Integrating multiple factors to optimize watchtower deployment for wildfire detection.

Area of Science:

• Environmental Science
• Computer Science
• Forestry

Background:

• Traditional human-vision watchtowers are being replaced by machine-vision systems.
• Machine-vision systems have smaller visual ranges, necessitating more towers and leading to increased overlap.
• Existing systems do not leverage viewshed redundancy or consider varying fire risk across areas.

Purpose of the Study:

• To develop an optimized watchtower deployment model for fire detection systems.
• To integrate viewshed analysis, location allocation, and fire risk mapping into a unified approach.
• To address the challenge of optimal placement given increased tower density and budget constraints.

Main Methods:

• Developed a novel watchtower deployment model incorporating viewshed analysis and fire risk.
• Utilized a fire risk model to generate a fire risk map.
• Integrated location allocation and multi-coverage strategies for high-fire-risk areas under budget constraints.
• Evaluated the approach using a real forest park dataset and compared it with FV-NB and XV-B algorithms.

Main Results:

• The proposed model achieves higher coverage gain and superior multiple-coverage of high-fire-risk areas.
• It effectively integrates viewshed and fire risk levels into location allocation while meeting coverage and budget requirements.
• Demonstrated 8.9-17.3% improvement in multiple-coverage of high-fire-risk areas in environments with moderate watchtower density.

Conclusions:

• The novel deployment model optimizes machine-vision watchtower systems by leveraging viewshed redundancy and fire risk data.
• This approach is particularly effective in scenarios with frequent overlaps, enhancing fire detection efficiency.
• The method provides a practical solution for cost-effective and comprehensive fire risk management in forest environments.