Annamária Laborczi

Compilation of 1:50,000 scale digital soil maps for Hungary based on the digital Kreybig soil information system

After several years of digital processing of legacy soil data collected by the Kreybig soil survey, the nationwide development of the digital Kreybig soil information system (DKSIS) made possible the compilation of soil property and function maps for the territory of Hungary at a scale of approximately 1:25,000–1:50,000. The Kreybig legacy data are spatially most detailed nationwide dataset related to soils which covers the whole area of the country. It simultaneously contains two types of geometric datasets: approximately 100,000 soil mapping units (SMUs) and 250,000 sampling plots. SMUs are characterized by several complex soil physical and chemical categories and detailed soil properties which are provided for soil profiles whose description in the digital environment is supported by a specific relational database. Primary digital soil maps can be compiled based on the polygons-type entities, while suitable spatial inference of profile-related variables makes the composition of secondary, regionalized digital soil maps possible, too. In our paper, we present example for both types.

Elaboration and applications of spatial soil information systems and digital soil mapping at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences

Hungary has long traditions in soil survey and mapping. Large amount of soil information is available in various dimensions and generally presented in maps, serving different purposes as to spatial and/or thematic aspects. Increasing the proportion of soil-related data has been digitally processed and organized into various spatial soil information systems (SSISs). The most current countrywide ones have been elaborated by and available at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences (RISSAC HAS). The existing maps, data and systems served the society for many years; however, the available data are no longer fully satisfactory for the recent needs of policy making. There were numerous initiatives for the digital processing, completion, improvement and integration of the existing soil datasets. In our paper, we briefly present the national SSISs developed and maintained by our institute and some examples are given regarding how their functionality was extended by digital soil mapping for the solution of specific soil-related demands.

Mapping geogenic radon potential by regression kriging.

Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.

Mapping of topsoil texture in Hungary using classification trees

ABSTRACT 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.

Variations for the Implementation of SCORPAN’s “S”

Development of DSM can be notably attributed to frequent limitations in the availability of proper soil information; consequently, it has been typically used in cases featured by limited soil data. Since SCORPAN equation includes other or previously measured properties of soil, the usage of legacy soil data supports the applicability of DSM and improves the accuracy of DSM products as well. Nevertheless, the occurrent abundance of available soil information poses new demands on and at the same time opens new possibilities in the application of DSM methods. A great amount of soil information has been collected in Hungary in the frame of subsequent surveys and assessments. The majority of these legacy soil data were integrated in various spatial soil information systems. Our paper presents three approaches for the application of Hungary’s most extended legacy soil data source in goal-oriented digital soil mapping.

Compilation of Functional Soil Maps for the Support of Spatial Planning and Land Management in Hungary

The tasks of national spatial planning (i.e., delineation of areas with natural constraints or areas with excellent productivity; support of irrigation strategies; flood, drought, and climate change impact risk assessment) increasingly demand advanced or new kinds of spatial soil information, which cannot be fully satisfied by legacy soil maps or formerly elaborated databases. Due to the lack of recent, extended, nationwide mapping, the data of previous surveys should be exploited thoroughly. Digital soil mapping integrates geographic information systems (GIS), geostatistical, and data mining tools and makes possible the elaboration of target-specific soil maps with improved and/or specific thematic, spatial, and temporal accuracy as opposed to former, more general soil maps. For the satisfaction of the recent demands, soil conditions of Hungary have been digitally mapped based on various available recent and legacy soil datasets, and spatially exhaustive, environmental, and auxiliary information. The produced digital soil property maps have been miscellaneously utilized in various regional planning activities.

Implementation and application of multiple potential natural vegetation models – a case study of Hungary

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 (META) 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 locations 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.

Spatial risk assessment of hydrological extremities: Inland excess water hazard, Szabolcs-Szatmár-Bereg County, Hungary

Inland excess water hazard was regionalized and digitally mapped using auxiliary spatial environmental information for a county in Eastern Hungary. Quantified parameters representing the effect of soil, geology, groundwater, land use and hydrometeorology on the formulation of inland excess water were defined and spatially explicitly derived. The complex role of relief was characterized using multiple derivatives computed from a DEM. Legacy maps displaying inland excess water events were used as a reference dataset. Regression kriging was applied for spatial inference with the correlation between environmental factors and inundation determined using multiple linear regressions. A stochastic factor derived through kriging the residual was added to the regression results, thus producing the final inundation hazard map. This may be of use for numerous land-related activities.

Spatial distribution of selected soil features in Hajdú-Bihar county represented by digital soil maps

With the ongoing DOSoReMI.hu project we aimed to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally general as well as specific national and international demands from the aspect of thematic, spatial and temporal accuracy. In addition to relevant and available auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened. In our paper we present some results in the form of brand new soil maps focusing on the territory of Hajdú-Bihar county.

Wetland habitats of the Kis-Sárrét 1860–2008 (Körös-Maros National Park, Hungary)

The manuscript presents maps of internationally important wetlands located in the Kis-Sárrét (Hungary) from 1860 to 2008. The study area is located in south-east Hungary, in the Körös-Maros National Park and covers 8048 ha. For the historic map review, we used digitized data of topographic maps from the period of two military surveys and the Second World War. We also made habitat maps of the area in 2007 and 2008. Data processing, and the establishment of a database of the mapped area, was made using QuantumGIS 1.7.0 and Esri ArcView GIS 3.2. Maps were produced using Esri ArcGIS 10.0 and show where and in what ratio the once extensive wetlands occurred, how they changed and in which part of the area they survived in different mapping periods. They provide a point of reference for the monitoring of wetlands, contributing to the long-term conservation of these valuable habitats. Maps and diagrams show that between 1860 and 1944 wetland extent decreased by half. The ratio of natural, ‘purely’ wet habitats reaches only 4.67% now. Wetlands typically occur in habitat complexes, therefore not ‘purely’ wet habitats (20.77%) also have to be taken into account. Considering this, and a recent habitat reconstruction, the extent of wetlands is more favourable today than it was in 1944. However, to sustain them requires care and well-planned management to which the maps presented here provide an important basis.