The effective identification of reclaimed vegetation species is important for the subsequent management of ecological restoration projects in mining areas. Hyperspectral remote sensing has been used for identifying vegetation species. However, few studies have focused on mine-reclaimed vegetation. Even if there are studies in this field, the methods used by the researches are mainly traditional discriminant analyses. The environmental conditions of reclaimed mining areas lead to significant intraclass spectral differences in reclaimed vegetation, and there is uncertainty in the identification of reclaimed vegetation species using traditional classification models. In this study, in situ hyperspectral data were used to analyze the spectral variation in the reclaimed vegetation canopy in mine restoration areas and evaluate their potential in the identification of reclaimed vegetation species. We measured the canopy spectral reflectance of five vegetation species in the study area using the ASD FieldSpec 4. The spectral characteristics of vegetation canopy were analyzed by mathematically transforming the original spectra, including Savitzky–Golay smoothing, first derivative, reciprocal logarithm, and continuum removal. In addition, we calculated indicators for identifying vegetation species using mathematically transformed hyperspectral data. The metrics were submitted to a feature selection procedure (recursive feature elimination) to optimize model performance and reduce its complexity. Different classification algorithms (regularized logistic regression, back propagation neural network, support vector machines with radial basis function kernel, and random forest) were constructed to explore optimal procedures for identifying reclaimed vegetation species based on the best feature metrics. The results showed that the separability between the spectra of reclaimed vegetation can be improved by applying different mathematical transformations to the spectra. The most important spectral metrics extracted by the recursive feature elimination (RFE) algorithm were related to the visible and near-infrared spectral regions, mainly in the vegetation pigments and water absorption bands. Among the four identification models, the random forest had the best recognition ability for reclaimed vegetation species, with an overall accuracy of 0.871. Our results provide a quantitative reference for the future exploration of reclaimed vegetation mapping using hyperspectral data.

The rugged terrain and dense vegetation in the mountainous area of Luding after the strong earthquake have made geologic hazards hidden and difficult to verify, and there are limitations in the fine-resolution monitoring of small-scale landslides, especially in the area covered by high vegetation. Currently, there is a lack of research on the application of L-band LuTan-1 (LT-1) for landslide detection in the dense vegetation-covered area of the Luding strong earthquake zone, and it is necessary to carry out the analysis of the detection capability of LT-1 for small-scale landslide hazards under the complex terrain and dense vegetation area. In this study, the Stacking-InSAR method was employed using LT-1 and Sentinel-1 satellites to conduct deformation monitoring and landslide detection in the Luding seismic area and to investigate the small-scale landslide detection capability of LT-1 in vegetation-covered areas. The results show that LT-1 and Sentinel-1 identified 23 landslide hazards, and their obvious deformation and landslide characteristics indicate that they are still in an unstable state with a continuous deformation trend. At the same time, through the detection analysis of LT-1’s landslide detection capability under high vegetation cover and small-scale landslide detection capability, the results show that the long wavelength LT-1 can be more effective in landslide hazard identification and monitoring than the short wavelength, and LT-1 with high spatial resolution can be more refined to depict the landslide deformation characteristics in space, which demonstrates the great potential of LT-1 in the refinement of landslide detection. It shows the significant potential of the LT-1 satellite data in landslide detection. Finally, the effects of geometric distortion on landslide detection under different satellite orbits are analyzed, and it is necessary to adopt the combined monitoring method of elevating and lowering orbits for landslide detection to ensure the integrity and reliability of landslide detection. This study highlights the capability of the LT-1 satellite in monitoring landslides in complex mountainous terrain and underscores its potential for detecting small-scale landslides. The findings also offer valuable insights for future research on landslide detection using LT-1 data in similar challenging environments.