Drone-borne hyperspectral monitoring of acid mine drainage. An example from the Sokolov lignite district.

This contributions aims to demonstrate the potential of unmanned aerial systems (UAS) to monitor areas affected by Acid mine drainage (AMD). The investigated area covers a recultivated tailing, which is and part of the Sokolov coalmine district in the Czech Republic. A high abundance of AMD minerals occurs in a confined space of the selected test site, which AMD minerals in high abundances can signal potential environmental predicament. The deposited mine waste material contains pyrite and itsthe consecutive weathering products, mainly iron hydroxides and oxides, which affect the natural pH values of the Earth’s surface. While previous research done in this area relies on satellite and air-borne data, our approach focuses on lightweight drone systems providing ground readiness within hours and, thus,, enabling rapid field campaigns. High spatial image resolutions and and precise target determination are additional advantages of UAS-based mapping. During April to September 2016, in total four field and flight campaigns were conducted. For validation, the waste heap was probed in-situ for pH, X-ray fluorescence (XRF) and, reflectance spectrometry. and sSampling points were surveyed by a differential GNSS global navigation satellite systems. Ground truth was achieved by collecting samples that were characterized for pH, X-ray diffraction and XRF in laboratory conditions. Sampling points were surveyed by a differential GNSS global navigation satellite systems. Hyperspectral data were processed and corrected for atmospheric, topographic and illumination effects. High-resolution point clouds and digital elevation models were built from drone-borne RGB data using Structure-from-Motion. The supervised classification of hyperspectral image (HSI) data suggests the presence of jarosite and goethite -, minerals associated with the acidic environmental conditions (pH range = 2.3 – 2.8 in situ). We identified specific iron absorption bands in the UAS-HS data, and was confirmed with ground-truth spectroscopy. The distribution of in-situ pH data supports the UAS-based mineral classification results. Evaluation of the applied methods highlights the drone surveying as a fast, non-invasive, inexpensive technique for multi-temporal environmental monitoring of the post-mining landscape.