ResumoO aproveitamento de óleos e gorduras saturadas, provenientes de frituras, em produtos como sabão, ração e biodiesel evita o lançamento destes no esgoto doméstico ou na forma bruta no solo e em cursos d'água. A produção de biodiesel a partir do óleo de fritura foi uma alternativa encontrada para a Associação Pro-Crep (Projeto Criar, Reciclar, Educar e Preservar), do bairro da Pinheira em Palhoça/SC, incrementar a renda de seus associados e preservar o meio ambiente. Através dos trabalhos desenvolvidos pela Unisul (Universidade do Sul de Santa Catarina) com apoio do CNPq e da UniSol/Santander foi possível implantar uma unidade de processamento de biodiesel. Inicialmente foi efetuado um diagnóstico da situação atual, usado o mapa comunitário para o entendimento espacial e localização dos fornecedores de óleo, calendário sazonal de pesca para a compreensão do tipo de embarcação usada pelos pescadores e o impacto do óleo diesel na composição dos custos da pesca. O processo produtivo como também os equipamentos foram desenvolvidos com a participação dos associados da Pro-Crep, alunos e professores da Unisul. O projeto foi desenvolvido dentro de uma perspectiva de desenvolvimento sustentável, buscando conciliar a dimensão econômica, social e ambiental. A partir dos trabalhos de educação ambiental, realizados nas escolas de ensino fundamental e médio, nas associações de bairro e nos grupos de terceira idade, criaram-se condições para manter o abastecimento da usina com óleo de fritura. As parcerias estabelecidas com os restaurantes também contribuíram significativamente com o projeto. O biodiesel produzido é usado para abastecer o trator que realiza a coleta de resíduos sólidos e do óleo e para abastecer os barcos de pesca artesanal da Pinheira.Palavras Chave: biodiesel, óleo de fritura, empoderamento.

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Soil maps are important to evaluate soil functions and support decision-making process, particularly for soil properties such as pH, carbon content (C), and cation exchange capacity (CEC), but the spatial resolution and soil depth should meet the needs of users. On another hand, the efficiency of statistical models to create soil maps, with an acceptable level of accuracy, often require a large number of samples with an appropriate distribution across the area of interest. However, accessibility for sampling can be a trouble in remote areas, such as the Itatiaia National Park (INP). The hypothesis of this work is that it is possible to obtain a viable result in soil mapping of areas with limited access by using DSM tools. The general objective of this paper was to create 2-and 3-D maps of the soil properties pH, carbon content, and CEC, with the correspondent spatial uncertainty, in the INP plateau. The sampling strategy was designed using conditioned Latin Hypercube Sample (cLHS), and different methods were tested to produce the soil properties maps. For calibration of the models: linear (MLR, multiple linear regression) and nonlinear (GAM, Generalised Additive Models). The results showed differences in predictive performance for all statistical methods and covariate selection approaches. The GAM, with covariates selection based on soil formation factors, was the best method for the limited number of soil samples. The greatest uncertainty was associated with areas with the lowest accessibility and, consequently, with low sampling density and/or noises in covariates. Even though the 2-and 3-D maps of soil properties, each associated with explicit uncertainty, can contribute to the INP decision makers/ managers by providing information not available before.

The accurate use of satellite images for mapping and environmental monitoring requires the image transformation to ground reflectance through atmospheric correction. However, it is a challenge to obtain the horizontal visibility, which is used by the atmospheric correction models to estimate the aerosol optical depth. The aim of this paper is to present the comparison of atmospheric correction of OLI Landsat 8 images using horizontal visibility from field observation and from airport data. OLI images were acquired from four dates, 02/26/2014, 02/10/2014, 10/11/2015 and 04/20/2016. Field work was conducted at the same time of satellite overpass and horizontal visibility was obtained by observing targets at different distances and recording the maximum distance at which targets could be identifiable by visual inspection yielding values from 12 to 17 km. For comparison the horizontal visibility was also downloaded from METAR database for the Galeão airport, which were up to 10km. Atmospheric correction was carried out for the two sources of horizontal visibility using the Atmcor4OLI program, adapted through the 6S code. These two methods were compared through graphs and a statistical test from samples of four targets using apparent and surface reflectance. The results show that the atmospheric correction is paramount to analyze the spectral response of targets as the atmosphere interferes with the spectral characteristics of the targets from the visible to the mid infrared. In the visible the additive effects predominate while in the near and mid infrared the subtractive effects dominate. The visibility of the airport and from field observation yielded surface reflectance values which were different by the test of means at 1% and not significant at the 5%, as the field observations were not much higher than 10 km. It is concluded that an accurate source of horizontal visibility is key for obtaining correct surface reflectance values, mainly when field observation at the time of satellite overpass was not possible.