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Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

L. Hardtke,P. D. Blanco,H. D. Valle,G. Metternicht,W. Sione

Published 2015 in International Journal of Applied Earth Observation and Geoinformation

ABSTRACT

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R² = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    International Journal of Applied Earth Observation and Geoinformation

  • Publication date

    2015-06-01

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1016/j.jag.2014.11.011
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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