Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R² of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens.

In recent decades, global climate change and rapid urbanization have aggravated the urban heat island (UHI) effect, affecting the well-being of urban citizens. Although this significant phenomenon is more pronounced in larger metropolitan areas due to extensive impervious surfaces, small- and medium-sized cities also experience UHI effects, yet research on UHI in these cities is rare, emphasizing the importance of land surface temperature (LST) as a key parameter for studying UHI dynamics. Therefore, this paper focuses on the evaluation of LST and land cover (LC) changes in the city of Prešov, Slovakia, a typical medium-sized European city that has recently undergone significant LC changes. In this study, we use the relationship between Landsat-8/Landsat-9-derived LST and spectral indices Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) derived from Landsat-8/Landsat-9 and Sentinel-2 to downscale LST to 10 m. Two machine learning (ML) algorithms, support vector machine (SVM) and random forest (RF), are used to assess image classification and identify how different types and LC changes in selected years 2017, 2019, and 2023 affect the pattern of LST. The results show that several decisions made during the last decade, such as the construction of new urban fabrics and roads, caused the increase in LST. The LC change evaluation, based on the RF classification algorithm, achieved overall accuracies of 93.2% in 2017, 89.6% in 2019, and 91.5% in 2023, outperforming SVM by 0.8% in 2017 and 4.3% in 2023. This approach identifies UHI-prone areas with higher spatial resolution, helping urban planning mitigate the negative effects of increasing urban LSTs.

Albedo is an important parameter in many environmental and renewable energy models. Satellite sensors can be used to derive broadband or narrowband albedos. However, the spatial resolution of such data can be insufficient in urban areas with complex morphology and land cover diversity. In this study, we propose the use of widely available aerial orthophotographs to derive visible band albedo in urban surfaces that can be effectively used in high-resolution applications. The solution is based on the estimation of the reflected irradiance captured by an RGB sensor and approximated by the brightness component in the hue-saturation-brightness (HSB) color model and incident solar irradiance modelled by the r.sun module in GRASS GIS. The visible band albedo values are calibrated by published reference values for selected land cover classes or, alternatively, by a spectroradiometer. The method is applied to the central part of Košice and compared to visible band albedo derived from the Landsat 8 OLI and Sentinel 2A sensors and previously published typical albedo values for various land cover classes, resulting in reasonable agreement. The proposed methodology is implemented using standard GIS tools that are easily applicable to any high-resolution urban data.

The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. Laser scanning technology (LiDAR - Light Detection and Ranging) enables such approach producing dense 3-D point clouds of millions of points, which, however, contain noise. Therefore, we developed a new approach allowing for a semi-automatic elimination of data noise resulting from motion of objects within the scanned scene such as persons. We used a connected-component labelling method to filter out the noise points from terrestrial laser scanning point clouds. Our approach was based on a step-by-step object classification with a proper parameterisation. In the first step, all points located close to the predicted terrain were selected. In the second step, the points representing the terrain and floor were classified using the surface filter tool implemented in the RiScan Pro software by RIEGL. The rest of points were classified using point cloud clustering via the connected-component labelling method implemented in the CloudCompare software. In the final step, the operator manually decides whether the point cluster represents the noise. The method was applied to the Cathedral of Saint Elizabeth, a sacral object located in the historical centre of the city of Košice in Slovakia during normal operating hours. We managed to capture approximately 80% of the data noise in total. The method provides a better flexibility in surveying overcrowded city locations using the laser scanning technology.

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.