WILDFIRE DETECTION BASED ON HIMAWARI-8 MULTI-TEMPORAL DATA

Wildfire is a serious natural disaster that poses a serious threat to the safety of human life and property. Currently, there are many researches related to satellite wildfire detection, but few can achieve near real-time monitoring results. Himawari-8 geostationary satellite can provide full disk data every 10 minutes, making near real-time monitoring of wildfires possible. In this paper, a wildfire detection method based on Himawari-8 for multi-temporal data is proposed. In our method, we use temporal convolutional network (TCN) to predict the brightness temperature and achieve excellent prediction results, the mean absolute error ( MAE) is 0.28 K, mean square error (MSE) is 0.30 K-2, and mean absolute percentage error (MAPE) is 0.10 %. Then, the predicted values combined with other features as model inputs, and machine learning classification models were used for wildfire detection. The experimental results showed that the combination of multi-layer perceptron (MLP) model and strategy 2 containing brightness temperature predicted values achieved an accuracy of 90.91% in wildfire detection.