Identification of forest fire points under clear sky conditions with Himawari-8 satellite data

ABSTRACT

Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring.

KEYWORDS:

• Forest fire
• Himawari-8
• threshold-based algorithm
• random forest model
• dynamic identification

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant [42230109], in part by the Yunling Scholar Project of the “Xingdian Talent Support Program” of Yunnan Province under Grant [41961053], and in part by the Platform Construction Project of High-Level Talent in the Kunming University of Science and Technology (KUST).