Abstract: The anthropic and natural elements have become more closely monitored and analysed through the use of remote sensing and GIS applications. In this regard, the study aims to feature a different approach to produce more and more thematic information, focusing on the development of built-up areas. In this paper, multispectral images and Synthetic Aperture Radar (SAR) images were the basis of a wide range of proximity analyses. These allow the extraction of data about the distribution of built-up space on the areas with potential for economic and social development. Application of interferometric coherence and supervised classifications have been accomplished on various territories, such as metropolitan areas of the most developed region of Romania, more specifically Transylvania. The results indicate accuracy values, which can reach 94 per cent for multispectral datasets and 93 per cent for SAR datasets. The accuracy of resulted data will reveal a variety of city patterns, depending mainly on local features regarding natural and administrative environments. In this way, a comparison will be made between the accuracy of both datasets to provide an analysis of the manner of built-up areas distribution to assess the expansion of the studied metropolitan areas. Therefore, this study aims to apply well-established methods from the remote sensing field to enhance the information and datasets in some areas lacking recent research.
Keywords: backscattering, metropolitan areas, supervised classification, urban footprint, built-up area
Abstract: This study evaluates the pattern of a night-time climate index, namely the tropical nights (T min ≥ 20 °C) during the 21st century in several different sized cities in the Carpathian Basin. For the modelling, MUKLIMO_3 microclimatic model and the cuboid statistical method were applied. In order to ensure the proper representation of the thermal characteristics of an urban landscape, the Local Climate Zone (LCZ) system was used as landuse information. For this work, LCZ maps were produced using WUDAPT methodology. The climatic input of the model was the Carpatclim dataset for the reference period (1981-2010) and EURO-CORDEX regional model outputs for the future time periods (2021-2050, 2071-2100) and emission scenarios (RCP4.5, RCP8.5). As results show, there would be a remarkable increase in the number of tropical nights along the century, and there is a clearly recognizable increase owing to urban landform. In the near past, the number of the index was 6-10 nights higher in the city core than the rural area where the number of this index was negligible. In the near future this urban-rural trend is the same, however, there is a slight increase (2-5 nights) in the index in city cores. At the end of the century, the results of the two emission scenarios become distinct. In the case of RCP4.5 the urban values are about 15-25 nights, what is less stressful compared to the 30-50 nights according to RCP8.5. The results clearly highlight that the effect of urban climate and climate change would cause serious risk for urban dwellers, therefore it is crucial to perform climate mitigation and adaptation actions on both global and urban scales.
Keywords: climate change, urban climate, Local Climate Zones, urban climate modelling
Abstract: The relationship between precipitation and elevation is a well-known topic in the field of geography and meteorology. Radar-based precipitation data are often used in hydrologic models, however, they have several inaccuracies, and elevation can be one of the additional parameters that may help to improve them. Thus, our aim in this article is to find a quantitative relationship between precipitation and elevation in order to correct precipitation data input into hydrologic models. It is generally accepted that precipitation increases with elevation, however, the real situation is much more complicated, and besides elevation, the precipitation is dependent on several other topographic factors (e.g., slope, aspect) and many other climatic parameters, and it is not easy to establish statistically reliable correlations between precipitation and elevation. In this paper, we examine precipitation-elevation correlations by using multiple regression analysis based on monthly climatic data. Further on, we present a method, in which these regression equations are combined with kriging or inverse distance weighting (IDW) interpolation to calculate precipitation fields, which take into account topographic elevations based on digital terrain models. Thereafter, the results of the different interpolation methods are statistically compared. Our study areas are in the hilly or low mountainous regions of Hungary (Bakony, Mecsek, Börzsöny, Cserhát, Mátra and Bükk montains) with a total of 52 meteorological stations. Our analysis proved that there is a linear relationship between the monthly sum of precipitation and elevation. For the North Hungarian Mountains, the correlation coefficients were statistically significant for the whole study period with values between 0.3 and 0.5. Multivariate regression analysis pointed out that there are remarkable differences among seasons and even months. The best correlation coefficients are typical of late spring-early summer and October, while the weakest linear relationships are valid for the winter period and August. The vertical gradient of precipitation is between one and four millimetres per 100 metres for each month. The statistical comparison of the precipitation interpolation had the following results: for most months, co-kriging was the best method, and the combined method using topography-derived regression parameters lead to only slightly better results than the standard kriging or IDW.
Keywords: precipitation, elevation, DTM, kriging, IDW, co-kriging, multivariate regression
Abstract: This study aimed to map soil organic carbon under erosion processes on an arable field in the Republic of Bashkortostan (Russia). To estimate the spatial distribution of organic carbon in the Haplic Chernozem topsoil, we applied Sentinel-2A satellite data and the linear regression method. We used 13 satellite bands and 15 calculated spectral indices for regression modelling. A regression model with an average prediction level has been created (R2 = 0.58, RMSE = 0.56, RPD = 1.61). Based on the regression model, cartographic materials for organic carbon content have been created. Water flows and erosion processes were determined using the calculated Flow Accumulation model. The relationship between organic carbon, biological activity, and erosion conditions is shown. The 13C-NMR spectroscopy method was used to estimate the content and nature of humic substances of different soil samples. Based on the 13C-NMR analysis, a correlation was established with the spectral reflectivity of eroded and non-eroded soils. It was revealed that the effect of soil organic carbon on spectral reflectivity depends not only on the quantity but also on the quality of humic substances and soil formation conditions.
Keywords: Soil organic carbon, remote sensing, sentinel, erosion, humic acids,13C-NMR
Abstract: This article investigates the interconnectedness between neighbourhood diversity and local business structures. For this purpose, interviews with residents and entrepreneurs were conducted in three European cities: Budapest, Copenhagen, and Milan. The results show that diversity in the economic structure of urban neighbourhoods is equally important with regards to residents’ quality of life, the image of the neighbourhood, and local social cohesion. Therefore, the main recommendation is that policy makers should act to preserve the diversity of local business structures, and that the concept of diversity itself should be understood in a broader sense, taking local peculiarities into account.
Keywords: urban diversity, local entrepreneurship, neighbourhood services, business ecosystem, comparative analysis
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