Innovative Applications of Remote Sensing and Machine Learning in Forest Fire Detection and Prevention

Dear Colleagues,

Wildland and forest fires, as significant ecological factors within ecosystems, play a pivotal role in maintaining the balance of the global ecosystem. While they contribute to natural ecological processes, uncontrolled fires pose a substantial threat to both the environment and human lives, leading to severe economic and ecological consequences. Therefore, there is an urgent need to enhance research on forest fire management systems.​

With the widespread integration of modern information technology, various aspects such as fire and smoke alarms, fire risk evaluation, fire behavior assessment, fire spread analysis, and post-fire forest degradation assessment have emerged as key strategies in forest fire management. These technological applications enable more proactive and scientific approaches to dealing with forest fires.​

In recent years, the utilization of remote sensing and machine learning for forest fire prediction, deep learning-based forest fire monitoring, and UAV-assisted forest fire severity classification have received growing attention in the fire management domain. These advanced technologies represent a significant leap forward in our ability to anticipate, detect, and analyze forest fires. To further develop smart fire management, continuous research, development, and application of more precise and efficient forest fire prediction and management methods are essential. By doing so, we can effectively reduce the risk of forest fires and respond promptly and effectively to forest fire emergencies. These technological advancements hold great promise for significantly improving forest fire management and prevention efforts, paving the way for a more sustainable approach to safeguarding our forests.

This Special Issue aims to cover the full range of applications in forest fire prediction and management. Possible topics include, but are not limited to, the following:

• Wildland and forest fire spreading analysis, monitoring, and prediction;
• Wildland and forest fire detection;
• UAV-based forest fire severity classification;
• Deep learning models for chronological analysis of forest succession;
• Pattern recognition techniques for forest parameter retrieval;
• Visible light smoke and fire recognition processing and intelligentization;
• Early fire detection;
• Accuracy of fire protection system positioning;
• UAV-based forest fire spreading, monitoring, and prediction;
• Forest aviation patrol.

Prof. Dr. Fuquan Zhang
Dr. Yiqing Xu
Guest Editors

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