Modelagem do terreno e mapeamento digital de solos por extrapolação das relações solo-paisagem

• Ano de defesa: 2016
• Autor(a) principal: Wolski, Mario Sergio
• Orientador(a): Dalmolin, Ricardo Simão Diniz
• Banca de defesa: Caten, Alexandre Ten , Kaiser, Douglas Rodrigo , Pedron, Fabrício de Araújo , Schenato, Ricardo Bergamo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência do Solo
• Departamento: Agronomia
• País: BR
• Palavras-chave em Português: Mapeamento convencional de solos; Mapeamento preditivo do solo; Área de referência e atributos de terreno
• Palavras-chave em Inglês: Conventional soil mapping; Predictive soil mapping; Reference area and terrain attributes
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/3379

Research on digital soil mapping (DSM) in Brazil is subject to the existence of legacy soil data obtained through conventional methods for extrapolation of soil-landscape relations. In areas with no available soil maps, it is necessary to develop methodologies for the acquisition of these data in a scale compatible with the needs of the users. In this context, the main objective of this study was to develop a methodology, through techniques of DSM, to predict soil classes at semidetailed level, in a region of gently undulating relief delimited by a topographic map in a scale of 1:50,000. Techniques for relief modeling were used to elaborate and evaluate the quality of the digital elevation models used in the area covered by the topographic map and in the reference area (RA). The RA technique was used to establish soil-landscape relations in the DSM. A basemap in a scale of 1:5,000 was created to support the implementation of soil mapping by conventional methods for the RA. Decision Tree (DT) technique was used to build the prediction model based on the soil map and eight terrain attributes of the RA. Two DSM strategies were tested in order to obtain the data to create the classification rules (DSM 1 and DSM 2). Each strategy employed the eight terrain attributes as predictor variables: elevation (ele), distance to channel network (dis), slope (dec), aspect (asp), topographic wetness index (twi), profile curvature (per), plan curvature (pla), and landform classes (lan). The previous selection of digital elevation models to extract the terrain attributes aggregated quality to the use of the predictor variables that participated in the production of the model. The use of RA in areas with limitation of data proved to be an efficient strategy to improve the understanding of soil-landscape relations for prediction of occurrence of soil classes through the DSM method. The comparison between field data and the digital soil map resulted in a global accuracy (GA) of 66.15% and Kappa of 0.35 for the DSM 1, and GA of 65.58% and Kappa of 0.27 for the DSM 2. The approach of soil survey through the conventional method in the RA proved appropriate, since it contributed to the knowledge of predominant soil categories, as well as reduced the number of field observations in the area covered by the topographic map.