Open-pit mine is still an unavoidable activity but can become unsustainable without the restoration of degraded sites. Monitoring the restoration after extractive activities is a legal requirement for mine companies and public administrations in many countries, involving financial provisions for environmental liabilities. The objective of this contribution is to present a rigorous, low-cost and easy-to-use application of Unmanned Aerial Systems (UAS) for supporting opencast mining and restoration monitoring, complementing the inspections with very high (<10 cm) spatial resolution multispectral imagery, and improving any restoration documentation with detailed land cover maps. The potential of UAS as a tool to control restoration works is presented in a calcareous quarry that has undergone different post-mining restoration actions in the last 20 years, representing 4 reclaimed stages. We used a small (<2 kg) drone equipped with a multispectral sensor, along with field spectroradiometer measurements that were used to radiometrically correct the UAS sensor data. Imagery was processed with photogrammetric and Remote Sensing and Geographical Information Systems software, resulting in spectral information, vegetation and soil indices, structural information and land cover maps. Spectral data and land cover classification, which were validated through ground-truth plots, aided in the detection and quantification of mine waste dumping, bare soil and other land cover extension. Moreover, plant formations and vegetation development were evaluated, allowing a quantitative, but at the same time visual and intuitive comparison with the surrounding reference systems. The protocol resulting from this research constitutes a pipeline solution intended for the implementation by public administrations and privates companies for precisely evaluating restoration dynamics in an expedient manner at a very affordable budget. Furthermore, the proposed solution prevents subjective interpretations by providing objective data, which integrate new technologies at the service of scientists, environmental managers and decision makers.

Mining is an essential activity that supports the provision of raw materials. However, the extraction process of mining has deep environmental impacts. For this reason, restoration actions are mandatory, and monitoring is a key step in ensuring the renaturalization of affected areas. Erosion processes are one of the main problems that affect restored areas in extractive activities due to the frequently steep slopes and the difficulty of revegetating the technosols constructed using mining debris. This research aims to develop a method for determining soil losses due to water erosion in mine-restored areas by using Geographic Information Systems (GIS) and Remote Sensing (RS) tools. For the study, images obtained using Unmanned Aerial Systems (UAS) in an open pit mine in the process of restoration are used, from which the Digital Elevation Model (DEM) of the current state of the slopes is obtained (0.10 m spatial resolution). With GIS techniques, ridges of the rills and gullies generated in the slopes are detected, whereby an estimation of a first DEM before the erosive process and a second DEM after the erosive process can be constructed. Each of these DEMs are evaluated individually in order to determine the height differences and estimate the volumetric loss. At the same time, the results are validated with the DEM derived from official mapping agencies’ airborne Lidar data (1.00 m spatial resolution), which yield consistent data in the volumetric quantification of the erosion despite the difference in spatial resolution. In conclusion, the high spatial resolution of drone images facilitated a detailed monitoring of erosive processes, obtaining data from vast and inaccessible slopes that are usually immeasurable with traditional field techniques, and altogether improving the monitoring process of mine restoration.

Unmanned Aerial Systems, or drones, are very helpful tools for managing open-pit mining operations and developing ecological restoration activities. This article presents a method for identifying water erosion processes in active quarries by means of drone imagery remote sensing, in the absence of pre-existing imagery or mapping for comparison. A Digital Elevation Model (DEM) with a spatial resolution (SR) >10 cm and an orthophoto with an SR >2.5 cm were generated from images captured with a drone and their subsequent photogrammetric processing. By using Geographical Information Systems tools to process the DEM, a detailed drainage network was obtained, the areas of detected water erosion were separated, and the watersheds in the gullies identified. Subsequently, an estimated DEM before the erosive processes was reconstructed by interpolating the gully ridges; this DEM serves as a reference for the relief before the erosion. To calculate the volume of eroded material, the DEM of Differences was calculated, which estimates the volume difference between the previously estimated DEM and the current DEM. Additionally, we calculated the material necessary for the geomorphological adaptation of the quarry and the slope map, which are two valuable factors closely related to the monitoring of erosive processes. The results obtained allowed us to identify the erosion factors quickly and accurately in this type of mining. In the case of water-filled quarries, it would be important to characterize the subsurface relief. Essentially, the presented method can be applied with affordable and non-invasive materials to create digital grid maps at 10 cm resolution, obtaining data ready for 3D metrics, being a very practical landscape modelling tool for characterizing the restoration evolution of open-pit mining spaces.

Mining is an important activity of the primary sector with strong economic and environmental impacts. All over the world, governments have made efforts to regulate mine restoration by monitoring and assessing the evolution of mined sites. Our work aims to synthesize various remote sensing applications into a single workflow in order to obtain cartographic products using Unmanned Aerial Systems (UAS), not only for mine restoration management, but also as a way of monitoring mining activity as a whole. The workflow performs image processing and terrain analysis calculations, which conduct a supervised classification of the land cover. The resulting mapping products include orthoimagery, Digital Surface Models (DSM), land cover maps, volume variation calculations, dust deposition, detection of erosion problems, and drainage network evaluation maps. The data obtained from red-green-blue (RGB) sensors has a spatial resolution of 4-10 cm, providing information that allows the characterization of land covers with an overall accuracy of 91%. In comparison, if using multispectral sensors with the same flight conditions than RGB, image spatial resolution diminishes and land cover characterization accuracy drops to 81%. The resulting digital maps can be fully integrated into Geographic Information Systems (GIS), allowing the quantification of environmental features and spatial changes. Our study provides the basis for creating a large-scale, replicable and ready-to-use workflow suited for monitoring the exploitation of minerals and mine restoration using RGB imagery obtained through drones.