Land cover mapping with emphasis to burnt area delineation using co-orbital ALI and Landsat TM imagery

George P. Petropoulos, Charalambos C. Kontoes, Iphigenia Keramitsoglou

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45 Citations (SciVal)

Abstract

In this study, the potential of EO-1 Advanced Land Imager (ALI) radiometer for land cover and especially burnt area mapping from a single image analysis is investigated. Co-orbital imagery from the Landsat Thematic Mapper (TM) was also utilised for comparison purposes. Both images were acquired shortly after the suppression of a fire occurred during the summer of 2009 North-East of Athens, the capital of Greece. The Maximum Likelihood (ML), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) classifiers were parameterised and subsequently applied to the acquired satellite datasets. Evaluation of the land use/cover mapping accuracy was based on the error matrix statistics. Also, the McNemar test was used to evaluate the statistical significance of the differences between the approaches tested. Derived burnt area estimates were validated against the operationally deployed Services and Applications For Emergency Response (SAFER) Burnt Scar Mapping service.

All classifiers applied to either ALI or TM imagery proved flexible enough to map land cover and also to extract the burnt area from other land surface types. The highest total classification accuracy and burnt area detection capability was returned from the application of SVMs to ALI data. This was due to the SVMs ability to identify an optimal separating hyperplane for best classes’ separation that was able to better utilise ALI's advanced technological characteristics in comparison to those of TM sensor. This study is to our knowledge the first of its kind, effectively demonstrating the benefits of the combined application of SVMs to ALI data further implying that ALI technology may prove highly valuable in mapping burnt areas and land use/cover if it is incorporated into the development of Landsat 8 mission, planned to be launched in the coming years.
Original languageEnglish
Pages (from-to)344-355
Number of pages11
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume18
Early online date21 Apr 2012
DOIs
Publication statusPublished - Aug 2012

Keywords

  • Burnt area mapping
  • Landsat TM
  • EO-1 Advanced Land Imager (ALI)
  • Support Vector Machines
  • Artificial Neural Networks
  • Maximum Likelihood
  • Greece

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