Multi-sensor data fusion for the detection of underground coal fires

Abstract

The spontaneous combustion of coal causes widespread underground coal fires in several countries, amongst which is China. These coal fires cause serious environmental, economic and safety problems. In northern China, the coal fires occur within a wide region stretching 5000 km east-west and 750 km north-south. Remote sensing therefore provides an ideal tool for monitoring this environmental hazard over such a large and remote area. As part of a research project to detect, measure, monitor and extinguish these coal fires, this paper describes a remote-sensing based multi-sensor data-fusion methodology for detecting the underground fires. The methodology is based on fusing a variety of satellite-based image types (optical, thermal, microwave) together with airborne data (optical and thermal infrared) and ancillary data sources such as geological and topographic maps. The results of the remote-sensing data fusion are presented, using pixel-based, feature-based and decision-based fusion approaches