The characterization of heavy metal polluted abandoned mining sites is a complicated assignment due to the variable spatial distribution of the pollutants, therefore complex integrated method is required in order to assess precisely the amount and the distribution of the contaminants. The examined area is flotation sludge reservoir of abandoned Pb-Zn mining site with serious heavy metal contamination. located in Gyöngyösoroszi, Northern Hungary.
The hyperspectral image of the flotation sludge is obtained by using a Digital Airborne Imaging Spectrometer DAIS 7915, in the frame of DLR HySens first Hungarian hyperspectral flight campaign (21/08/2002). Parallel to the flight campaign heavy metal content of soil samples were examined from the area of the flotation sludge. The analysis of hyperspectral data was verified by the examination of mine tailing samples by FPXRF (Field Portable X-ray Fluorescence spectrometry) (NITON XL-703).
Determinations of heavy metal containing minerals are based on the spectral profiles of the pixels of the area with using USGIS standard spectral profiles of the examined materials (galena, pyrite, sphalerite, goethite and jarosit).
Applying the Spectral Angle Mapper with BandMax classification the distribution of minerals (galena, pyrite, sphalerite, goethite, jarosit) in the area was defined. The mineral formation occurs especially at the levees and the barren places of the Szárazvölgyi flotation sludge reservoir. Based on the statistic results of the samples, principal component analysis and correlation coefficient between the different metal content of the samples were calculated. The highest correlations were found between Pb-Zn, Fe-Zn and between Fe-Pb. This prove the results of the principal component analysis, where usually Pb, Zn, Fe introduce the main component.
Canopy analysis was also carried out with the hyperspectal image in order to classify the differences between vegetation types at the Szárazvölgy flotation sludge reservoir and analyse the applicability of it. Supervised classification methods were used to distinguish 8 vegetation types based on the spectral properties of the area. The results of the classifications were compared to a ground truth image, based on ortophoto, topographic map, and GPS based field data collection. According to results of the comparison, the paralellpiped classification method is proved to be appropriate method based on the overall accuracy of canopy classification, which was 54% due to heterogeneity of the vegetation.
The results of hyperspectral data and FPXRF analysis suggest that Pb, Zn and Fe containing minerals have similar spatial distribution in the examined and barren area.
Based on this study hyperspectral remote sensing is likely to be an effective tool for the characterization and modeling the distribution of Pb, Zn and Fe containing minerals at the examined heavy metal polluted sites. Further more, based on the vegetation analysis plant species for phytoremediation can be defined.
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 carried 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.
The more widely use of GIS, remote sensing technology provides appropriate data acquisition and data processing tools to build several national and international biodiversity monitoring system of environmental protection and natur conservation. The ChangeHabitats 2 is a similar international project, which uses airborne hyperspectral and airborne laser scanning (airborne LiDAR) sources beyond traditional data collection methods to build a monitoring system of Natura 2000 habitats. The goal of our research, on one hand, was to separate the most typical species of trees which can be found in the largest coverage in the research plots of Debreceni Nagyerdő Nature Reserve from field and airborne remote sensing data, use image classification that based on spectral and geometry (height) characteristics of the trees. On the other hand our goal was to evaluate the efficient use of the integration of mobilGIS, airborne hyperspectral and airborne LiDAR data collecting methods to complement or substitut of the traditional, field data collecting methods. We used ArcGIS 10.2 and Exelis 5.0 GIS software for data evaluation, in which the mosaicing, the selection of plots and the spectral image processing were carried out.
The use of remote sensing in forest management and agriculture is becoming more prominent. The rapid development of technology allowed the emergence of database suitable for precision application in addition to the previously used low-resolution and low data content images. The high resolution, hyperspectral images are not only suitable for separating the different land use categories and vegetation types but also for examining the soil characteristics and biophysical features of plants (Blackburn and Steel, 1999; Condit, 1970). We processed a multispectral satellite image (Landsat 7 ETM+) and a hypespectral areal image (DAIS 7915) about a farm on the plains and evaluated the different image classification methods. During our examinations, we examined the geometrical and radiometrical characteristics of images first, then assigning the training areas, we determined the spectral characteristics of land use categories. We performed a multispectral analysis for checking land use, where we compared controlled and uncontrolled classification systems to check their reliability. We used areal and spectral reductions to make the classifications more accurate and to reduce the length of calculations.
Hyper and multispectral imaging systems are widely used in agricultural and environmental protection. Remote sensing techniques are suitable for evaluating environmental protection hazarsd, as well as for agriculture resource exploration. In our research we compared aerial hyper and multispectral images, as well as multispectral digital camera images with the background data from the test site. Hyperspectral records were obtained using a new 80-channeled aerial spectrometer (Digital Airborne Imaging Spectrometer /DAIS 7915/. We have chosen two farms where intensive crop cultivation takes place, as test sites, so soil degradation and spreading of weeds can be intensive as a result of land use and irrigation. We took additional images of air and ground with a TETRACAM ADC wide band multispectral camera, which can sense blue, green and near infrared bands. We had detailed GIS database about the test site. Weed and vegetation map of the area in the spring and the summer was made in 2002. For soil salt content analysis, we gathered detailed data frome an 80x100 m area. When analyzing the images, we evaluated image reliability, and the connection between the bands and the soil type, pH and salt content, and weed mapping. In the case of hyperspectral images, our aim was to choose and analyze the appropriate band combinations. With a TETRACAM ADC camera, we made images at different times, and we calculated canopy, NDVI and SAVI indexes. Using the background data mentioned above, the aim of our study was to develop a spectral library, which can be used to analyze the environmental effects of agricultural land use.