László PÁSZTOR - Annamária LABORCZI - Katalin TAKÁCS - Gábor SZATMÁRI - Endre DOBOS - Gábor ILLÉS - Zsófia BAKACSI - József SZABÓ

10.15201/hungeobull.64.1.5

Due to former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project we would like to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. In addition to the 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 incorporating and eventually integrating geostatistical, data mining and GIS tools. Regression kriging has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matter content. Classification and regression trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identified and expressed in decision rules made the compilation of spatially refined category-type soil maps (like genetic soil type and soil productivity maps) possible with the aid of high resolution environmental auxiliary variables. In our paper, we give a short introduction to soil mapping and information management concentrating on the driving forces for the renewal of soil spatial data infrastructure provided by the framework of Digital Soil Mapping. The first results of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project are presented in the form of brand new national and regional soil maps.

classification and regression trees, digital soil mapping, regression kriging, spatial soil information

2015. 04. 20.

Hide references

Ács, F., Gyöngyösi, A.Z., Breuer, H., Horváth, Á., Mona, T. and Rajkai, K. 2014. Sensitivity of WRF-simulated planetary boundary layer height to land cover and soil changes. Meteorologische Zeitschrift, PrePub
AGROTOPO 1994. AGROTOPO database of RISSAC. Budapest, RISSAC HAS
Baumgardner, M.F. 2011. Soil databases. In Handbook of Soil Sciences: Resource Management and Environmental Impacts. Eds.: Huang, P.M., Li, Y. and Sumner, M.E. Boca Raton, CRC Press, 21-35.
Blum, W.E.H. 2005. Functions of soil for society and the environment. Reviews in Environmental Science and Biotechnology 4. 75-79.
Boettinger, J.L., Howell, D.W., Moore, A.C., Hartemink, A.E. and Kienast-Brown, S. Eds. 2010. Digital Soil Mapping: Bridging Research, Environmental Application, and Operation. Heidelberg, Springer, 473 p.
Böhner, J., Köthe, R., Conrad, O., Gross, J., Ringeler, A. and Selige, T. 2002. Soil regionalisation by means of terrain analysis and process parameterisation. In Soil Classification 2001. Eds.: Michéli, E., Nachtergaele, F. and Montanarella, L. EUR 20398 EN. The European Soil Bureau, Joint Research Centre, Ispra, 213-222.
Bou Kheir, R., Bocher, P.K., Greve, M.B. and Greve, M.H. 2010. The application of GIS based decisiontree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data. Hydrology and Earth System Sciences 14. 847-857.
Breiman, L. 2001. Decision-tree forests. Machine Learning 45. (1): 5-32.
Bullock, P. 1999. Soil resources of Europe - An overview. In Soil Resources of Europe. Eds.: Bullock, P., Jones, R.J.A. and Montanarella, L. European Soil Bureau Research Report 6. Luxembourg, Office for Official Publications of the European Communities, 15-25.
Dobos, E. and Hengl, T. 2009. Soil mapping applications. In Geomorphometry - Concepts, Software, Applications. Eds.: Hengl, T. and Reuter, H.I. Development in Soil Science series 33. 461-479.
Dobos, E., Bialkó, T., Micheli, E. and Kobza, J. 2010. Legacy soil data harmonization and database development. In Digital Soil Mapping Bridging Research Environmental Application, and Operation: Eds.: Boettinger, J.L., Howell, D.W., Moore, A.C., Hartemink, A.E. and Kienast-Brown, S. Heidelberg, Springer, 309-323.
Dobos, E., Carré, F., Hengl, T., Reuter, H.I. and Tóth, G. Eds.. 2006. Digital soil mapping as a support to production of functional maps. EUR 22123 EN. Luxembourg, Office for Official Publications of the European Communities, 68 p.
Farkas, Cs., Gelybó, Gy., Bakacsi, Zs., Horel, Á., Hagyó, A., Dobor, L., Kása, I. and Tóth, E. 2014. Impact of expected climate change on soil water regime under different vegetation conditions. Biologia, Manuscript, accepted for publication.
Fodor, N., Pásztor, L. and Németh, T. 2014. Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agroecological characteristics of Hungary. International Journal of Digital Earth 7. (5): 391-410.
Gessler, P.E., Moore, I.D., McKenzie, N.J. and Ryan, P.J. 1995. Soil-landscape modelling and spatial prediction of soil attributes. International Journal of Geographical Information Systems 9. (4): 421-432.
