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Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest

Author

Listed:
  • Minerva Singh

    (The Centre for Environmental Policy, Imperial College London, The Weeks Building, 16–18 Princes’ Gardens, London SW7 1NE, UK)

  • Zhuhua Huang

    (Department of Earth Science and Engineering, Imperial College London, The Weeks Building, 16–18 Princes’ Gardens, London SW7 1NE, UK)

Abstract

The fire susceptibility of the Atlantic Forest has largely increased over the past two decades due to a combination of climate change and anthropogenic factors such as land cover change and human modification. High rates of forest fragmentation have contributed to escalating fires in this imperilled global biodiversity hotspot. Understanding fire patterns is essential to developing an effective forest fire management strategy. In this research, we utilized the Random Forest (RF) machine learning approach for identifying the role of climatic and anthropogenic factors in influencing fire occurrence probability and mapping the spatial distribution of fire risk. We found that the Normalized Difference Vegetation Index value and climate variables (i.e., temperature and solar radiation) were significant drivers of fire occurrence risk. Results also confirm that forest fragmentation increases with fire density in the region.

Suggested Citation

  • Minerva Singh & Zhuhua Huang, 2022. "Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest," Sustainability, MDPI, vol. 14(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:992-:d:726247
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    Citations

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    Cited by:

    1. Ghafar Salavati & Ebrahim Saniei & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    2. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3797-3816, April.
    3. Y. Supriya & Thippa Reddy Gadekallu, 2023. "Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires," Sustainability, MDPI, vol. 15(2), pages 1-19, January.

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