New Methods of Active Fire Detection Using MODIS Data

Active fire detection using satellite thermal sensors usually involves thresholding the detected brightness temperature in several bands. The frequently used features for fire detection are the brightness temperature in the 4-micron wavelength band (T4) and the brightness temperature difference between the 4-micron and 11-micron bands (dT=T4-T11). The thresholds used are determined empirically without any theoretical considerations, and thus may not be optimal for a given fire detection task. In this paper, we discuss a new approach in active fire detection based on a stochastic model for target detection. This approach considers the probability density functions of the fire and background pixels and optimal thresholds are derived depending on the specific objectives of the detection tasks. For example, the optimal threshold can be found by minimizing a cost function which is a weighted sum of the omission and commission errors. Alternatively, the threshold can also be derived based on the maximum likelihood criterion. The implementation of the new methods and the results of comparison with the conventional algorithms will be described.