Evaluation of Key Remote Sensing Features for Bushfire Analysis - Ziyi Yang; Husam Al-Najjar; Ghassan Beydoun; Bahareh Kalantar; Mohsen Zand; Naonori Ueda (Remote Sensing)
This study evaluates remote sensing features to resolve problems associated with feature redundancy, low efficiency, and insufficient input feature analysis in bushfire detection. It calculates spectral features, remote sensing indices, and texture features from Sentinel-2 data for the Blue Mountains region of New South Wales, Australia. Feature separability was evaluated with three measures: J-M distance, discriminant index, and mutual information, leading to an assessment of the best remote sensing features. The results show that for post-fire smoke detection, the best features are the normalized difference vegetation index (NDVI), the B1 band, and the angular second moment (ASM) in the B1 band, with respective scores of 0.900, 0.900, and 0.838. For burned land detection, the best features are NDVI, the B2 band, and correlation (Corr) in the B5 band, with corresponding scores of 1.000, 0.9436, and 0.9173. These results demonstrate the effectiveness of NDVI, the B1 and B2 bands, and specific texture features in the post-fire analysis of remote sensing data. These findings provide valuable insights for the monitoring and analysis of bushfires and offer a solid foundation for future model construction, fire mapping, and feature interpretation tasks.