Conclusion

The goal of our study was mainly to highlight the individual and combinatorial influence of the spectral and of the temporal components of remotely sensed images reflectances in land cover classification. As our aim was not to propose an operational classifier directed at thematic mapping based on the most efficient combination of reflectance inputs, we intentionally restricted our experimental framework to continental Portugal. In the course, we were led to define a distance-based topography to rank these features in terms of their relevant contribution to a discriminatory representation space. The chosen Mahalanobis median distance, although it was not shown to strictly optimize any classification criterion, deemed among all the possible arbitrary choices a reasonable indicator of the classes’

dispersion and of the clusters compactness. Then, following this resulting arrangement of features inputs to sequentially train a Support Vector Machine classifier, we were able to demonstrate that the Enhanced Vegetation Index (EVI) calculated in August was the most informative combination of one spectral band with one date to characterize the land cover classes that we retained to describe the Portuguese mainland. Continuing to gradually include the remaining bands and dates, we also exposed the context dependent advantages of each new component to the classification performances, and thus proved the multitemporality assets and limitations. In this way, we showed that spectral diversity is a richer source of information than time variety. In fact, the multitemporal factor has a significant effect when coupled with combinations of few spectral bands, but it turns negligible as soon as the full spectral information is exploited. In contrast, even with a full year measurement, there is always substantial interest in considering no less than three spectral channels. As a by-product of our study, we evidenced the poor adequacy of spectral and temporal recourses at differentiating certain land cover classes. A situation often pointed out by previous investigations in diverse bioclimatic study areas, and due to baffling temporal and spectral similarities between distinct classes’ phenologies.

3.6. Acknowledgements

This study was carried out in the framework of the project "LANDEO - User driven land cover characterisation for multi-scale environmental monitoring using multi-sensor earth observation data (PDCTE/MGS/49969/2003)" funded by "Programa Dinamizador das Ciências e Tecnologias para o Espaço" from "Fundação para a Ciência e Tecnologia", and from the "Announcement of Opportunity for the Utilisation of ERS and ENVISAT Data"

from European Space Agency (ESA). Research by Hugo Carrão was founded by the

"Fundação para a Ciência e Tecnologia" (SFRH/BD/18447/2004). This work was partially

performed while Hugo Carrão was visiting INRIA Rhône-Alpes granted with a six month INRIA scholarship.

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