A map of high-altitude wetlands in the world’s major mountain regions

Abstract

We present a first global high-resolution map (30 m x 30 m) of high-altitudinal wetlands in the world’s major mountain regions, i.e. the Andes, Rocky Mountains, Alps and High Mountain Asia. To map these wetlands, we employed a supervised classification approach using a random forest machine learning model and a selected set of predictors including vegetation, topographic, and surface moisture features. The predictors were derived from freely available radar and optical satellite imagery (Sentinel-1 and Sentinel-2), SRTM elevation data, and the global ecoregion map RESOLVE. We identify a total area of >30,500 km2 of high-mountain wetlands. With this map we aim to enhance the understanding of wetland distribution in remote and often inaccessible mountain regions and enable a more reliable understanding of their role in the ecosystem functioning and water cycles of high mountain areas.

Metadata

Item Type:	Article
Authors/Creators:	Becker, R. https://orcid.org/0000-0002-9040-1355 Kropáček, J. Ross, A.C. Gribbin, T. Drenkhan, F. https://orcid.org/0000-0002-9443-9596 Sotelo, L.H. Mendoza, M.M. Davies, B. https://orcid.org/0000-0002-8636-1813 Ely, J. https://orcid.org/0000-0003-4007-1500 Buytaert, W. https://orcid.org/0000-0001-6994-4454
Copyright, Publisher and Additional Information:	© The Author(s) 2026. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Geomatic Engineering; Biological Sciences; Engineering; Machine Learning and Artificial Intelligence; Life on Land
Dates:	Accepted: 3 March 2026 Published (online): 11 March 2026 Published: 11 March 2026
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Geography and Planning
Funding Information:	Funder Grant number NATURAL ENVIRONMENT RESEARCH COUNCIL NE/X004031/1
Date Deposited:	18 Mar 2026 16:07
Last Modified:	28 Apr 2026 13:48
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1038/s41597-026-07020-w
Related URLs:	PubMed URL Dataset
Sustainable Development Goals:
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:239241