Probability-based harmonization of digital maps to produce conceptual soil maps

Sisák, István; Benő, András

doi:10.1556/agrokem.63.2014.1.10

Cited by 7 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Presently soil data requirements are fulfi lled with the recently available datasets either by their direct usage or aft er certain specifi c and generally fortuitous, thematic and/or spatial inference (Szabó, J. et al 2007;Dobos, E. et al 2010;Sisák, I. and Benő, A. 2014;Waltner, I. et al 2014).…”

Section: Spatial Soil Information In Hungarymentioning

confidence: 99%

Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

et al. 2015

View full text Add to dashboard Cite

Due to former soil surveys and mapping activities signifi cant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfi lled with these available datasets either by their direct usage or aft er certain specifi c 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 signifi cantly extend the potential, how soil information requirements could be satisfi ed in Hungary. We started to compile digital soil maps, which fulfi l 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 matt er content. Classifi cation 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 identifi ed and expressed in decision rules made the compilation of spatially refi ned 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 fi rst 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.

show abstract

Section: Spatial Soil Information In Hungarymentioning

confidence: 99%

Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

et al. 2015

View full text Add to dashboard Cite

show abstract

“…2000;McBratney, A.B. et al 2003;Lagacherie, P. et al 2006;Szatmári, G. et al 2013;Pásztor, L. and Takács, K. 2014;Sisák, I. and Benő, A. 2014;Szatmári, G. and Pásztor, L. 2016).…”

Section: Introductionunclassified

A novel approach for mapping WRB soil units – A methodology for a global SOTER coverage

et al. 2019

View full text Add to dashboard Cite

Traditional soil maps present soil information in the form of categorical classes of soil types classified on the appropriate level of the applied classification system corresponding to the scale. Soil complexes and associations have been used to describe polygons. This kind of data structure is useful to characterise an area by explaining its soil resources. However, it is difficult to convert these complex categorical units into a simple digital variable, the usage of this kind of data in a digital environment is limited. Users often need single properties instead of the complex classes. Additionally, the problem becomes more complicated when soil information of different origin, based on different classification systems has to be integrated into a common, harmonised database. The presented methodology is part of the efforts to develop a global SOTER (World Soil and Terrain database) coverage and contribute to the global soil observing s as part of the Global Earth Observing System of Systems (GEOSS). The aim is to determine and map the relevant soil properties, horizons and materials following the diagnostic concepts of the World Reference Base (WRB) for soil resources and derive the occurrence probability of soil classes (WRB reference soil groups) of certain spots with the application of remote sensing and digital soil mapping tools. The developed method is referred as the e-SOTER approach and is capable of producing a stack of soil diagnostic element layers with the likelihood of their occurrence within each pixel and a layer of WRB reference soil groups (RSG). This new approach may provide better input for modellers and predict the spatial continuum of the soil cover in a much better resolution than the traditional polygon based approaches. At the same time the diagnostic elements, as building blocks of the classification systems, help the correlation of the national soil classes into integrated databases and maps.

show abstract