Application of the multifractal microcanonical formalism to the detection of fire plumes in NOAA–AVHRR data

Abstract

In this article it is shown that the multifractal microcanonical formalism (herein referred to as MMF) has strong potential for bringing new solutions to a known problem in the analysis of some remotely sensed datasets: the determination of fire plumes in NOAA-AVHRR data. It has been proven that NOAA-AVHRR data can be used to detect plumes caused by fire accidents of different kinds. This work builds on previous studies and uses the MMF to introduce novel methods for the determination of plumes. The MMF can be used to derive geometrical superstructures (like certain multifractal topological manifolds and most importantly the so-called reduced signals) that are able to deal with the multiscale properties of turbulent geophysical fluid flows. These multiscale properties make use of the spatial distribution of grey-level values in the datasets and they are used in conjunction with previous pixel-based descriptors to enhance the determination of plume pixels.

Scaling Analysis of Ocean Surface Turbulent Heterogeneities from Satellite Remote Sensing: Use of 2D Structure Functions

“…Multifractal methods have been widely applied to time series, but there are not many studies applying such approaches to 2D data, especially in the field of ocean color remote sensing. Some of them considered a local gradient transform in order to identify currents and oil spills [ 10 – 13 ]. Other studies transformed satellite Chl-a or SST image data into a positive singular field using a gradient modulus transform [ 14 , 15 ].…”