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Comparative analysis of Landsat TM, ETM+, OLI and EO-1 ALI satellite images at the Tisza-tó area, Hungary
53-62Views:339Satellite 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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Heavy metal content of flood sediments and plants near the River Tisza
120-131Views:76The River Tisza is Hungary’s especially important river. It is significant not only because of the source 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 the people living near the river and depend considerably on the quality of the sediments settled by the river. Several sources of pollution can be found in the catchment area of the River Tisza and some of them significantly contribute to the pollution of the river and its active floodplain. In this paper we study the concentration of zinc, copper, nickel and cobalt in sediments settled in the active floodplain and the ratio of these metals taken up by plants. Furthermore, our aim was to study the vertical distribution of these elements by the examination of soil profiles. The metal content of the studied area does not exceed the critical contamination level, except in the case of nickel, and the ratio of metals taken up by plants does not endanger the living organisms. The vertical distribution of metals in the soil is heterogeneous, depending on the ratio of pollution coming from abroad and the quality of flood.
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Land cover analysis based on descriptive statistics of Sentinel-2 time series data
1-9Views:385In our paper we examined the opportunities of a classification based on descriptive statistics of NDVI throughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 images in 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, in accordance with Corine Land Cover (CLC) nomenclature. The result of classification had a 76.2% overall accuracy. We described the reasons for the disagreement in case of the most remarkable errors.
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Water chemical analysis of the oxbow lakes near the Upper-Tisza River
36-42Views:92The 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 from the section of Tarpa to Rakamaz. 45 oxbow lakes were sampled and the chemical parameters were determined. Sodium was used as a pollutant (sewage water) indicator and 2 lakes were found extremely polluted. The lakes outside the dam were slightly polluted because of the lack of renewal of the water body and the ones in the active floodplain had good quality parameters.
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Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery
66-75Views:607In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.
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Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories
194-202Views:1186The remote sensing techniques provide a great possibility to analyze the environmental processes in local 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) were investigated from the aspect of land cover types: water body (W); plough land (PL); forest (F); vineyard (V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilarities and the correlation of spectral indices were examined. In BU – GL – F categories similar NDVI values were 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 other categories ranged from narrower interval. Weak correlation were found among the indices due to the differences caused by the water land cover class. Statistically, most land cover types differed from each other, but in several cases similarities can be found when delineating vegetation with various water content. MNDWI was found as the most effective in highlighting water bodies.