• Economic and Environmental Geology
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• v.54 no.2
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• pp.177-186
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• 2021

For disaster management and mitigation of earthquakes in Korea Peninsula, active fault investigation has been conducted for the past 5 years. In particular, investigation of sediment-covered active faults integrates geomorphological analysis on airborne LiDAR data, surface geological survey, and geophysical exploration, and unearths subsurface active faults by trench survey. However, the fault traces revealed by trench surveys are only available for investigation during a limited time and restored to the previous condition. Thus, the geological data describing the fault trench sites remain as the qualitative data in terms of research articles and reports. To extend the limitations due to temporal nature of geological studies, we utilized a terrestrial LiDAR to produce 3D point clouds for the fault trench sites and restored them in a digital space. The terrestrial LiDAR scanning was conducted at two trench sites located near the Yangsan Fault and acquired amplitude and reflectance from the surveyed area as well as color information by combining photogrammetry with the LiDAR system. The scanned data were merged to form the 3D point clouds having the average geometric error of 0.003 m, which exhibited the sufficient accuracy to restore the details of the surveyed trench sites. However, we found more post-processing on the scanned data would be necessary because the amplitudes and reflectances of the point clouds varied depending on the scan positions and the colors of the trench surfaces were captured differently depending on the light exposures available at the time. Such point clouds are pretty large in size and visualized through a limited set of softwares, which limits data sharing among researchers. As an alternative, we suggested Potree, an open-source web-based platform, to visualize the point clouds of the trench sites. In this study, as a result, we identified that terrestrial LiDAR data can be practical to increase reproducibility of geological field studies and easily accessible by researchers and students in Earth Sciences.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.727-736
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• 2022

Ice wedges are subsurface ice mass structures that formed mainly by freezing precipitation with airborne dust and surrounding soil particles flowed through the active layer into the cracks growing by repeating thermal contractions in the deeper permafrost layer over time. These ice masses characteristically contain high concentrations of solutes and solids. Because of their unique properties and distribution, the possibility of harnessing ice wedges as an alternative archive for reconstructing paleoclimate and paleoenvironment has been recently suggested despite limited studies. It is imperative to preserve the physicochemical properties of the ice wedge (e.g., solute concentration, mineral particles) without any potential alteration to use it as a proxy for reconstructing the paleo-information. Thawing the ice wedge samples is prerequisite for the assessment of their physicochemical properties, during which the paleo-information could be unintentionally altered by any methodological artifact. This study examined the effect of thawing conditions and procedures on the physicochemical properties of solutes and solid particles in ice wedge samples collected from Cyuie, East Siberia. Four different thawing conditions with varying temperatures (4 and 23℃) and oxygen exposures (oxic and anoxic) for the ice wedge sample treatment were examined. Ice wedge samples thawed at 4℃ under anoxic conditions, wherein biological activity and oxidation were kept to a minimum, were set as the standard thawing conditions to which the effects of temperature and oxygen were compared. The results indicate that temperature and oxygen exposure have negligible effects on the physicochemical characteristics of the solid particles. However, the chemical features of the solution (e.g., pH, electric conductivity, alkalinity, and concentration of major cations and trace elements) at 4℃ under oxic conditions were considerably altered, compared to those measured under the standard thawing conditions. This study shows that the thawing condition of ice wedge samples can affect their chemical features and thereby the geochemical information therein for the reconstruction of the paleoclimate and/or paleoenvironment.

• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.106-114
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• 2023

With rising concerns about pesticide spray drift by aerial application, this study attempt to evaluate aerodynamic property and collection efficiency of spray drift according to the leaf area index (LAI) of crop for preventing undesirable pesticide contamination by the spray-drift tunnel experiment. The collection efficiency of the plant with 'Low' LAI was measured at 16.13% at a wind speed of 1 m·s^-1. As the wind speed increased to 2 m·s^-1, the collection efficiency of plant with the same LAI level increased 1.80 times higher to 29.06%. For the 'Medium' level LAI, the collection efficiency was 24.42% and 43.06% at wind speed of 1 m·s^-1 and 2 m·s^-1, respectively. For the 'High' level LAI, it also increased 1.24 times higher as the wind speed increased. The measured results indicated that the collection of spray droplets by leaves were increased with LAI and wind speed. This also implied that dense leaves would have more advantages for preventing the drift of airborne spray droplets. Aerodynamic properties also tended to increase as the LAI increased, and the regression analysis of quadric equation and power law equation showed high explanatory of 0.96-0.99.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.437-449
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• 2023

