Hungarian Geographical Bulletin

Hungarian Geographical Bulletin
ISSN 2064-5031, E-ISSN 2064-5147

2015. VOL. 64. No 1.

Cikk adatai
Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools
László PÁSZTOR - Annamária LABORCZI - Katalin TAKÁCS - Gábor SZATMÁRI - Endre DOBOS - Gábor ILLÉS - Zsófia BAKACSI - József SZABÓ
Megjelenési adatok
64/1. 49-64. (2015)
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 (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
Online közzététel
2015. 04. 20.