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The Amazon Rainforest, the largest tropical forest in the world, is located in the northern region of South America, spanning several countries, viz., Brazil, Bolivia, Colombia, Ecuador, Guyana, French Guiana, Peru, Suriname and Venezuela. Approximately 60 % of the forest’s area falls under the Brazilian territory.
Thiago Nunes Kehl; Viviane Todt; Maurício Roberto Veronez; Silvio César Cazella. Introduction. An Introduction to Distance Geometry applied to Molecular Geometry 2015, 1 -4.
AMA StyleThiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio César Cazella. Introduction. An Introduction to Distance Geometry applied to Molecular Geometry. 2015; ():1-4.
Chicago/Turabian StyleThiago Nunes Kehl; Viviane Todt; Maurício Roberto Veronez; Silvio César Cazella. 2015. "Introduction." An Introduction to Distance Geometry applied to Molecular Geometry , no. : 1-4.
The main purpose of this work was the development of a tool to detect daily deforestation in the Amazon rainforest, using satellite images from the MODIS/TERRA [1] sensor and Artificial Neural Networks. The developed tool provides the parameterization of the configuration for the neural network training to enable us to find the best neural architecture to address the problem. The tool makes use of confusion matrixes to determine the degree of success of the network. Part of the municipality of Porto Velho, in Rondônia state, is located inside the tile H11V09 of the MODIS/TERRA sensor, which was used as the study area. A spectrum-temporal analysis of this area was made on 57 images from 20 of May to 15 of July 2003 using the trained neural network. This analysis allowed us to verify the quality of the implemented neural network classification as well as helping our understanding of the dynamics of deforestation in the Amazon rainforest. The great potential of neural networks for image classification was perceived with this work. However, the generation of consistent alarms, in other words, detecting predatory actions at the beginning; instead of firing false alarms is a complex task that has not yet been solved. Therefore, the major contribution of this paper is to provide a theoretical basis and practical use of neural networks and satellite images to combat illegal deforestation.
Thiago Nunes Kehl; Viviane Todt; Mauricio Roberto Veronez; Silvio César Cazella. Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images. Sustainability 2012, 4, 2566 -2573.
AMA StyleThiago Nunes Kehl, Viviane Todt, Mauricio Roberto Veronez, Silvio César Cazella. Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images. Sustainability. 2012; 4 (10):2566-2573.
Chicago/Turabian StyleThiago Nunes Kehl; Viviane Todt; Mauricio Roberto Veronez; Silvio César Cazella. 2012. "Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images." Sustainability 4, no. 10: 2566-2573.
The identification of lithofacies from well is usually an interpretative process based on geophysical logs since core and sidewall samples are not usually available. Despite being always sampled and described, cuttings are useful only as a reference for determining the rocks because a number of problems occur during the drilling and sampling activities. Well logs are in situ continuous records of different physical properties of the drilled rocks, which can be associated with different lithofacies by experienced log analysts. This task needs a relatively great amount of time and it is likely to be imperfect because the human analysis is subjective. Thus, any alternative method of classification with high accuracy and promptness is very welcome by the log analysts. This paper is based on Neural Networks (NNs) applied in well data from the Leão Coal Mine, southern Brazil, in order to classify organic mudrocks, coals and siliciclastic sandstones, the main rocks present in the Rio Bonito and Palermo formations, by using their well logs as database. The training and validation set of the NN contain data from eight cored and logged boreholes. The input included 409 values of depth and logs of gamma-ray, spontaneous potential, resistance and resistivity for each electrofacies. The neural network model was the feedforward multilayer perceptron (MLP) and the neural networks were trained with variations of the backpropagation algorithm: Levenberg-Marquardt and Resilient backpropagation. Although an accuracy of approximately 80% had been achieved in the general classification, discrepant accuracies in the classification of the different electrofacies are discussed in order to better understand the reasons that affected negatively the NN performance.
Paula Schmitt; Mauricio Roberto Veronez; Francisco Tognoli; Viviane Todt; Ricardo C. Lopes; Carlos A. U. Silva. Electrofacies Modelling and Lithological Classification of Coals and Mud-bearing Fine-grained Siliciclastic Rocks Based on Neural Networks. Earth Science Research 2012, 2, p193 .
AMA StylePaula Schmitt, Mauricio Roberto Veronez, Francisco Tognoli, Viviane Todt, Ricardo C. Lopes, Carlos A. U. Silva. Electrofacies Modelling and Lithological Classification of Coals and Mud-bearing Fine-grained Siliciclastic Rocks Based on Neural Networks. Earth Science Research. 2012; 2 (1):p193.
Chicago/Turabian StylePaula Schmitt; Mauricio Roberto Veronez; Francisco Tognoli; Viviane Todt; Ricardo C. Lopes; Carlos A. U. Silva. 2012. "Electrofacies Modelling and Lithological Classification of Coals and Mud-bearing Fine-grained Siliciclastic Rocks Based on Neural Networks." Earth Science Research 2, no. 1: p193.
The main purpose of this work was the development of a tool to detect in real time (daily) deforestation in the Amazon rainforest, using satellite images from the MODIS/TERRA sensor and Artificial Neural Networks. The developed tool provides the parameterization of the configuration for the neural network training to enable finding the best neural architecture to address the problem and makes use of confusion matrixes to determine the degree of success of the network. Part of the city of Porto Velho, in Rondônia state, makes up the tile H11V 09 of the MODIS/TERRA sensor, which was used as the study area. A spectrum-temporal analysis of this area was made on 57 images from 20 of May to 15 of July 2003 using the trained neural network. This analysis allowed verifying the quality of the implemented neural network classification as well as helped the understanding of the dynamics of deforestation in the Amazon rainforest. The great potential of neural networks for image classification was perceived with this work. However, the generation of consistent alarms, in other words, detecting predatory actions at the beginning; instead of firing false alarms is a complex task that is not yet solved. Therefore, the major contribution of this paper is to provide a theoretical basis and practical use of neural networks and satellite images to combat illegal deforestation.
Viviane Todt; Silvio Cazella; Mauricio Veronez; Thiago Kehl. Amazonian Forest Deforestation Detection Tool in Real Time Using Artificial Neural Networks and Satellite Images. Proceedings of The 1st World Sustainability Forum 2011, 1 .
AMA StyleViviane Todt, Silvio Cazella, Mauricio Veronez, Thiago Kehl. Amazonian Forest Deforestation Detection Tool in Real Time Using Artificial Neural Networks and Satellite Images. Proceedings of The 1st World Sustainability Forum. 2011; ():1.
Chicago/Turabian StyleViviane Todt; Silvio Cazella; Mauricio Veronez; Thiago Kehl. 2011. "Amazonian Forest Deforestation Detection Tool in Real Time Using Artificial Neural Networks and Satellite Images." Proceedings of The 1st World Sustainability Forum , no. : 1.