• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1503-1517
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• 2018

Mt. Baekdu is a stratovolcano located at the border between China and North Korea and is known to have formed through its differentiation stage after the Oligocene epoch in the Cenozoic era. There has been a growing interest in the magma re-activity of Mt. Baekdu volcano since 2010. Several research projects have been conducted by government such as Korea Meteorological Administration and Korea Institute of Geoscience and Mineral Resources. Because, however, the Mt. Baekdu volcano is located far from South Korea, it is quite difficult to collect in-situ observations by terrestrial equipment. Remote sensing is a science to analyze and interpret information without direct physical contact with a target object. Various types of platform such as automobile, unmanned aerial vehicle, aircraft and satellite can be used for carrying a payload. In the past several decades, numerous volcanic studies have been conducted by remotely sensed observations using wide spectrum of wavelength channels in electromagnetic waves. In particular, radar remote sensing has been widely used for volcano monitoring in that microwave channel can gather surface's information without less limitation like day and night or weather condition. Radar interferometric technique which utilized phase information of radar signal enables to estimate surface displacement such as volcano, earthquake, ground subsidence or glacial movement, etc. In 2018, long-term research project for collaborative observation for Mt. Baekdu volcano between Korea and China were selected by Korea government. A volcanic specialized research center has been established by the selected project. The purpose of this paper is to introduce about remote sensing techniques for volcano monitoring and to review selected studies with remote sensing techniques to monitor Mt. Baekdu volcano. The acquisition status of the archived observations of six synthetic aperture radar satellites which are in orbit now was investigated for application of radar interferometry to monitor Mt. Baekdu volcano. We will conduct a time-series analysis using collected synthetic aperture radar images.

• Asian Journal of Atmospheric Environment
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• v.7 no.2
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• pp.120-128
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• 2013

The concentration and composition of particulate matter (PM) in the atmosphere can directly reflect the environmental pollution. The atmospheric pollution in some Cameroonian cities is increasing with the industrial development and urbanization. Air pollution is inherently complex, containing PM of varied size and composition. This PM exists as a dynamic cloud interacting with sunlight and is modified by the meteorology. The reflectometer and the EDXRF spectrometry are applied to determine the concentration of some specific elements at four sites in the city of Yaound$\acute{e}$. The particular aim of the present work is to put in place data base on air pollution in urban area and elaborate regulations on the emissions issued to industrial and vehicle activities. This study provides an overview of the concentration of black carbon and some specific elements in the air, which have impacts on human health. The measurement was done by distinguishing the size of particle. So that, the particle with aerodynamic diameter between $2.5-10{\mu}m$ (so-called coarse particle) and aerodynamic diameter < $2.5{\mu}m$ (so-called fine particle) were considered to obtain more information about levels of the inhalable fraction of the location. The results obtained in four locations of the city of Yaound$\acute{e}$ show that the black carbon concentration is very considerable, the element sulfur is a major pollutant and the concentration of fine particle is very greater. The results obtained of fine and coarse filters range from $5-17{\mu}g/m^3$ and $10-18{\mu}g/m^3$ for the black carbon. S, Cu, Zn, Pb, Cd, As, Se and Hg are the specific findings of this work. The pollutants with a greater concentration are S, Pb, and Zn. These later seem to be non-uniformly, non-regular in some location and high compared to other countries. This work allows us to make a potential relation between pollutants and emission sources. In this framework, some suggestions have been proposed to reduce emissions for an improvement of the air quality in the environment and thus, the one of the city of Yaound$\acute{e}$.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.59-64
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• 2008

The urban is possessing of various landuses such as commercial, industrial, residential and official areas. All of these landuses is including the paved areas that are roads and parking lots. The NPS (nonpoint sources) pollutants are generally originated from pavement areas in urban by human activities. Especially the roads are stormwater intensive landuses because of high vehicle activities and high imperviousness. The main NPS pollutants from roads are particulates and metals from vehicles and pavements. The Korea MOE (Ministry of Environment) is developing the NPS control program to reduce the NPS pollutants from the basins. However, it is not easy to control the NPS because it has high uncertainty by characteristics of rainfalls and watersheds. Therefore, this research was conducted on characterizing the runoff and providing mean EMC from roads. The monitoring were performed for total 16 rainfall events from a road in Youngin City since 2006. The results show that the TSS is highly correlated with other pollutant parameters. The statistical regression models using TSS EMC have been developed to easily determine the EMC of other pollutant parameters.