This study measured PM₁₀ concentrations and wind speeds in buffer green spaces and neighborhood parks located along the road, and compared them with roadside measurementresults to understand the effect of mitigating PM₁₀ concentrations by type of green space and the influence of wind speeds on it. As a result of the analysis, the effect of mitigating PM₁₀ concentration was different depending on the type of roadside green space, and an increase in wind speed had a significant effect on reducing PM₁₀ concentration. In buffer green areas with high planting density, wind speed was low and PM₁₀ stagnated inside, resulting in the highest concentration. On the other hand, green areas in neighborhood parks with relatively low planting density had high wind speeds and the lowest PM₁₀ concentration. The non-green area within the neighborhood park recorded the highest wind speed, which was advantageous for the spread of PM₁₀, but the concentration was higherthan that of the green area. Therefore, in orderto reduce PM₁₀ concentration in roadside green space, it is necessary to create green space with good ventilation, and the combined effect of green space and wind speed seems to be more advantageous in reducing PM₁₀ concentration. Green spaces capture and remove PM inside, contributing to reducing the concentration of PM outside. In order to manage PM in the entire city and on roads, it is necessary to increase planting density and leaf area in roadside green spaces, such as buffer green spaces, so that PM can be removed within the green spaces. However, in green spaces such as neighborhood parks that are actively used by city residents, in orderto minimize damage to users due to PM, it is desirable to create green spaces with a structure that allows PM to spread to the outside rather than stagnate inside.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.3
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• pp.380-391
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• 2015

Objectives: The objective of this study is to assess airborne particulate matter pollution and its effect on health of residents living near Ansim Briquette Fuel Complex and its vicinities. Also, this study measured and analyzed the concentration of TSP, $PM_{10}$, $PM_{2.5}$, and heavy metals which influences on the environmental and respiratory disease in Ansim Briquette Fuel Complex, Daegu, Korea. Methods: In this study, we analyzed various environmental pollutants such as particulate matter and heavy metals from Ansim Briquette Fuel Complex that adversely affected local residents's health. In particular, we verified the concentration distribution and characteristics of exposure for TSP, $PM_{10}$, and $PM_{2.5}$ among particulate matters, and heavy metals(Cd, Cr, Cu, Mn, Ni, Pb, Fe, Zn, and Mg). In that regard, the official test method on air pollution in Korea for analysis of particulate matter and heavy metal in atmosphere were conducted. The large capacity air sampling method by the official test method on air pollution in Korea were applied for sampling of heavy metals in atmosphere. In addition, we evaluated the concentration of seasonal environmental pollutants for each point of residence in Ansim Briquette Fuel Complex and surrounding area. The sampling measured periods for air pollutants were from August 11, 2013 to February 21, 2014. Furthermore, we measured and analyzed the seasonal concentrations(summer, autumn and winter). Results: The average concentration for TSP, $PM_{10}$, and $PM_{2.5}$ by direct influence area at Ansim Briquette Fuel Complex were 1.7, 1.4 and 1.9 times higher than reference region. In analysis results of seasonal concentrations for particulate matter in four direct influence and reference area, concentration levels for winter were generally somewhat higher than concentrations for summer and autumn. The average concentrations for Cd, Cr, Mn, Ni, Pb, Fe, and Zn in direct influence area at Ansim Briquette Fuel Complex were $0.0008{\pm}0.0004{\mu}g/Sm^3$, $0.0141{\pm}0.0163{\mu}g/Sm^3$, $0.0248{\pm}0.0059{\mu}g/Sm^3$, $0.0026{\pm}0.0011{\mu}g/Sm^3$, $0.0272{\pm}0.0084{\mu}g/Sm^3$, $0.4855{\pm}0.1862{\mu}g/Sm^3$, and $0.3068{\pm}0.0631{\mu}g/Sm^3$, respectively. In particularly, the average concentrations for Cd, Cr, Mn, Ni, Pb, Fe, and Zn in direct influence area at Ansim Briquette Fuel Complex were 1.9, 3.6, 2.1, 1.9, 1.4, 2.6, and 1.2 times higher than reference area, respectively. The continuous monitoring and management were required for some heavy metals such as Cr and Ni. Moreover, the average concentration in winter for particulate matter in direct influence area at Ansim Briquette Fuel Complex were generally higher than concentrations in summer and autumn. Also, average concentrations for TSP, $PM_{10}$, and $PM_{2.5}$ were from 1.5 to 2.0 times, 1.2 to 1.8 times, and 1.1 to 2.3 times higher than reference area, respectively. In results for seasonal atmospheric environment, TSP, $PM_{10}$, $PM_{2.5}$, and heavy metal concentrations in direct influence area were higher than reference area. Especially, the concentrations in C station were a high level in comparison with other area. Conclusions: In the results, some particulate matters and heavy metals were relatively high concentration, in order to understand the environmental pollution level and health effect in surrounding area at Ansim Briquette Fuel Complex. The concentration of some heavy metals emitted from direct influence area at Ansim Briquette Fuel Complex were relatively higher than reference area. In particular, average concentration for heavy metals in this study were higher than average concentrations in air quality monitoring station for heavy metal for 7 years in Deagu metropolitan region. Especially, the residents near Ansim Briquette Fuel Complex may be exposed to the pollutants(TSP, $PM_{10}$, $PM_{2.5}$, and heavy metals, etc) emitted from the factories in Ansim Briquette Fuel Complex.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.1
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• pp.225-285
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• 2017

