Self-Adjusting Thresholding for Burnt Area Detection Based on Optical Images

Abstract

Mapping of regional fires would make it possible to analyse their environmental, social and economic impact, as well as to develop better fire management systems. However, automatic mapping of burnt areas has proved to be a challenging task, due to the wide diversity of vegetation cover worldwide and the heterogeneous nature of fires themselves. Here, we present an algorithm for the automatic mapping of burnt areas using medium-resolution optical images. Although developed for Landsat images, it can be also applied to Sentinel-2 images without modification. The algorithm draws upon the classical concept of differences in pre- and post-fire reflectance, but also takes advantage of the object-oriented approach and a new threshold calculation method. It consists of four steps. The first concerns the calculation of spectral indices and their differences, together with differences in spectral layers based on pre- and post-fire images. In the second step, multiresolution segmentation and masking are performed (clouds, water bodies and non-vegetated areas are removed from further analysis). Thirdly, ‘core’ burnt areas are detected using automatically-adjusted thresholds. Thresholds are calculated on the basis of specific functions established for difference layers. The last step combines neighbourhood analysis and patch growing to define the final shape of burnt areas. The algorithm was tested in 27 areas located worldwide, and covered by various types of vegetation. Comparisons with manual interpretation show that the fully-automated classification is accurate. Over 82% of classifications were considered satisfactory (overall accuracy > 90%; user and producer accuracy > 70%).