Exploration of Suitable Spectral Bands and Indices for Forest Fire Severity Evaluation Using ZY-1 Hyperspectral Data

Satellite remote sensing has been widely recognized as an effective tool for estimating fire severity. Existing indies predominantly rely on broad-spectrum multispectral data, limiting the ability to elucidate the intricate relationship between fire severity and spectral response. To address this challenge, the optimal spectral bands and indices for fire severity assessment were explored using ZY-1 hyperspectral data, which captured pre- and post-fire conditions of a forest fire site in Yuxi City, Yunnan Province, China. Separability contrast and threshold segmentation methods were applied to perform a sensitivity analysis on the original spectral bands and constructed indices derived from surface reflectance of the post-fire image and the pre- and post-fire image combination, respectively. The findings indicate the following: (1) The spectral bands of the post-fire image exhibited superior spectral separability and classification capabilities compared to the pre- and post-fire difference image, with the highest forest fire severity classification accuracy of 78.99% achieved at the 800 nm central wavelength. (2) The difference of normalized difference index category for the pre- and post-fire image combination outperformed the vegetation indices of the post-fire image and the other vegetation indices using the pre- and post-fire image combination, with the highest forest fire severity classification accuracy of 83.39% achieved with the combination of 2048 nm and 1106 nm central wavelength. (3) Unburned areas exhibited strong separability, facilitating effective segmentation, but burned areas showed poor separability between fire severities, particularly between low and moderate&ndash:high severity, which remains the primary limitation in fire severity assessment. In conclusion, this study advances the understanding of fire severity and spectral response by leveraging the narrow-band advantages. It aims to enhance the accuracy of satellite-based fire severity estimation, offering valuable technical guidance and theoretical insights for assessing forest fire impacts and vegetation recovery.