This work discusses the historical development of landscape typologies of Slovenia, focusing on methodology, terminology, criteria for the division of territory, and landscape type hierarchy. It ...presents all five macrotypologies of Slovenia created between 1946 and 2013, Slovenia’s classification in nine selected macrotypologies of Europe produced between 1995 and 2016, and eight examples of microtypologies of smaller areas of Slovenia made between 1985 and 2020. It compares and evaluates similar typologies. If, in addition to the landscape typology, a geographical regionalization was also produced, common points are sought between the two. The macrotypologies and microtypologies of Slovenia are accompanied by an original and updated map.
Based on digital data on relief, rock, and vegetation, the most significant elements of the internal structure of Slovenian landscapes and at the same time of their external appearance, a geographic ...information system and verification in the field were used to create several natural landscape typologies of Slovenia with a varying number of types. The most generalized typology is based on the spatial overlap of four relief, seven lithological, and seven vegetation units. It has twenty-four landscape types: four flat, eleven low hilly, six hilly, and three mountainous types.
Based on digital data on relief, rock, and vegetation, the most significant elements of the internal structure of Slovenian landscapes, and their external appearance, a geographic information system ...was used to calculate landscape diversity of Slovenia. Areas with high landscape diversity are landscape hotspots, and areas with low landscape diversity are landscape coldspots. One-tenth of Slovenia with the highest landscape diversity was defined as landscape hotspots, and one-tenth of Slovenia with the lowest landscape diversity was defined as landscape coldspots. Most landscape hotspots are located in the Alpine part of Slovenia (more than two-thirds of their total area), and most landscape coldspots in the Dinaric part of Slovenia (almost half of their total area).
Supervised and unsupervised classification methods can be a useful tool in determining various geographical spatial divisions, especially regionalizations and typifications. Because Slovenia is ...geographically very diverse, its divisions are a particularly significant and interesting research challenge. The main objective of this article is to determine the effectiveness of unsupervised classification methods, and therefore we compare the well-established landscape typology of Slovenia from 1996 with landscape typologies that were modeled using various unsupervised classification methods. Our results show that landscape typologies modeled using unsupervised classification methods deviate more from the original landscape typology of Slovenia than landscape typologies modeled using random and expert-supervised classification methods.
Various ice bodies are an important source of paleoenvironmental data, and their study improves the understanding of present and future environmental conditions. Their changes are an important ...indicator of climate change. This special issue of Acta geographica Slovenica draws attention to the changing and disappearing cryosphere across the globe, with an emphasis on the southeastern Alps, and the necessity to conduct research in this field before the ice disappears forever. This paper briefly summarizes the current body of knowledge on glaciers, permafrost, cave ice, lake and river ice, and snow in the southeastern Alps, and it presents the contribution of Acta geographica Slovenica to this research and the main highlights of all five papers included in this special issue.
Some landscape classifications officially determine financial obligations; thus, they must be objective and precise. We presume it is possible to quantitatively evaluate existing manually constructed ...classifications and correct them if necessary. One option for achieving this goal is a machine learning method. With (re)modeling of the landscape classification and an explanation of its structure, we can add quantitative proof to its original (qualitative) description. The main objectives of the paper are to evaluate the consistency of the existing manually constructed natural landscape classification with a machine learning-based approach and to test the newly developed general black-box explanation method in order to explain variable importance for the differentiation between natural landscape types. The approach consists of training a model of the existing classification and a general method for explaining variable importance. As an example, we evaluated the existing natural landscape classification of Slovenia from 1998, which is still officially used in the agricultural taxation process. Our results showed that the modeled classification confirms the original with a high rate of agreement--94%. The complementary map of classification uncertainty (entropy) gave us more information on the areas where the classification should be checked, and the analysis of the variable importance provided insight into the differentiation between types. Although the selection of the exclusively climatic variables seemed unusual at first, we were able to understand "the computer's logic" and support geographical explanations for the model. We conclude that the approach can enhance the explanation and evaluation of natural landscape classifications and can be transparently transferred to other areas.
The city of Ljubljana lies at the intersection of various geomorphological regions that have strongly influenced its spatial organization. Prehistoric settlements were built on marshland, a Roman ...town was built on the first river terrace of the Ljubljanica River, and in the Middle Ages a town was built in a strategic position between the Ljubljanica River and Castle Hill. The modern city absorbed all usable space between the nearby hills. This paper reviews some relief features in Ljubljana, their influence on the city’s spatial development, and urban geoheritage. The results indicate new possibilities for urban geoheritage tourism in the Slovenian capital and its surroundings.
This paper presents an analysis of data on damage caused by natural disasters in Slovenia. The data were systematically collected by the Statistical Office of the Republic of Slovenia from 1991 to ...2008 for fourteen categories of disasters: earthquakes, floods, fires, drought, windstorms, hail, frost, glaze, landslides and avalanches, epidemics, epizootics, damage caused by various pests and diseases, ecological disasters, and other natural disasters. Data by statistical regions (NUTS 3) are available for 1992 to 2008, and data by municipalities (LAU 2) and administrative units (LAU 1) are available for 1992 to 2005. Analysis of the data shows that from 1991 to 2008 direct damage caused by natural disasters amounted to an average of 0.48% of annual GDP, or an average of €45 per capita a year.
The proposed European Union indicators for defining areas less suitable for agriculture in Slovenia are not entirely appropriate because taking them into account would omit some distinctly and ...clearly unsuitable areas–for example, Suha krajina (Dry Carniola) and Bela krajina (White Carniola)–and farmers would be unjustifiably financially harmed. In such a case, every European Union member state has the right to propose an additional indicator to reduce such discrepancies. With regard to actual natural conditions, in Slovenia especially some karst landscapes would be unjustifiably omitted, and so we have proposed a karst indicator as an additional criterion based on the distribution of karst (i.e., carbonate) rocks. Through spatial coverage of karst rocks and soils, we determined whether more reasonable and less strict application of European criteria regarding soil could be satisfactory for better results in defining areas less suitable for agriculture in Slovenia.
This article examines various annual trends in climate and hydrological changes in Slovenia's mountain regions between 1961 and 2018. Climate changes are primarily reflected in the increase in ...average annual temperatures and significantly decreased duration of snow cover, and hydrological changes in the decrease in the minimum and mean annual discharge, whereas the maximum discharge is increasing in some places. Among the factors affecting the reduction in the annual water volume in rivers, land-use changes (i.e, increased forest cover) especially stand out. In addition to the water volume, rivers' discharge regimes are also changing. In nearly all locations, the autumn maximum discharge now exceeds the spring maximum discharge, which was once one of the basic characteristics of mountain snow-rain discharge regimes.
PDF
