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Real time deforestation detection using ANN and Satellite images

The Amazon Rainforest study case

  • Book
  • © 2015

Overview

Authors:
  1. Thiago Nunes Kehl

    1. Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

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  2. Viviane Todt

    1. Universidade do Vale do Rio dos Sinos -, São Leopoldo, Brazil

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  3. Maurício Roberto Veronez

    1. Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

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  4. Silvio Cesar Cazella

    1. UFCSPA, Porto Alegre, Brazil

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  • Development of a tool to detect daily deforestation in the Amazon rainforest, using satellite images from the MODIS/TERRA sensor and Artificial Neural Networks
  • Tool provides parameterization of the configuration for the neural network training to select the best neural architecture to address the problem
  • The tool uses confusion matrices to determine the degree of success of the network
  • A spectrum-temporal analysis of the study area was done on 57 images from May 20 to July 15, 2003 using the trained neural network

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella
    Pages 1-4

  3. Literature Review

    • Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella
    Pages 5-18

  4. Method

    • Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella
    Pages 19-31

  5. Modeling and Tool Use

    • Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella
    Pages 33-38

  6. Results and Discussion

    • Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella
    Pages 39-49

  7. Back Matter

    Pages 51-67
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Keywords

About this book

The foremost aim of the present study was the development of a tool to detect daily deforestation in the Amazon rainforest, using satellite images from the MODIS/TERRA sensor and Artificial Neural Networks. The developed tool provides parameterization of the configuration for the neural network training to enable us to select the best neural architecture to address the problem. The tool makes use of confusion matrices to determine the degree of success of the network. A spectrum-temporal analysis of the study area was done on 57 images from May 20 to July 15, 2003 using the trained neural network. The analysis enabled verification of quality of the implemented neural network classification and also aided in understanding the dynamics of deforestation in the Amazon rainforest, thereby highlighting the vast potential of neural networks for image classification. However, the complex task of detection of predatory actions at the beginning, i.e., generation of consistent alarms, instead of false alarms has not been solved yet. Thus, the present article provides a theoretical basis and elaboration of practical use of neural networks and satellite images to combat illegal deforestation.

Authors and Affiliations

  • Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

    Thiago Nunes Kehl, Maurício Roberto Veronez

  • Universidade do Vale do Rio dos Sinos -, São Leopoldo, Brazil

    Viviane Todt

  • UFCSPA, Porto Alegre, Brazil

    Silvio Cesar Cazella

Bibliographic Information

  • Book Title: Real time deforestation detection using ANN and Satellite images

  • Book Subtitle: The Amazon Rainforest study case

  • Authors: Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella

  • Series Title: SpringerBriefs in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-15741-2

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing Switzerland 2015

  • Softcover ISBN: 978-3-319-15740-5Published: 08 May 2015

  • eBook ISBN: 978-3-319-15741-2Published: 25 April 2015

  • Series ISSN: 2191-5768

  • Series E-ISSN: 2191-5776

  • Edition Number: 1

  • Number of Pages: X, 67

  • Number of Illustrations: 4 b/w illustrations, 21 illustrations in colour

  • Topics: Remote Sensing/Photogrammetry, Artificial Intelligence

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