A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection

The present study proposes and improved self-adaptive algorithm (ISAA) for the detection of active fires using only channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR). ISAA is specifically devised for the detection of small fires. The fire detection procedure is mainly based on the multitemporal approach (TN-ALT) devised by Cuomo et al. (2001a) and makes use of statistical analyses of real fires from different regions of the Italian peninsula. Such analyses allow the characterization of these fires as well as the computation of dynamic threshold values, which are variable in time and space and calibrated on local environmental conditions. ISAA was developed using an initial data sample of 1000 fires that occurred in 1996, and then in order to achieve a highly satisfactory performance in fire detection, the statistical analyses are updated yearly, so that a wider data sample can be considered for subsequent years. The evaluation tests made use of multitemporal satellite data (from 1997 to 1999) and ground observations provided by the Italian Forestry Service. The results obtained in different regions of North and South Italy demonstrated that ISAA detected about 80% of fires (with a low rate of false alarms at 15%) and showed a high fire discrimination capability both in the worst and good light conditions. The most recent contextual methods of fire detection were applied to significant test cases and compared with the results obtained from ISAA. This comparison showed that ISAA was able to find an increased number of fires as well as to reduce false alarms in all different light conditions.