Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs).

Area of Science:

• Remote Sensing
• Computer Vision
• Forestry

Background:

• Forest fires pose significant environmental and economic threats.
• Current detection methods, including satellite and helicopter surveillance, have limitations in terms of cost, accessibility, and real-time capabilities.
• The need for efficient and accurate early fire detection systems is critical for effective wildfire management.

Purpose of the Study:

• To introduce a novel color index, the Forest Fire Detection Index (FFDI), for enhanced forest fire detection.
• To adapt existing vegetation classification methods for identifying flame and smoke tonalities.
• To evaluate the precision and processing speed of the FFDI method for real-time applications.

Main Methods:

• Development of the Forest Fire Detection Index (FFDI) based on vegetation classification principles.
• Adaptation of the index to detect flame and smoke colors, with adaptive inclusion of smoke into Regions of Interest (RoIs).
• Testing the FFDI method on database imagery, including early-stage fires, and measuring detection precision and processing time.

Main Results:

• Achieved a detection precision of 96.82% for 960 × 540 pixel images with a processing time of 0.0447 seconds.
• Demonstrated potential for high frame rates (22-54 f/s) with maintained detection precision.
• Attained a precision rate of 96.62% for early-stage forest fires.

Conclusions:

• The FFDI method provides a highly precise and efficient approach to forest fire detection.
• The system is well-suited for real-time implementation in Unmanned Aerial Systems (UASs), offering cost-effective and safer monitoring solutions.
• Future work involves integrating the FFDI into commercially available drones for practical deployment.