Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

Disposal sites without adequate engineering controls pose a significant risk to the environment. In the present study, the environmental hazards of an abandoned and unrecultivated liquid waste disposal are investigated with a special focus on soil and shallow groundwater contamination. After a period of operation from 1994 to 2010, when the wastewater collection of the municipality was regulated, the disposal site was subsequently decommissioned without further action. Eight monitoring wells have been established in the disposal basins and in the surrounding area to determine the contamination of the site. Sampling took place in the summers of 2020 and 2021. The results of the analysis of the soil and water samples collected showed a high level of contamination in the area. In the borehole profile of the infiltration basin, a well-developed leachate nitrate profile was observed, with a concentration above 3000 mg/kg NO₃⁻. The soil phosphate content was also significant, with a value of over 1900 mg/kg in the upper 40 cm layer. Extremely high concentrations of ammonium (>45 mg/L) and organic matter (>90 mg/L) were detected in the groundwater of the basins, indicating that contaminated soil remains a major source of pollutants more than 10 years after closure. For all micro- and macroelements present in detectable concentrations, a significant increase was observed in the infiltration basin. Our results have revealed that the surroundings are also heavily contaminated. NO₃⁻ concentrations above the contamination limit were measured outside the basins. Recultivation of liquid waste disposal sites of similar characteristics is therefore strongly recommended.

Black locust (Robinia pseudoacacia L.) breeding is an important component of plantation forestry in Central and Eastern Europe; however, clone trials are still mainly evaluated using conventional field surveys, and the application of high-resolution red-edge satellite indices at the intraspecific level remains rarely applied. As a result, less information is available on the phenological status of black locust clones derived from red-edge satellite data. This study evaluates a clone trial established in Eastern Hungary on slightly acidic Arenosol soil, assessing the growth performance and seasonal spectral dynamics of newly bred black locust clones during their fifth growing season by integrating field measurements with PlanetScope-derived Normalized Difference Red-Edge Index (NDRE) time series. Clone NK2 exhibited the most vigorous growth, reaching a mean height of 11.1 ± 0.15 m and a diameter at breast height (DBH) of 11.21 ± 0.19 cm, which were 35.4% greater in height and 19.0% larger in DBH than those (8.2 ± 0.12 m height, and 9.42 ± 0.23 cm diameter) of the control (‘Üllői’ cultivar). Clone PL251 also exceeded the control by 25.6% in height and 19.2% in DBH. Spectral analysis (NDRE value ± standard error) revealed marked differences in phenological development: in the early stage (April 15), NK1 and PL040 had the highest NDRE values (0.472 ± 0.020 and 0.461 ± 0.019), whereas NK2 showed delayed leaf emergence (0.398 ± 0.019). By June 21, PL251 had reached an NDRE value of 0.692 ± 0.013, which was higher than that of the control (0.673 ± 0.016). In mid-July, NDRE peaked for NK2 and NK1 (0.732 ± 0.012 and 0.731 ± 0.013), with ‘Üllői’ showing consistently lower values across the season. In the final stage, NK2 maintained the highest NDRE values (October 22: 0.618 ± 0.015; November 9: 0.466 ± 0.021), indicating prolonged photosynthetic activity, while NK1 and ‘Üllői’ declined earlier (e.g., November 9: 0.354 ± 0.018 and 0.390 ± 0.027, respectively). These findings highlight NK2 and PL251 as superior candidates for high-yield, climate-resilient tree plantations because of their strong growth and extended physiological activity.

The use of the small format digital action cameras has been increased in the past few years in various applications, due to their low budget cost, flexibility and reliability. We can mount these small cameras on several devices, like unmanned air vehicles (UAV) and create 3D models with photogrammetric technique. Either creating or receiving these kind of databases, one of the most important questions will always be that how accurate these systems are, what the accuracy that can be achieved is. We gathered the overlapping images, created point clouds, and then we generated 21 different digital surface models (DSM). The differences based on the number of images we used in each model, and on the flight height. We repeated the flights three times, to compare the same models with each other. Besides, we measured 129 reference points with RTK-GPS, to compare the height differences with the extracted cell values from each DSM. The results showed that higher flight height has lower errors, and the optimal air base distance is one fourth of the flying height in both cases. The lowest median was 0.08 meter, at the 180 meter flight, 50 meter air base distance model. Raising the number of images does not increase the overall accuracy. The connection between the amount of error and distance from the nearest GCP is not linear in every case.