The monitoring of the environment's status at continental scale involves the integration of information derived by the analysis of multiple, complex, multidisciplinary, and large-scale phenomena. Thus, there is a need to define synthetic Environmental Indicators (EIs) that concisely represent these phenomena in a manner suitable for decision-making. This research proposes a flexible system to define EIs based on a soft fusion of contributing environmental factors derived from multi-source spatial data (mainly Earth Observation data). The flexibility is twofold: the EI can be customized based on the available data, and the system is able to cope with a lack of expert knowledge. The proposal allows a soft quantifier-guided fusion strategy to be defined, as specified by the user through a linguistic quantifier such as 'most of'. The linguistic quantifiers are implemented as Ordered Weighted Averaging operators. The proposed approach is applied in a case study to demonstrate the periodical computation of anomaly indicators of the environmental status of Africa, based on a 7-year time series of dekadal Earth Observation datasets. Different experiments have been carried out on the same data to demonstrate the flexibility and robustness of the proposed method.
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Document Type: Research Article
IREA CNR, Institute for Electromagnetic Sensing of the Environment, Milan, Italy
IDPA CNR, Institute for the Dynamics of Environmental Processes, I24044 Dalmine (Bg), Italy
European Commission-DG Joint Research Centre, Institute for Environment and Sustainability, I-21020 Ispra (VA), Italy
Publication date: 2008-01-01
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