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Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method

Received: 26 June 2019     Accepted: 30 July 2019     Published: 24 December 2019
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Abstract

Land cover classification analysis from satellite imagery methods are important because they are the basis for characterizing surface conditions and evolution, supporting the management and optimization of land resources, evaluating global climate and environmental changes, and facilitating sustainable regional economic and social development. In order to address these necessities, artificial neural networks have been used extensively. In addition, other methods based on computer vision are very useful to solve this task. In this paper, the authors propose an approach based on Monte Carlo method and artificial neural networks in order to classify regions of small forest reserves from drones’ images and calculate their respective areas. Next to the small forest reserve will be extended a standard rectangular tarpaulin of 250 square meters and based on this reference it will be possible to calculate the area of the forest reserve if the ground is relatively flat. The proposed approach will be compared with a method based on watershed algorithm. The automatic calculation of the forest area through images generated by drones has much practical application for environmental engineers, for example, for the calculation of environmental impact and determination of carbon loss if such forests are consequently deforested.

Published in Mathematics and Computer Science (Volume 4, Issue 6)
DOI 10.11648/j.mcs.20190406.13
Page(s) 112-129
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Remote Sensing, Artificial Neural Networks, Monte Carlo Methods, Watershed Algorithm, Unmanned Aerial Vehicles (UAVs), Drone-based Imagery

References
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Cite This Article
  • APA Style

    Paulo Marcelo Tasinaffo, Afonso Henriques Moreira Santos, Elias Cavalcante Junior, Carlos Henrique Quartucci Forster, Rafael Augusto Lopes Shigemura, et al. (2019). Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method. Mathematics and Computer Science, 4(6), 112-129. https://doi.org/10.11648/j.mcs.20190406.13

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    ACS Style

    Paulo Marcelo Tasinaffo; Afonso Henriques Moreira Santos; Elias Cavalcante Junior; Carlos Henrique Quartucci Forster; Rafael Augusto Lopes Shigemura, et al. Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method. Math. Comput. Sci. 2019, 4(6), 112-129. doi: 10.11648/j.mcs.20190406.13

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    AMA Style

    Paulo Marcelo Tasinaffo, Afonso Henriques Moreira Santos, Elias Cavalcante Junior, Carlos Henrique Quartucci Forster, Rafael Augusto Lopes Shigemura, et al. Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method. Math Comput Sci. 2019;4(6):112-129. doi: 10.11648/j.mcs.20190406.13

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  • @article{10.11648/j.mcs.20190406.13,
      author = {Paulo Marcelo Tasinaffo and Afonso Henriques Moreira Santos and Elias Cavalcante Junior and Carlos Henrique Quartucci Forster and Rafael Augusto Lopes Shigemura and Rafael Jacomel and Victor Ulisses Pugliese and Bruno Koshin Vazquez Iha and Adilson Marques da Cunha and Gildarcio Sousa Goncalves and Luiz Alberto Vieira Dias},
      title = {Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method},
      journal = {Mathematics and Computer Science},
      volume = {4},
      number = {6},
      pages = {112-129},
      doi = {10.11648/j.mcs.20190406.13},
      url = {https://doi.org/10.11648/j.mcs.20190406.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190406.13},
      abstract = {Land cover classification analysis from satellite imagery methods are important because they are the basis for characterizing surface conditions and evolution, supporting the management and optimization of land resources, evaluating global climate and environmental changes, and facilitating sustainable regional economic and social development. In order to address these necessities, artificial neural networks have been used extensively. In addition, other methods based on computer vision are very useful to solve this task. In this paper, the authors propose an approach based on Monte Carlo method and artificial neural networks in order to classify regions of small forest reserves from drones’ images and calculate their respective areas. Next to the small forest reserve will be extended a standard rectangular tarpaulin of 250 square meters and based on this reference it will be possible to calculate the area of the forest reserve if the ground is relatively flat. The proposed approach will be compared with a method based on watershed algorithm. The automatic calculation of the forest area through images generated by drones has much practical application for environmental engineers, for example, for the calculation of environmental impact and determination of carbon loss if such forests are consequently deforested.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method
    AU  - Paulo Marcelo Tasinaffo
    AU  - Afonso Henriques Moreira Santos
    AU  - Elias Cavalcante Junior
    AU  - Carlos Henrique Quartucci Forster
    AU  - Rafael Augusto Lopes Shigemura
    AU  - Rafael Jacomel
    AU  - Victor Ulisses Pugliese
    AU  - Bruno Koshin Vazquez Iha
    AU  - Adilson Marques da Cunha
    AU  - Gildarcio Sousa Goncalves
    AU  - Luiz Alberto Vieira Dias
    Y1  - 2019/12/24
    PY  - 2019
    N1  - https://doi.org/10.11648/j.mcs.20190406.13
    DO  - 10.11648/j.mcs.20190406.13
    T2  - Mathematics and Computer Science
    JF  - Mathematics and Computer Science
    JO  - Mathematics and Computer Science
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    EP  - 129
    PB  - Science Publishing Group
    SN  - 2575-6028
    UR  - https://doi.org/10.11648/j.mcs.20190406.13
    AB  - Land cover classification analysis from satellite imagery methods are important because they are the basis for characterizing surface conditions and evolution, supporting the management and optimization of land resources, evaluating global climate and environmental changes, and facilitating sustainable regional economic and social development. In order to address these necessities, artificial neural networks have been used extensively. In addition, other methods based on computer vision are very useful to solve this task. In this paper, the authors propose an approach based on Monte Carlo method and artificial neural networks in order to classify regions of small forest reserves from drones’ images and calculate their respective areas. Next to the small forest reserve will be extended a standard rectangular tarpaulin of 250 square meters and based on this reference it will be possible to calculate the area of the forest reserve if the ground is relatively flat. The proposed approach will be compared with a method based on watershed algorithm. The automatic calculation of the forest area through images generated by drones has much practical application for environmental engineers, for example, for the calculation of environmental impact and determination of carbon loss if such forests are consequently deforested.
    VL  - 4
    IS  - 6
    ER  - 

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Author Information
  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • Electrical Engineering Institute, Federal University of Itajuba (UNIFEI), Itajuba, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • IBM Brazil, S?o Paulo, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • Mathematics and Statistics Institute, Sao Paulo University (USP), Sao Paulo, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

  • Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), S?o Jose dos Campos, Brazil

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