A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego

Abstract

Early detection of forest fire is helpful for monitoring the spread of fire promptly, minimizing the loss of forests, wild animals, human life, and economy. The performance of brightness temperature (BT) prediction determines the accuracy of fire detection. Great efforts have been made on BT prediction model building, but there still remains some uncertainty. Based on the widely used contextual BT prediction model (CM) and temporal-contextual BT prediction model (TCM), we proposed a spatio-temporal contextual BT prediction model (STCM), which involves historical images to contrast the BT correlation matrix between the pixel to be predicted and its background pixels within a dynamic window, and the spatial distance factor was introduced to modify the BT correlation matrix. We applied the STCM to a fire-prone area in San Diego, California, US, and compared it with CM and TCM. We found that the average RMSE of STCM was 12.54% and 9.12% lower than that of CM and TCM, and the standard deviation of RMSE calculated by STCM was reduced by 12.04% and 15.57% compared with CM and TCM, respectively. In addition, the bias of STCM was concentrated around zero and the range of bias of STCM was 88.7% and 15.3% lower than that of CM and TCM, respectively. The results demonstrated that the STCM can be used to obtain the highest BT prediction accuracy and most robust performance, followed by TCM, and CM performed worst. Our research on the BT prediction of potential fire pixels is helpful for improving the fire detection accuracy and is potentially useful for the prediction of other environmental variables with high spatial and temporal autocorrelation. However, the requirement of high-quality continuous data will limit the application of STCM in cloudy and rainy areas.