Enhancing active fire detection in Sentinel 2 imagery using GLCM texture features in random forest models.

The array of wildfire activities instigated by human endeavors has emerged as a significant source of atmospheric pollution, posing considerable risks to both public health and property safety. This study harnesses Sentinel-2 satellite data, employing a variety of methods including spectral index methods, thresholding, and the Random Forest (RF) model for active fire spot detection. The research encompasses a wide range of land cover types across various Chinese regions. Utilizing the Gini coefficient, the study assesses the importance of spectral and texture features in the RF, culminating in the selection of an optimal feature combination for the construction of a bespoke RF model tailored for active fire detection. The research utilized texture features based on the Grey Level Co-occurrence Matrix (GLCM), demonstrating their significant contribution to enhancing the accuracy of fire detection using the RF model. Our analysis reveals that GLCM-based texture features, which form 40% of the model's final feature set, are crucial for improving detection accuracy. The optimized RF model demonstrates a marked superiority in identifying active fires, achieving an overall accuracy of 86.1%. The study results demonstrate that the bespoke RF model is suitable for detecting active fire across various land cover environments in China.