Linear fractional-based filter as a pre-classifier to map burned areas in Mediterranean countries

Abstract

Wildfires in Southern Europe burn thousands of square kilometres every year, causing extensive economic and ecological losses. Accurate mapping of fire-burned areas is a decisive factor in guiding forest management decisions. In this work a linear fractional-based filter is considered as a pre-classifier for burned area estimation from Moderate Resolution Imaging Spectroradiometer (MODIS) accurate satellite imagery. Three vegetation indices (normalized difference vegetation index, enhanced vegetation index, and global environment monitoring index) and three spectral indices specifically designed for burned area identification (normalized burnt ratio, burned area index, and burned area adapted for MODIS) have been used as inputs for the proposed filter. The filtered images were classified and the accuracy of the burned area estimates was computed by kappa () statistic. Burned area perimeters measured on the ground by global positioning system were used as reference truth. A linear Gaussian pre-classification filter was used as reference to check the burned area estimates accuracy improvements. When using the proposed fractional-type filter, an accurate estimation () of areas burned by forest fires in the four study areas located in Central Spain was achieved. The results showed that the non-local filter, used as a pre-classifier, allowed higher accuracy than the same inputs both without filtering and with the Gaussian filter ( index increased up to 30% with statistical significance). McNemar test confirmed that such accuracy improvement had statistical significance, and a statistical separability test showed that filtering increased the inter-class distance, helping to improve the latter classification. Burned areas in Central Spain were accurately mapped when the linear fractional-based filter proposed here was used as a pre-classifier.

Additional information

Funding

The research of the first author was supported by MICINN project MTM2010-19510/MTM.