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Vol 10No 22016
Satellite images are important information sources of land cover analysis or land cover change monitoring. We used the sensors of four different spacecraft: TM, ETM+, OLI and ALI. We classified the study area using the Maximum Likelihood algorithm and used segmentation techniques for training area selection. We validated the results of all sensors... to reveal which one produced the most accurate data. According to our study Landsat 8’s OLI performed the best (96.9%) followed by TM on Landsat 5 (96.2%) and ALI on EO-1 (94.8%) while Landsat 7’s ETM+ had the worst accuracy (86.3%).
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Vol 2No 22008
The River Tisza is Hungary’s especially important river. It is significant not only because of thesource of energy and the value insured by water (hydraulical power, shipping route, stock of fish,aquatic environment etc.) but the active floodplain between levees as well. Ploughlands, orchards,pastures, forests and oxbow lakes can be found here. ...They play a significant role in the life of thepeople living near the river and depend considerably on the quality of the sediments settled by theriver. Several sources of pollution can be found in the catchment area of the River Tisza and some ofthem significantly contribute to the pollution of the river and its active floodplain. In this paper westudy the concentration of zinc, copper, nickel and cobalt in sediments settled in the active floodplainand the ratio of these metals taken up by plants. Furthermore, our aim was to study the verticaldistribution of these elements by the examination of soil profiles. The metal content of the studiedarea does not exceed the critical contamination level, except in the case of nickel, and the ratio ofmetals taken up by plants does not endanger the living organisms. The vertical distribution of metalsin the soil is heterogeneous, depending on the ratio of pollution coming from abroad and the qualityof flood.
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Vol 12No 22018
In our paper we examined the opportunities of a classification based on descriptive statistics of NDVIthroughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 imagesin 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, inaccordance with Corine Land Cover (CLC)... nomenclature. The result of classification had a 76.2% overallaccuracy. We described the reasons for the disagreement in case of the most remarkable errors. .
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Vol 1No 12007
The Tisza river plays an important role in the life of Eastern Hungary. Beside the river there are several oxbow lakes, cut off meanders. In this paper the water quality of these lakes was examined fromthe section of Tarpa to Rakamaz. 45 oxbow lakes were sampled and the chemical parameters weredetermined. Sodium was used as a pollutant (sewage indicator and 2 lakes were found extremely polluted. The lakes outside the dam were slightly polluted because of the lack of renewal ofthe water body and the ones in the active floodplain had good quality parameters.
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Vol 10No 3-42016
The remote sensing techniques provide a great possibility to analyze the environmental processes inlocal or global scale. Landsat images with their 30 m resolution are suitable among others for landcover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) wereinvestigated from the aspect of land cover types: body (W); plough land (PL); forest (F); vineyard(V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilaritiesand the correlation of spectral indices were examined. In BU – GL – F categories similar NDVI valueswere calculated, but the other land cover types differed significantly. The water related indices (NDWI,MNDWI) were more effective (especially the MNDWI) to enhance water features, but the values of othercategories ranged from narrower interval. Weak correlation were found among the indices due to thedifferences caused by the water land cover class. Statistically, most land cover types differed from eachother, but in several cases similarities can be found when delineating vegetation with various watercontent. MNDWI was found as the most effective in highlighting water bodies.
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