CLASSIFYING AND TRACKING DUST PLUMES FROM PASSIVE REMOTE SENSING

摘要

Recent studies emphasize the role mineral dust aerosols play in terms of the earth’s climate system, its radiation budget and microbial nutrition cycles. In order to gain further insight into the genesis and long term characteristics of dust events, processing setellite imagery is inevitable. We propose a fully Bayesian multispectral classification method that significantly facilitates this task. Using MSG-SEVIRI imagery we show that our technique allows to extract dust activity well enough to pave the way for a tracking scheme. Based on this procedure we derive an approach to identify regions that are likely to be the origin of emerging dust plumes.