Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data

作者：Da Zhang, Chunlin Huang, Juan Gu, Jinliang Hou, Ying Zhang, Weixiao Han, Peng Dou, Yaya Feng · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15061541 · 被引用次数：35 · 研究领域：Fire effects on ecosystems、Fire Detection and Safety Systems、Remote Sensing in Agriculture

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy...