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An early warning system to forecast the close of the spring burning window from satellite-observed greenness
Spring represents the peak of human-caused wildfire events in populated boreal forests, resulting in catastrophic loss of property and human life. Human-caused wildfire risk is anticipated to increase in northern forests as fuels become drier, on average, under warming climate scenarios and as popul...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660258/ https://www.ncbi.nlm.nih.gov/pubmed/29079804 http://dx.doi.org/10.1038/s41598-017-14730-0 |
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author | Pickell, Paul D. Coops, Nicholas C. Ferster, Colin J. Bater, Christopher W. Blouin, Karen D. Flannigan, Mike D. Zhang, Jinkai |
author_facet | Pickell, Paul D. Coops, Nicholas C. Ferster, Colin J. Bater, Christopher W. Blouin, Karen D. Flannigan, Mike D. Zhang, Jinkai |
author_sort | Pickell, Paul D. |
collection | PubMed |
description | Spring represents the peak of human-caused wildfire events in populated boreal forests, resulting in catastrophic loss of property and human life. Human-caused wildfire risk is anticipated to increase in northern forests as fuels become drier, on average, under warming climate scenarios and as population density increases within formerly remote regions. We investigated springtime human-caused wildfire risk derived from satellite-observed vegetation greenness in the early part of the growing season, a period of increased ignition and wildfire spread potential from snow melt to vegetation green-up with the aim of developing an early warning wildfire risk system. The initial system was developed for 392,856 km(2) of forested lands with satellite observations available prior to the start of the official wildfire season and predicted peak human-caused wildfire activity with 10-day accuracy for 76% of wildfire-protected lands by March 22. The early warning system could have significant utility as a cost-effective solution for wildfire managers to prioritize the deployment of wildfire protection resources in wildfire-prone landscapes across boreal-dominated ecosystems of North America, Europe, and Russia using open access Earth observations. |
format | Online Article Text |
id | pubmed-5660258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56602582017-11-01 An early warning system to forecast the close of the spring burning window from satellite-observed greenness Pickell, Paul D. Coops, Nicholas C. Ferster, Colin J. Bater, Christopher W. Blouin, Karen D. Flannigan, Mike D. Zhang, Jinkai Sci Rep Article Spring represents the peak of human-caused wildfire events in populated boreal forests, resulting in catastrophic loss of property and human life. Human-caused wildfire risk is anticipated to increase in northern forests as fuels become drier, on average, under warming climate scenarios and as population density increases within formerly remote regions. We investigated springtime human-caused wildfire risk derived from satellite-observed vegetation greenness in the early part of the growing season, a period of increased ignition and wildfire spread potential from snow melt to vegetation green-up with the aim of developing an early warning wildfire risk system. The initial system was developed for 392,856 km(2) of forested lands with satellite observations available prior to the start of the official wildfire season and predicted peak human-caused wildfire activity with 10-day accuracy for 76% of wildfire-protected lands by March 22. The early warning system could have significant utility as a cost-effective solution for wildfire managers to prioritize the deployment of wildfire protection resources in wildfire-prone landscapes across boreal-dominated ecosystems of North America, Europe, and Russia using open access Earth observations. Nature Publishing Group UK 2017-10-27 /pmc/articles/PMC5660258/ /pubmed/29079804 http://dx.doi.org/10.1038/s41598-017-14730-0 Text en © The Author(s) 2017 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/. |
spellingShingle | Article Pickell, Paul D. Coops, Nicholas C. Ferster, Colin J. Bater, Christopher W. Blouin, Karen D. Flannigan, Mike D. Zhang, Jinkai An early warning system to forecast the close of the spring burning window from satellite-observed greenness |
title | An early warning system to forecast the close of the spring burning window from satellite-observed greenness |
title_full | An early warning system to forecast the close of the spring burning window from satellite-observed greenness |
title_fullStr | An early warning system to forecast the close of the spring burning window from satellite-observed greenness |
title_full_unstemmed | An early warning system to forecast the close of the spring burning window from satellite-observed greenness |
title_short | An early warning system to forecast the close of the spring burning window from satellite-observed greenness |
title_sort | early warning system to forecast the close of the spring burning window from satellite-observed greenness |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660258/ https://www.ncbi.nlm.nih.gov/pubmed/29079804 http://dx.doi.org/10.1038/s41598-017-14730-0 |
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