Abstract: The Permanent Protection Areas (PPA) are relevant to ensure vegetation around the drainage network. This paper presents an automated methodology for the extraction of drainage from the river and automated generation of PPA, and analysis of environmental adequacy. The methodology is based on geoprocessing and remote sensing techniques applied to RapidEye satellite images. The analyzed area covers a portion of the Paraíba do Sul river basin, located in the city of São José dos Campos (Southern Brazil). Land use and land cover were determined using a digital classifier and estimated within the APP of four rural properties bordering the river. The digital classification of the RapidEye images was evaluated based on the visual interpretation of high spatial resolution airborne orthophotos, as well as through random points that enabled the generation of the Kappa index and global accuracy, showing high agreement. The analysis shows the inadequate land use practice in some properties analyzed, indicating changes in the areas of PPA over the years analyzed. The results of this research show that the proposed methodology can be used for supervision purposes in properties declared in the Rural Environmental Registry (CAR), thus assisting in the decision-making process.

O objetivo deste trabalho foi descrever um procedimento de modelagem de fertilidade do solo que integra propriedades químicas do solo utilizando-se do método de Monte Carlo. A espacialização das propriedades químicas foi obtida por procedimento geoestatístico de simulação estocástica, com modelagem das incertezas associadas às estimativas. As incertezas das propriedades químicas foram propagadas para o modelo de fertilidade resultante, possibilitando a geração de mapas de fertilidade condicionados a níveis de risco prédefinidos. O método aqui apresentado é ilustrado por um estudo de caso de fertilidade para cultura de soja, no Estado de Santa Catarina, considerando as seguintes propriedades químicas do solo: alumínio trocável, potássio e capacidade de troca catiônica.

This work described a methodology of fertility modeling that integrates chemical properties of the soil through the Monte Carlo method. The chemical attributes are spatialized, along with their estimation uncertainties, by a geostatistical procedure of random simulation. Uncertainties of the chemical properties are propagated to the resulting fertility model allowing the generation of fertility maps conditioned to preset risk levels. The method presented here is illustrated by a case study of fertility for soy culture, at the State of Santa Catarina, Brazil, considering the following chemical properties of the soil: exchangeable aluminum, potassium and cation exchange capacity.