• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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• pp.93-101
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• 2021

Recently, with the urban redevelopment and the spread of the planned cities, there is increasing interest in the wind environment, which is related not only to design of buildings and landscaping but also to the comfortability of pedestrians. Numerical analysis for wind environment prediction is underway in many fields, such as dense areas of high-rise building or composition of the apartment complexes, a precisive 3D building model is essentially required in this process. Many studies conducted for wind environment analysis have typically used the method of creating a 3D model by utilizing the building layer included in the GIS (Geographic Information System) data. These data can easily and quickly observe the flow of atmosphere in a wide urban environment, but cannot be suitable for observing precisive flow of atmosphere, and in particular, the effect of a complicated structure of a single building on the flow of atmosphere cannot be calculated. Recently, drone photogrammetry has shown the advantage of being able to automatically perform building modeling based on a large number of images. In this study, we applied photogrammetry technology using a drone to evaluate the flow of atmosphere around two buildings located close to each other. Two 3D models were made into an automatic modeling technique and manual modeling technique. Auto-modeling technique is using an automatically generates a point cloud through photogrammetry and generating models through interpolation, and manual-modeling technique is a manually operated technique that individually generates 3D models based on point clouds. And then the flow of atmosphere for the two models was compared and analyzed. As a result, the wind environment of the two models showed a clear difference, and the model created by auto-modeling showed faster flow of atmosphere than the model created by manual modeling. Also in the case of the 3D mesh generated by auto-modeling showed the limitation of not proceeding an accurate analysis because the precise 3D shape was not reproduced in the closed area such as the porch of the building or the bridge between buildings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.319-325
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• 2021

A fine dust measurement using drones is becoming an increasingly common technology, and air pollutants can be identified through dust monitoring in partial industrial areas. A station for measuring fine dust provides information at large construction site offices. On the other hand, it was difficult to check the fine dust in the pollutant source accurately. Therefore, the drone took measurements directly after been placed at the site. While measuring fine dust, monitoring noise occurred due to the influence of the drone's down-wind during landing, but the measurements were similar to the numerical value of the grounded pollution source on the height of 30 m. The field applicability to the study area has limitations in periodic updates using satellite images because the terrain was constantly changing due to considerable flattening fieldwork. Therefore, this study implemented a system that can reflect real-time field information through GIS mapping using drones.

• Atmosphere
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• v.31 no.1
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• pp.113-141
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• 2021

The impacts of ice clouds on the energy budget of the Earth and their representation in climate models have been identified as important and unsolved problems. Ice clouds consist almost exclusively of non-spherical ice crystals with various shapes and sizes. To determine the influences of ice clouds on solar and infrared radiation as required for remote sensing retrievals and numerical models, knowledge of scattering and microphysical properties of ice crystals is required. A conventional method for representing the radiative properties of ice clouds in satellite retrieval algorithms and numerical models is to combine measured microphysical properties of ice crystals from field campaigns and pre-calculated single-scattering libraries of different shapes and sizes of ice crystals, which depend heavily on microphysical and scattering properties of ice crystals. However, large discrepancies between theoretical calculations and observations of the radiative properties of ice clouds have been reported. Electron microscopy images of ice crystals grown in laboratories and captured by balloons show varying degrees of complex morphologies in sub-micron (e.g., surface roughness) and super-micron (e.g., inhomogeneous internal and external structures) scales that may cause these discrepancies. In this study, the current idealized models representing morphologies of ice crystals and the corresponding numerical methods (e.g., geometric optics, discrete dipole approximation, T-matrix, etc.) to calculate the single-scattering properties of ice crystals are reviewed. Current problems and difficulties in the calculations of the single-scattering properties of atmospheric ice crystals are addressed in terms of cloud microphysics. Future directions to develop physically consistent ice-crystal models are also discussed.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1339-1348
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• 2020

Phytoplankton controls marine ecosystems in terms of nutrients, photosynthetic rate, carbon cycle, etc. and the degree of its influence on the marine environment depends on their physical size. