Difficult to map regions in 30 m global land cover mapping determined with a common validation dataset

Abstract

© 2018 Informa UK Limited, trading as Taylor & Francis Group. The free availability of decametre global satellite images and high-performance supercomputing provides opportunities for the development of many global products, including land cover, forest change, water, and cropland. However, some regions are particularly hard to map. Identification of these regions aids the understanding of map accuracy issues. In this study, we analysed seven maps produced with different algorithms/approaches but using the same classification system and training samples. A common validation dataset was used to identify regions incorrectly classified by all maps. These were defined as difficult to map regions (DMRs). They covered around 16% of the world’s ice-free terrestrial areas. Our analysis indicated that (1) grassland, shrubland, forest, and cropland were the most common land-cover types that could not be correctly classified, but impervious surfaces had the greatest proportion of misclassification; (2) incorrect classification mainly occurred in tropical/subtropical grassland/savanna/shrub-land and desert/xeric shrubland; (3) the spatial distribution of DMRs was almost consistent with slope/elevation changes along latitude/longitude; and (4) the hotspot areas of land-cover mapping studies did not align with the DMRs. Our results suggest that there is a need for further work on DMRs to improve global land-cover mapping accuracy.