Analysis of the Effects of Different Spectral Transformation Methods on the Estimation of Chlorophyll Content of Reclaimed Vegetation in Rare Earth Mining Areas

Ion adsorption rare earths are an important strategic resource, but their leach mining causes post-mining wastelands and tailings to suffer from soil sanding, acidification, and heavy metal contamination. This makes natural vegetation recovery difficult, relying mainly on artificial reclamation; however, the reclaimed vegetation grows poorly due to environmental stress. Hyperspectral remote sensing technology, with its high efficiency, non-destructive nature, and wide-range monitoring capability, can accurately estimate the physiological parameters of reclaimed vegetation. This provides support for environmental regulation in mining areas. In this study, three typical types of reclaimed vegetation in the Lingbei Rare Earth Mining Area, Dingnan County, Ganzhou City, were analyzed. Hyperspectral data and the corresponding chlorophyll content were collected to compare the spectral differences between reclaimed and normal vegetation. The spectral data were processed using mathematical transformation, fractional order differentiation, discrete wavelet transform, and continuous wavelet transform. Sensitive bands were extracted, and multispectral transformed feature bands were integrated. Linear and machine learning regression models were used to estimate chlorophyll content. The effects of different spectral processing methods on chlorophyll estimation were then analyzed. The results showed that reclaimed vegetation had higher spectral reflectance than normal vegetation, with the red valley shifting towards the long-wave direction and a steeper red edge slope. Different spectral transformation methods impact the accuracy of chlorophyll content estimation. Using appropriate methods can improve estimation accuracy. Fusing multi-spectral transformation features can achieve relatively good results. Among the models, the random forest regression model provides the best performance in estimating the chlorophyll content of reclaimed vegetation. This study provides a scientific basis for rapid and accurate monitoring of reclaimed vegetation growth in rare earth mining areas, supporting environmental management and decision-making and contributing to ecological restoration.