| Peer-Reviewed

Robust Method for Deforestation Analysis of Satellite Images

Received: 25 November 2015     Accepted: 4 December 2015     Published: 25 December 2015
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Abstract

The aim is to design a robust method for tracking real time deforestation in a local area under satellite observation. Deforested areas are obtained by a procedure of differentiating between two successive images (temporal). The resulting method proves to be robust, the analyzed satellite image having multiple alterations: cutting (minus 3-10%), translation (5-10%), rotation (2-10 degrees), parasite random noise (5-15%), different brightness and contrast (5-10%) and cloudy areas (15-20%).

Published in International Journal of Environmental Monitoring and Analysis (Volume 3, Issue 6)
DOI 10.11648/j.ijema.20150306.16
Page(s) 420-424
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), 2015. Published by Science Publishing Group

Keywords

Satellite Images, Digital Image Processing Deforestation, Forest Satellite Surveillance

References
[1] www.fao.org - FAO Document Repository: Forestry Paper 169.
[2] Ni-Bin Chang (Editor), Environmental Remote Sensing and Systems Analysis, CRC Press, 2012.
[3] Ioannis Manakos, Mathias Braun. Land Use and Land Cover Mapping in Europe Practices & Trends, Springer, 2014.
[4] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008.
[5] Goshtasby, Ardeshir, "Piecewise linear mapping functions for image registration," Pattern Recognition, Vol. 19, 1986, pp. 459-466.
[6] Goshtasby, Ardeshir, "Image registration by local approximation methods," Image and Vision Computing, Vol. 6, 1988, pp. 255-261.
[7] O. Chum and J. Matas. Optimal randomized RANSAC, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(8) Ș1472-1482, 2008.
[8] Gregory P. Asner, David E. Knapp, Aravindh Balaji, Guayana Páez-Acosta. Automated mapping of tropical deforestation and forest degradation: CLASlite, Journal of Applied Remote Sensing, Vol. 3, 033543, 2009.
[9] Ben DeVries, Jan Verbesselt, Lammert Kooistra and Martin Herold, Detecting Tropical Deforestation and Degradation Using Landsat Time Series, papers submitted to Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.
[10] J. G. Masek, E. F. Vermote, N. E. Saleous, R. Wolfe, F. G. Hall, K. F. Huemmrich, F. Gao, J. Kutler, and T.-k. Lim, \A Landsat Surface Reectance Dataset," Geoscience and Remote Sensing Letters, vol. 3, no. 1, pp. 68-72, 2006.
[11] C. Huang, N. Thomas, S. N. Goward, J. G. Masek, Z. Zhu, J. R. G. Townshend, and J. E. Vogelmann, Automated masking of cloud and cloud shadow for forest change analysis using Landsat images," International Journal of Remote Sensing, vol. 31, no. 20, pp. 5449-5464, Oct. 2010.
[12] A. K. Sah, B. P. Sah, K. Honji, N. Kubo, S. Senthil. Semi-automated cloud/shadow removal and land cover change detection using satellite imagery, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, Melbourne, Australia.
[13] M. A. Wulder, J. A. Dechka, M. A. Gillis, J.E. Luther3, R. J. Hall, A. Beaudoin, S. E. Franklin. Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program, NOVEMBER/DECEMBER 2003, VOL. 79, NO. 6 pp. 2075-1083, THE FORESTRY CHRONICLE.
[14] Land.copernicus.eu - Copernicus Global Land Service.
[15] www.eea.europa.eu - European Environment Agency; GIO Land Service; CORINE Land Cover.
[16] cce.nasa.gov - NASA Carbon Cycle & Ecosystems - LCLUC Program.
[17] www.ospo.noaa.gov – NOAA (National Oceanic and Atmospheric Administration) Office of Satellite and Product Operations; Hazard Mapping System Fire and Smoke Product.
Cite This Article
  • APA Style

    Ioan Ispas, Eduard Franti, Florin Lazo, Elteto Zoltan. (2015). Robust Method for Deforestation Analysis of Satellite Images. International Journal of Environmental Monitoring and Analysis, 3(6), 420-424. https://doi.org/10.11648/j.ijema.20150306.16

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    ACS Style

    Ioan Ispas; Eduard Franti; Florin Lazo; Elteto Zoltan. Robust Method for Deforestation Analysis of Satellite Images. Int. J. Environ. Monit. Anal. 2015, 3(6), 420-424. doi: 10.11648/j.ijema.20150306.16

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    AMA Style

    Ioan Ispas, Eduard Franti, Florin Lazo, Elteto Zoltan. Robust Method for Deforestation Analysis of Satellite Images. Int J Environ Monit Anal. 2015;3(6):420-424. doi: 10.11648/j.ijema.20150306.16

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  • @article{10.11648/j.ijema.20150306.16,
      author = {Ioan Ispas and Eduard Franti and Florin Lazo and Elteto Zoltan},
      title = {Robust Method for Deforestation Analysis of Satellite Images},
      journal = {International Journal of Environmental Monitoring and Analysis},
      volume = {3},
      number = {6},
      pages = {420-424},
      doi = {10.11648/j.ijema.20150306.16},
      url = {https://doi.org/10.11648/j.ijema.20150306.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20150306.16},
      abstract = {The aim is to design a robust method for tracking real time deforestation in a local area under satellite observation. Deforested areas are obtained by a procedure of differentiating between two successive images (temporal). The resulting method proves to be robust, the analyzed satellite image having multiple alterations: cutting (minus 3-10%), translation (5-10%), rotation (2-10 degrees), parasite random noise (5-15%), different brightness and contrast (5-10%) and cloudy areas (15-20%).},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Robust Method for Deforestation Analysis of Satellite Images
    AU  - Ioan Ispas
    AU  - Eduard Franti
    AU  - Florin Lazo
    AU  - Elteto Zoltan
    Y1  - 2015/12/25
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijema.20150306.16
    DO  - 10.11648/j.ijema.20150306.16
    T2  - International Journal of Environmental Monitoring and Analysis
    JF  - International Journal of Environmental Monitoring and Analysis
    JO  - International Journal of Environmental Monitoring and Analysis
    SP  - 420
    EP  - 424
    PB  - Science Publishing Group
    SN  - 2328-7667
    UR  - https://doi.org/10.11648/j.ijema.20150306.16
    AB  - The aim is to design a robust method for tracking real time deforestation in a local area under satellite observation. Deforested areas are obtained by a procedure of differentiating between two successive images (temporal). The resulting method proves to be robust, the analyzed satellite image having multiple alterations: cutting (minus 3-10%), translation (5-10%), rotation (2-10 degrees), parasite random noise (5-15%), different brightness and contrast (5-10%) and cloudy areas (15-20%).
    VL  - 3
    IS  - 6
    ER  - 

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Author Information
  • Centre for New Electronic Architecture, Research Institute for Artificial Intelligence, Bucharest, Romania

  • Centre for New Electronic Architecture, Research Institute for Artificial Intelligence, Bucharest, Romania

  • Centre for New Electronic Architecture, Research Institute for Artificial Intelligence, Bucharest, Romania

  • Centre for New Electronic Architecture, Research Institute for Artificial Intelligence, Bucharest, Romania

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