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 sens...ors 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%).
The 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
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
In 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 Co
accuracy. We described the reasons for the disagreement in case of the most remarkable errors.
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 from
the section of Tarpa to Rakamaz. 45 oxbow lakes were sampled and the chemical parameters were
determined. Sodium was used as a pollutant
the water body and the ones in the active floodplain had good quality parameters.
In 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 spect...ral 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.
The 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 land
cover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) were
investigated from the aspect of land c
(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.