Image mining for drought monitoring in eastern Africa using Meteosat SEVIRI data

We propose an image mining approach to monitor drought using Meteosat Spinning Enhanced Visible and InfraRed Imager (SEVIRI) image data. SEVIRI image data provide frequent Normalized Difference Vegetation Index (NDVI) time series which are important to assess the evolution of drought conditions. Vegetation condition is characterized in space by the deviation of the current NDVI observations at locations from their temporal mean values. In this paper we assume a gradual evolution of vegetation stress caused by drought and hence address this aspect with the use of a membership function applied to vegetation stress values to model drought. Our approach is implemented on subset image data of eastern Africa. Vegetated sites in a drought prone area of the region serve as an illustration using the drought spell at the end of 2005. This study shows that the use of a membership function allows capturing the gradual evolution of drought and can be used to model drought from observed vegetation conditions.