With the emergence and development of unmanned aerial vehicles (UAVs), different sensors have become more miniaturized and intelligent. UAVs equipped with various sensors are now an important approach for acquiring spatial data. Many advantages, such as low cost, short revisiting cycle, flexibility and high precision, have made UAVs powerful tools in geological, agricultural, ecological and forestry growth monitoring, as well as evaluation. Now, UAVs are a hotspot in scientific research. Their application in mining areas (MA), although still in its infancy, is developing rapidly in terms of speed, scale and service scope. This research examines aspects such as UAV platforms, different sensors and their application fields, as well as reviewing the advances of scientific research in MA at the present time. By combining current research and the functions of multiple sensors, an application framework for UAV monitoring in MA is constructed. Finally, the challenge and prospects for the development of UAVs and sensors are also considered. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring in MA, as well as an assessment of applications in mining, reclamation and environment.

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km² of land from grassland, water, etc., to barren earth, and only 23.54 km² was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas.