Wildfire Detection Based on the Spatiotemporal and Spectral Features of Himawari-8 Data

Wildfire is a severe natural disaster that poses a significant threat to the natural environment, as well as the safety of human life and property. The timely detection of wildfires plays a critical role in minimizing their detrimental impact. Himawari-8, a geostationary satellite equipped with an advanced Himawari imager (AHI) sensor, can provide full-disk data every 10 min, thus enabling near real-time and large-scale monitoring of wildfires. In this article, a wildfire detection method based on the spatiotemporal features of Himawari-8 data is proposed. First, a temporal convolutional network (TCN) is employed to predict the brightness temperature of the bands related to wildfire detection, achieving prediction results with a mean absolute error (MAE) of 0.28 K, a mean square error (MSE) of 0.30 K2, and a mean absolute percentage error (MAPE) of 0.10%. Then, various feature strategies are devised from spectral, spatial, and temporal aspects, and machine learning models are utilized for wildfire detection research. Among the considered strategies, strategy 4, which integrates spectral, spatial, and temporal features with the random forest (RF) algorithm, exhibits the most effective wildfire detection performance. It achieves a precision of 0.62, an omission of 0.34, and an F1-score of 0.64. Compared with the threshold method, precision increased by 0.05, omission decreased by 0.31, and F1-score increased by 0.21. To further evaluate practical applicability, the combination of strategy 4 and the RF is employed for wildfire detection near power grid transmission lines. In this scenario, out of the 295 real wildfires, 253 are successfully detected, resulting in a recall of 0.86. These experimental results affirm the effectiveness of the proposed method for wildfire detection.