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Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska
Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5892374/ https://www.ncbi.nlm.nih.gov/pubmed/29633984 http://dx.doi.org/10.1038/sdata.2018.58 |
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author | Lara, Mark J. Nitze, Ingmar Grosse, Guido McGuire, A. David |
author_facet | Lara, Mark J. Nitze, Ingmar Grosse, Guido McGuire, A. David |
author_sort | Lara, Mark J. |
collection | PubMed |
description | Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling. |
format | Online Article Text |
id | pubmed-5892374 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-58923742018-04-13 Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska Lara, Mark J. Nitze, Ingmar Grosse, Guido McGuire, A. David Sci Data Data Descriptor Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling. Nature Publishing Group 2018-04-10 /pmc/articles/PMC5892374/ /pubmed/29633984 http://dx.doi.org/10.1038/sdata.2018.58 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Lara, Mark J. Nitze, Ingmar Grosse, Guido McGuire, A. David Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska |
title | Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska |
title_full | Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska |
title_fullStr | Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska |
title_full_unstemmed | Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska |
title_short | Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska |
title_sort | tundra landform and vegetation productivity trend maps for the arctic coastal plain of northern alaska |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5892374/ https://www.ncbi.nlm.nih.gov/pubmed/29633984 http://dx.doi.org/10.1038/sdata.2018.58 |
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