Mapeamento digital de solos: Metodologias para atender a demanda por informação espacial em solos

• Ano de defesa: 2011
• Autor(a) principal: Caten, Alexandre Ten
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Agronomia; UFSM; Programa de Pós-Graduação em Ciência do Solo
• Palavras-chave em Português: Ondaleta; Árvore de decisão; Pedometria; Levantamento de solos; Wavelet; Decision tree; Pedometric; Soil survey; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO
• Link de acesso: http://repositorio.ufsm.br/handle/1/3326

Soil has increasingly being recognized as having an important role in ecosystems as well as for food production and global climate regulation. For this reason, the demand for relevant and updated information on soil is increasing. Digital Soil Mapping (DSM) provides this information at different spatial resolution with associated quality indicators. The aim of this study was to analyze the main methodological approaches used for DSM of soil classes through a literature review of national researches and to propose procedures for data analysis in DSM projects of soil classes. The use of DSM techniques for mapping soil classes in Brazil is recent, the first publication on this subject occurred only in 2006. Among the predictive functions, logistic regressions is the predominantly used technique. Quality evaluation of the predictive models employed error matrix and kappa index in most cases. The use of wavelet transform proved to be a methodology of great potential for analyzing the spatial resolution of terrain attributes maximum variability. The proposed methodology of data exclusion for environmental covariates located too near at the border of soil classes polygons has enabled the generation of less complex and more accurate Decision Tree (DT) models. It was also shown that the amount of data required for DT model training is between five and 15% of the total data set. Collected field observations indicated a predicted accuracy close to 70% for DT models produced by those sampling densities.