Satellite Collaborative Mission Planning Method for Forest Fire Prevention and Suppression

Forest fire monitoring is crucial for ecological environmental protection and disaster prevention. Traditional methods of forest fire prevention and suppression often suffer from delayed responses and limited coverage. This makes it difficult to meet the demands of real-time monitoring. The development of remote sensing satellite technology offers a promising solution through multi-source collaborative observation. However, existing methods for calculating satellite revisit intervals have limitations in accuracy and adaptability. Multi-satellite collaborative observation can significantly enhance monitoring efficiency. To optimize satellite observation efficiency in forest fire monitoring. This paper proposes a new scheduling method based on multi-satellite collaboration. This method establishes a complementary monitoring system through coordinated multi-satellite observations. Three sets of comparative experiments were conducted in the southwestern forest region. These experiments systematically evaluated the monitoring effectiveness of different observation strategies. The proposed method addresses issues such as low resource utilization and poor timeliness in multi-satellite collaborative observation. It provides scientific decision-making support for forest fire emergency response. The experimental results show that the proposed method outperforms other approaches across all evaluation metrics. It significantly enhances the system’s response capability to sudden fire incidents. This method holds significant practical value for improving forest fire monitoring capabilities. It has the potential to play a crucial role in enhancing the overall effectiveness of forest fire management and emergency response.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/ccai65422.2025.11189845
Primary Topic
Robotic Path Planning Algorithms
Type
article
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Satellite Collaborative Mission Planning Method for Forest Fire Prevention and Suppression

Suju Li, Cong Li, Zhenjia Chen, Kun Liu et al.
Robotic Path Planning Algorithms
article

Satellite Collaborative Mission Planning Method for Forest Fire Prevention and Suppression

Suju Li, Cong Li, Zhenjia Chen, Kun Liu, Xianyu Wang
article en

Abstract

Forest fire monitoring is crucial for ecological environmental protection and disaster prevention. Traditional methods of forest fire prevention and suppression often suffer from delayed responses and limited coverage. This makes it difficult to meet the demands of real-time monitoring. The development of remote sensing satellite technology offers a promising solution through multi-source collaborative observation. However, existing methods for calculating satellite revisit intervals have limitations in accuracy and adaptability. Multi-satellite collaborative observation can significantly enhance monitoring efficiency. To optimize satellite observation efficiency in forest fire monitoring. This paper proposes a new scheduling method based on multi-satellite collaboration. This method establishes a complementary monitoring system through coordinated multi-satellite observations. Three sets of comparative experiments were conducted in the southwestern forest region. These experiments systematically evaluated the monitoring effectiveness of different observation strategies. The proposed method addresses issues such as low resource utilization and poor timeliness in multi-satellite collaborative observation. It provides scientific decision-making support for forest fire emergency response. The experimental results show that the proposed method outperforms other approaches across all evaluation metrics. It significantly enhances the system’s response capability to sudden fire incidents. This method holds significant practical value for improving forest fire monitoring capabilities. It has the potential to play a crucial role in enhancing the overall effectiveness of forest fire management and emergency response.

China Academy of Space Technology (CN), Hainan University (CN), China National Commission for Disaster Reduction (CN)
Openalex Percentile: Top 72%
Robotic Path Planning Algorithms
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