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Sökning: WFRF:(Boyd Doreen S.) > Optical and radar E...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005173naa a2200397 4500
001oai:DiVA.org:umu-218475
003SwePub
008231220s2023 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-2184752 URI
0247 a https://doi.org/10.5194/bg-20-4221-20232 DOI
040 a (SwePub)umu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Sjögersten, Sofieu School of Biosciences, University of Nottingham, College Road, Sutton Bonington, Loughborough, United Kingdom4 aut
2451 0a Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden
264 1b Copernicus Publications,c 2023
338 a electronic2 rdacarrier
500 a Errata: https://doi.org/10.5194/bg-20-4221-2023-corrigendum
520 a Permafrost thaw in Arctic regions is increasing methane (CH4) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing permafrost thaw, associated ecosystem change and increased CH4 emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider. Landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions, and it is difficult to detect the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high-resolution unoccupied aerial vehicle (UAV) data and Sentinel-1 and Sentinel-2 data to extrapolate field measurements of CH4 emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high-resolution UAV data can map spatial variation in vegetation relevant to variation in CH4 emissions and extrapolate these across the wider landscape. We further show how this can be integrated with Sentinel-1 and Sentinel-2 data. By way of a soft classification and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH4 emissions closely matched the results generated using the UAV data. Interferometric synthetic-aperture radar (InSAR) assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH4 emissions. The transition of a 50 ha area currently experiencing subsidence to fen vegetation is estimated to increase emissions from 116 kg CH4 per season to emissions as high as 6500 to 13 000 kg CH4 per season. The key outcome from this study is that a combination of high- and low-resolution EO data of different types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types which are vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 emissions from the Arctic over space and time with confidence.
650 7a NATURVETENSKAPx Geovetenskap och miljövetenskapx Naturgeografi0 (SwePub)105072 hsv//swe
650 7a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Physical Geography0 (SwePub)105072 hsv//eng
7001 a Ledger, Marthau School of Biosciences, University of Nottingham, College Road, Sutton Bonington, Loughborough, United Kingdom4 aut
7001 a Siewert, Matthias B.,d 1985-u Umeå universitet,Institutionen för ekologi, miljö och geovetenskap4 aut0 (Swepub:umu)masi0110
7001 a De La Barreda-Bautista, Betsabéu School of Biosciences, University of Nottingham, College Road, Sutton Bonington, Loughborough, United Kingdom; School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom4 aut
7001 a Sowter, Andrewu Terra Motion Ltd, Ingenuity Centre, Triumph Rd, Nottingham, United Kingdom4 aut
7001 a Gee, Davidu Terra Motion Ltd, Ingenuity Centre, Triumph Rd, Nottingham, United Kingdom4 aut
7001 a Foody, Gilesu School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom4 aut
7001 a Boyd, Doreen S.u School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom4 aut
7102 a School of Biosciences, University of Nottingham, College Road, Sutton Bonington, Loughborough, United Kingdomb Institutionen för ekologi, miljö och geovetenskap4 org
7730 t Biogeosciencesd : Copernicus Publicationsg 20:20, s. 4221-4239q 20:20<4221-4239x 1726-4170x 1726-4189
8564 u https://doi.org/10.5194/bg-20-4221-2023y Fulltext
8564 u https://umu.diva-portal.org/smash/get/diva2:1821432/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-218475
8564 8u https://doi.org/10.5194/bg-20-4221-2023

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