Handling heterogeneous bipolar information for modelling environmental syndromes of global change

Spatial assessment of environmental phenomena at regional/global scale involves the analysis and fusion of multiple, complex, multidisciplinary, and large-scale information. Since very often reliable models of such phenomena are lacking, the ''syndrome approach'' has been adapted to this purpose. In this context, there is a strong need for frameworks capable of handling data from heterogeneous sources in order to fuse them into synthetic indicators by modelling the uncertain and incomplete knowledge of the phenomenon. The approach here proposed models a syndrome by soft revision of bipolar information having heterogeneous role: a set of contextual conditions constraining the flourishing of the syndrome (negative information), identified on the basis of the expert's knowledge, and a typical pattern of notable symptoms (positive information) that are indeed proxies of observations of the syndrome occurrence. Specifically, three soft revision strategies are defined in the paper to combine negative and positive information whose overall aim is to define indicators of occurrence of an environmental syndrome with distinct objectives. The main concern of the first two revisions is to reduce the incoherence of bipolar information by taking into account the information reliability: the first strategy models a cautious decision attitude by eliminating incoherence and by modelling priorities of either negative or positive information and their partial trust, while the second strategy models a cautious and precautionary decision attitude giving credit to observations by taking into account False Negatives and False Positives. Finally, the third strategy models decision attitudes characterized by distinct level of risk. The proposed methodology is exemplified by a real case study whose objective is to define an indicator of the Rural Exodus syndrome for the Mediterranean region. In this case the method fuses biophysical related variables derived from Earth Observation (EO) data with geophysical and socioeconomic conditions based on the proposed strategies.