Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

The cave map, as a result of mapping in limited cave spaces, is a specific cartographic product characterized by a high degree of cartographic abstraction and subjectivity of the cave features. Over the last decade, remote sensing methods have been increasingly used in cave mapping. Specifically, the laser scanning technology can effectively record the vastly fragmented interior of the cave at a high level of detail. The presented paper demonstrates a methodology of making the high-scale cave map from LiDAR point clouds. The innovativeness of the presented approach is in the use of highly detailed model of a cave floor derived from a point cloud as a base data layer for identification of the cave features. The main benefit of the final cave map is in the diminution of the authoŕs subjective perception during the cave mapping resulting in the generalization of the cave spaces geometry and cave features.

Over the last few years, many European countries experienced a rapid growth of photovoltaic (PV) power plants. For example, more than 20, 000 new PV power plants were built in the Czech Republic. The high spatial and temporal variability of the solar resource and subsequent PV power plant production, poses new challenges for the reliability and predictability of the power grid system. In this paper, we analyse the most recent data on PV power plants built in the Czech Republic and Slovakia, with a focus on the spatial distribution of these installations. We have found that these power plants scarcely follow the solar resource potential and, apparently, other factors affect decisions for their location. Recent changes in the support schemes for solar applications also influence these patterns, with new installations mostly confined to built-up areas. These changes will require new tools to assess the appropriate locations of PV systems.

The presented study uses the recent colour aerial photographs, historical black and white aerial photographs, and detailed digital elevation model to assess the spatial distribution and long-term temporal dynamics of soil loss in agriculturally intensively exploited loess hilly land with a subcontinental temperate climate. The strongly eroded soils appear in the studied area as bright patterns, surrounded by darker soils, and they are well visible on aerial photos. Three approaches of interpretation of aerial photographs were tested: visual interpretation, pixel-based image classification, and object-based image classification. All three methods provided detailed maps of soil redistribution patterns. The bright areas as the areas of soil degradation characterized by erosion increased from 1949 until 2011 by 76%. A detailed map of areal erosion patterns was used for the validation of water erosion models. LS-factor of USLE and ED’ index of USPED were selected for expressing the relation of real erosion to the terrain. The relationship between surface morphology and real erosion is very complex, and the tested water erosion models do not express it sufficiently. Therefore, the first and second-order directional derivative of the surface elevations with respect to the tillage direction has been tested. The absolute value of the first-order directional derivative showed better results and better corresponded with the real erosion pattern than the other morphometric characteristics. The findings suggest that tillage is the dominant erosion factor in the area.

Karst dolines, as geomorphologically diverse natural landforms, usually exhibit more or less steep microclimatic gradients that provide a mosaic of diverse microhabitat conditions, resulting in a high diversity of soil biota with numerous rare endemic and/or relict species occupying these habitats. In this study, we investigated the spatial patterns of Collembola abundance, species richness, community structure and distribution of functional groups at topographically and microclimatically different sites across three open (unforested) karst dolines in a north-south direction in the Slovak Karst, Slovakia. We also assessed the refugial capacity of dolines for collembolan communities. The Friedman ANOVA test confirmed the significant differences in soil mean temperatures between the sites of all the dolines selected. The diverse soil microclimatic conditions within the dolines supported higher Collembola diversity (species numbers, diversity indices) compared with sites on the karst plateau and showed a potential to facilitate the persistence of some species that are absent or very rare in the surrounding landscape. In dolines with circular morphology and comparable size, the topography and soil microclimate had a stronger effect on community composition and structure than soil organic carbon. Shallow solution dolines provided microhabitats for various functional groups of soil Collembola in relation to the microclimatic character of the individual sites. It was observed that such landforms can also function as microclimatic refugia for cold-adapted species through the accumulation of colder air and the buffering of the local microclimate against the ambient mesoclimate, thus underlying the necessity of adequate attention in terms of the conservation of the karst natural phenomena.

Measuring and mapping vegetation structure is essential for understanding the functioning of terrestrial ecosystems and for informing environmental policies. Recent years have seen a growing demand for high‐resolution data on vegetation structure, driving their prediction at fine resolutions (1–30 m) at state, continental, and global spatial extents by combining satellite data with machine learning. As these initiatives expand, it is crucial to actively discuss the quality and usability of these products. Here, we briefly summarize current efforts to map vegetation structure and show that continental‐to‐global canopy height models (CHMs) exhibit significant errors in canopy heights compared to national airborne laser scanning (ALS) data. We recommend that regions with abundant ALS data, such as Europe, prioritize using ALS‐based canopy height metrics rather than relying on less accurate predictions from satellite products. Despite variations in ALS data characteristics, such as temporal inconsistencies and differences in acquisition characteristics and classification accuracy, the generation of spatially contiguous canopy height products in raster format at fine spatial resolution is necessary and feasible. This requires coordinating efforts for data and survey harmonization, developing standardized processing pipelines and continent‐wide ALS products, and ensuring free access for research and environmental policy. We show that ALS data now cover most of Europe, with newer surveys achieving higher point densities, improving their suitability for vegetation mapping. Beyond numerous applications in forestry, ecology, and conservation, such data sets are crucial for calibrating future Earth Observation missions, making them essential for producing reliable and accurate global, fine‐resolution vegetation structure data.