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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.