Fuzzy integration of satellite data for detecting environmental anomalies across Africa

New opportunities for the detection of anomalous environmental conditions at continental and global scale are offered from the exploitation of time series of satellite remote sensing data coupled with appropriate geoinformation processing. The assessment of environmental conditions is generally based on complex models that require large dataset and whose performance depends on expert knowledge and specific tuning. In this paper we propose a syn-thetic indicator which is obtained by aggregating the scores of diverse observable factors that reinforce the convergence of anomaly evidence. The factors evaluation and aggregation are framed within fuzzy set theory and approximate reasoning methods so as to take into account the uncertainty and incompleteness affecting the collection of factors, the estimation of their importance and the complexity of their interrelationship. The methodology based on parameters derived from the analysis of time series of low resolution satellite data concerning water avail-ability and vegetation phenology is described and the results obtained from its application in the detection of anomalous environmental conditions over the African continent are presented and discussed.