A Novel Spatiotemporal Fire Detection Algorithm Based on Himawari-8 Satellite Data

As one of the key links in maintaining the balance of ecosystems, natural fires in nature are often extensive and unpredictable. When they get out of control and turn into wildfires, the threats they pose to ecosystems, the atmospheric environment, and human health are incalculable. Fires lead to a continuous reduction in forest coverage, while a large amount of harmful gases produced by forest combustion are emitted into the atmosphere. This causes enormous harm to the ecological environment, economic development, and the safety of human lives and property. Therefore, timely and accurate detection of forest fires, as well as grasping specific characteristics such as the exact occurrence time, location, and spatiotemporal evolution of fires, helps to explore the causes and patterns of fires, and is of great significance for the sustainable management of forests supported by fire prevention management.

This study proposes a novel fire detection algorithm integrating spatiotemporal information, utilizing data from Himawari-8, a next-generation geostationary satellite. By combining contextual information and a dynamic threshold detection method, the algorithm achieves real-time detection and scientific prediction of fire points through improving the slope deviation of infrared channels. A forest fire that occurred in Yuxi City, Yunnan Province, from April 11 to April 15, 2023, was selected as a research case for fire detection analysis. The results demonstrate that the proposed fire point detection method reduces edge false detections compared to WLF, the official fire point product of Himawari-8. Meanwhile, it shows significantly higher recognition accuracy and a notably lower false detection rate than the pre-improved algorithm.

The experimental results show that this improved forest fire detection algorithm can quickly and effectively detect fire point information. Compared with the pre-improved algorithm, it has higher detection accuracy. Meanwhile, the improvement of infrared gradient provides new ideas and methods for realizing effective disaster situation monitoring.