National Level Land-Use Changes in Functional Urban Areas in Poland, Slovakia, and Czechia

Land-use and cover change (LUCC) impacts global environmental changes. Therefore, it is crucial to obtain cross-national level LUCC data that represents past and actual LUCC. As urban areas exhibit the most significant dynamics of the changes, accompanied by such processes as urban sprawl, it seems desirable to take into account LUCC information from such areas to acquire national level information. The paper analyses land-use changes (LUCs) in urban areas in Czechia, Poland, and Slovakia. The analysis is based on functional urban area (FUA) data from the European Urban Atlas project for 2006 and 2012. The area of urbanised land grew at the expense of agricultural areas, semi-natural areas, and wetlands over the investigated period in all three countries. The authors determined LUC direction models in urban areas based on the identified land-use change. The proposed LUC direction models for the investigated period and area should offer national level LUC data for such purposes as modelling of future changes or can be the point of reference for planning analyses. The paper proposes the following models: mean model, median model, weighted mean model where the weight is the urbanised to vegetated area ratio, and weighted mean model where the weight is the share of urbanised areas. According to the proposed LUC models, areas considered as urbanised grow in FUAs on average in six years by 5.5900‰ in Czechia, 7.5936‰ in Poland, and 4.0769‰ in Slovakia. Additionally, the change models facilitated determination of a LUC dynamics ratio in each country. It reached the highest values in Poland and the lowest in Slovakia

Regional Differentiation of Long-Term Land Use Changes: A Case Study of Czechia

The major topic of this article is the evaluation of the regional differentiation of the long-term changes in land use in Czechia. This study searches the spatial and temporal differentiation of the changes and their driving forces since the 19th century. The comprehensive land use land cover change database (LUCC Czechia Database) which comprises cadastral data on the land use in the years 1845, 1896, 1948, 1990, 2000, and 2010 for more than 8000 units, was the main data source. The chief benefit of this article can be seen in the methodical procedures of the application of the “Rate of heterogeneity” (H) derived from the Gini coefficient in the research of the differentiation/inequality of the long-term land use change. GIS modeling tools were used to calculate the selected geographical characteristics (altitude and slope) of the examined units for the purpose of searching the factors of the land use changes. The results show a strong trend in the differentiation of the long-term land use changes. Two main antagonistic processes took place in the land use structure during the observed period of 1845–2010. The fertile regions experienced agricultural intensification with the concentration of the arable land in these regions. On the other hand, the infertile regions experienced extensification, accompanied by afforestation and grass planting during the last decades. The influence of natural conditions (altitude and slope) on the distribution of the land use has been growing—the arable land has been concentrated into the lower altitudes and, more significantly, into less steep areas. Grasslands and forests predominantly occupy the less favored areas with higher altitudes and steeper slopes. The built-up areas have been strongly concentrated and regionally polarized. In 1845, half of the Czech built-up areas were concentrated in 31% of the total country area, whereas in 2010, it was in 21%

Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks

This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines

This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity. The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO). The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm. For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines

This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines

This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity. The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO). The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm. For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas

Historical land use dataset of the Carpathian region (1819–1980)

We produced the first spatially explicit, cross-border, digital map of long-term (160 years) land use in the Carpathian Ecoregion, the Hungarian part of the Pannonian plains and the historical region of Moravia in the Czech Republic. We mapped land use in a regular 2 × 2 km point grid. Our dataset comprises of 91,310 points covering 365,240 km2 in seven countries (Czechia, Slovakia, Austria, Hungary, Poland, Ukraine and Romania). We digitized three time layers: (1) for the Habsburg period, we used maps of the second Habsburg military survey from years 1819–1873 at the scale 1:28,800 and the Szatmari's maps from years 1855–1858 at scale 1:57,600; (2) The World Wars period was covered by national topographic maps from years 1915–1945 and scales here ranged between 1:20,000–1:100,000; and (3) the Socialist period was mapped from national topographic maps for the years 1950–1983 at scales between 1:25,000–1:50,000. We collected metadata about the years of mapping and map sources. We used a hierarchical legend for our maps, so that the land use classification for the entire region consisted of 9 categories at the most general level and of 22 categories depending on the period and a country

Comparison of Global and Continental Land Cover Products for Selected Study Areas in South Central and Eastern European Region

Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products