probability-based harmonization of digital maps to produce conceptual soil maps
TRANSCRIPT
AGROKÉMIA ÉS TALAJTAN 63 (2014) 1 89–98
Correspondence to: ISTVÁN SISÁK, Georgikon Faculty, University of Pannonia, H-8316 Keszthely, 16 Deák F. út. Hungary. E-mail: [email protected]
Probability-based harmonization of digital maps to produce conceptual soil maps
I. SISÁK and A. BENŐ
Georgikon Faculty, University of Pannonia, Keszthely (Hungary)
Introduction
Knowledge on the spatial distribution of soils and soil attributes is crucial for environmental and soil management (HÄRING et al., 2012). The spatial distribution of soils and their properties may change markedly within short distances due to the continuous nature of soil. Soils do not occur in nature as discrete bodies with dis-tinct boundaries. In spite of that, soils have been mapped as categorical map units, because this practice allows for structuring our knowledge by classification. The usual approach in soil mapping is the aggregation of several soil types into one single complex soil map unit, depending on the specific mapping scale and on the small-scale heterogeneity of soils (Soil Atlas of Europe, 2005).
Three nationwide soil maps were elaborated in Hungary between 1953 and 1988 with the nominal scales of 1:100,000 for one map and 1:200,000 for two maps. Since then, traditional soil mapping has practically ceased and digital soil mapping methods have gained only slow advance due to data ownership questions and the low level of digitization. A strong discrepancy has developed by now between soil data needs and supply in spite of the fact that highly detailed soil information is abundant in Hungary (SISÁK & BÁMER, 2008). Therefore, the aim of present study was to develop a method to harmonize, improve and refine existing nationwide soil maps of Hungary. The CHAID (CHi-squared Automatic Interaction Detection) classification tree method was used, as well as geology, land cover and digital ele-vation model as ancillary data.
Methods for predictive digital soil mapping were summarized, among others, by MCBRATNEY et al. (2003) and SCULL et al. (2003). According to the authors, deci-sion tree analysis is rapidly gaining popularity as a means for developing prediction rules that can be rapidly and repeatedly evaluated. The method provides the follow-ing advantages over standard statistical techniques: 1. It is easier to interpret when explanatory variables are both nominal and continuous; 2. it is invariant to mono-tone re-expressions (transformations) of predictor variables; 3. it deals more satis-factorily with missing data values, outliers and redundant information; 4. it is more adept at capturing non-additive and non-linear behaviour; 5. it doesn’t make any