An empirical model for the raw wood assortment price predicting – case study in Slovakia

Sales of timber, which represent the main source of forest management income, are essential for the economic welfare of forest businesses. Planning the timber sale management faces a certain amount of uncertainty and risk in such difficult conditions of climate change. Model scenarios make preparation for potential future development possible. The aim of the study was to create a prediction model of coniferous and non-coniferous sawlogs for the area of the Central Europe. The objective of the model was to estimate the variations in the price of coniferous or non-coniferous sawlogs following a linear regression equation in the analysed time series from 2001 to 2017. The price of coniferous sawlogs was significantly affected in a negative way by the amount of incidental fellings and in a positive way by the Gross Domestic Product. The price of the non-coniferous sawlogs was significantly affected in a positive way by the GDP and the volume of non-coniferous sawlog export. These factors caused a non-elastic response of the coniferous sawlog price. The impact of these factors depends to a great extent on the wood species composition of the forests in the Slovak Republic. The model also can be set for conditions of other countries when considering their economic indicators.

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Roof’s Potential and Suitability for PV Systems Based on LiDAR: A Case Study of Komárno, Slovakia

Sustainability ◽

10.3390/su122310018 ◽

2020 ◽

Vol 12 (23) ◽

pp. 10018

Author(s):

Marcela Bindzarova Gergelova ◽

Zofia Kuzevicova ◽

Slavomir Labant ◽

Stefan Kuzevic ◽

Diana Bobikova ◽

...

Keyword(s):

3D Model ◽

Spatial Information ◽

Slovak Republic ◽

Slope Aspect ◽

Pv System ◽

Pv Systems ◽

Subsidy Scheme ◽

Gis Environment ◽

The Impact

The case study focuses on evaluating the suitability of roof surfaces in terms of their solar potential based on their geometric parameters. The selected processing methodology detects segments of roof surfaces from the LiDAR base, supplemented with spatial information (orthophoto map, real estate cadastre (REC)—footprint, basic database for the geographic information system (ZBGIS)—classification of buildings—current use). The approach based on spatial analyses takes into account the limit conditions for determining the impact of solar radiation resulting from the roof area, slope, aspect, and hillshade. Considering to the available subsidy scheme for family houses in the conditions of the Slovak Republic, a narrower sample of 35 family houses was selected from the total number of typologically represented buildings (194). A 3D model of the building created by combining REC and LiDAR substrates shows the roof surface without overlap, while another 3D model made of LiDAR substrates alone represents the actual dimension of the roof surface. The results presented for each selected building show good agreement with each other, and their visualizations were obtained using two GIS environment approaches. In the area of family houses, up to 94% of the roof areas of buildings registered in the REC meet the conditions for the installation of a PV system with an output of 2.6/3.3 kW.

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Impact of dispersed settlement on the structure and diversity of rural landscape (Case study of village Hrušov, Slovak Republic)

Geographia Polonica ◽

10.7163/gpol.0192 ◽

2021 ◽

Vol 94 (1) ◽

pp. 29-46

Author(s):

Ján Hanušin

Keyword(s):

Land Cover ◽

Driving Force ◽

Agricultural Land ◽

Slovak Republic ◽

Rural Landscape ◽

Landscape Diversity ◽

Primary Hypothesis ◽

The Impact ◽

Different Levels

The impact of a dispersed settlement on the changes of the land cover (LC) and landscape diversity (LDI) in the years 1950, 1986 and 2016 was analyzed on four spatially different levels: on the level of the whole cadastral area, 60 circular areas – hinterlands of hamlets, 15 circular areas in agricultural land outside hamlets and areas outside circular areas. The primary hypothesis that the landscape with a dispersed settlement is internally differentiated in terms of LC and LDI changes and that a dispersed settlement itself is an important driving force of these changes has been confirmed.

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A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

Symmetry ◽

10.3390/sym12010139 ◽

2020 ◽

Vol 12 (1) ◽

pp. 139 ◽

Cited By ~ 1

Author(s):

Xiaobin Ren ◽

Lianyan Li ◽

Yang Yu ◽

Zhihua Xiong ◽

Shunzhou Yang ◽

...

Keyword(s):

Climate Change ◽

Pearson Correlation ◽

Extreme Weather ◽

Biological Species ◽

Convincing Evidence ◽

Support Vector ◽

Human Society ◽

Change Model ◽

Climate Change Model ◽

The Impact

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

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