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Wildfires have become increasingly frequent and severe worldwide, causing substantial human casualties and economic losses as well as having far-reaching impacts on global carbon cycles, air quality, and ecosystem health. The recent large-scale wildfires in the United States, Australia, and China further underscore the urgency of this issue. In the context of global warming, prolonged droughts, vegetation changes, and shifts in land use patterns have substantially augmented the frequency and intensity of wildfires, making hazard prevention and control more complex.
Yet the rapid advancement of technology has introduced new tools and methodologies that are transforming wildfire detection, monitoring, and management. Satellite imagery, remote sensing, artificial intelligence, and big data analytics have revolutionized the way we track and predict fire outbreaks, allowing for real-time monitoring and more accurate risk assessment. These technological innovations offer unprecedented opportunities to enhance early warning systems, improve situational awareness, and enable proactive mitigation efforts, but they also present new challenges in terms of data integration, interpretation, and effective deployment.
This Collection aims to delve deeply into the driving mechanisms of wildfire hazards, their dynamic evolution, monitoring and prediction technologies, as well as mitigation strategies. It seeks to bring together the latest progress from both academic and practical fields, providing theoretical support and technical solutions to wildfire hazards.
This Collection welcomes Original Research articles as well as Reviews, Perspectives, and Comments to address the following specific areas of focus that include, but are not limited to:
Causes and driving mechanisms of wildfire hazards
Dynamic evolution of wildfire spread and its modeling development
Technological innovations in high-resolution, multi-scale monitoring and detecting wildfires
Technological innovations in wildfire prevention and control