Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Remote Sensing Based Land Cover Map of Watersheds in the Swedish Arctics: Study of Spatial and Temporal Variabilities of Land Cover

Yves AUDA Auda
* ORCID logo ,
Erik J. Lundin
,
David Gustafsson
,
Oleg S. Pokrovsky
,
Simon Cazaurang
,
Laurent Orgogozo
Version 1 : Received: 20 April 2023 / Approved: 20 April 2023 / Online: 20 April 2023 (10:51:55 CEST)

How to cite: Auda, Y.A.; Lundin, E.J.; Gustafsson, D.; Pokrovsky, O.S.; Cazaurang, S.; Orgogozo, L. Remote Sensing Based Land Cover Map of Watersheds in the Swedish Arctics: Study of Spatial and Temporal Variabilities of Land Cover. Preprints 2023, 2023040653. https://doi.org/10.20944/preprints202304.0653.v1 Auda, Y.A.; Lundin, E.J.; Gustafsson, D.; Pokrovsky, O.S.; Cazaurang, S.; Orgogozo, L. Remote Sensing Based Land Cover Map of Watersheds in the Swedish Arctics: Study of Spatial and Temporal Variabilities of Land Cover. Preprints 2023, 2023040653. https://doi.org/10.20944/preprints202304.0653.v1

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. The Random Forest algorithm correctly identified 83% of polygon pixels reserved for testing. The developed workflow relied solely on open source software and acquired ground observations. Space organization was directed by the altitude as shown 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 demonstrates some trends of vegetation cover evolution in response to climate change in the considered area. The potential applications in terms of permafrost modeling (hiperborea.omp.eu) are finally discussed.

Keywords

land cover; sentinel-2 images; random forest; boreal forest; alpine tundra

Subject

Environmental and Earth Sciences, Remote Sensing

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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