In regard to the regulations related to the RPA(Remotely Piloted Aircraft), which is sometimes called in other countries as UA(Unmanned Aircraft), ICAO stipulates the regulations in the 'RPAS manual (2015)' in detail based on the 'Chicago Convention' in 1944, and enacts provisions for the Rules of UAS or RPAS. Other contries stipulates them such as the Federal Airline Rules (14 CFR), Public Law (112-95) in the United States, the Air Transport Act, Air Transport Order, Air Transport Authorization Order (through revision in "Regulations to operating Rules on unmanned aerial System") based on EASA Regulation (EC) No.216/2008 in the case of unmanned aircaft under 150kg in Germany, and Civil Aviation Act (CAA 1998), Civil Aviation Act 101 (CASR Part 101) in Australia. Commonly, these laws exclude the model aircraft for leisure purpose and require pilots on the ground, not onboard aricraft, capable of controlling RPA. The laws also require that all managements necessary to operate RPA and pilots safely and efficiently under the structure of the unmanned aircraft system within the scope of the regulations. Each country classifies the RPA as an aircraft less than 25kg. Australia and Germany further break down the RPA at a lower weight. ICAO stipulates all general aviation operations, including commercial operation, in accordance with Annex 6 of the Chicago Convention, and it also applies to RPAs operations. However, passenger transportation using RPAs is excluded. If the operational scope of the RPAs includes the airspace of another country, the special permission of the relevant country shall be required 7 days before the flight date with detail flight plan submitted. In accordance with Federal Aviation Regulation 107 in the United States, a small non-leisure RPA may be operated within line-of-sight of a responsible navigator or observer during the day in the speed range up to 161 km/hr (87 knots) and to the height up to 122 m (400 ft) from surface or water. RPA must yield flight path to other aircraft, and is prohibited to load dangerous materials or to operate more than two RPAs at the same time. In Germany, the regulations on UAS except for leisure and sports provide duty to avoidance of airborne collisions and other provisions related to ground safety and individual privacy. Although commercial UAS of 5 kg or less can be freely operated without approval by relaxing the existing regulatory requirements, all the UAS regardless of the weight must be operated below an altitude of 100 meters with continuous monitoring and pilot control. Australia was the first country to regulate unmanned aircraft in 2001, and its regulations have impacts on the unmanned aircraft laws of ICAO, FAA, and EASA. In order to improve the utiliity of unmanned aircraft which is considered to be low risk, the regulation conditions were relaxed through the revision in 2016 by adding the concept "Excluded RPA". In the case of excluded RPA, it can be operated without special permission even for commercial purpose. Furthermore, disscussions on a new standard manual is being conducted for further flexibility of the current regulations.