A nonparametric regression — random forest model for the estimation of fractional vegetation coverage (FVC) in a complex topographic area is presented based on low-altitude unmanned aerial vehicle (UAV) hyperspectral imagery. In order to collect a large number of sufficient training samples required for random forest algorithm, the UAV equipped with an optical camera was used to vertically capture the images of land covers in several inaccessible areas such as high mountains, water body and densely forested areas, to increase the density of the ground sampling. The RGBVI (red-green-blue vegetation index) was calculated first and then the Otsu method was adopted to extract the FVC values of the samples from the UAV optical images and ground photos. After that, the hyperspectral images captured by the UAV GaiaSky-mini2 hyperspectral imaging system in the Youlougou Mining area, Chayouzhong County, Inner Mongolia on August 16?18, 2018 were used to extract feature variables, and this feature set was filtered by recursive feature elimination algorithm based on the importance of the variables. On the basis of the optimized feature set and extended training samples using the proposed UAV-ground cosampling approach, the random forest estimation model was constructed to estimate the FVC in the study area. Results indicated that the model achieved a determinant coefficient (R²) of 0.923 and a RMSE of 0.087 on the testing sample set and outperformed the commonly used Pixel Dichotomy method. It can be used in the fast and accurate monitoring of vegetation dynamics in mining areas.