Image Mining for Modeling of Forest Fires From Meteosat Images

Abstract

Meteosat satellites with the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) sensor onboard provide remote-sensing images nowadays every 15 min. This paper investigates and applies image-mining methods to make an optimal use of images. It develops a simple, time-efficient, and generic model to facilitate pattern discovery and analysis. The focus of this paper is to develop a model for monitoring and analyzing forest fires in space and time. As an illustration, a diurnal cycle of fire in Portugal on July 28, 2004 was analyzed. Kernel convolution characterized the hearth of the fire as an object in space. Objects were extracted and tracked over time automatically. The results thus obtained were used to make a linear model for fire behavior with respect to vegetation and wind characteristics as explanatory variables. This model may be useful for predicting hazards at an almost real-time basis. The research illustrates how image mining improves information extraction from the Meteosat SEVIRI images