Hyperspectral characterization and chlorophyll content inversion of reclaimed vegetation in rare earth mines.

Abstract

The special physical and chemical properties of the reclaimed land caused by the disturbance of rare earth mining and the environmental stress caused by the mining of rare earth lead to the inhibition of the physiological functions of the reclaimed vegetation and the severe challenge of vegetation ecological restoration. This study focuses on the Lingbei rare earth mining area in Dingnan County, Jiangxi Province, and investigates the original spectrum, derivative spectrum, and the continuum-removed spectrum of reclaimed vegetation. The spectral characteristics and trends and the typical reclaimed vegetation are analyzed, the correlation between the chlorophyll content and the spectral indices of the reclaimed vegetation is determined, and the sensitive spectral parameters are extracted. Partial least squares algorithm, a back propagation neural network algorithm, and a sparse autoencoder network are used to estimate the chlorophyll content, and the model's accuracies are compared. The vegetation spectrum of the reclaimed vegetation is characterized by high reflectance in the visible region, a redshift of the green peak and red valley positions, and a blueshift of the red edge positions, a relatively low spectral variation in. The variability of the sensitive spectral parameters of different vegetation type is extracted. The sparse autoencoder network is the optimal estimation model (the R2 value of the three vegetations are 0.9117, 0.7418, and 0.9815, respectively). The results provide a scientific basis for monitoring and managing the growth of different types of reclaimed vegetation in rare earth mining areas under environmental stress and can guide the ecological restoration of reclaimed mining areas.