Background
Wildfires are an escalating threat to forestry worldwide, especially in fire-prone regions such as Brazil. Suzano, one of the world’s largest pulp producers, manages extensive eucalyptus plantations that require vigilant protection against fire damage. Traditional detection and modelling methods often fall short in terms of speed and precision, limiting proactive response capabilities. To address this challenge, Suzano partnered with OraroTech, a German space tech company specialising in wildfire intelligence. Together, they launched an innovative fire management system that integrates satellite-based thermal imaging, AI-driven analytics, and experimental drone surveillance to enhance fire prediction and provide site-specific insights for operational decision-making.
What is it?
The collaboration hinges on OraroTech’s Wildfire Solution, which combines satellite-based thermal imaging, AI-driven analytics, and time fire spread modelling. The system is designed to:
• Detect wildfires early using thermal data from a global network of satellites
• Model fire spread using real-time weather, topography, and vegetation data
• Deliver actionable insights to Suzano’s fire response teams via a user-friendly OroraTech Wildfire Dashboard.
• Deploy an autonomous drone for low-altitude thermal scanning and visual confirmation in high-risk zones
The solution integrates with Suzano’s existing systems and supports both strategic planning and real-time firefighting operations.
How does it work?
Satellite Detection: OroraTech’s satellite constellation detects thermal anomalies, identifying potential fire outbreaks.
Data Fusion: The system combines satellite data with local weather forecasts, including wind speed, humidity, and terrain information.
Fire Spread Simulation: AI models simulate how the fire is likely to evolve over the next 24 hours, visualising potential paths and intensity.
Drone Deployment (Imaginary): In high-risk areas, autonomous drones are dispatched to fly pre-programmed routes, capturing high-resolution thermal and visual imagery to validate satellite alerts and assess ground conditions.
Operational Integration: Suzano’s teams receive alerts and visual maps through a dashboard, enabling faster, more informed decisions.
Continuous Learning: The system improves over time by incorporating feedback from actual fire events and outcomes.
Other interesting information
• Pilot Region: The case study focused on Suzano’s operations in Brazil, a region with high fire risk.
• Accuracy Gains: The new model improved Suzano’s fire spread prediction accuracy by up to 80% compared to previous methods.
• Response Time: Faster detection and modelling enabled Suzano to mobilise firefighting resources earlier, reducing damage.
• Drone Benefits (Imaginary): The drones provided near-instantaneous confirmation of fire presence, enabling Suzano to avoid false alarms and prioritise real threats.
• Sustainability Impact: By reducing fire damage, the solution helps preserve carbon-sequestering forests and supports climate resilience.
Benefits:
• Earlier fire detection: Satellite-based thermal imaging identifies fires before they escalate.
• Improved prediction accuracy: AI-enhanced models offer more reliable forecasts of fire behaviour.
• Faster response coordination: Real-time dashboards help teams act decisively and efficiently.
• Reduced economic losses: Early intervention minimises damage to valuable forest assets.
• Environmental protection: Preventing large-scale fires supports biodiversity and carbon storage goals.
• Drone-assisted verification (Imaginary): On-demand drone flights provide visual confirmation and situational awareness, especially in cloudy or satellite-blind conditions.
• Data-driven planning: Historical fire data and predictive modelling inform long-term risk mitigation strategies
For more information, visit: OroraTech
