Fuzzy soil mapping based on prototype category theory

Feng, Qianjin; Zhu, A‐Xing; Harrower, Mark; Burt, Janis M.

doi:10.1016/j.geoderma.2006.06.001

Cited by 94 publications

(63 citation statements)

References 39 publications

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“…However, making comparisons within same soil property, the methods performed in contrast, with extreme high values of MPE and RMSPE in some cases. Considering results from the literature related with soil physical properties, most results showed suitable accuracy indexes, similar to those presented in this work, but they are mostly related to soil texture mapping (Akumu et al, 2015;Qi et al, 2006;Zhu et al, 2010). Thus, it is not possible to make any specific comparison between MPE and RM-SPE values.…”

Section: Anova Testsupporting

confidence: 71%

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Menezes

Silva

Mello

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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ABSTRACT:The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme;2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.

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Section: Anova Testsupporting

confidence: 71%

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Menezes

Silva

Mello

et al. 2018

Sci. agric. (Piracicaba, Braz.)

View full text Add to dashboard Cite

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“…The knowledge-based digital soil mapping approach has been successfully applied in the prediction of A horizon depth , drainage classes (McKay et al, 2010), A horizon silt and sand contents (Qi et al, 2006), soil transmissivity , hydraulic conductivity (Zhu and McKay, 2001), and solum depth (Quinn et al, 2005;Zhu and McKay, 2001). While the information about surface topography can nowadays be derived from easily accessible DEMs in different spatial resolutions and accuracies (Hengl and MacMillan, 2009), the use of aerial photography interpretation is becoming harder due to the limited number of pedologists trained in this methodology and the difficulty of acquiring aerial photographs on adequate scales in comparison to widely available high resolution satellite images.…”

Section: Validation and Accuracy Assessment Of The Predicted Solum Depthmentioning

confidence: 99%

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Menezes

Silva

Mello

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledgebased digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soillandscape relationship explicit.

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“…It is very difficult to compare the existing maps or use them for validation since the mapping units are different and often combine several soil types. Still, similar approaches have shown better results for mapping continuous soil parameters than extracting them from classical soil maps (Qi et al 2006). One of the most often used intermediate parameters is the risk of near surface epikarst.…”

Section: Resultsmentioning

confidence: 99%

GIS-based analysis of geo-resources and geo-hazards for urban areas - the example of the northern periphery of Belo Horizonte (capital of Minas Gerais, Brazil)

et al. 2017

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Easily understandable thematic maps of geo-scientific parameters are important for land use decision making. If several parameters are relevant and have to be compared, it is important that they are consistent with each other, available at the same spatial range and detail and normed to a common data range. In the current study, geological and topographical data have been used to derive a set of 90 geo-scientific maps for an area of 400 km² in the northern part of the metropolitan area of Belo Horizonte. Each parameter has been transferred to a common data range between 0 and 1 using a Semantic Import Model strategy and afterwards combined to derive new parameters for soil hydrology and hydrogeology. From these, many intermediate geo-scientific parameters, maps of geo-resources (sand/gravel, carbonates, fertile soils) and geo-hazards (erosion, groundwater pollution) have been derived that they can be used as base information for a participatory and sustainable land use planning. The workflow is transparently stored in GIS-tools and can be modified and updated if new information is available.

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Fuzzy soil mapping based on prototype category theory

Cited by 94 publications

References 39 publications

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

GIS-based analysis of geo-resources and geo-hazards for urban areas - the example of the northern periphery of Belo Horizonte (capital of Minas Gerais, Brazil)

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