Multifractal pre-processing of AVHRR images to improve the determination of smoke plumes from large fires

In this study, we show how different spectral channels of NOAA-AVHRR acquired data can be used to produce a synthetized signal aimed at helping the characterization of plumes associated to fire events. The synthetized signal is computed using a reconstruction formula in the multifractal microcanonical formalism (herein referred to as MMF). The MMF is a recent development in the analysis of complex signals, well adapted to the study of turbulent acquired data, for instance geophysical fluids. It allows the computation, at each point of the signal's domain, of a singularity exponent, characteristic of the scale behaviour of the signal around that point; singularity exponents provide information about the strengths of the transitions inside a signal, and they are related to the multifractal hierarchy associated to structure functions in Fully Developped Turbulence (FDT). In the MMF, it is possible to reconstruct a turbulent signal from the manifold of most singular exponents. We make use of this property by computing supergeometric structures from a thermal infrared channel in NOAA-AVHRR acquired data, and we use the signal¿s gradient coming from other channels to reconstruct a signal in which plume pixels are easier to detect. This methodology is based on the turbulent properties of the plume accessible from the thermal infrared band; the algorithm is detailed and applied on a specific example, showing a new spatially-based method for helping the determination of plume pixels in NOAA-AVHRR data.