KEYWORDS: Unmanned aerial vehicles, Mining, Point clouds, 3D modeling, 3D acquisition, Photogrammetry, 3D metrology, Satellites, Data acquisition, Land mines
Mining companies worldwide routinely monitor their excavation activity. Until a few years ago terrestrial measurements, aerial photogrammetry and remote sensing using very high-spatial resolution satellite data were the usual methodologies. In particular, executing precise terrestrial measurements with topographic equipment of Differential GNSS constitutes a time-consuming procedure. Although the absolute precision of individual points is extremely high (mm level), it is challenging to survey large land areas. At the same time, Terrestrial Laser Scanners (TLSs) provide comparable accuracy by collecting millions of points per second, decreasing the surveying time substantially; yet, deploying these sensors inside the quarries continues to be problematic. While costly, with aerial photogrammetry data from large quarry areas is collected at a cm level accuracy. Satellite data present the same pros and cons as aerial photogrammetry in terms of area coverage, accuracy, and cost. The advent of Unmanned Aerial Vehicles (UAVs) and the development of high-accuracy cameras and light-wise LiDAR sensors open new opportunities for the monitoring of quarries. In the present study we evaluate and compare the 3D point clouds derived from high-accuracy UAV cameras to the respective data collected by TLS. An open pit bauxite mine in Greece, monitored in the frame of the m4mining project, is selected as the study area. “Μ4mining” is an EU-funded project that aims at confining the resolution gap between satellite- and UAV-acquired data for mine monitoring. The 3D point clouds derived from UAV flight campaigns and TLS measurements are compared in terms of point density and fidelity of topographic representation. The current work proposes an effective and precise methodology to accurately 3D map a site, using cost-efficient data, acquired by UAV and TLS.
HySpex is presenting an integrated solution for hyperspectral drill core imaging. The system’s mineral mapping capabilities are presented in close cooperation with renowned academic and industrial partners through the Center to Advance the Science of Exploration to Reclamation in Mining (CASERM) led by the Colorado School of Mines and Virginia Tech. Utilizing HySpex cameras covering the spectral range between 400 and 2500 nm, the system is capable of scanning full core boxes in seconds. Using Prediktera’s new Breeze-GEO Software, real-time mineral mapping of the highest quality is achieved. Apart from different interactive qualitative and quantitative data analysis tools offered by Breeze, the platform includes the publicly available USGS Material Identification and Classification Algorithm (MICA) for mineral identification, as well as the Minimum Wavelength Mapping (MWL) algorithm. The scanner’s capabilities are demonstrated using drill cores from the LaRonde-Penna deposit. The deposit is located within the Archean Abitibi greenstone belt of Ontario and Quebec, Canada, which is home to numerous Volcanogenic Massive Sulfide (VMS) deposits. LaRonde-Penna contains an endowment of 71 Mt of ore grading on average 3.9 g/t Au and economic grades of Zn, Cu and Pd. Because the deposits have been studied extensively over the past decades, cores from the deposit open up a unique opportunity for research and development.
Hyperspectral cameras are capable of obtaining highly useful data for geology, agriculture, urban planning, and many other applications. Several satellite-based hyperspectral cameras are currently operational, providing hyperspectral data to various users. Even large instruments usually have relatively large ground sampling distance (GSD): 10m or larger in 400 to 1000nm range and 30m or larger in 900 to 2500nm range. GSD is even coarser in hyperspectral cameras for microsatellites. Based on the information from PRISMA 2021 Workshop and our customer’s feedback, the most requested feature for satellite-based hyperspectral cameras is significantly improved GSD. Also, there is a strong demand for smaller microsatellite-compatible hyperspectral cameras. Due to lower mission cost, such cameras can provide hyperspectral data to more users. Additionally, microsatellite constellations could provide swath and revisit time that would be impossible for a single large satellite. Creating a hyperspectral camera with acceptable Signal-to-Noise Ratio (SNR) and small GSD, that would be still compatible with a small platform, is a big challenge. Our approach has been to create a hyperspectral camera that would surpass the current limitations of small satellite platforms, and would provide data that, for some specifications, exceed what is available for free from large instruments. Our focus has been on providing significantly improved GSD, small spatial and spectral misregistration, while keeping acceptable spectral sampling and SNR. The instrument development has been funded by the Norwegian Space Agency. One of the proposed instruments has been selected by the Norwegian Space Agency as the primary payload on an upcoming Norwegian In-Orbit Demonstrator satellite.
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