Frequent waterlogging disasters can have serious effects on regional ecology, food safety, and socioeconomic sustainable development. Early monitoring of waterlogging stress levels is vital for accurate production input management and reduction of crop production-related risks. In this study, a pot experiment on winter wheat was designed using three varieties and seven gradients of waterlogging stress. Hyperspectral imagery of the winter wheat canopy in the jointing stage, heading stage, flowering stage, filling stage, and maturation stage were measured and then classified. Wavebands of imaging data were screened. Waterlogging stress level was assessed by a combined harmonic analysis method, and application of this method at field scale was discussed preliminarily. Results show that compared to the k-nearest neighbor and support vector machine algorithms, the random forest algorithm is the best batch classification method for hyperspectral imagery of potted winter wheat. It can recognize waterlogging stress well in the wavebands of red absorption valley (RW: 640–680 nm), red-edge (RE: 670–737 nm), and near-infrared (NIR: 700–900 nm). In the RW region, amplitudes of the first three harmonic sub-signals (c1, c2, and c3) can be used as indexes to recognize the waterlogging stress level that each winter wheat variety undertakes. The third harmonic sub-signal amplitude c3 of the RE region is also suitable for judging stress levels of JM31 (one of the three varieties which is highly sensitive to water content). This study has important theoretical significance and practical application values related to the accurate control of waterlogging stress, and functions as a new method to monitor other types of environmental stress levels such as drought stress, freezing stress, and high-temperature stress levels.
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 (Fv′/Fm′) 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.
Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, 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 results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.
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