Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications

This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account. The legends of the two maps are first reconciled by creating a legend lookup table that shows how the legends map onto one another. Where there is overlap, the specific definitions for each legend class are used to calculate the degree of overlap between legend classes. In this way, one-to-many mappings are accounted for unlike in most methods where the legend definitions are often forced into place. Another advantage over previous map comparison methods is that application-specific requirements are captured using expert input, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application. This user-defined matrix in conjunction with the degree of overlap between legend classes is applied on a pixel-by-pixel basis to create maps of spatial disagreement and uncertainty. The user can then highlight the areas of highest thematic uncertainty and disagreement between the different land cover maps allowing for areas that require further detailed examination to be readily identified. It would also be possible for several users to input their knowledge into the process, leading to a potentially more robust comparison of land cover products. The methodology of map comparison is illustrated using different land cover products including Global Land Cover 2000 (GLC-2000) and the MODIS land cover data set. Two diverse applications are provided including the estimation of global forest cover and monitoring of agricultural land. In the case of global forest cover, an example was provided for Columbia, which showed that the MODIS land cover map overestimates forest cover in comparison with the GLC-2000. The agricultural example, on the other hand, served to illustrate that for Sudan, MODIS tends to underestimate crop areas while GLC-2000 overestimates them.