Pub Date : 2021-04-06DOI: 10.15201/HUNGEOBULL.70.1.4
A. Suleymanov, I. Gabbasova, R. Suleymanov, E. Abakumov, V. Polyakov, P. Liebelt
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 213C-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.
{"title":"Mapping soil organic carbon under erosion processes using remote sensing","authors":"A. Suleymanov, I. Gabbasova, R. Suleymanov, E. Abakumov, V. Polyakov, P. Liebelt","doi":"10.15201/HUNGEOBULL.70.1.4","DOIUrl":"https://doi.org/10.15201/HUNGEOBULL.70.1.4","url":null,"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 213C-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.","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"70 1","pages":"49-64"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45319076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-06DOI: 10.15201/HUNGEOBULL.70.1.6
K. Mezentsev
{"title":"Karácsonyi, D., Taylor, A., and Bird, D. (eds.): The Demography of Disasters, Impacts for Population and Place","authors":"K. Mezentsev","doi":"10.15201/HUNGEOBULL.70.1.6","DOIUrl":"https://doi.org/10.15201/HUNGEOBULL.70.1.6","url":null,"abstract":"","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"70 1","pages":"81-84"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41904897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-06DOI: 10.15201/HUNGEOBULL.70.1.5
Szabolcs Fabula, Rikke Skovgaard Nielsen, E. Barberis, L. Boros, Anne Hedegaard Winther, Z. Kovács
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.
{"title":"Diversity and local business structure in European urban contexts","authors":"Szabolcs Fabula, Rikke Skovgaard Nielsen, E. Barberis, L. Boros, Anne Hedegaard Winther, Z. Kovács","doi":"10.15201/HUNGEOBULL.70.1.5","DOIUrl":"https://doi.org/10.15201/HUNGEOBULL.70.1.5","url":null,"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.","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"70 1","pages":"65-80"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43306984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-06DOI: 10.15201/HUNGEOBULL.70.1.7
M. Rzeszewski
{"title":"Travis, C., Ludlow, F. and Gyuris, F. (eds.): Historical Geography, GIScience and Textual Analysis. Landscapes of Time and Place","authors":"M. Rzeszewski","doi":"10.15201/HUNGEOBULL.70.1.7","DOIUrl":"https://doi.org/10.15201/HUNGEOBULL.70.1.7","url":null,"abstract":"","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"70 1","pages":"85-88"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48477320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-06DOI: 10.15201/HUNGEOBULL.70.1.3
Tamás Schneck, T. Telbisz, I. Zsuffa
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.
{"title":"Precipitation interpolation using digital terrain model and multivariate regression in hilly and low mountainous areas of Hungary","authors":"Tamás Schneck, T. Telbisz, I. Zsuffa","doi":"10.15201/HUNGEOBULL.70.1.3","DOIUrl":"https://doi.org/10.15201/HUNGEOBULL.70.1.3","url":null,"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.","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"70 1","pages":"35-48"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49602217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-06DOI: 10.15201/HUNGEOBULL.70.1.1
Constantin Oșlobanu, M. Alexe
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.
{"title":"Built-up area analysis using Sentinel data in metropolitan areas of Transylvania, Romania","authors":"Constantin Oșlobanu, M. Alexe","doi":"10.15201/HUNGEOBULL.70.1.1","DOIUrl":"https://doi.org/10.15201/HUNGEOBULL.70.1.1","url":null,"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.","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"70 1","pages":"3-18"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47037956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.15201/hungeobull.69.4.5
A. Uzzoli, Z. Egri, Dániel Szilágyi, Viktor Pál
The availability of health care services is an important issue, however, improving availability of health care services does not necessarily mean better accessibility for everybody. The main aim of this study is to find out how better availability in the care of acute myocardial infarction vary with accessibility of patients’ geographical location within Hungary. We applied statistical analysis and interview techniques to unfold the role of spatiality in the conditions of access to health care. Results of statistical analysis indicate significant health inequalities in Hungary. Decreasing national mortality rates of acute myocardial infarction, has been coupled by increasing spatial inequalities within the country especially at micro-regional level. According to in-depth interviews with local health care stakeholders we defined factors that support access to health care as well as important barriers.The supporting factors are related to the improvement of availability (i.e. infrastructural developments), while geographical distance, lack of material and human resources, or low level of health literacy proved to be the most relevant barriers. Main conclusion is that barriers to accessibility and availability are not only spatial but are also based on individual stages of acute myocardial infarction care. The development of cardiac catheter centres in Hungary has improved the short-term chances of infarction survival, but long-term survival chances have worsened in recent years due to deficiencies in rehabilitation care as well as low level of health literacy.
