Cao, X, Chen, J, Matsushita, B, Imura, H, Wang, L (2009). An automatic method for burn scar mapping using support vector machines. INTERNATIONAL JOURNAL OF REMOTE SENSING, 30(3), 577-594.

Abstract
Wildfires release large amounts of carbon, smoke and aerosols that strongly impact the global climatic system. Burn scar is an important parameter when modelling the impact of wildfires on the ecosystem and the climatic system. We have developed an automatic burn scar mapping method using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data, in which the Global Environment Monitoring Index (GEMI), a vegetation index VI3T and a new index, GEMI-Burn scar (GEMI-B), were used together to enhance the differences between burned and unburned pixels related to vegetation photosynthesis, surface temperature and vegetation water content, respectively, and an automatic region growing method based on Support Vector Machines (SVMs) was used to classify burn scars without any predefined threshold. A case study was carried out to validate the new method at the border area between Mongolia and China, where a wildfire took place in May 2003. The results show that the burn scar area extracted by the new method is consistent with that from Landsat Thematic Mapper (TM) data with high accuracy. The sound performance of the new technique is due to the following reasons: (1) multiple features of burn scar spectra were combined and used, (2) a reasonable assumption was made stating that the neighbourhoods of active fires (hotspots) are most likely to be burn scars, (3) an SVM classifier was adopted that works well with a small number of training samples, and (4) an iterative classification procedure was developed that is capable of running continuous training for the SVM classifier to deal with the transitionary features of burn scar pixels. The results suggest that the new index GEMI-B and automatic mapping method based on SVMs have the potential to be applied to near real-time burn scar mapping in grassland areas.

DOI:
10.1080/01431160802220219

ISSN:
0143-1161