Abstract
Heavy mineral deposits occur in several coastal areas of the world, formed over a long period due to variations in mean sea level, wave action, and winds. These are the main sources of ilmenite (FeTiO3), which in turn is the source of more than 80% of the TiO2 produced and applied in various industries, most recently in nanotechnology. The present study mapped heavy mineral deposits on the coast of Rio Grande do Sul in southern Brazil using integrated proximal and orbital thermal infrared (TIR) remote sensing techniques. Mineral groups, such as oxides and silicates, have spectral features in the TIR wavelengths. Using laboratory spectroscopy at TIR using Nicolet 6700 Thermo Scientific Spectrometer, we measured the spectral signature of the local sample of heavy minerals (between 8 and 14 μm) and identified a diagnostic spectral feature at 10.75 μm. The signature was resampled to be compatible with the Advanced Spaceborne Thermal Emission Radiometer (ASTER) sensor bandwidth values and used as a reference endmember for the Spectral Angle Mapper (SAM) and Linear Spectral Unmixing (LSU) digital image classification algorithms. Thus, we identified the presence of the reference endmember (heavy minerals) in the pixels of the ASTER scene. In pixels classified by SAM as the presence of heavy minerals, LSU was applied to estimate the surface concentration within the pixel. The results showed a concentration of up to 20% of heavy minerals, with the highest concentration on the beach and dune fields. Opaque minerals such as ilmenite do not have spectral reflectance features in visible, near-infrared, and short-wave infrared, which makes their identification by remote sensing difficult. The present study showed that the integration of proximal and orbital as well as hyperspectral and multispectral thermal data can be considered as an alternative for detecting and mapping heavy minerals in coastal areas.