ISBN: 1-84626-170-8, ISBN13: 978-1-84626-170-1 A Fuzzy Synthetic Evaluation Approach for Land Cover Cartography Accuracy Assessment Pedro Sarmento 1, 2 , Hugo Carrão 1, 2 and Mario Caetano 1, 2 + 1 Portuguese Geographic Institute (IGP), Remote Sensing Unit (RSU), Rua Artilharia Um, 107, 1099-052 Lisboa, Portugal 2 CEGI, Instituto Superior de Estatística e Gestão de Informação, ISEGI, Universidade Nova de Lisboa, 1070-312 Lisboa, Lisboa, Portugal Abstract. The accuracy assessment of land cover maps is traditionally based on reference sample observations randomly selected over the study area. It is assumed that reference sample observations, representing the “real” land cover at Earth’s surface, are free of errors. However, some of these may be erroneous. These errors are sometimes due to an uncertainty in the identification of the most adequate reference land cover classes by visual interpretation of aerial images and/or field work. This uncertainty is caused by landscape fragmentation and/or presence of more than one land cover class in sampled areas. The introduction of uncertainty in thematic accuracy measures remains an issue, but ignoring this uncertainty can significantly influence the land cover maps accuracy reported to end users. In this paper we propose a very simple and understandable method for thematic accuracy assessment of land cover maps that uses reference uncertainty as input feature. This fuzzy synthetic evaluation (FSE) approach is based on the combination of linguistic fuzzy operators. Specifically, we evaluate errors magnitudes per land cover class and weight their importance in map accuracy assessment process. In the sequence, we compare our approach with most traditional accuracy assessment measures and evaluate methodological gains and disadvantages. To achieve this goal we present a case study based on a land cover map of Continental Portugal derived from automatic classification of MERIS images. We demonstrate that trough the use of the fuzzy synthetic evaluation approach we provide accuracy descriptors that are more comprehensible for map users. In fact, this approach allows end users to easier decide if a land cover map satisfies their needs and to become more conscientious about map error extension and its particular impacts. Keywords: fuzzy synthetic evaluation, reference databases uncertainty, land cover maps, accuracy assessment. 1. Introduction Land cover maps are nowadays of major importance in many studies and decision-making processes. However, the production of up to date maps that cover extensive areas of the Earth’s surface, with reduced temporal gaps and with reduced costs, is only possible trough satellite images. Moreover, [1] refers that data obtained from satellites are nowadays an increasing source of information to produce land cover maps. Land cover maps production is an important task, but the thematic accuracy assessment of these maps is many times ignored by the producers. If land cover maps are used for decision-making, the quality of this data would certainly affect the type of the decisions. Traditionally, the accuracy assessment of land cover maps was made trough the comparison of produced maps with a reference sample database [2] that represents the “real” land cover at Earth’s surface. Generally, this comparison is represented in a confusion matrix, where the reference classes are introduced in the columns and the map classes in the rows of the matrix. This approach assumes that only one reference land cover class is considered appropriate for each geographic area of the map [3]. However, and many times, the most prominent reference land cover class at each sample observation is not clear, and often there are doubts about which class to assign. These doubts are due to certain characteristics of land cover (e.g. existence of more than one land cover class in the sampled area, + Corresponding author. Tel.: +351 21 381 9600; fax: +351 21 381 9699. E-mail address: mario.caetano@igeo.pt Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences Shanghai, P. R. China, June 25-27, 2008, pp. 348-355