A methodology for optimal volume computation for the environmental management of waste stockpiles was derived by integrating the terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) technologies. Among the UAV-based point clouds generated under various flight scenarios, the most accurate point cloud was selected for analysis. The root mean square errors (RMSEs) of the TLS- and UAV-based methods were 0.202 and 0.032 m, respectively, and the volume computation yielded 41,226 and 41,526 m³, respectively. Both techniques showed high accuracy but also exhibited drawbacks in terms of their spatial features and efficiency. The TLS and UAV methods required 800 and 340 min, respectively, demonstrating the high efficiency of the UAV method. The RMSE and volume obtained using the TLS/UAV fusion model were calculated as 0.030 m and 41,232 m³, respectively. The UAV approach generally yielded high point cloud accuracy and volume computation efficiency.

The rapid development of drone technologies, such as unmanned aerial systems (UASs) and unmanned aerial vehicles (UAVs), has led to the widespread application of three-dimensional (3D) point clouds and digital surface models (DSMs). Due to the number of UAS technology applications across many fields, studies on the verification of the accuracy of image processing results have increased. In previous studies, the optimal number of ground control points (GCPs) was determined for a specific area of a study site by increasing or decreasing the amount of GCPs. However, these studies were mainly conducted in a single study site, and the results were not compared with those from various study sites. In this study, to determine the optimal number of GCPs for modeling multiple areas, the accuracy of 3D point clouds and DSMs were analyzed in three study sites with different areas according to the number of GCPs. The results showed that the optimal number of GCPs was 12 for small and medium sites (7 and 39 ha) and 18 for the large sites (342 ha) based on the overall accuracy. If these results are used for UAV image processing in the future, accurate modeling will be possible with minimal effort in GCPs.

Urban areas play a crucial role in carbon absorption, while also producing a considerable amount of carbon emissions. However, there has been a lack of research that has systematically examined the carbon storage and sequestration in green spaces located within urban environments, at a spatial scale. This study analyzes carbon storage and sequestration in Yurim Park, Daejeon, South Korea on a grid basis to fill the research gap. The research compares the variation in sequestration capacity across different grids and provides insights into the development of sustainable urban parks in urban planning. The classification of grids is based on specific site characteristics, such as land cover, tree distribution, type, and density. This results in a total of seven distinct types. The study employs a combination of the I-tree eco model, drone-based modeling, and on-site surveys to estimate carbon storage and sequestration in urban parks. The results show that the average carbon storage per unit area in the entire park was 15.3 tons of carbon per hectare, ranging from a minimum of 5.0 to a maximum of 21.4 tons per hectare. For the planted area, the average carbon storage was 8.6 tons per hectare. Grids with green areas dominated by broad-leaved trees and closed canopy cover had the highest carbon sequestration and storage values. The planting area ratio and the type of trees planted were found to directly influence the carbon sequestration capacity per unit area of urban parks. This study stands out from previous research by conducting a detailed area-based comparison and analysis of carbon sequestration capacity in urban parks using sophisticated measurement techniques. The findings offer direct insights into strategies and policies for securing future urban carbon sinks and can be of practical use in this regard.

Rapid urbanization has led to several severe environmental problems, including so-called heat island effects, which can be mitigated by creating more urban green spaces. However, the temperature of various surfaces differs and precise measurement and analyses are required to determine the “coolest” of these. Therefore, we evaluated the accuracy of surface temperature data based on thermal infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs), which have recently been utilized for the spatial analysis of surface temperatures. Accordingly, we investigated land surface temperatures (LSTs) in green spaces, specifically those of different land cover types in an urban park in Korea. We compared and analyzed LST data generated by a thermal infrared (TIR) camera mounted on an unmanned aerial vehicle (UAV) and LST data from the Landsat 8 satellite for seven specific periods. For comparison and evaluation, we measured in situ LSTs using contact thermometers. The UAV TIR LST showed higher accuracy (R² 0.912, root mean square error (RMSE) 3.502 °C) than Landsat TIR LST accuracy (R² value lower than 0.3 and RMSE of 7.246 °C) in all periods. The Landsat TIR LST did not show distinct LST characteristics by period and land cover type; however, grassland, the largest land cover type in the study area, showed the highest accuracy. With regard to the accuracy of the UAV TIR LST by season, the accuracy was higher in summer and spring ($R^2$ 0.868–0.915, RMSE 2.523–3.499 °C) than in autumn and winter ($R^2$ 0.766–0.79, RMSE 3.834–5.398 °C). Some land cover types (concrete bike path, wooden deck) were overestimated, showing relatively high total RMSEs of 4.439 °C and 3.897 °C, respectively, whereas grassland, which has lower LST, was underestimated—showing a total RMSE of 3.316 °C. Our results showed that the UAV TIR LST could be measured with sufficient reliability for each season and land cover type in an urban park with complex land cover types. Accordingly, our results could contribute to decision-making for urban spaces and environmental planning in consideration of the thermal environment.

Research on the risks of climate change to urban regeneration projects has been insufficient to date. Therefore, this study aims to compare and analyze the degree of risk of climate change impact on areas with and without urban regeneration projects (for Eup, Myeon, and Dong regional units) in Busan, South Korea. In this study, (1) climate change risk indicators were extracted based on the concept of risk (hazard, vulnerability, and exposure), (2) a spatial analysis was performed using a graphic information system (GIS), and (3) the primary influencing factors were derived through a logistic regression analysis. The principal results show that urban regeneration areas have a higher risk of climate change impact than other areas. The results indicate that urban regeneration areas have a higher population density per area and more impermeable or flooded areas can increase the risk of climate change impacts. We also discuss strategies to develop resilient cities and climate change adaptation policies for future urban regeneration projects.