Amazon Rainforest Deforestation Daily Detection Tool Using Artificial Neural Networks and Satellite Images

Thiago Nunes Kehl, Viviane Todt, Mauricio Roberto Veronez and Silvio César Cazella
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Thiago Nunes Kehl: Universidade do Vale do Rio dos Sinos (UNISINOS), Ciências Exatas e Tecnológicas, Curso de Graduação em Ciência da Computação, Av. Unisinos, 950, Cep 93022-000 São Leopoldo, RS, Brasil
Viviane Todt: Universidade do Vale do Rio dos Sinos (UNISINOS), Ciências Exatas e Tecnológicas, Programa de Pós-Graduação em Geologia, Av. Unisinos, 950, Cep 93022-000 São Leopoldo, RS, Brasil
Mauricio Roberto Veronez: Universidade do Vale do Rio dos Sinos (UNISINOS), Ciências Exatas e Tecnológicas, Programa de Pós-Graduação em Geologia, Av. Unisinos, 950, Cep 93022-000 São Leopoldo, RS, Brasil
Silvio César Cazella: Universidade do Vale do Rio dos Sinos (UNISINOS), Ciências Exatas e Tecnológicas, Curso de Graduação em Ciência da Computação, Av. Unisinos, 950, Cep 93022-000 São Leopoldo, RS, Brasil

Sustainability, 2012, vol. 4, issue 10, 1-8

Abstract: 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.

Keywords: Artificial Neural Networks; satellite images classification; deforestation detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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