Background: Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion.
Methods: The study simulated the heat-stress environment of a coal gangue dump reclamation area through a temperature gradient experiment. We collected leaf spectrum and water content data on alfalfa plants commonly planted in such areas. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and least absolute shrinkage operator (lasso) regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa's heat stress level.
Results: Comparing three leaf water content indicators, we found that the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, vegetation indexes and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525, 1771), DVI (1412, 740) and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa. The accuracy of the training set was > 95% and the accuracy of the verification set was about 90%.
Conclusion: The results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.