A weighted contextual active fire detection algorithm based on Himawari-8 data

Himawari-8, a geostationary satellite, is equipped with the Advanced Himawari Imager (AHI) sensor, which offers significant advantages for forest fire monitoring. This study proposes a weighted contextual fire detection algorithm (AHI_WFDA) that can apply to the AHI sensor as a global fire detection algorithm. Unlike the traditional pass-by screening algorithms, the algorithm takes into account the characteristics of different bands and assigns different weights and corresponding thresholds to the test conditions based on the bands' sensitivity to fire. To validate the algorithm's performance, we tested it on fires in five target areas. We regard MODIS data as the ground truth data and it was used as the benchmark for comparison with the AHI_WFDA , the Himawari-8 official product WLF, and the traditional spatial contextual algorithm (the reproduced SEVIRI algorithm). The results show that the AHI_WFDA significantly reduces the commission error rate compared to the WLF product. While our algorithm's accuracy rate is not superior to the SEVIRI algorithm, it detects more fire incidents correctly. Compared with the MODIS active fire product, the AHI_WFDA's omission error rate is about 63%. In contrast, the relative commission error rate is about 12%, which is in line with the results of some previous studies. In addition, we conducted detailed verification of our algorithm's results with the support of the Landsat series and Sentinel-2 data. The results show that the algorithm in this paper can effectively exploit the fire detection capability of AHI sensors and provide a new idea for the subsequent algorithms.