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metadata.artigo.dc.title: Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches
metadata.artigo.dc.creator: Menezes, Michele Duarte de
Silva, Sérgio Henrique Godinho
Mello, Carlos Rogério de
Owens, Phillip Ray
Curi, Nilton
metadata.artigo.dc.subject: Soil mapping
Image analysis
Remote sensing
Fuzzy logic
Mapeamento do solo
Análise de imagem
Sensoriamento remoto
Lógica fuzzy
metadata.artigo.dc.publisher: Universidade de São Paulo: Escola Superior de Agricultura "Luiz de Queiroz" Jul-2014
metadata.artigo.dc.identifier.citation: MENEZES, M. D. de et al. Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches. Scientia Agricola, Piracicaba, v. 71, n. 4, p. 316-323, July/Ago. 2014.
metadata.artigo.dc.description.abstract: 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 knowledge-based 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 soil-landscape relationship explicit.
metadata.artigo.dc.language: en_US
Appears in Collections:DEG - Artigos publicados em periódicos

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