Characterization of heavy metal polluted abandoned mining sites is complicated, as the spatial distribution of pollutants often changes dramatically. In our study, a hyperspectral data analysis of the Gyöngyösoroszi abandoned Pb-Zn mine, located in northern Hungary, where Záray (1991) reported serious heavy metal contamination, was carrie
...d out using ENVI 4.3. In this area, galena (PbS), goethite (FeO(OH)), jarosite (KFe3(SO4)2(OH)6), sphalerite ((Zn, Fe)S) and pyrite (FeS2) were the predominant minerals in the alteration zones was chosen as the target mineral. Spectral angle mapper (SAM) and BandMax classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixels in the hyperspectral image to a reference spectrum. As a result of hyperspectral imagery the distribution of pyritic minerals (sphalerite, galena) in the area was defined. Both of the mineral formations occur, especially in mine tailings, the area of the ore preparatory, and the Szárazvölgyi flotation sludge reservoir. According to the results, jarosite and goethite have similar distributions to sphalerite and galena. The results showed that hyperspectral remote sensing is an effective tool for the characterization of Pb, Zn and Fe containing minerals at the examined polluted sites and for modelling the distribution of heavy metals and minerals in extensive areas. This classification method is a basis of further detailed investigations, based on field measurements, to map the heavy metal distribution of the studied area and to quantify the environmental risks caused by erosion, which include DEM (digital elevation model) and climatic and hydrological data sources. Furthermore, it can be used primarily to support the potentially applicable phytostabilization technique and to isolate hot spots where only ex-situ remediation techniques can be applied.