In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q₂–Q₃: 0.63–0.71; NDMI Q₂–Q₃: 0.10–0.19) and the TCW index had negative values (Q₂–Q₃: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

Land use, land-use change and forestry (LULUCF) is a greenhouse gas inventory sector that evaluates greenhouse gas changes in the atmosphere from land use and land-use change. This study focuses on the development of a Sentinel-2 data classification according to the LULUCF requirements on the cloud-based platform Google Earth Engine (GEE). The methods are tested in selected larger territorial regions (two Czech NUTS 2 units) using data collected in 2018. The Random Forest method was used for classification. In terms of classification accuracy, a combination of these parameters was tested: The Number of Trees (NT), the Variables per Split (VPS) and the Bag Fraction (BF). A total of 450 combinations of different parameters were tested. The highest accuracy classification with an overall accuracy = 89.1% and Cohen’s Kappa = 0.84 had the following combination: NT = 150, VPS = 3 and BF = 0.1. For classification purposes, a mosaic was created using the median method. The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution. Altitude values derived from SRTM and NDVI variance values were also included in the classification. These added bands were the most significant in terms of Gini importance.

Diachronic studies play a key role in the research and documentation of cultural heritage and its changes, ranging from architectural fragments to landscape. Regarding the reconstructions of lost cultural heritage sites, the determination of landscape conditions in the reconstructed era goes frequently unheeded. Often, only ruins and detached archeological artefacts remain of the built heritage. Placing them correctly within the reconstructed building complex is of similar importance as placing the lost monument in the context of the landscape at that time. The proposed method harmonizes highly heterogeneous sources to provide such a context. The solution includes the fusion of referential terrain models of different levels of detail (LODs) as well as the fusion of diverse 3D data sources for the reconstruction of the built heritage. Although the combined modeling of large landscapes and small 3D objects of a high detail results in very large datasets, we present a feasible solution, whose data structure is suitable for Geographic Information Systems (GIS) analyses of landscapes and also provides a smooth and clear 3D visualization and inspection of detailed features. The results are demonstrated in the case study of the island monastery, the vanished medieval town of Sekanka, and the surrounding landscape, which is located in Czechia and was the subject of intensive changes over time.

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%.

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.