai; János Mészáros; Zsófia Kovács; Eric C. Brevik; László Pásztor; Csongor I. Gedeon. Automatic detection and mapping of European ground squirrel burrows on UAV-based multi- and hyperspectral imagery with classification methods. 2020, 1 .
AMA StyleMátyás Árvai, János Mészáros, Zsófia Kovács, Eric C. Brevik, László Pásztor, Csongor I. Gedeon. Automatic detection and mapping of European ground squirrel burrows on UAV-based multi- and hyperspectral imagery with classification methods. . 2020; ():1.
Chicago/Turabian StyleMátyás Árvai; János Mészáros; Zsófia Kovács; Eric C. Brevik; László Pásztor; Csongor I. Gedeon. 2020. "Automatic detection and mapping of European ground squirrel burrows on UAV-based multi- and hyperspectral imagery with classification methods." , no. : 1.
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).
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 StyleLászló 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 StyleLászló 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.