Continuous soil maps — a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models

Gruijter, J.J. de; Walvoort, D.J.J.; Gams, P.F.M. van

doi:10.1016/s0016-7061(97)00021-9

Cited by 101 publications

(52 citation statements)

References 24 publications

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“…As concluded by Burrough et al (1997) and De Gruijter et al (1997), the FKM classification can produce meaningful soil typological results. It partitions soil profiles into the given k classes, where profile properties in the class centre are represented by centroids.…”

Section: Methodsmentioning

confidence: 83%

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Digital soil mapping from conventional field soil observations

Balkovič¹,

Rampašeková²,

Hutar³

et al. 2013

Soil Water Res.

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Balkovič J., Rampašeková Z., Hutár V., Sobocká J., Skalský R. (2013): Digital soil mapping from conventional field soil observations. Soil & Water Res., 8: 13-25.We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Rišňovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A-D: (i) Luvic Phaeozems (Anthric), (ii) Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A-D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R 2 ranging from 23% to 47% (P < 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R 2 = 14%, P < 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A "leave-one-out" cross-validation was calculated for the output maps yielding R 2 of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P < 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations.

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Section: Methodsmentioning

confidence: 83%

“…We used the pixel-mixture technique (De Gruijter et al 1997) to visualize the soil type distribution using the algorithm published for R programme at http://spatial-analyst.net/wiki/ index.php?title=Uncertainty_visualization.…”

Section: Methodsmentioning

confidence: 99%

Digital soil mapping from conventional field soil observations

Balkovič¹,

Rampašeková²,

Hutar³

et al. 2013

Soil Water Res.

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“…Examples of fuzzy spatial objects include mountains, valleys, biotopes, and oceans, which cannot be rigorously bounded by a sharp line. The usefulness of fuzzy concepts in geoscience applications from a modeling standpoint has, for example, been demonstrated in Burrough, van Gaans & Macmillan (2000) for modeling geomorphological units, De Gruijter, Walvoort & Vangaans (1997), Lagacherie, Andrieux & Bouzigues (1996) for representing soil types and boundaries, Cheng, Molenaar & Lin (2001) for designing landscape objects, Brown (1998) for describing forest types, Hendricks Franssen, van Eijnsbergen & Stein (1997) for determining soil pollution classes in environmental applications, and Bogàrdi, Bárdossy & Duckstein (1990) for performing hydrological studies. Approaches nearer to computer science have been given by Burrough (1996) introducing fuzzy geographical objects for modeling natural objects with indeterminate boundaries, Dutta (1989Dutta ( , 1991 and Kollias & Voliotis (1991) dealing with qualitative spatial and temporal reasoning using fuzzy logic, Edwards (1994) and Wang & Hall (1996) dealing with fuzzy representations of geographical boundaries in GIS, Usery (1996) defining concepts like core and boundary of a fuzzy region, and Wang, Hall & Subaryono (1990) and Wang (1994) presenting a fuzzy query approach in order to introduce more natural language expressions into GIS user interfaces.…”

Section: A Classification Of Models For Vague Spatial Objectsmentioning

confidence: 99%

Soft Computing Techniques in Spatial Databases

Schneider

2010

Soft Computing Applications for Database Technologies

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Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. Examples are air polluted areas, temperature zones, and lakes. These applications require novel concepts due to the lack of adequate approaches and systems. In this chapter, we show how soft computing techniques can provide a solution to this problem. We give an overview of two type systems or algebras that can be integrated into database systems and utilized for the modeling and handling of spatial vagueness. The first type system, called Vague Spatial Algebra (VASA), is based on well known, general, and exact models of crisp spatial data types and introduces vague points, vague lines, and vague regions. This enables an exact definition of the vague spatial data model since we can build it upon an already existing theory of spatial data types. The second type system, called Fuzzy Spatial Algebra (FUSA), leverages fuzzy set theory and fuzzy topology and introduces novel fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions. This enables an even more fine-grained modeling of spatial objects that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. This chapter provides a formal definition of the structure and semantics of both type systems. Further, we introduce spatial set operations for both algebras and obtain vague and fuzzy versions of geometric intersection, union, and difference. Finally, we describe how these data types can be embedded into extensible databases and show some example queries.

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“…After kriging the order relations are corrected by post-processing with the GSLIB POSTIK routine (Deutsch and Journel, 1992). Gruijter et al (1997) proposed an alternative method (Compositional Kriging) for guaranteeing the correct order relations and the constant sum of the proportions (i.e. 1).…”

Section: Model For the Optimisation Functionmentioning

confidence: 99%

Optimal estuarine sediment monitoring network design with simulated annealing

Nunes

Caeiro²,

Cunha

et al. 2006

Journal of Environmental Management

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An objective function based on geostatistical variance reduction, constrained to the reproduction of the probability distribution functions of selected physical and chemical sediment variables, is applied to the selection of the best set of compliance monitoring stations in the Sado river estuary in Portugal. These stations were to be selected from a large set of sampling stations from a prior field campaign. Simulated annealing was chosen to solve the optimisation function model. Both the combinatorial problem structure and the resulting candidate sediment monitoring networks are discussed, and the optimal dimension and spatial distribution are proposed. An optimal network of sixty stations was obtained from an original 153-station sampling campaign. q

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Continuous soil maps — a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models

Cited by 101 publications

References 24 publications

Digital soil mapping from conventional field soil observations

Digital soil mapping from conventional field soil observations

Soft Computing Techniques in Spatial Databases

Optimal estuarine sediment monitoring network design with simulated annealing

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