Near Real-Time Monitoring of Fire Spots Using a Novel SBT-FireNet Based on Himawari-8 Satellite Images

Abstract

Detailed and timely monitoring of the location and intensity of the fire is critical to reducing the destructive impacts of a fire. Satellite imagery platforms, in particular geostationary satellites with high temporal resolution, allow for real-time fire monitoring. However, because of the coarse resolution of geostationary satellite images, even when deep learning models are applied, precision still remains limited. Thus, the prediction models easily fall into a local optimal solution because of the insufficient semantic information from low spatial resolution. Therefore, in this study, we proposed a novel deep learning model, SBT-FireNet, to monitor fire spots from Himawari-8 satellite images. Specifically, the extraction modules of spatial, band, and time-series features were designed and integrated into the model. The spatial feature extraction module served to collate information about fires and their surrounding environment, while the band and time-series features were designed to obtain fire-sensitive band and time information, respectively. The newly structured SBT-FireNet model was tested in four fire-prone areas with high forest cover. The precision of SBT-FireNet in four test areas is 35.2% higher than other methods. The model yielded significant improvements through the combination of the modules of spatial, band, and time-series features and their fire-tailored design. The advantages of the high temporal resolution of geostationary satellite images were fully integrated into the model to ensure that the model monitors the possibility of fire in an automated way in a near-real-time manner.

Keywords

• Convolutional neural network (CNN)
• deep learning
• LSTM
• near-real-time monitoring
• transformer
• wildfire