{"title":"Does better availability mean better accessibility? Spatial inequalities in the care of acute myocardial infarction in Hungary","authors":"A. Uzzoli, Z. Egri, Dániel Szilágyi, Viktor Pál","doi":"10.15201/hungeobull.69.4.5","DOIUrl":"https://doi.org/10.15201/hungeobull.69.4.5","url":null,"abstract":"The availability of health care services is an important issue, however, improving availability of health care services does not necessarily mean better accessibility for everybody. The main aim of this study is to find out how better availability in the care of acute myocardial infarction vary with accessibility of patients’ geographical location within Hungary. We applied statistical analysis and interview techniques to unfold the role of spatiality in the conditions of access to health care. Results of statistical analysis indicate significant health inequalities in Hungary. Decreasing national mortality rates of acute myocardial infarction, has been coupled by increasing spatial inequalities within the country especially at micro-regional level. According to in-depth interviews with local health care stakeholders we defined factors that support access to health care as well as important barriers.The supporting factors are related to the improvement of availability (i.e. infrastructural developments), while geographical distance, lack of material and human resources, or low level of health literacy proved to be the most relevant barriers. Main conclusion is that barriers to accessibility and availability are not only spatial but are also based on individual stages of acute myocardial infarction care. The development of cardiac catheter centres in Hungary has improved the short-term chances of infarction survival, but long-term survival chances have worsened in recent years due to deficiencies in rehabilitation care as well as low level of health literacy.","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":"69 1","pages":"401-418"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43038063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.15201/hungeobull.69.4.2
Szabolcs Balogh, T. Novák
The transformation of the landscapes due to the anthropogenic activities is increasing worldwide. These changes are also manifested in the change of soil-forming processes. The land cover (LC) changes evaluated according to their influence on anthropogenic features of soils allows to distinguish between LC changes resulting increased and decreased human impact (HI). In our study, we assess the changes of HI on landscapes and its spatial distribution across Hungary. The changes were evaluated by using LC data of four periods between 1990 and 2018 reclassified based on the related anthropogenic soil features. To identify the hotspots of the changes 1×1 grids were applied in which the direction (increasing, neutral or decreasing HI) and frequency (number of landscape patches with LC changes) of changes were evaluated. In our research, the hotspots were identified over the studied four periods. We point out that the spatial distribution of hotspots is very different. The hotspots of the increased human impact are 2,449 cells (643.0 km2 ) between 1990 and 2018, and the most of it localized in the Pest Plain (67), Csepel Plain (64) and Nagykálló-Nyírség (60). Most of the multiple hotspots are in the outskirts of Budapest to Kiskunlacháza, Bugyi, Délegyháza. As we examine the decreasing hotspot data we found 1,679 cells (1,524.9 km2 ) between 1990 and 2018. In largest number, they occur on the Kiskunság Sand Ridge (38), Majsa–Szabadka Sand Ridge (37) and Nagykállói-Nyírség (36). Multiple hotspots are located in settlements Izsák, Ásotthalom, Vatta and Nyírmihályi. Regions with numerous hotspots require special management to moderate its negative consequences on soils to consider both increased anthropisation, but also extensification of land use and their consequences.
{"title":"Trends and hotspots in landscape transformation based on anthropogenic impacts on soil in Hungary, 1990–2018","authors":"Szabolcs Balogh, T. Novák","doi":"10.15201/hungeobull.69.4.2","DOIUrl":"https://doi.org/10.15201/hungeobull.69.4.2","url":null,"abstract":"The transformation of the landscapes due to the anthropogenic activities is increasing worldwide. These changes are also manifested in the change of soil-forming processes. The land cover (LC) changes evaluated according to their influence on anthropogenic features of soils allows to distinguish between LC changes resulting increased and decreased human impact (HI). In our study, we assess the changes of HI on landscapes and its spatial distribution across Hungary. The changes were evaluated by using LC data of four periods between 1990 and 2018 reclassified based on the related anthropogenic soil features. To identify the hotspots of the changes 1×1 grids were applied in which the direction (increasing, neutral or decreasing HI) and frequency (number of landscape patches with LC changes) of changes were evaluated. In our research, the hotspots were identified over the studied four periods. We point out that the spatial distribution of hotspots is very different. The hotspots of the increased human impact are 2,449 cells (643.0 km2 ) between 1990 and 2018, and the most of it localized in the Pest Plain (67), Csepel Plain (64) and Nagykálló-Nyírség (60). Most of the multiple hotspots are in the outskirts of Budapest to Kiskunlacháza, Bugyi, Délegyháza. As we examine the decreasing hotspot data we found 1,679 cells (1,524.9 km2 ) between 1990 and 2018. In largest number, they occur on the Kiskunság Sand Ridge (38), Majsa–Szabadka Sand Ridge (37) and Nagykállói-Nyírség (36). Multiple hotspots are located in settlements Izsák, Ásotthalom, Vatta and Nyírmihályi. Regions with numerous hotspots require special management to moderate its negative consequences on soils to consider both increased anthropisation, but also extensification of land use and their consequences.","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48643819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.15201/hungeobull.69.4.7
Bálint Kronstein
{"title":"Baran-Szołtys, M., Glosowitz, M. and Konarzewska, A. (eds.): Imagined Geographies: Central European Spatial Narratives between 1984 and 2014","authors":"Bálint Kronstein","doi":"10.15201/hungeobull.69.4.7","DOIUrl":"https://doi.org/10.15201/hungeobull.69.4.7","url":null,"abstract":"","PeriodicalId":38149,"journal":{"name":"Hungarian Geographical Bulletin","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44312896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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