Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction

This paper presents two complementing algorithms for remote sensing based coal fire research and the results derived thereof. Both are applicable on Landsat, ASTER and MODIS data. The first algorithm automatically delineates coal fire risk areas from multispectral satellite data. The second automatically extracts local coal fire related thermal anomalies from thermal data. The presented methods aim at the automated, unbiased retrieval of coal fire related information. The delineation of coal fire risk areas is based on land cover extraction through a knowledge based spectral test sequence. This sequence has been proven to extract coal fire risk areas not only in time series of the investigated study areas in China, but also in transfer regions of India and Australia. The algorithm for the extraction of thermal anomalies is based on a moving window approach analysing sub-window histograms. It allows the extraction of thermally anomalous pixels with regard to their surrounding background and therefore supports the extraction of very subtle, local thermal anomalies of different temperature. It thus shows clear advantages to anomaly extraction via simple thresholding techniques. Since the thermal algorithm also does extract thermal anomalies, which are not related to coal fires, the derived risk areas can help to eliminate false alarms. Overall, 50% of anomalies derived from night-time data can be rejected, while even 80% of all anomalies extracted from daytime data are likely to be false alarms. However, detection rates are very good. Over 80% of existing coal fires in our first study area were extracted correctly and all fires (100%) in study area two were extracted from Landsat data. In MODIS data extraction depends on coal fire types and reaches 80% of all fires in our study area with hot coal fires of large spatial extent, while in another region with smaller and 'colder' coal fires only the hottest ones (below 20%) can be extracted correctly. The success of the synergetic application of the two methods has been proven through our detection of so far unknown coal fires in Landsat 7 ETM+ remote sensing data. This is the first time in coal fire research that unknown coal fires were detected in satellite remote sensing data exclusively and were validated later subsequently during in situ field checks.