Files and links (1)
Published VersionCC BY V4.0, Open Access
Journal article
Active wildfire detection via satellite imagery and machine learning: an empirical investigation of Australian wildfires
Natural Hazards , Vol.121, pp.9777-9800
2025
Appears in UniSC Supported Open Access Outputs
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.
Details
- Title
- Active wildfire detection via satellite imagery and machine learning: an empirical investigation of Australian wildfires
- Authors
- Harikesh Singh (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringLi-Minn Ang - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringSanjeev Kumar Srivastava - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Natural Hazards , Vol.121, pp.9777-9800
- Publisher
- Springer Cham
- Date published
- 2025
- DOI
- 10.1007/s11069-025-07163-w
- ISSN
- 1573-0840; 0921-030X
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Data Availability
- The data supporting the findings of this study are available upon reasonable request.
- Organisation Unit
- School of Science, Technology and Engineering; Engage Research Lab; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 991110914802621
- Output Type
- Journal article
Metrics
6 File views/ downloads
30 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Geosciences, Multidisciplinary
- Meteorology & Atmospheric Sciences
- Water Resources
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites