From Land Management to AI: New Solutions to Reduce Wildfire Risk
A webinar organised by SERN in the context of the RESIST project – co-funded by the EU – presented practical solutions to help territories reduce wildfire risk, from landscape management and early warning systems to drones and AI-powered detection.
As temperatures rise and wildfires become more frequent and intense, innovative solutions for early detection is only one part of the challenge. Reducing wildfire risk also means limiting the conditions that allow fires to spread, strengthening local preparedness, and helping emergency teams act faster when a fire starts.
This was one of the key messages emerging from a recent RESIST thematic webinar organised by SERN, which brought together experts working on landscape-based and tech solutions for early wildfire detection and management. RESIST is a five-year EU co-funded project to find and share solutions to make regions more resilient to climate change.
Productive Fuel Breaks: Reducing Fire Risk at Landscape Level
In northern Extremadura, Spain, the RESIST pilot area is exploring how landscape management can reduce fire hazards before ignition occurs. The approach focuses on productive fuel breaks: areas where vegetation and fuel continuity are reduced through activities such as grazing, cropping, timber harvesting, biomass removal, and agroforestry. Unlike conventional firebreaks, these areas can be maintained by local actors while also generating social, economic, and environmental benefits.
ARGOS and AEGPs: Using Data and Land Planning Before Fires Start
In Baixo Alentejo, Portugal, the discussion turned to ARGOS, an early warning system that connects real-time temperature data, fire information, landscape management, and territorial planning to support better decision-making. The session also highlighted AEGPs, or Integrated Areas of Landscape Management, a Portuguese territorial planning instrument rather than a standalone technology. AEGPs are designed to bring together landowners, public authorities, and local actors to manage agricultural, forest, and silvopastoral areas in a more coordinated way. By reducing fuel accumulation, supporting ecosystem restoration, and promoting more sustainable land use, they aim to make high-risk rural territories less vulnerable to severe wildfires.
FRED: Bringing Drones Into Emergency Response
The Portuguese experience also drew on the FRED – Fire Free Med project, where drones and advanced ICT tools were tested to support wildfire prevention and emergency response. These tools helped with the identification of ignition points, risk mapping, evacuation support, and operational coordination between emergency teams. The experience underlined a wider lesson: technology is more effective when it is embedded in trusted local systems, including firefighters, municipalities, and communities.
Fire Tracking: Spotting Fires in the First Minutes with AI
The session also examined Fire Tracking, an AI-powered camera system capable of detecting and locating fire ignitions within seconds. By providing rapid alerts, map-based information, images of the ignition point, and indications of nearby infrastructure at risk, the system can help emergency teams make faster decisions during the first critical minutes of a fire.
Taken together, these approaches point to a broader shift in wildfire resilience: from reacting to fires once they escalate, to combining prevention, detection, local knowledge, and rapid response. While no single tool can address the challenge alone, solutions such as productive fuel breaks, ARGOS, AEGPs, FRED, and Fire Tracking offer practical examples that other territories may be able to adapt to their own landscapes, risks, and operational needs.
To explore more tested and emerging solutions for climate resilience, visit the RESIST Handbook of Solutions.
RESIST is co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor CINEA can be held responsible for them.