Giasson, E. Sarmento, E.C., Weber, E., Flores, C.A. and Hasenack, H. 2011. Decision trees for digital soil mapping on subtropical basaltic steeplands. Scienta Agricola 68. (2): 167-174.
Goovaerts, P. 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology 228. (1-2), 113-129.
Greve, M.H., Kheir, R.B., Greve, M.B. and Bocher, P.K. 2012. Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data: The case study of Denmark. Ecological Indicators 18. 1-10.
Grunwald, S. 2009. Multi-criteria characterization of recent digital soil mapping and modelling approaches. Geoderma 152. 195-207.
Gyalog, L. and Síkhegyi, F. Eds. 2005. Magyarország geológiai térképe, 1:100 000 (Geological Map of Hungary, 1:100 000), Budapest, Geological Institute of Hungary. Digital version
Hartemink, A.E., Mcbratney, A.B. and Mendonça- Santos, M.De L. Eds. 2008. Digital Soil Mapping with Limited Data. Springer, The Netherlands, 445 p.
Henderson, B.L., Bui, E.N., Moran, C.J. and Simon, D.A.P. 2005. Australia-wide predictions of soil properties using decision trees. Geoderma 124. (3-4): 383-398.
Hengl, T., Heuvelink, G. and Stein, A. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 122. (1-2), 75-93.
Hengl,T. 2009. A Practical Guide to Geostatistical Mapping. Amsterdam, University of Amsterdam, 291 p.
Heuvelink, G.B.M. and Webster, R. 2001. Modelling soil variation: past, present, future. Geoderma 100. 269-301.
Illés, G., Kovács, G. and Heil, B. 2011. Comparing and evaluating digital soil mapping methods in a Hungarian forest reserve. Canadian Journal of Soil Science 91. (4): 615-626.
Illés, G., Kovács, G., Laborczi A. and Pásztor L. 2014. Zala megye egységes talajtípus adatbázisának összeállítása (Developing a unified soil type database for Zala County using classification algorithms). Erdészettudományi Közlemények. (in press)
Jenny, H. 1941. Factors of Soil Formation. New York, McGraw-Hill, 281 p.
Kozma, Zs., Derts, Zs., Kardos, M. and Koncsos, L. 2012. A mezőgazdasági termelés mint ökoszisztéma-szolgáltatás értéke: hidrológiai modellhez kapcsolt számítási módszertan (The value of agricultural crops as an ecosystem service: calculation methodology connected to a hydrological Model) Tájökológiai Lapok 10. (1): 55-69.
Kreybig, L. 1937. A Magyar Királyi Földtani Intézet talajfelvételi, vizsgálati és térképezési módszere (The survey, analytical and mapping method of the Hungarian Royal Institute of Geology). Magyar Királyi Földtani Intézet Évkönyve 31. 147-244.
Laborczi, A., Szatmári, G., Takács, K. and Pásztor, L. 2015. Topsoil texture class map of Hungary compiled using classification trees. Journal of Maps (submitted paper)
Lagacherie P. 2008. Digital soil mapping: A state of art. In Digital Soil Mapping with Limited Data. Eds.: Hartemink, A.E., Mcbratney, A.B. and Mendonça-Santos, M.De L. Dordrecht, Springer, 3-14.
Lagacherie, P. and Mcbratney, A.B. 2007. Spatial Soil Information Systems and Spatial Soil Inference Systems: perspectives for digital soil mapping. In Digital soil mapping: an introductory perspective. Eds.: Lagacherie, P., Mcbratney, A.B. and Voltz, M., Amsterdam, Elsevier, 3-22.
Lagacherie, P., Mcbratney, A.B. and Voltz, M. Eds. 2007. Digital soil mapping: an introductory perspective. Amsterdam, Elsevier, 658 p.
Lawrence, R., Bunn, A., Powell, S. and Zambon, M. 2004. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing of the Environment 90. 331-336.
Makó A., Tóth, B., Hernádi, H., Farkas, Cs. and Marth, P. 2010. Introduction of the Hungarian Detailed Soil Hydrophysical Database (MARTHA) and its use to test external pedotransfer functions. Agrokémia és Talajtan 59. (1): 29-38.
Mark, D.M. and Csillag, F. 1989. The nature of boundaries on 'area-class' maps. Cartographica 26. (1): 65-78.
