An approach for land cover mapping with multi-temporal MERIS imagery

In this paper we present a study exploring the medium spatial resolution images of recently launched ENVISAT MERIS for land cover characterization in Portugal. The goal is to take advantage of enhanced spectral and temporal resolutions of images acquired by this sensor to discriminate properly between 19 land cover classes. We test both unitemporal and multitemporal classifications, trying to augment land cover classes' discrimination through the use of temporal variations of classes' characteristic spectral reflectances along one year period. We discuss about classifier's accuracy and map accuracy, showing the existence of noticeable differences between achieved values in both evaluations. The assessment of the final map resulted in an overall accuracy of 70%, considering a final set of 9 land cover classes; these classes were selected along the study as being the more adequate to map the Portuguese landscape at MERIS spatial resolution. Best classification results were attained by removing spectral bands 1, 2, 3, and 13 from input dataset for classification, and using the Maximum Likelihood as a supervised classifier.