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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
Main Authors: de Santana, Mariana Martins Medeiros, de Vasconcelos, Rodrigo Nogueira, Mariano Neto, Eduardo, da Franca Rocha, Washington de Jesus Sant’Anna
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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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ispartof Fire (Basel, Switzerland), 2024-10, Vol.7 (10), p.338
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recordid cdi_doaj_primary_oai_doaj_org_article_a3894b91b24e4cc1a39860f75b6a7793
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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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