McBratney, A.B. and Odeh, I.O.A. 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77. 85-113.
McBratney, A.B., Mendonça-Santos, M.L. and Minasny, B. 2003. On digital soil mapping. Geoderma 117. 3-52.
McKenzie, N.J. and Ryan, P.J. 1999. Spatial prediction of soil properties using environmental correlation. Geoderma 89. (1-2): 67-94.
Mermut, A.R. and Eswaran, H. 2000. Some major developments in soil science since the mid-1960s. Geoderma 100. 403-426.
Minasny, B. and McBratney, A.B. 2010. Methodologies for Global Soil Mapping, Dordrecht, Springer Netherlands, 429-436.
Minasny, B., Malone, B.P. and McBratney, A.B. Eds. 2012. Digital Soil Assessments and Beyond. London, Taylor and Francis Group, 466 p.
Montanarella, L. 2010. Need for interpreted soil information for policy making. In 19th World Congress of Soil Science, Soil Solutions for a Changing World, 1-6 August 2010, Brisbane, Australia. Published
on DVD.
Moran, C.J. and Bui, E.N. 2002. Spatial data mining for enhanced soil map modelling. International Journal of Geographic Information Science 16. 533-549.
Mulder, V.L., de Bruin, S., Schaepman, M.E. and Mayr, T.R. 2011. The use of remote sensing in soil and terrain mapping. - A review. Geoderma 162. 1-19.
Nemes, A., Pachepsky, Y.A. and Timlin, D.J. 2011. Toward improving global estimates of field soil water capacity. Soil Science Society of America Journal 75. (3): 807-812.
Omuto, C., Nachtergaele, F. and Rojas, R.V. 2013. State of the Art Report on Global and Regional Soil Information: Where are we? Where to go? Global Soil Partnership Technical Report. Rome, FAO, 69 p.
Panagos, P., van Liedekerke, M., Jones, A. and Montanarella, L. 2012. European Soil Data Centre: Response to European policy support and public data requirements. Land Use Policy 29. 329-338.
Pásztor, L., Bakacsi, Zs., Laborczi, A. and Szabó, J. 2013b. Kategória típusú talajtérképek térbeli felbontásának javítása kiegészítő talajtani adatok és adatbányászati módszerek segítségével (Downscaling of categorical soil maps with the aid of auxiliary spatial soil information and data mining methods). Agrokémia és Talajtan 62. (1): 205-218.
Pásztor, L., Dobos, E., Szatmári, G., Laborczi, A., Takács, K., Bakacsi, Zs. and Szabó, J. 2014. Application of legacy soil data in digital soil mapping for the elaboration of novel, countrywide maps of soil conditions. Agrokémia és Talajtan 63. (1): 79-88.
Pásztor, L., Szabó, J. and Bakacsi, Zs. 2010. Digital processing and upgrading of legacy data collected during the 1:25 000 scale Kreybig soil survey. Acta Geodaetica et Geophysica Hungarica 45. 127-136.
Pásztor, L., Szabó, J., Bakacsi, Zs. and Laborczi, A. 2013a. Elaboration and applications of spatial soil information systems and digital soil mapping at the Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences. Geocarto International 28. (1): 13-27.
Pásztor, L., Szabó, J., Bakacsi, Zs., Matus, J. and Laborczi, A. 2012. Compilation of 1:50 000 scale digital soil maps for Hungary based on the digital Kreybig soil information system. Journal of Maps 8. (3): 215-219.
Rajkai, K. and Kabos, S. 1999. A talaj víztartóképességfüggvény (pF-görbe) talajtulajdonságok alapján történő becslésének továbbfejlesztése (Estimation of Soil Water Retention Characteristics (pF Curves) From Other Soil Properties. Agrokémia és Talajtan 48. (1-2): 15-32.
Sanchez, P.A., Ahamed, S., Carré, F., Hartemink, A.E., Hempel, J., Huising, J., Lagacherie, P., McBratney, A.B., McKenzie, N.J., Mendonça-Santos, M.L., Minasny, B., Montanarella, L., Okoth, P., Palm, C.A., Sachs, J.D., Shepherd, K.D., Vagen, T.G., Vanlauwe, B., Walsh, M.G., Winowiecki, L.A. and Zhang, G.L. 2009. Digital soil map of the world. Science 325. 680-681.
Saxton, K.E. and Rawls, W.J. 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Science Society of America Journal 70. (5): 1569-1578.
