Advanced Search
MyIDEAS: Login

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

Contents:

Author Info

  • 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)

Registered author(s):

    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 Rond�nia 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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.mdpi.com/2071-1050/4/10/2566/pdf
    Download Restriction: no

    File URL: http://www.mdpi.com/2071-1050/4/10/2566/
    Download Restriction: no

    Bibliographic Info

    Article provided by MDPI, Open Access Journal in its journal Sustainability.

    Volume (Year): 4 (2012)
    Issue (Month): 10 (October)
    Pages: 2566-2573

    as in new window
    Handle: RePEc:gam:jsusta:v:4:y:2012:i:10:p:2566-2573:d:20476

    Contact details of provider:
    Web page: http://www.mdpi.com/

    Related research

    Keywords: Artificial Neural Networks; satellite images classification; deforestation detection;

    Find related papers by JEL classification:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. Jingwei Song & Xinyuan Wang & Ying Liao & Jing Zhen & Natarajan Ishwaran & Huadong Guo & Ruixia Yang & Chuansheng Liu & Chun Chang & Xin Zong, 2014. "An Improved Neural Network for Regional Giant Panda Habitat Suitability Mapping: A Case Study in Ya’an Prefecture," Sustainability, MDPI, Open Access Journal, vol. 6(7), pages 4059-4076, June.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:4:y:2012:i:10:p:2566-2573:d:20476. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.