Research of relationship between light reflectance and surface moisture content in tailings samples using hyperspectral images

Abstract

Proper tailings storage facilities (TSF) management can be done by monitoring various variables, such as moisture, which influences at physical and chemical stability phenomena. Nowadays, there are no continuous moisture controls due to the dangers associated with taking samples in tailings, generally in low-density states, making access difficult. Due to the recent hyperspectral imaging technologies and the successful use in monitoring interest variables (moisture, fines content, mineralogy) in areas such as hydrology and agriculture, therefore, this research uses hyperspectral images to estimate surface moisture in TSF using copper and iron tailings samples, which are subjected to active illumination from a 980 nm laser and monitored in a desiccation process in the moisture range from saturation to dry state, including density and fine content effects. Results show a parabolic relationship between moisture and light reflection, which, added to the use of the normalized difference vegetation index (NDVI), allow estimating the surface samples moisture content, generating an auspicious method for the continuous monitoring of humidity in TSF.