Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A New Land Cover Map of Two Watersheds under Long-Term Environmental Monitoring in the Swedish Arctic Using Sentinel-2 Data

Yves Auda
* ORCID logo ,
Erik J. Lundin
,
Jonas Gustafsson
,
Oleg S. Pokrovsky
,
Simon Cazaurang
,
Laurent Orgogozo
Version 1 : Received: 25 August 2023 / Approved: 25 August 2023 / Online: 28 August 2023 (04:09:58 CEST)

A peer-reviewed article of this Preprint also exists.

Auda, Y.; Lundin, E.J.; Gustafsson, J.; Pokrovsky, O.S.; Cazaurang, S.; Orgogozo, L. A New Land Cover Map of Two Watersheds under Long-Term Environmental Monitoring in the Swedish Arctic Using Sentinel-2 Data. Water 2023, 15, 3311. Auda, Y.; Lundin, E.J.; Gustafsson, J.; Pokrovsky, O.S.; Cazaurang, S.; Orgogozo, L. A New Land Cover Map of Two Watersheds under Long-Term Environmental Monitoring in the Swedish Arctic Using Sentinel-2 Data. Water 2023, 15, 3311.

Abstract

A land cover map of two arctic catchments, nearby the Abisko Scientific Research Station, was obtained from a classification of a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. Two classification algorithms, support vector machine and random forest, were tested and gave very similar results. The percentage of correctly classified pixels was over 88% in both cases. The developed workflow relies solely on open source software and acquired ground observations. Space organization was directed by the altitude as demonstrated by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 shows some trends of vegetation cover evolution in response to climate change in the considered area. This land cover map is key input data for permafrost modeling, and hence for the quantification of climate change impacts in the studied area.

Keywords

land cover; sentinel-2 images; support vector machine; random forests; boreal forest; alpine tundra

Subject

Environmental and Earth Sciences, Environmental Science

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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