Active wildfire detection via satellite imagery and machine learning: an empirical investigation of Australian wildfires

Harikesh Singh (), Li-Minn Ang and Sanjeev Kumar Srivastava
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Harikesh Singh: University of the Sunshine Coast
Li-Minn Ang: University of the Sunshine Coast
Sanjeev Kumar Srivastava: University of the Sunshine Coast

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 8, No 32, 9777-9800

Abstract: Abstract Forests worldwide play a critical role in biodiversity conservation and climate regulation, yet they face unprecedented challenges, particularly from wildfires. Early wildfire detection is essential for preventing rapid spread, protecting lives, ecosystems, and economies, and mitigating climate change impacts. Traditional wildfire detection methods relying on human surveillance are limited in scope and efficiency. However, advancements in remote sensing technologies offer new opportunities for more efficient and comprehensive detection. This study highlights the integration of satellite sensors, capable of detecting thermal anomalies, smoke plumes, and vegetation health changes, with machine learning, particularly Support Vector Machines (SVMs), to enhance detection efficiency and accuracy. These algorithms analyse satellite data to identify fire patterns and provide near real-time alerts. SVMs’ adaptability over time improves performance, making them suitable for evolving fire regimes influenced by climate change. Focusing on the Wolgan Valley in Eastern Australia, the study utilised Landsat-8 imagery and SVMs to detect active fires and classify burned areas. Results demonstrated that combining various spectral bands, such as the Shortwave Infrared (SWIR) and Near-Infrared (NIR), enhances the identification of active fires and smoke. The introduction of the Normalized Difference Fire Index (NDFI) further refines detection capabilities by leveraging distinct spectral characteristics from Landsat 8 imagery. Despite the promise of these technologies, challenges such as data availability and model interpretability remain. Future research should focus on integrating diverse data sources, advancing machine learning techniques, developing real-time monitoring systems, addressing model interpretability, integrating unmanned aerial vehicles, and considering climate change impacts. This study underscores the potential of machine learning algorithms and innovative indices like NDFI to improve wildfire detection and management strategies, ultimately enhancing our ability to protect lives and ecosystems in fire-prone regions.

Keywords: Active fire detection; Landsat-8 imagery; SVM; Machine learning; Normalized difference fire index (NDFI) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07163-w

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