A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping

The scope of this paper is to demonstrate, evaluate and compare two burn scar mapping (BSM) approaches developed and applied operationally in the framework of the RISK-EOS service element project within the Global Monitoring for Environment and Security (GMES) program funded by ESA (http://www.risk-eos.com). The first method is the BSM_NOA, a fixed thresholding method using a set of specifically designed and combined image enhancements, whilst the second one is the BSM_ITF, a decision tree classification approach based on a wide range of biophysical parameters. The two methods were deployed and compared in the framework of operational mapping conditions set by RISK-EOS standards, based either on sets of uni- or multi-temporal satellite images acquired by Landsat 5 TM and SPOT 4 HRV. The evaluation of the performance of the two methods showed that either in uni- or multi-temporal acquisition mode, the two methods reach high detection capability rates ranging from 80% to 91%. At the same time, the minimum burnt area detected was of 0.9–1.0 ha, despite the coarser spatial resolution of Landsat 5 TM sensor. Among the advantages of the satellite-based approaches compared to conventional burn scar mapping, are cost-efficiency, repeatability, flexibility, and high spatial and thematic accuracy from local to country level. Following the catastrophic fire season of 2007, burn scar maps were generated using BSM_NOA for the entirety of Greece and BSM_ITF for south France in the framework of the RISK-EOS/GMES Services Element project.