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Tracking and classifying Amazon fire events in near real time
Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near–real-time a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337759/ https://www.ncbi.nlm.nih.gov/pubmed/35905176 http://dx.doi.org/10.1126/sciadv.abd2713 |
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author | Andela, Niels Morton, Douglas C. Schroeder, Wilfrid Chen, Yang Brando, Paulo M. Randerson, James T. |
author_facet | Andela, Niels Morton, Douglas C. Schroeder, Wilfrid Chen, Yang Brando, Paulo M. Randerson, James T. |
author_sort | Andela, Niels |
collection | PubMed |
description | Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near–real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies. |
format | Online Article Text |
id | pubmed-9337759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93377592022-08-09 Tracking and classifying Amazon fire events in near real time Andela, Niels Morton, Douglas C. Schroeder, Wilfrid Chen, Yang Brando, Paulo M. Randerson, James T. Sci Adv Earth, Environmental, Ecological, and Space Sciences Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near–real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies. American Association for the Advancement of Science 2022-07-29 /pmc/articles/PMC9337759/ /pubmed/35905176 http://dx.doi.org/10.1126/sciadv.abd2713 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Earth, Environmental, Ecological, and Space Sciences Andela, Niels Morton, Douglas C. Schroeder, Wilfrid Chen, Yang Brando, Paulo M. Randerson, James T. Tracking and classifying Amazon fire events in near real time |
title | Tracking and classifying Amazon fire events in near real time |
title_full | Tracking and classifying Amazon fire events in near real time |
title_fullStr | Tracking and classifying Amazon fire events in near real time |
title_full_unstemmed | Tracking and classifying Amazon fire events in near real time |
title_short | Tracking and classifying Amazon fire events in near real time |
title_sort | tracking and classifying amazon fire events in near real time |
topic | Earth, Environmental, Ecological, and Space Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337759/ https://www.ncbi.nlm.nih.gov/pubmed/35905176 http://dx.doi.org/10.1126/sciadv.abd2713 |
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