Scull, P., Franklin, J. and Chadwick, O.A. 2005. The application of classification tree analysis to soil type prediction in a desert landscape. Ecological Modelling 181. 1-15.
Scull, P., Franklin, J., Chadwick, O.A. and McArthur, D. 2003. Predictive soil mapping: a review. Progress in Physical Geography 27. (2): 171-197.
Shirazi, M.A. and Boersma, L. 1984. A Unifying Quantitative Analysis of Soil Texture. Soil Science Society of America Journal 48. (1): 142-147.
Sisák, I. and Benő, A. 2014. Probability-based harmonization of digital maps to produce conceptual soil maps. Agrokémia és Talajtan 63.(1): 89-98.
Specifications Version 1 GlobalSoilMap.net products, Release 2.1, (2014)
Szabó, J., Pásztor, L. and Bakacsi, Zs. 2011. Demand, feasibility and construction stages of a national spatial soil information system. Agrokémia és Talajtan 60. (Suppl.), 149-160.
Szabó, J., Pásztor, L., Bakacsi, Zs., László, P. and Laborczi, A. 2007. A Kreybig Digitális Talajinformációs Rendszer alkalmazása térségi szintű földhasználati kérdések megoldásában (Application of the Kreybig Digital Soil Information System to solve land use problems at regional level). Agrokémia és Talajtan 56. (1): 5-20.
Szatmári, G. and Barta, K. 2013. Csernozjom talajok szervesanyag-tartalmának digitális térképezése erózióval veszélyeztetett mezőföldi területen (Digital mapping of the organic matter content of chernozem soils on an area endangered by erosion in the Mezőföld region). Agrokémia és Talajtan 62. (1): 47-60.
Szatmári, G., Laborczi, A., Illés, G. and Pásztor, L. 2013. A talajok szervesanyag-készletének nagyléptékű térképezése regresszió krigeléssel Zala megye példáján (Large-scale mapping of soil organic matter content by regression kriging in Zala County). Agrokémia és Talajtan 62. (2): 219-234.
Tobler,W. 1970. A computer movie simulating urban growth in the Detroit region, Economic Geography 46. (2): 234-240.
Tóth, B., Makó, A. and Tóth, G. 2014. Role of soil properties in water retention characteristics of main Hungarian soil types. Journal of Central European Agriculture 15. (2): 137-153.
Tóth, G., Montanarella, L., Stolbovoy, V., Máté, F., Bódis, K., Jones, A., Panagos, P. and van Liedekerke, M. 2008. Soils of the European Union. EUR 23439 EN. Luxembourg, Office for Official Publications of the European Communities, 85 p.
USDA 1987. Soil Mechanics Level I. Module 3 - USDA Textural Soil Classification Study Guide. National Employee Development Staff, Soil Conservation Service, United States Department of Agriculture, USA, 232 p.
Van Orshoven, J., Terres, J-M. and Tóth. T. 2013. Updated common bio-physical criteria to define natural constraints for agriculture in Europe. Definition and scientific justification for the common criteria. Technical Factsheets. Luxembourg: Office for Official Publications of the European Communities 2012. Scientific and Technical Research, EUR 25203 EN. Joint Research Centre, Institute for Environment and Sustainability.
Várallyay, Gy. 2011. Water storage capacity of Hungarian soils. Agrokémia és Talajtan 60. (Suppl.), 7-26.
Várallyay, Gy. 2012. Talajtérképezés, talajtani adatbázisok. Agrokémia és Talajtan 61. (Suppl.), 249-267.
Várallyay, Gy., Szűcs, L., Zilahy, P., Rajkai, K. and Murányi, A. 1985. Soil factors determining the agro-ecological potential of Hungary. Agrokémia és Talajtan 34. (Suppl.), 90-94.
Vereecken, H., Maes, J., Feyen, J. and Darius, P. 1989. Estimating the soil moisture retention characteristic from texture, bulk density, and carbon content. Soil Science 148. 389-403.
Waltner, I., Micheli, E., Fuchs, M., Láng, V., Pásztor, L., Bakacsi, Zs., Laborczi, A. and Szabó, J. 2014. Digital mapping of selected WRB units based on vast and diverse legacy data. In Global Soil Map: Basis of the Global Spatial Soil Information System. Eds.: Arrouays, D., McKenzie, N., Hempel, J., Richer de Forges, A. and McBratney, A.B. London, Taylor and Francis Group, 313-318.