The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

Context. Solar wind disturbances such as interplanetary coronal mass ejections (ICMEs) and corotating interaction regions (CIRs) cause short-term cosmic ray depressions, generally denoted as Forbush decreases. Aims. We conduct a systematic statistical study of various aspects of Forbush decreases. The analysis provides empirical background for physical interpretations of short-term cosmic ray modulations. Methods. Firstly, we analyzed the effects of different types of solar wind disturbances, and secondly, we focused on the phenomenon of over-recovery (the return of the cosmic ray count to a value higher than the pre-decrease level). The analysis is based on groundbased neutron monitor data and the solar wind data recorded by the Advanced Composition Explorer. The correlations between various cosmic ray depressions and solar wind parameters as well as their statistical significance are analyzed in detail. In addition, we performed a normalized superposed epoch analysis for depressions and magnetic field enhancements. Results. The analysis revealed differences in the relationship between different solar wind disturbances and cosmic ray depression parameters. The amplitude of the depression for ICMEs was found to correlate well with the amplitudes of magnetic field strength and fluctuations, whereas for CIRs we found only the correlation between the amplitude of the depression and the solar wind disturbance dimension proxy vt B . Similar behavior was found for shock and no-shock events, respectively. The CIR/ICME composites show a specific behavior that is a mixture of both ICMEs and CIRs. For all analyzed categories we found that the duration of the depression correlates with the duration of the solar wind disturbance. The analysis of the over-recovery showed that there is no straightforward relationship to either "branching-effect" or geomagnetic effects, therefore we propose a scenario where the "branching-effect" is caused by several factors and is only indirectly related to the over-recovery.

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Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time.

Satellite imagery with different spatial resolutions and global daily revisit time provide much information of earth surfaces on a large scale in a short time. Thereby it is necessary to determine the horizontal accuracy of the satellite imagery to enable the possibility of their future everyday use in different application fields like environmental assessment, urban monitoring, forestry management, etc. In this research multispectral (MS) imagery from PlanetScope (PS), RapidEye (RE) and WorldView-2 (WV2) satellites was used for horizontal accuracy assessment. The imagery was obtained at different processing levels (basic-non-orthorectified, ortho-orthorectified).