A research paper recently published in the journal Fire explored wildfire dynamics in the Amazon, focusing on current fire probabilities and predicting future risks using Machine Learning (ML) under various climate change scenarios.
Study: Machine Learning Model Reveals Land Use and Climate’s Role in Amazon Wildfires: Present and Future Scenarios. Image Credit: M. Federico/Shutterstock.com
The researchers used advanced machine learning techniques to combine multiple predictors and fire data, aiming to improve fire management strategies and climate projections. This study is essential for developing effective fire control methods and understanding how global warming could impact fire patterns in the Amazon.
Fire Dynamics in the Amazon
Understanding fire dynamics in the Amazon is crucial due to the region's environmental, social, and economic importance. In recent decades, the area has experienced increased fire frequency, leading to environmental damage, habitat loss, greenhouse gas emissions, and negative impacts on local communities.
Human activities such as deforestation and agriculture, along with climatic factors like reduced rainfall and rising temperatures, drive these changes. This situation threatens biodiversity and ecosystem services, making it important to predict wildfire patterns for effective prevention and adaptation strategies.
Technological Advances in Fire Prediction
Recent advancements in artificial intelligence (AI), particularly machine learning, have transformed the analysis of environmental and climate data, providing deeper insights into wildfire dynamics.
These models can combine various factors to predict fire occurrence and spread accurately. They also enable simulations of future scenarios based on different land-use trajectories and climate conditions, offering valuable tools for discussing the future frequency and intensity of fires in the Amazon.
Research Methodologies
In this paper, the authors employed the maximum entropy (MaxEnt) model to analyze fire probabilities across the Legal Amazon by combining predictors with fire occurrences from 1985 to 2022. MaxEnt, a presence-only modeling approach, estimates the likelihood of an event based on environmental factors, making it ideal for ecological studies where the absence of data is difficult to obtain.
The primary goal was to identify key drivers of current wildfire activity and predict future fire risks under various climate scenarios. The study created detailed maps highlighting high-risk areas for preventive actions and monitoring.
The researchers selected the Legal Amazon as the study area to capture variations in vegetation and fuel conditions. This region, designated by the Brazilian government for environmental management and planning, covers about 5 million square kilometers and features diverse vegetation and climates, making it suitable for research on fire dynamics.
The study used fire occurrence data from the MapBiomas dataset, which tracks fire scars using Landsat imagery. From this dataset, 1,000 points, primarily based on land cover and land use data, were randomly selected to create layers for assessing how the landscape influences fire patterns. Additionally, historical and future bioclimatic variables were obtained from the WorldClim database.
The MaxEnt model was trained with 70% of the data and tested with 30%. Its performance was evaluated using the area under the curve (AUC) metric, which measures the model's ability to distinguish between presence and absence locations. Furthermore, a genetic algorithm optimized the model’s hyperparameters, ensuring reliable predictions for future climate conditions.
Key Outcomes
The authors found that approximately 26.5% of the Amazon has a "moderate" to "very high" fire risk, particularly in the southern and southeastern regions known as the "Arc of Deforestation." Key predictors included "Distance to Farming" (53.4%), "Distance to Non-Vegetated Areas" (11.2%), and "Temperature Seasonality" (9.3%). These results highlighted the significant influence of human activities alongside climatic factors on fire patterns.
Future projections suggest that fire-prone areas may expand, especially in the southern border regions and along the Amazon riverbanks. The models demonstrated excellent predictive performance, with AUC values consistently above 0.85.
The optimistic scenario showed a slight decrease in fire probabilities in some regions, while the pessimistic scenario indicated a potential increase in fire risks. Kappa concordance results showed moderate agreement between current conditions and future scenarios, with higher concordance observed between the two future scenarios.
The response curves indicated that proximity to farming and non-vegetated areas increases fire probability, while lower annual precipitation levels were linked to a higher fire risk.
Additionally, moderate temperature seasonality was associated with increased fire incidents, likely due to the accumulation of flammable biomass. These findings emphasized incorporating ecological and human factors into fire management strategies to address future risks effectively.
Applications
This research provided valuable insights into public policies, conservation programs, and integrated fire management strategies. By identifying high-risk areas, resources can be allocated more efficiently to fire prevention and suppression efforts.
The spatial mapping and findings can serve as essential tools for technical panels involved in integrated fire management, helping to develop strategies by considering diverse ecosystems.
Conclusion and Future Scope
In summary, this paper offered critical insights into the drivers of wildfire occurrences in the Amazon and provided practical measures for mitigating future fire risks.
Advanced modeling techniques and data analysis established a new standard for research in this field, supporting the long-term sustainability of Amazon’s vital ecosystems. Utilizing these findings, stakeholders can implement more effective measures to protect Amazon’s ecosystems and promote sustainable development.
Future work should focus on refining predictive models by incorporating data on fire behavior, land use changes, and vegetation dynamics. Additionally, examining the impact of fire on biodiversity and ecosystem services will further strengthen conservation strategies.
Journal Reference
De Santana., & et, al. (2024) Machine Learning Model Reveals Land Use and Climate’s Role in Amazon Wildfires: Present and Future Scenarios. Fire 2024, 7, 338. doi: 10.3390/fire7100338, https://www.mdpi.com/2571-6255/7/10/338
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.