Abstract
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.
Data availability
Consensus datasets are openly available in the Zenodo repository at (https://doi.org/10.5281/zenodo.13823832). The code used for data processing, analysis, and all other datasets stored in Google Earth Engine is publicly accessible in the GitHub repository at https:/github.com/simonopravil/MAC-Land.
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Acknowledgements
This study was made possible using multiple existing land cover datasets, including Google© Dynamic World, ESA WorldCover, ESRI© Land Cover, CLMS CLC+, ELC10, and S2GLC. We acknowledge Google, the European Space Agency, ESRI, the Copernicus Land Monitoring Service, and other data providers for making these datasets publicly available. All maps used in this study are accessible under their respective open data licenses.
Funding
This research was funded by the project ‘G4B: Grasslands for biodiversity: supporting the protection of the biodiversity-rich grasslands and related management practices in the Alps and Carpathians’ funded by Biodiversa+, the European Biodiversity Partnership under the 2021–2022 BiodivProtect joint call for research proposals, co-funded by the European Commission (GA N°101052342) and with the funding organisations SNSF, DFG, NCN, PROV BZ, SAS and UEFISCDI; by the project of Slovak Research and Development Agency-21-0226: “Species-rich Carpathian grasslands: mapping, history, drivers of change and conservation” pursued at the Institute of Geography of the Slovak Academy of Sciences; by the Slovak Scientific Grant Agency VEGA under Grant 2/0043/23 “Detection of landscape diversity and its changes in Slovakia based on remote sensing data in the context of the European Green Deal.”
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Conceptualization, S.O., M.B., T.K., R.P; methodology, S.O., M.B., T.B., R.P.; software, S.O.; validation, S.O., T.G., H.A.; formal analysis, S.O. and R.P.; investigation, S.O., T.G., H.A., R.P., resources, S.O., T.G.; data curation, S.O.; writing—original draft preparation, S.O. and R.P.; writing—review and editing, M.B., T.G., H.A., T.K.; visualization, S.O.; supervision, R.P. and T.K.; project administration, R.P, and T.K.; funding acquisition, R.P. and T.K. All authors have read and agreed to the published version of the manuscript.
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The expert annotations used in this study were produced by the co‑authors Šimon Opravil, Tomáš Goga, and Hamid Afzali. Each co‑author provided informed consent for the use of their annotations in this manuscript and in any related supplementary materials. No personal or sensitive information was collected; the annotations concern land‑cover categories only.
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Opravil, Š., Baumann, M., Goga, T. et al. Consensus land-cover mapping improves grassland classification in European mountain landscapes. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39197-w
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DOI: https://doi.org/10.1038/s41598-026-39197-w
