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Delineating northern peatlands using Sentinel-1 time series and terrain indices from local and regional digital elevation models
Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences. Linköping University, Centre for Climate Science and Policy Research, CSPR.ORCID iD: 0000-0002-3926-3671
Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0003-1107-3929
Stockholm Univ, Sweden.
LSCE, France.
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2019 (English)In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 231, article id UNSP 111252Article in journal (Refereed) Published
Abstract [en]

The spatial extent of northern peatlands remains highly uncertain in spite of rapidly developing satellite observation datasets. This is limiting progress in the understanding of fundamental biogeochemical processes, such as the global carbon (C) cycle and climate feedback effects on C fluxes. In this study, we evaluated the capabilities of two new satellite datasets that enable regional scale mapping of peatland extent at high spatial resolution, including Sentinel-1 synthetic aperture radar (SAR) and the Arctic digital elevation model (ArcticDEM). Terrain indices and temporal features derived from these datasets provided input to Random Forest models for delineating four main land cover classes (forest, open upland, water and peatland) in an area in northern Sweden consisting of both lowland and mountainous terrain. The contribution of ArcticDEM to the classification accuracy was assessed by comparing the results with those derived when a high quality LiDAR based DEM (LiDEM) was used as alternative model input. This study shows that multi-seasonal SAR alone can produce reasonable classification results in terms of overall accuracy (OA; 81.6%), but also that it has limitations. The inclusion of terrain indices improved classification performance substantially. OA increased to 87.5% and 90.9% when terrain indices derived from ArcticDEM and LiDEM were included, respectively. The largest increase in accuracy was achieved for the peatland class, which suggests that terrain indices do have the ability to capture the features in the geographic context that aid the discrimination of peatland from other land cover classes. The relatively small difference in classification accuracy between LiDEM and ArcticDEM is encouraging since the latter provides circumpolar coverage. Thus, the combination of Sentinel-1 time series and terrain indices derived from ArcticDEM presents opportunities for substantially improving regional estimates of peatland extent at high latitudes.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2019. Vol. 231, article id UNSP 111252
Keywords [en]
Sentinel-1; Satellite image time series; Digital elevation model; Arctic DEM; Terrain index; Land cover mapping; Wetland; Peatland
National Category
Physical Geography
Identifiers
URN: urn:nbn:se:liu:diva-160586DOI: 10.1016/j.rse.2019.111252ISI: 000484643900026OAI: oai:DiVA.org:liu-160586DiVA, id: diva2:1362726
Note

Funding Agencies|Swedish Research Council VRSwedish Research Council [VR 2012-48]; IZOMET project [VR 2014-6584]; Swedish Research Council for Sustainable Development (Formas)Swedish Research Council Formas [2018-00570, 2018-01794]

Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21

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Karlson, MartinGålfalk, MagnusBastviken, David
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Tema Environmental ChangeFaculty of Arts and SciencesCentre for Climate Science and Policy Research, CSPR
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