Assessing Potential Spontaneous Combustion of Coal Gangue Dumps after Reclamation by Simulating Alfalfa Heat Stress Based on the Spectral Features of Chlorophyll Fluorescence Parameters

The spontaneous combustion of coal gangue dumps after reclamation causes severe harm to the ecological environment surrounding mining areas. Using remote sensing technology to determine vegetation heat stress levels is an important way to evaluate the probability of a spontaneous combustion disaster. The canopy spectra and chlorophyll fluorescence (ChlF) parameters of alfalfa were collected through pot experiments to simulate different heat stress levels. Time series analyses of three ChlF (chlorophyll fluorescence) parameters showed that the regularity of the quantum efficiency of photosystem II (PSII) in light-adapted conditions (${\mathrm{Fv}}^{\prime }/{\mathrm{Fm}}^{\prime }$) was stronger during the monitoring period. The correlation coefficients between the three ChlF parameters and the canopy raw spectrum, first derivative spectrum, and vegetation indices were calculated, and the spectral features were found to be more correlated. Lasso regression was used to further screen spectral features, and the optimal spectral features were the raw spectral value at 741 nm (abbreviated as RS (741)) and NDVI (652, 671). To discriminate among heat stress levels accurately and automatically, we built a time convolution neural network. The classification results showed that when the sequence length is 3, the heat stress is divided into three categories, and the model obtains the highest accuracy. In combination with relevant research conclusions on the temperature distribution law of spontaneous combustion in coal gangue dumps, three heat stress levels can be used to assess the potential of spontaneous combustion in coal gangue dumps after reclamation. The research results provide an important theoretical basis and technical support for early warnings regarding spontaneous combustion disasters in reclaimed coal gangue dumps.