• Economic and Environmental Geology
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• v.48 no.3
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• pp.205-212
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• 2015

We have verified applicability of UAV(Unmanned Aerial Vehicle) photogrammetry to a mining engineering. The test mine is a smectite mine located at Gyeongju city in Gyeongnam province, Koera. 448 photos over area of $600m{\times}380m$ were taken with overlapped manner using Cannon Mark VI equipped to multicopter DJI S1000, which were processed with AgiSoft Photoscan software to generate orthophoto and DEM model of the study area. photogrammetry data with 10 cm resolution were generated using 6 ground control positions, which were exported to the 3D geological modeling software to make a topographic surface object. Monitoring of amount of ore production and landsliding could be done with less than 1 hours photographing as well as low cost. A direct link between UAV photogrammetry and 3D geological modeling technology might increase productivity of a mine due to appling the topographical surface change immediately according to the mining operation.

• International Journal of Highway Engineering
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• v.8 no.1 s.27
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• pp.99-106
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• 2006

During the dry periods, many types of pollutant are accumulating on the paved surface by vehicle activities. Particularly, the highways are stormwater intensive landuses because of high imperviousness and high pollutant mass emissions from vehicles. The accumulated pollutants in highways are washed-off during a rainfall event and are highly contributing on water quality of receiving water bodies. The stormwater runoff from the highways are containing various pollutants such as metals, oil & grease and toxic chemicals originated from vehicles. Therefore, this research is performed to find pollutant characteristics in the magnitude of statistical pollutant concentrations during storm periods. During the monitoring periods, the first-flush phenomenon is visibly occurred on most storm events, which is confirmed from hydro- and pollute-graphs. The 95% confidence intervals of washed-off pollutant concentration are ranged to 154.7-257.1 mg/L for 755,138.9-197.6 mg/L for COD, 3.5-6.4 mg/L for oil & grease, 6.3-9.2 mg/L for TN and 2.3-3.31 mg/L for TP. The first flush effect is mostly occurred within initial 30 min of storm duration.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.2
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• pp.161-168
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• 2011

The heating, ventilating, air conditioning (HVAC) system is a very important part of an automotive vehicle: it controls the microclimate inside the passenger's compartment and removes the frost or mist that is produced in cold/rainy weather. In this study, the numerical analysis of the defrost duct in an HVAC system and the de-icing pattern is carried out using commercial CFX-code. The mass flow distribution and flow structure at the outlet of the defrost duct satisfied the duct design specification. For analyzing the de-icing pattern, additional grid generation of solid domain of ice and glass is pre-defined for conductive heat transfer. The flow structure near the windshield, streakline, and temperature fields clearly indicate that the de-icing capacity of the given defrost duct configuration is excellent and that it can be operated in a stable manner. In this paper, the unsteady changes in temperature, water volume fraction, and static enthalpy at four monitoring points are discussed.

• Atmosphere
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• v.27 no.1
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• pp.41-54
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• 2017

Organic carbon (OC) and elemental carbon (EC) in $PM_{2.5}$ were measured using Sunset OC/EC Field Analyzer at Seoul Hwangsa Monitoring Center from March to April, 2016. The mean concentrations of OC and EC during the entire period were $4.4{\pm}2.0{\mu}gC\;m^{-3}$ and $1.4{\pm}0.6{\mu}gC\;m^{-3}$, respectively. OC/EC ratio was $3.4{\pm}1.0$. The average concentrations of $PM_{10}$ and $PM_{2.5}$ were $57.4{\pm}25.9$ and $39.7{\pm}19.8{\mu}g\;m^{-3}$, respectively, which were detected by an optical particle counter. The OC and EC peaks were observed in the morning, which were impacted by vehicle emission, however, their diurnal variations were not noticeable. This is determined to be contributed by the long-range transported OC or secondary formation via photochemical reaction by volatile organic compounds at afternoon. A conditional probability function (CPF) model was used to identify the local source of pollution. High concentrations of $PM_{10}$ and $PM_{2.5}$ were observed from the westerly wind, regardless of wind speed. When wind velocity was high, a mixing plume of dust and pollution during long-range transport from China in spring was observed. In contrast, pollution in low wind velocity was from local source, regardless of direction. To know the effect of long-range transport on pollution, a concentration weighted trajectory (CWT) model was analyzed based on a potential source contribution function (PSCF) model in which 75 percentiles high concentration was picked out for CWT analysis. $PM_{10}$, $PM_{2.5}$, OC, and EC were dominantly contributed from China in spring, and EC results were similar in both PSCF and CWT. In conclusion, Seoul air quality in spring was mainly affected by a mixture of local pollution and anthropogenic pollutants originated in China than the Asian dust.