Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA

The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the underst...

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Published in:Remote Sensing
Main Authors: Davidson, S.J., Santos, M.J., Sloan, V.L., Watts, J.D., Phoenix, G.K., Oechel, W.C., Zona, D.
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2016
Subjects:
Online Access:https://eprints.whiterose.ac.uk/110085/
https://eprints.whiterose.ac.uk/110085/1/remotesensing-08-00978.pdf
https://doi.org/10.3390/rs8120978
id ftleedsuniv:oai:eprints.whiterose.ac.uk:110085
record_format openpolar
spelling ftleedsuniv:oai:eprints.whiterose.ac.uk:110085 2023-05-15T14:24:52+02:00 Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA Davidson, S.J. Santos, M.J. Sloan, V.L. Watts, J.D. Phoenix, G.K. Oechel, W.C. Zona, D. 2016-11-26 text https://eprints.whiterose.ac.uk/110085/ https://eprints.whiterose.ac.uk/110085/1/remotesensing-08-00978.pdf https://doi.org/10.3390/rs8120978 en eng MDPI https://eprints.whiterose.ac.uk/110085/1/remotesensing-08-00978.pdf Davidson, S.J., Santos, M.J., Sloan, V.L. et al. (4 more authors) (2016) Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA. Remote Sensing, 8 (12). ISSN 2072-4292 cc_by_4 CC-BY Article PeerReviewed 2016 ftleedsuniv https://doi.org/10.3390/rs8120978 2023-01-30T21:50:12Z The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450-510 nm, 630-690 nm and 705-745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450-510 nm and 630-690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance ... Article in Journal/Newspaper Arctic Arctic Climate change Tundra Alaska White Rose Research Online (Universities of Leeds, Sheffield & York) Arctic Remote Sensing 8 12 978
institution Open Polar
collection White Rose Research Online (Universities of Leeds, Sheffield & York)
op_collection_id ftleedsuniv
language English
description The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450-510 nm, 630-690 nm and 705-745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450-510 nm and 630-690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance ...
format Article in Journal/Newspaper
author Davidson, S.J.
Santos, M.J.
Sloan, V.L.
Watts, J.D.
Phoenix, G.K.
Oechel, W.C.
Zona, D.
spellingShingle Davidson, S.J.
Santos, M.J.
Sloan, V.L.
Watts, J.D.
Phoenix, G.K.
Oechel, W.C.
Zona, D.
Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA
author_facet Davidson, S.J.
Santos, M.J.
Sloan, V.L.
Watts, J.D.
Phoenix, G.K.
Oechel, W.C.
Zona, D.
author_sort Davidson, S.J.
title Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA
title_short Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA
title_full Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA
title_fullStr Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA
title_full_unstemmed Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA
title_sort mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in north alaska, usa
publisher MDPI
publishDate 2016
url https://eprints.whiterose.ac.uk/110085/
https://eprints.whiterose.ac.uk/110085/1/remotesensing-08-00978.pdf
https://doi.org/10.3390/rs8120978
geographic Arctic
geographic_facet Arctic
genre Arctic
Arctic
Climate change
Tundra
Alaska
genre_facet Arctic
Arctic
Climate change
Tundra
Alaska
op_relation https://eprints.whiterose.ac.uk/110085/1/remotesensing-08-00978.pdf
Davidson, S.J., Santos, M.J., Sloan, V.L. et al. (4 more authors) (2016) Mapping arctic tundra vegetation communities using field spectroscopy and multispectral satellite data in North Alaska, USA. Remote Sensing, 8 (12). ISSN 2072-4292
op_rights cc_by_4
op_rightsnorm CC-BY
op_doi https://doi.org/10.3390/rs8120978
container_title Remote Sensing
container_volume 8
container_issue 12
container_start_page 978
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