Ján Kaňuk

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.

Slovenská akadémia vied vykonáva špičkový základný výskum, dlhodobý strategický a aplikovaný výskum a vývoj a poskytuje prístup k vedeckej infraštruktúre.

Accurate modeling of subcanopy solar radiation is vital for ecological modeling, forest management, and urban planning, as it influences vegetation growth, energy balance, and environmental dynamics. This study provides a comprehensive evaluation of two solar radiation models: PCSRT and r.sun, leveraging LiDAR datasets from terrestrial (TLS), unmanned aerial (ULS), and airborne (ALS) scanning. The results demonstrate that the choice of modeling approach and data source substantially impacts the accuracy of solar radiation estimates, particularly in complex forested environments. PCSRT, with its voxel-based 3D modeling, excels in capturing intricate subcanopy radiation dynamics, especially when combined with high-density LiDAR data such as TLS and ULS. In contrast, the raster-based r.sun model, while computationally efficient and scalable, is better suited for broader regional analyses, particularly in less heterogeneous environments such as urban areas. This research underscores the critical role of LiDAR data density in determining model accuracy, with ULS providing the most reliable results, TLS capturing detailed local variations but facing coverage limitations, and ALS offering scalability but with reduced precision in dense canopy structures. Practical implications of this study include tailored recommendations for selecting modeling tools and LiDAR datasets based on the objectives and spatial scale of the study. PCSRT is recommended for high-resolution ecological studies requiring detailed subcanopy analysis, whereas r.sun is preferable for large-scale applications where computational efficiency is prioritized. However, limitations of each approach are acknowledged, including the computational intensity of PCSRT and the lower precision of r.sun in capturing canopy interactions.Keywords: subcanopy solar radiation, solar radiation models, LiDAR, r.sun, PCSRT

This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches—r.sun and the Point Cloud Solar Radiation Tool (PCSRT)—in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments.

Hyperspectral (HS) and LiDAR sensing provide complementary information for vineyard monitoring. HS imagery captures detailed spectral signals related to canopy physiology and biochemistry but is often affected by contamination from inter-row soil and weeds. LiDAR offers precise measurements of canopy structure yet lacks biochemical sensitivity. Their integration has the potential to overcome these limitations, and remotely piloted (unpiloted) aerial vehicles (UAVs) provide the flexible platform needed to collect both datasets at very high resolution over vineyards. In this study, UAV-based hyperspectral and laser scanning data were collected in the Slovak part of the Tokaj wine region to evaluate their combined potential for distinguishing vine from non-vine areas, producing dense point clouds with more than 600 points per square metre and hyperspectral imagery consisting of 172 bands at 0.1 m spatial resolution. Four datasets were prepared: hyperspectral imagery alone, hyperspectral imagery combined with canopy height, simulated natural colour imagery alone, and simulated natural colour imagery combined with canopy height. All datasets were transformed using principal component analysis, and the resulting features were classified with a supervised maximum likelihood classifier. Accuracy was evaluated using 1,000 field-validated reference points. The classification based only on the hyperspectral data reached 89% overall accuracy but performed poorly for vine detection, with a producer’s accuracy of 48.9% and an F1-score of 0.61. When canopy height information was included, performance improved to 96% overall accuracy, a Kappa coefficient of 0.85, and an F1-score of 0.88. Simulated natural-colour imagery combined with canopy height achieved intermediate results, with 93% overall accuracy and an F1-score of 0.79. These findings confirm that integrating spectral and structural information enhances vineyard mapping and provides a reliable basis for precision viticulture applications.

Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide.

Sal ‘e Porcus is the largest natural salt flat in Europe, located in Sardinia (Italy) in the southwest region of the Mediterranean area. It spans over an area of more than 3.3 square kilometers. Its homogeneity and size characteristics make it a potentially suitable spot for use as a satellite sensor calibration/validation site. For this purpose, a campaign of spectral, mineralogical and LiDAR data acquisition has been organized in the framework of a dedicated International Remote Sensing Summer School in 2023. Furthermore, the study of this site has been funded by the Italian Spatial Agency through the Project: System for the “COnsOlidation of L2 products from the PRISMA-SG mission (COOL)”. New orbiting hyperspectral sensors such as ASI-PRISMA and EnMAP DLR and near future continuity missions (ASI-PRISMA SG, ESA CHIME) request ground reflectance spectra to rely on the remote sensed data. In this study, the first results of the study of the spectral and LiDAR data acquired on the site are described and discussed, focusing on two main outputs: i) the first release of the spectral library of the minerals of the salt flat; ii) the morphological setting of the surface. The collection of the spectra has been coupled with sediment samples for mineralogical characterization. During these campaigns, UAV-based LiDAR data were acquired. First results show that the topography of the surface changes from west to east, giving the idea of a geological control of this flat area. Moreover, first results of the spectral characterization of this surface using field, UAV and satellite data are discussed.

Enclosed surficial karst dolines are geomorphologically diverse habitats with the potential to strongly shape community structure and the distribution of functional groups of organisms. Forest habitats in karst landscapes are characterized by lower soil temperatures and microclimatic fluctuations compared to open habitats; therefore, they may provide favorable conditions for cold-adapted/psychrophilic species, which are highly vulnerable to climate warming. We compared the diversity and structure of soil Collembola communities at sites along microclimatic gradients in open and forested karst dolines. The effect of topography and related environmental conditions on the distribution of ecological groups of soil Collembola was analyzed, and the function of the dolines as potential climate refugia for psychrophilic arthropods was assessed. The karst dolines revealed significant habitat heterogeneity, including warm plateaus and S-facing slopes and colder and wetter bottoms and N-facing slopes. The inner sites of the dolines had an overall higher abundance and species richness compared to the plateau sites. Nonmetric multidimensional scaling ordination revealed a clear delimitation of Collembola communities in terms of warmer and cooler sites in the open dolines, while the forested dolines did not reveal such a clear pattern. The studied dolines showed a potential to serve as microrefugia in the context of global climate change. Dolines could support the long-term persistence of at least some species such as cold-adapted species. Karst dolines in the temperate zone play a key role in maintaining biodiversity; therefore, they should be central in biodiversity conservation programs.