The extraction of lithological information in areas with high vegetation coverage presents numerous challenges, particularly in identifying concealed lithological features. This study focuses on a typical high-vegetation coverage area in Taiwan Province, China, utilizing multi-source data from Sentinel-2, Sentinel-1, and DEM, and using the Random Forest algorithm for lithological mapping. The results demonstrate that the optimal combination of Sentinel-2 and DEM significantly enhances the classification performance, achieving an overall accuracy (OA) of 84.30% and a Kappa coefficient of 0.83 in the validation set. Geological conditions have specific limiting effects on ecosystems, as spectral features (such as B2 and NDBI) and topographic features (such as elevation) contribute significantly to the classification results. This study provides valuable reference information for lithological information extraction in areas with high vegetation coverage.

Coal is the main mineral resource, but over-exploitation will cause a series of geological disasters. Interferometric synthetic aperture radar (InSAR) technology provides a superior monitoring method to compensate for the inadequacy of traditional measurements for mine surface deformation monitoring. In this study, the whole process of mining a working face in Huaibei Mining District, Anhui Province, is taken as the object of study. The ALOS PALSAR satellite radar image data and ground measurements were acquired, and the ISK-DPIM-InSAR deformation monitoring model with the dynamic probabilistic integral model (DPIM) was proposed by combining the probabilistic integral method (PIM) and the improved segmented Knothe time function (ISK). The ISK-DPIM-InSAR model constructs the inversion equations of InSAR line-of-sight deformation, north–south and east–west horizontal movement deformation, vertical deformation, inverts the optimal values of the predicted parameters of the workforce through the particle swarm algorithm, and substitutes it into the ISK-DPIM-InSAR model for predicting the three-dimensional dynamic deformation of a mining face. Simulated workface experiments determined the feasibility of the model, and by comparing the level observation results of the working face, it is confirmed that the ISK-DPIM-InSAR model can accurately monitor the three-dimensional deformation of the surface in the mining area.