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#7086. Analysis and discrimination of hyperspectral characteristics of typical vegetation leaves in a rare earth reclamation mining area
| December 2026 | publication date |
| Proposal available till | 10-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Nature and Landscape Conservation; Management, Monitoring, Policy and Law; Environmental Engineering; |
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Abstract:
Large area hyperspectral remote sensing monitoring of physiological parameters of reclaimed vegetation is an important technology for environmental regulation in mining areas, but accurate identification of reclaimed tree species is a prerequisite. In this study, we collected hyperspectral data of six typical vegetation leaves at the Jiazibei reclamation site and analyzed the characteristics of the original vegetation spectrum, including its reciprocal logarithm, first derivative, and continuum removal. Additionally, on the basis of the mean confidence interval method that reduces the dimensionality of the hyperspectral data to select the characteristic bands, the t-test detection method was used to select the best characteristic bands on the basis of the significant difference among the characteristic bands. Finally, three discrimination models, Fisher, stepwise discrimination (SD), and multi-layer perceptron (MLP), were constructed to discriminate typical vegetation types based on th e best feature bands. The results show that the first derivative and continuum removal processing can intensify the characteristic difference of the spectral curve and that the blueshift in the vegetation spectrum based on the first derivative of the red edge feature indicates that the Camellia is the most severely stressed in the mining area. Compared with other research on the characteristic bands selected based on reclaimed vegetation in the mining area, we obtained richer water content information for vegetation types. Among the three discriminate models, the SD method based on the hyperspectral measured leaf sample data in the mining area had the best effect in identifying the reclaimed vegetation in the study area, in which the average discriminative accuracy of Wetland pine is 93.6%. The identification and analysis of reclaimed vegetation provide technical support and a theoretical basis for the inversion of physiological parameters of reclaimed vegetation in rare earth mining areas and the monitoring of reclamation effects, which is of significance for realizing large-scale monitoring of the ecological environment of mining areas.
Keywords:
Multi-layer perceptron; Rare earth mining area; Reclaimed vegetation; Stepwise discrimination method
Contacts :
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Abstract:
Large area hyperspectral remote sensing monitoring of physiological parameters of reclaimed vegetation is an important technology for environmental regulation in mining areas, but accurate identification of reclaimed tree species is a prerequisite. In this study, we collected hyperspectral data of six typical vegetation leaves at the Jiazibei reclamation site and analyzed the characteristics of the original vegetation spectrum, including its reciprocal logarithm, first derivative, and continuum removal. Additionally, on the basis of the mean confidence interval method that reduces the dimensionality of the hyperspectral data to select the characteristic bands, the t-test detection method was used to select the best characteristic bands on the basis of the significant difference among the characteristic bands. Finally, three discrimination models, Fisher, stepwise discrimination (SD), and multi-layer perceptron (MLP), were constructed to discriminate typical vegetation types based on th e best feature bands. The results show that the first derivative and continuum removal processing can intensify the characteristic difference of the spectral curve and that the blueshift in the vegetation spectrum based on the first derivative of the red edge feature indicates that the Camellia is the most severely stressed in the mining area. Compared with other research on the characteristic bands selected based on reclaimed vegetation in the mining area, we obtained richer water content information for vegetation types. Among the three discriminate models, the SD method based on the hyperspectral measured leaf sample data in the mining area had the best effect in identifying the reclaimed vegetation in the study area, in which the average discriminative accuracy of Wetland pine is 93.6%. The identification and analysis of reclaimed vegetation provide technical support and a theoretical basis for the inversion of physiological parameters of reclaimed vegetation in rare earth mining areas and the monitoring of reclamation effects, which is of significance for realizing large-scale monitoring of the ecological environment of mining areas.
Keywords:
Multi-layer perceptron; Rare earth mining area; Reclaimed vegetation; Stepwise discrimination method
Contacts :
