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Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
Why the world needs a wildfire risk prediction system based on plant functional traits and moisture—before fires ignite
npj Natural Hazards, Vol.2, pp.1-4
2025
Abstract
This perspective proposes a new wildfire risk prediction system by integrating plant functional traits with microwave-derived vegetation optical depth to detect critical moisture thresholds before ignition. By shifting from reactive, thermal-based detection to proactive, ecology-driven forecasting, this approach could enhance preparedness, guide targeted mitigation, and improve resource allocation globally and would help reduce wildfire impacts on biodiversity, infrastructure, and communities in an era of intensifying climate-driven fire risk.
Details
- Title
- Why the world needs a wildfire risk prediction system based on plant functional traits and moisture—before fires ignite
- Authors
- Shawkat I. Sohel (Corresponding Author) - University of the Sunshine Coast, Queensland, Forest Research InstituteAndrew R. Marshall - University of the Sunshine Coast, Queensland, Forest Research Institute
- Publication details
- npj Natural Hazards, Vol.2, pp.1-4
- Publisher
- Nature Publishing Group
- Date published
- 2025
- DOI
- 10.1038/s44304-025-00149-5
- ISSN
- 2948-2100
- Copyright note
- This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.
- Data Availability
- No datasets were generated or analysed during the current study.
- Organisation Unit
- Tropical Forests and People Research Centre; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991179279902621
- Output Type
- Journal article
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