Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe

Abstract

One of the major drawbacks of land cover products is the lack of interoperability among them. Since their development was driven by different national or international initiatives, they were developed for different purposes and hold diverse technical characteristics. Thus, comparison among products and quality monitoring is necessary in assessing their usefulness. This paper provides a methodology to compare global land cover maps that allows for differences in legend definitions among products.Two different approaches were considered for map comparison, a Boolean approach and a new methodology based on fuzzy set theory in which the Land Cover Classification System (LCCS) acted as a general bridging system. The fuzzy approach let us reconcile legends in terms of a set of nine selected attributes. The methodology is illustrated over Europe using four different land cover products: CORINE, GLC2000, MODIS land cover (MODISLC) and GlobCover.Overall accuracies between datasets based on a generalized nomenclature ranged from 35% (GlobCover–MODISLC) to 57% (CORINE–GLC2000). A further assessment based on a flexible Boolean comparison allowed us to minimize part of the uncertainty introduced by ambiguity of legends, resulting in an overall increase of agreement of around 10% in absolute terms. Reduction of positional errors produced an agreement increase of around 5%. The fuzzy approach allowed us to examine certain criteria used in classification systems to better understand the causes of discrepancies among datasets and express them in terms of relevant land attributes, irrespective of class names. This method served to highlight the consistency of the land cover maps under consideration, especially for datasets that showed higher divergences. In particular, it allows the identification of areas that are very close in land attributes, even when a Boolean comparison shows them as different.