Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems
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 measureme...
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ftdoajarticles:oai:doaj.org/article:e684676235e749b394b183dd780cc154 2023-05-15T14:56:39+02:00 Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems Scott J. Davidson Maria J. Santos Victoria L. Sloan Kassandra Reuss-Schmidt Gareth K. Phoenix Walter C. Oechel Donatella Zona 2017-11-01T00:00:00Z https://doi.org/10.3390/rs9121227 https://doaj.org/article/e684676235e749b394b183dd780cc154 EN eng MDPI AG https://www.mdpi.com/2072-4292/9/12/1227 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs9121227 https://doaj.org/article/e684676235e749b394b183dd780cc154 Remote Sensing, Vol 9, Iss 12, p 1227 (2017) Arctic tundra methane flux vegetation communities upscaling footprint modelling multispectral imagery Alaska Science Q article 2017 ftdoajarticles https://doi.org/10.3390/rs9121227 2022-12-31T16:18:21Z 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. Article in Journal/Newspaper Arctic Tundra Alaska Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 9 12 1227 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic tundra methane flux vegetation communities upscaling footprint modelling multispectral imagery Alaska Science Q |
spellingShingle |
Arctic tundra methane flux vegetation communities upscaling footprint modelling multispectral imagery Alaska Science Q Scott J. Davidson Maria J. Santos Victoria L. Sloan Kassandra Reuss-Schmidt Gareth K. Phoenix Walter C. Oechel Donatella Zona Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems |
topic_facet |
Arctic tundra methane flux vegetation communities upscaling footprint modelling multispectral imagery Alaska Science Q |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Scott J. Davidson Maria J. Santos Victoria L. Sloan Kassandra Reuss-Schmidt Gareth K. Phoenix Walter C. Oechel Donatella Zona |
author_facet |
Scott J. Davidson Maria J. Santos Victoria L. Sloan Kassandra Reuss-Schmidt Gareth K. Phoenix Walter C. Oechel Donatella Zona |
author_sort |
Scott J. Davidson |
title |
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems |
title_short |
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems |
title_full |
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems |
title_fullStr |
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems |
title_full_unstemmed |
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems |
title_sort |
upscaling ch4 fluxes using high-resolution imagery in arctic tundra ecosystems |
publisher |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/rs9121227 https://doaj.org/article/e684676235e749b394b183dd780cc154 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra Alaska |
genre_facet |
Arctic Tundra Alaska |
op_source |
Remote Sensing, Vol 9, Iss 12, p 1227 (2017) |
op_relation |
https://www.mdpi.com/2072-4292/9/12/1227 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs9121227 https://doaj.org/article/e684676235e749b394b183dd780cc154 |
op_doi |
https://doi.org/10.3390/rs9121227 |
container_title |
Remote Sensing |
container_volume |
9 |
container_issue |
12 |
container_start_page |
1227 |
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1766328732868411392 |