How IoT sensors and machine learning help prevent wildfires risks before flames appear
Wildfires are one of Spain’s most severe environmental threats, putting more than 15 million hectares of forest at risk every year.
For public authorities, infrastructure owners and land managers, the difference between a contained incident and a catastrophic fire often comes down to minutes.
Traditional detection methods are no longer enough on their own.
This is where advanced, sensor-based solutions can transform wildfire management — shifting from reactive firefighting to proactive, early intervention.
When every minute counts in forest fire protection
Spain’s climate, vegetation and geography create a perfect storm for wildfires.
Rising temperatures and prolonged droughts increase both the frequency and intensity of fires. At the same time, many forests are remote, difficult to access and hard to monitor continuously.
This was exactly the situation faced by a Securitas client responsible for managing forest areas in Spain, including zones with rugged terrain and high exposure to wildfires during peak summer months.
The challenge was clear: detecting fires faster — and ideally before they fully develop.
Relying solely on patrols, cameras or satellite imagery meant that fires were often detected only once smoke or flames were visible. Besides the terrain creates “shadow zones” that cannot be covered by traditional means. By then, valuable time had already been lost, especially in remote areas where response times are longer.
From detection to anticipation: the real challenge
The core issue was not just detecting fires — but detecting them early enough to make a difference.
Traditional systems are mostly reactive. Cameras detect smoke, satellites detect heat, and people report visible flames. By that point, especially in dry and windy conditions, the fire may already be spreading rapidly.
This is why the client needed a solution that could:
- Detect early signs of fire risk before flames spread
- Operate autonomously in remote areas
- Reduce the need for constant human monitoring
- Minimize false alarms
- Support a sustainable, long-term prevention strategy
Beyond early detection, the system also needed to monitor large, forested areas continuously, provide reliable real-time insights, integrate with existing response structures, and operate in remote environments with limited infrastructure.
At the same time, the solution had to be robust, cost efficient, low-maintenance and sustainable. Many of the forests in question have limited access to power or communications networks, making traditional systems difficult to deploy at scale.
The challenge was to combine advanced detection capabilities with practical, field-ready deployment — without adding complexity for response teams.
IoT sensors that learn the “scent” of the forest
To meet these requirements, Securitas implemented a solution based on IoT sensors equipped with machine learning capabilities.
These sensors are designed to “learn” the natural chemical and environmental profile — the “scent” — of the forest in which they are installed.
Over time, they build a baseline of what is normal in that specific environment.
They continuously analyse environmental data and detect deviations that may indicate early signs of fire or conditions that could lead to one comparing them to a lab created database of fire “scents” for different types of vegetation.
When an anomaly is detected, the system automatically sends an alert to the control center.
Several key design choices made this solution particularly effective:
- Machine learning–driven detection → sensors adapt to local conditions instead of relying on fixed thresholds
- Solar-powered operation → enabling autonomous use in remote areas without external infrastructure
- Automatic alerting → anomalies are transmitted instantly, without requiring on-site monitoring
Day-to-day operation: how technology and people work together
In daily operation, the system is designed to be seamless for the client and their teams.
Sensors are installed at strategic locations across the forest. They operate continuously, powered by solar energy, collecting and analysing environmental data while refining their understanding of normal conditions.
When an anomaly is detected, such as a change in air composition or other indicators of fire risk, an alert is automatically generated and sent to the control center.
From there, the alert is assessed and prioritized, relevant stakeholders such as operations teams, authorities or emergency services are informed, and response teams can be deployed if needed.
This workflow eliminates the need for constant human presence in the field.
Technology ensures continuous monitoring, while people focus on decision-making and response.
Impact: sustainability, prevention and faster intervention
The value of the solution is both measurable and strategic.
For the client, it delivers:
- Strong preventive capability → earlier detection enables faster intervention and limits escalation
- Improved sustainability → solar-powered sensors reduce environmental impact and infrastructure needs
- Scalable deployment → suitable for large territories such as Spain’s 15 million hectares of forest
- Enhanced risk management → continuous data supports more proactive, informed decision-making
Conclusion: from reacting to fires to preventing them
Wildfires will remain a critical challenge for Spain and many other countries in the years ahead.
However, as this case shows, advanced IoT and machine learning technologies can fundamentally change how organizations detect and prevent fires.
By learning the “scent” of the forest and providing continuous, real-time monitoring, the solution enables earlier detection, faster decisions and more effective intervention.
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Let’s explore what this could mean for you
If you are responsible for protecting forests, infrastructure or large natural areas, early detection is no longer just a technical question.
It’s a strategic one.
Securitas supports organizations in designing monitoring solutions adapted to their environment, risks and operational constraints.
If you’d like to explore how intelligent sensing and monitoring can strengthen your wildfire prevention strategy, we’d be happy to discuss your situation.