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 framework for 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 of the system is twofold; the EI can be customised based on the available data, and the ability to cope with a lack of expert knowledge when defining the EI. The framework applies fuzzy logic to define a soft quantifier-guided fusion strategy, where these soft constraints allow a non-linear scaling of the contributing factors from which the EI is composed. Soft fusion criteria are specified by linguistic quantifiers that correspond to fuzzy majorities, and are implemented as Ordered Weighted Averaging (OWA) operators. The approach is applied in a case study to demonstrate the periodical computation of anomaly indicators of the environmental status of Africa, based on a seven year time series of dekadal Earth Observation datasets.

CARRARA Paola;  BORDOGNA Gloria;  BOSCHETTI Mirco;  BRIVIO Pietro;  NELSON Andrew;  STROPPIANA Daniela; 

2008-08-27

TAYLOR & FRANCIS LTD

JRC35532

1365-8816,   

http://dx.doi.org/10.1080/13658810701703183,    https://publications.jrc.ec.europa.eu/repository/handle/JRC35532,   

10.1080/13658810701703183,