Publisher © Czech Geological Survey, ISSN: 2336-5757 (online), 0514-8057 (print)
Using image spectroscopy to determine parameters of surface mining waters
Abstract
Water has been traditionally monitored by in situ point measurement, but point samples are not adequate to observe spatial and temporal variations in large areas or in polluted regions where the water quality can change dramatically and needs to be monitored at a regular basis. Image spectroscopy (hyperspectral remote sensing) provides a new way to obtain continuous information on water quality at regional scales. In our study we tested the feasibility of mapping properties of surface waters affected by long-term mining activities using airborne multi-flight-line HyMap hyperspectral (HS) datasets. The work was carried on the Sokolov lignite mines as they represent a site with extreme dynamics, material heterogeneity, high pH gradients and wide varieties of surface water pollution. For eight different water bodies physical and chemical properties were determined and associated with the spectral properties of pure image pixels. To estimate the selected water parameters (content of Fe, solid particles and dissolved organic carbon) a sub-pixel method, the linear spectral unmixing (LSU), was tested and, as sufficient accuracy was achieved, semi-quantitative maps were created.
