A new algorithm for global forest fire detection using multispectral images

Fire detection has been an active research field for many years and a number of algorithms have been proposed. These algorithms, however, are often inflexible in dealing with the spatial and temporal heterogeneity of the environment. Different biomes, seasons, and temperatures usually cause the performance of these algorithms to vary dramatically. In this paper, we propose a new algorithm for fire detection based on the Mahalanobis distance that exploits the statistical properties of multi-spectral images. The distinguishing feature of our algorithm is its robustness. It can effectively differentiate fire from background in various environments, using a single, fixed threshold. We evaluate our algorithm by comparing it to three state-of-the-art existing algorithms: the MODVOLC normalized fire index algorithm, the Arino's threshold algorithm, and the contextual MODIS algorithm. All algorithms are tested using MODIS images taken in different parts of the world as well as at different times. Experimental results demonstrate that our algorithm consistently achieves the best performance, showing a low and constant false alarm rate.