Consensus land-cover mapping improves grassland classification in European mountain landscapes

Accurate land-cover information is essential for biodiversity monitoring, yet existing 10-m global and continental land-cover datasets vary in accuracy and thematic consistency, particularly for grasslands in complex mountain environments. We assessed six state-of-the-art land-cover products (Dynamic World, ESA WorldCover, Esri Land Cover, Corine Land Cover + Backbone, ELC10, S2GLC) across the Alps and Carpathians and developed three consensus maps using weighted voting, accuracy-confusion weighting, and an accuracy-weighted Random Forest ensemble. All datasets were validated against an independent set of expert-interpreted reference samples. Individual products showed large discrepancies in grassland extent, elevation distribution, and landscape structure. Global datasets (Dynamic World, Esri Land Cover) underestimated grassland extent, whereas ESA WorldCover and Corine Land Cover + reported higher proportions. Consensus approaches substantially reduced these inconsistencies. The Random Forest ensemble achieved the highest accuracy (overall 90–92%), outperforming individual datasets and improving both user’s and producer’s accuracies for grassland (> 84%). Consensus datasets also better captured expected elevation and slope gradients, producing more spatially coherent and ecologically realistic grassland patterns. By integrating multiple land-cover sources, consensus approaches effectively mitigated dataset-specific biases and increased the reliability of grassland mapping in heterogeneous mountain systems. Consequently, consensus land-cover products provide a robust and ecologically meaningful alternative to single-source datasets for environmental assessments in complex mountain regions.