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Assessment of MODIS-Based NDVI-Derived Index for Fire Susceptibility Estimation in Northern China

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Computational Science and Its Applications -- ICCSA 2015 (ICCSA 2015)

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Abstract

Some satellite-based indices are useful for fire susceptibility estimation in some regions. However, the obtained results are region-dependent to some extent. The aim of this study is to assess the effectiveness of two NDVI-derived indices: the relative greenness index (RGI) and the vegetation danger index (VDI) applied to the northern China. Thus, the Moderate Resolution Imaging Spectroradiometer sensor (MODIS) data MYD13Q1, which is the 16-days composite product, were used. The results indicated that the RGI values were higher than 70% during the before-fire period from spring up to autumn, whereas it have decreased sharply when fire happened and after a period time and also it was below normal level during a period of after-fire. The VDI values were negative when fire happened and after a short period and that of the before fire and after fire were positive. Thus, it can be concluded that the two MODIS-based NDVI-derived indices have a higher possibility for fire susceptibility estimation even though it needs to combine other fire-related parameters.

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Authors and Affiliations

  1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, China

    Xiaolian Li & Weiguo Song

  2. Institute of Methodologies for Environmental Analysis, National Research Council of Italy, Potenza, Italy

    Antonio Lanorte, Luciano Telesca & Rosa Lasaponara

Authors
  1. Xiaolian Li
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  2. Antonio Lanorte
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  3. Luciano Telesca
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  4. Weiguo Song
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  5. Rosa Lasaponara
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Corresponding author

Correspondence to Rosa Lasaponara .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Canaanland, Nigeria

    Sanjay Misra

  4. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  5. University of Minho, Braga, Portugal

    Ana Maria Alves Coutinho Rocha

  6. Polytechnic University, Bari, Italy

    Carmelo Torre

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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© 2015 Springer International Publishing Switzerland

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Li, X., Lanorte, A., Telesca, L., Song, W., Lasaponara, R. (2015). Assessment of MODIS-Based NDVI-Derived Index for Fire Susceptibility Estimation in Northern China. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9158. Springer, Cham. https://doi.org/10.1007/978-3-319-21410-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-21410-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21409-2

  • Online ISBN: 978-3-319-21410-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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