No abstract

The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonoše Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and objectbased approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

This paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonoše Mts. National Park. Two legends were proposed: the detailed one with twelve classes, and simplified legend with eight classes. Aerial orthorectified images (orthoimages) with very high spatial resolution (12.5 cm) and four spectral bands have been examined using the object based classification. Satellite data WorldView-2 (WV-2) with high spatial resolution (2 metres) and eight spectral bands have been examined using object based classification and per-pixel classification. Per-pixel classification has been applied also to the freely available Landsat 8 data (spatial resolution 30 metres, seven spectral bands). Of the algorithms for per-pixel classification, the following classifiers were compared: maximum likelihood classification (MLC), support vector machine (SVM), and neural net (NN). The object based classification utilized the example-based approach and SVM algorithm (all available in ENVI 5.2). Both legends (simplified and detailed ones) show best results in the case of orthoimages (overall accuracy 83.56% and 71.96% respectively, Kappa coefficient 0.8 and 0.65 respectively). The WV-2 classification brought best results using the object based approach and simplified legend (68.4%); in the case of per-pixel classification it was the SVM method (RBF) and detailed legend (60.82%). Landsat data were best classified using the MLC (78.31%). Our research confirmed that Landsat data are sufficient to get a general overview of basic land cover classes above the tree line in the Krkonoše Mts. National Park. Based on the comparison of the data with different spectral and spatial resolution we can however conclude that very high spatial resolution is the decisive feature that is essential to reach high overall classification accuracy in the detailed level.

95-115. -The article analyses land cover changes along the Iron Curtain in the period 1990-2006. CORINE land cover state and land cover change datasets are used to evaluate differences in land cover structure in 1990 and in land cover changes between the eastern (from former German Democratic Republic to Hungary) and western (former Federal Republic of Germany and Austria) border sections along the Iron Curtain. The results confirm different representation of individual land cover categories on the eastern and western sides. Different intensity of changes at the eastern and western border sections has been confirmed, too. More intense land cover changes were detected in the "East" after 1990. The highest intensity of changes was recorded at the Czech border sections where rather strong process of afforestation took place, together with retreat of intensive agriculture (changes on more than 8% of the area between 1990 and 2000). On the contrary, the Austrian border section was the most stable area (changes only on 0.13% of the area). KEY WORDS: land cover -change -Iron Curtain -1990Curtain - -2006

This study focuses on the assessment of forest cover and disturbance changes in the heavily polluted Ore Mountains (Czechia, Central Europe) during the second half of the 20th century and onward. It analyzes the driving forces of forest changes with reference to environmental, societal and political development in the region. Anthropogenic air pollution, prevalently SO 2 from adjacent coal-burning industry, caused extensive forest decline, especially between the 1970s and 1980s. The most affected tree species was the main economical timber species, Norway spruce, which proved to be remarkably pollution-sensitive. We used Landsat time series, and a combination of an integrated forest Z-score and Disturbance Index (DI), to analyze the forest cover change and disturbance development during 1985-2016. In 1994, the forest cover reached its minimum there. The breakdown of communism in the 1990s implied fulfilling EU pollution standards via air protection regulations, investment in power plant desulphurization, and forest management measures, which were the main drivers of the forest recovery. The forest recovery continued till about 2005; however, fluctuations in forest cover and DI have continued during the last decade. Apparently, forests weakened by old loads are prone to new stress factors. Landsat time series represent a powerful data source to monitor the impact of these drivers on forests on a regional scale. Originally, the severely damaged eastern part with heavier acidic load and large forest decline recovered faster after remarkable lowering of air pollution loads compared to the western part, with lower loads and less damaged forests. However, the interactions of persisting driving forces (soil acidification, adverse meteorological events, climate change factors, air pollution, tree species composition and physiological state, pest outbreaks) still threaten the forests there, which remain moderately damaged in both parts of the Ore Mountains. This may lead to unpredictable forest development independently of societal and political driving forces.

Changes in the cultural landscape provide essential evidence about the manner and intensity of the interactions between humans and nature. Czechia has a specific location in Central Europe. It is positioned at the crossroads of European landscape changes. These changes can be documented based on a unique database that shows the development of land use since the middle of the 19th century. In this study, we aimed to address the major processes of landscape change that occurred during four periods over the past 165 years, at the cadastral level on the territory of present-day Czechia. Further we identify and discuss proximate and underlying driving forces of the landscape changes. We used land use data from the year 1845, 1896, 1948, 1990, and 2010 that correspond to key events in Czech history. The major processes and intensity of landscape change were evaluated based on calculations of increases and decreases in land use classes between the first and last year of each examined period. The period 1845–1896 was the only period in which arable land increased, and the most recent period, 1990–2010, was the only period during which a grassing over process was recorded. Afforestation was recorded in all periods. The communist period was characterized by unified changes—urbanization, afforestation, arable land decrease, and landscape devastation. The post-communist period was, in some respects, beneficial to the landscape (e.g., grassing over and afforestation, particularly in mountain areas), but it also led to negative processes, such as strong urbanization and land abandonment. Such changes lead to landscape polarization. The landscape changes in Czechia during the period 1845–2010 reflect many important historical events in Europe. In our analysis, we demonstrate the essential impact of underlying drivers and also identify driving forces specific to the development of the Czech territory.

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.

The paper deals with mapping of landscape transition after the collapse of Communism in Czechia on national and local levels. Three maps demonstrate the main trends of landscape transition on the national level (by cadastral units) in the period 1990-2010. The Main Map shows the four most important Processes of landscape change (afforestation, grassing over, intensification, and urbanization). The second map demonstrates the proportion of area where any kind of land use change occurred (Index of change) and the third map (Extensification) indicates the shift to less intensive use of land (increase of forests and grasslands). Two main processes were mapped on the local level, that is, by parcels. The case of Jirny showed strong sububanization: fertile agricultural land has been turned into residential and commercial areas, roads; soil sealing was taking place. On the contrary, grassing over and afforestation was detected in Hošťka where arable land almost disappeared it was either abandoned or replaced mainly by pastures between 1990 and 2010.