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Abstract. Deforestation in tropical rainforests is a major source of carbon dioxide emissions, an important driver of climate change. For decades, the Brazilian government has maintained monitoring programs for deforestation detection in the Brazilian Legal Amazon area based on remotely sensed optical images in a protocol that involves considerable efforts of visual interpretation. However, the Amazon region is covered with clouds for most of the year, and deforestation assessment can rely only on images acquired in the dry season when cloud-free images are more likely to capture. One possibility to lessen that restriction and enable deforestation detection throughout the year is to synthesize cloud-free optical images from corresponding SAR images, which are only marginally influenced by atmospheric conditions. This work compares a set of such image synthesis methods, considering deforestation detection in the Amazon forest as the target application. Specifically, we evaluate three deep learning methods for cloud removal in Sentinel-2 images: a conditional Generative Adversarial Network (cGAN) based on the pix2pix architecture; an extension of that method, which uses atrous convolutions (Atrous cGAN) to enhance fine image details; and a non-generative method (DSen2-CR) based on residual networks. In the evaluation, we assess both the quality of the generated images and the accuracy obtained when performing deforestation detection from those images. We further compare those methods with an image aggregation tool available in Google Earth Engine (GEE Tool), which creates cloud-free mosaics from sequences of images acquired at nearby dates. In this study, we considered two sites in the Brazilian Amazon, characterized by distinct vegetation and deforestation patterns. In terms of the quality metrics and classification accuracy, the Atrous cGAN was the best performing deep learning method. The GEE Tool outperformed all those methods when dealing with images from the dry season but turned out to be the poorest performing method in the wet season.

Alternativas que reduzam a dependência da matriz energética mundial aos derivados do petróleo são discutidas cada vez mais. Existe uma grande quantidade de dados acadêmicos relacionando consequências perniciosas ao meio ambiente devido a indústria de petróleo e seus componentes refinados. A substituição dos óleos a base mineral pela base vegetal é uma medida seguindo essa tendência. O óleo vegetal é uma substância biodegradável com a capacidade comprovada de ser decomposta em um intervalo de até um ano, o que faz com que seja um produto menos tóxico e agressivo ao meio ambiente do que os de base minerais. O óleo vegetal escolhido para esse trabalho foi o de dendê, por ser um óleo popularmente conhecido na culinária brasileira, principalmente baiana. É um dos óleos vegetais mais consumidos no mundo, atrás apenas dos óleos de soja e canola. O objetivo desse estudo foi comparar as propriedades e comportamentos do óleo mineral 15w-40 com o óleo de dendê através de um ensaio PIN-ON-DISK com presença do filme de óleo. Após os ensaios, os óleos utilizados foram analisados através dos dados de espectrometria, ferrografia, viscosidade de Saybolt, monitor de partículas ferrosas e dos sinais coletados pelo software, gerados pelo tribômetro. Pelos resultados obtidos, observou-se que o lubrificante vegetal, óleo de dendê, tem grande potencial para ser usado na substituição do óleo mineral. Os dados apresentaram a real possibilidade da utilização do óleo livre de qualquer tipo de tratamento adicional a fim de torná-lo mais eficiente, como mistura de aditivos ou envelhecimento.Palavras-chave: Tribologia. Lubrificante vegetal. Desgaste.Viability analysis of the use of dende oil in automotive lubrication through the pin-on-disk testAbstractAlternatives that reduce the dependence of the world energy resources on mineral oil derivatives are increasingly discussed. There is a great deal of academic data relating pernicious consequences to the environment due to the oil industry and its refined components. The substitution of mineral based oils for the vegetable base is a measure following this trend. Vegetable oil is a biodegradable substance with a proven ability to decompose within an interval up to one year, making it a less toxic and environmentally aggressive product than mineral based ones. The vegetable oil chosen for this study is extracted from the palm tree (Elaeisguineenses). This oil is popularly known in Brazilian cuisine, mainly in Bahia. It is one of the most consumed vegetable oils in the world, soy and canola oils are in the upper positions. The objective of this study was to compare the properties and behaviors of 15w-40 mineral oil against palm oil through a PIN-ON-DISK test with presence of the oil film. After the tests, the oil used was analyzed using spectrometric, ferrography, Saybolt's viscosity, ferrous debris monitor data and signals from the tribometer collected by the software. From the results obtained, it was observed that the palm oil lubricant has great potential to replace mineral oil. The data showed the real possibility of using palm oil without any additional treatment in order to make it more efficient, such as mixture of additives or artificial aging.Keywords: Tribology. Vegetable lubricant. Wear.

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