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Machine Learning Model Reveals Land Use and Climate’s Role in Amazon Wildfires: Present and Future Scenarios
Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. This study aimed to analyze recent fire probabilities and project future “fire niches” under global warming scenarios across the Legal...
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Published in: | Fire (Basel, Switzerland) Switzerland), 2024-10, Vol.7 (10), p.338 |
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description | Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. This study aimed to analyze recent fire probabilities and project future “fire niches” under global warming scenarios across the Legal Amazon, a scale chosen for its relevance in social and economic planning. Utilizing the maximum entropy method, this study combined a complex set of predictors with fire occurrences detected during 1985–2022. It allowed for the estimation of current fire patterns and projecting changes for the near future (2020–2040) under two contrasting socioeconomic pathways. The results showed strong model performance, with AUC values consistently above 0.85. Key predictors included “Distance to Farming” (53.4%), “Distance to Non-Vegetated Areas” (11.2%), and “Temperature Seasonality” (9.3%), revealing significant influences from human activities alongside climatic predictors. The baseline model indicated that 26.5% of the Amazon has “moderate” to “very high” fire propensity, especially in the southern and southeastern regions, notably the “Arc of Deforestation”. Future projections suggest that fire-prone areas may expand, particularly in the southern border regions and near the Amazon riverbanks. The findings underscore the importance of incorporating both ecological and human factors into fire management strategies to effectively address future risks. |
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This study aimed to analyze recent fire probabilities and project future “fire niches” under global warming scenarios across the Legal Amazon, a scale chosen for its relevance in social and economic planning. Utilizing the maximum entropy method, this study combined a complex set of predictors with fire occurrences detected during 1985–2022. It allowed for the estimation of current fire patterns and projecting changes for the near future (2020–2040) under two contrasting socioeconomic pathways. The results showed strong model performance, with AUC values consistently above 0.85. Key predictors included “Distance to Farming” (53.4%), “Distance to Non-Vegetated Areas” (11.2%), and “Temperature Seasonality” (9.3%), revealing significant influences from human activities alongside climatic predictors. The baseline model indicated that 26.5% of the Amazon has “moderate” to “very high” fire propensity, especially in the southern and southeastern regions, notably the “Arc of Deforestation”. Future projections suggest that fire-prone areas may expand, particularly in the southern border regions and near the Amazon riverbanks. The findings underscore the importance of incorporating both ecological and human factors into fire management strategies to effectively address future risks.</description><identifier>ISSN: 2571-6255</identifier><identifier>EISSN: 2571-6255</identifier><identifier>DOI: 10.3390/fire7100338</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agriculture ; Amazon fire dynamics ; Amazon River region ; Climate change ; Datasets ; Deforestation ; disturbance ; Economic planning ; Ecosystems ; Environmental aspects ; fire susceptibility analysis ; Forest & brush fires ; Global warming ; Grasslands ; Human factors ; Influence ; Land use ; Land use management ; Machine learning ; Maxent ; Maximum entropy ; Maximum entropy method ; Precipitation ; Probability ; Project management ; pyrogeography ; River banks ; Seasonal variations ; Social organization ; Vegetation ; Wildfires</subject><ispartof>Fire (Basel, Switzerland), 2024-10, Vol.7 (10), p.338</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c291t-1dfa369c14ac72517559f63b5f9e7c568720c9a4506cf799f7088b834773cd73</cites><orcidid>0000-0002-5218-7140 ; 0000-0002-4204-0882 ; 0000-0002-2175-2792 ; 0000-0002-1368-6721</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3120643375/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3120643375?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,25736,27907,27908,36995,44573,74877</link.rule.ids></links><search><creatorcontrib>de Santana, Mariana Martins Medeiros</creatorcontrib><creatorcontrib>de Vasconcelos, Rodrigo Nogueira</creatorcontrib><creatorcontrib>Mariano Neto, Eduardo</creatorcontrib><creatorcontrib>da Franca Rocha, Washington de Jesus Sant’Anna</creatorcontrib><title>Machine Learning Model Reveals Land Use and Climate’s Role in Amazon Wildfires: Present and Future Scenarios</title><title>Fire (Basel, Switzerland)</title><description>Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. 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subjects | Agriculture Amazon fire dynamics Amazon River region Climate change Datasets Deforestation disturbance Economic planning Ecosystems Environmental aspects fire susceptibility analysis Forest & brush fires Global warming Grasslands Human factors Influence Land use Land use management Machine learning Maxent Maximum entropy Maximum entropy method Precipitation Probability Project management pyrogeography River banks Seasonal variations Social organization Vegetation Wildfires |
title | Machine Learning Model Reveals Land Use and Climate’s Role in Amazon Wildfires: Present and Future Scenarios |
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