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Remote Sens. 2017, 9(12), 1227;

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK
Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA
Department of Innovation, Environmental and Energy Sciences, Utrecht University, 3512 JE Utrecht, The Netherlands
College of Environmental Sciences, University of Exeter, Exeter EX4 4RU, UK
Author to whom correspondence should be addressed.
Received: 25 October 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Arctic tundra ecosystems are a major source of methane (CH4), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100–1000 m, whereas model estimates are typically made at the ~100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales. View Full-Text
Keywords: Arctic; tundra; methane flux; vegetation communities; upscaling; footprint modelling; multispectral imagery; Alaska Arctic; tundra; methane flux; vegetation communities; upscaling; footprint modelling; multispectral imagery; Alaska

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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 (CC BY 4.0).

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Davidson, S.J.; Santos, M.J.; Sloan, V.L.; Reuss-Schmidt, K.; Phoenix, G.K.; Oechel, W.C.; Zona, D. Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems. Remote Sens. 2017, 9, 1227.

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