Surface Roughness and Land Use in Slovenia

The article describes characteristics of several relief indicators (surface height, surface slope, surface aspect, height coefficient of surface roughness, slope coefficient of surface roughness, and aspect coefficient of surface roughness) relative to various types of land use in Slovenia and establishes the con-nection between surface roughness and land use and between relief indicators and individual types of land use

The usefulness of unsupervised classification methods for landscape typification: The case of Slovenia

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

Suitability of Hammond's method for determining landform units in Slovenia

Landform is often the most important factor in distinguishing between regions and an important element of geographic classification, typification, and regionalization; this is why morphological classification has had a long tradition in Slovenia and abroad. One of the best-known classifications was developed by the American geographer Edwin H. Hammond, who classified the landforms of the United States in great detail. Later on, his method was applied several times using a geographic information system and digital elevation model. Computer land-surface classification became more objective, whereas the selection of classification elements and their classes remained subjective. Hammond’s method of determining landform units is known throughout the world and this is why it has also been tested in Slovenia. First, the original classification elements were taken into account and only thirteen units of the twenty-one landform units specified by Hammond were selected. Due to weaknesses that were revealed, Hammond’s original method was suitably adapted: the form and size of the basic window and the boundaries between classification element classes were changed. Nineteen landform units were thus identified in Slovenia using the adapted method

Differences between 100-meter and 25-meter digital elevation models according to types of relief in Slovenia

Studying relief and landscapes, we often employ digital elevation models. Their applicability is primarily linked to their accuracy. In this paper, we compare the older 100-meter digital elevation model of Slovenia and the more recent 25-meter digital elevation model. We assess applicability relative to differences in surface heights, surface slopes, and surface aspects for all of Slovenia and for four areas with different relief. We compare the frequency of distribution, arithmetic mean, standard deviation, range, and the coefficient of variation. The statistical analysis of the two digital elevation models of Slovenia indicates that the smallest differences exist relative to surface heights, there are larger differences relative to surface aspects, and the largest differences exist relative to surface slopes

Podnebne in hidrloške spremembe v slovenskem Podravju med letoma 1961 in 2018

This article examines different annual trends in the climate and hydrological changes in the Slovenian part of the Drava Basin (Sln. Podravje) between 1961 and 2018. Climate change is primarily reflected in the rising average annual temperatures and a significantly shorter duration of snow cover. In terms of hydrological changes, a decrease in the average annual minimum and mean annual discharge can be observed, whereas the average maximum and absolute discharge is increasing in places. In addition to the water volume, changes can also be observed in the rivers’ discharge regimes, which may indicate a smaller probability of spring floods, but conversely a higher probability of fall floods.V prispevku obravnavamo različne letne trende podnebnih in hidroloških spremenljivk na območju slovenskega dela porečja Drave med letoma 1961 in 2018. Prve se odražajo predvsem v rasti povprečne letne temperature in močno skrajšanem trajanju snežne odeje. Pri drugih pa je opazno padanje povprečnih minimalnih in srednjih letnih pretokov, povprečni maksimalni in absolutni maksimalni pretoki pa ponekod naraščajo. Poleg vodnih količin se pri rekah spreminjajo tudi pretočni režimi, ki po eni strani lahko kažejo manjšo možnost spomladanskih poplav, po drugi strani pa večjo možnost jesenskih poplav

Podnebne in hidrloške spremembe v slovenskem Podravju med letoma 1961 in 2018

This article examines different annual trends in the climate and hydrological changes in the Slovenian part of the Drava Basin (Sln. Podravje) between 1961 and 2018. Climate change is primarily reflected in the rising average annual temperatures and a significantly shorter duration of snow cover. In terms of hydrological changes, a decrease in the average annual minimum and mean annual discharge can be observed, whereas the average maximum and absolute discharge is increasing in places. In addition to the water volume, changes can also be observed in the rivers’ discharge regimes, which may indicate a smaller probability of spring floods, but conversely a higher probability of fall floods.V prispevku obravnavamo različne letne trende podnebnih in hidroloških spremenljivk na območju slovenskega dela porečja Drave med letoma 1961 in 2018. Prve se odražajo predvsem v rasti povprečne letne temperature in močno skrajšanem trajanju snežne odeje. Pri drugih pa je opazno padanje povprečnih minimalnih in srednjih letnih pretokov, povprečni maksimalni in absolutni maksimalni pretoki pa ponekod naraščajo. Poleg vodnih količin se pri rekah spreminjajo tudi pretočni režimi, ki po eni strani lahko kažejo manjšo možnost spomladanskih poplav, po drugi strani pa večjo možnost jesenskih poplav

Landscape characteristics of common land in Slovenia

Common land is land with an ancient form of ownership. At one time it was economically important, but now its importance lies in the preservation of cultural landscapes and ecological balance. This article uses the geographic information system to analyze selected landscape indicators with respect to various types of agricultural land and determine whether and to what extent the assertion holds true that in Slovenia common agricultural land is preserved primarily in areas with poorer natural conditions for agriculture

Evaluating existing manually constructed natural landscape classification with a machine learning-based approach

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\u27s 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