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The early stages of forest fires are usually accompanied by large amounts of smoke, so smoke identification and concentration inversion can effectively warn and locate the fire point. To achieve smoke identification and concentration inversion, this paper first analysed the spectral differences between smoke and underlying background features based on the samples selected from Landsat 8/OLI fire data of Qiqian forest in Inner Mongolia, China, andNew South Wales, Australia. Then, the sensitive band of smoke identification was screened and the relative smoke concentration indices were established. Finally, a mathematical model between the indices and smoke concentration level was constructed and the best inversion model was established by accuracy test based on samples in each background. The results show that (1) the reflectance increases with increasing smoke concentration in B1-B4 bands under different backgrounds (vegetation, water, bare ground), and the reflectance was significantly different between different concentrations of smoke; (2) the smoke concentration inversion model form is y = kx + b with R 2  &gt; 0.8 (x denotes the smoke concentration level, y denotes the relative smoke concentration index), and the visible brightness index (VBI = B1 + B2 + B3 + B4) was the best relative smoke concentration index under different background; (3) the model’s accuracy was tested using each background smoke sample with a known level of smoke concentration, and the inversion accuracy was above 90%. The results show that the VBI can effectively reflect the spectral variation between different smoke concentrations and background features; the variation is closely related to smoke concentration which determines that the mathematical correlation model between VBI and relative smoke concentration can be established; the model is accuracy and can be used for the relative smoke concentration inversion of forest fire cases in different study areas.

Research on smoke relative concentration identification method based on Landsat8-OLI multispectral data and multivariate analysis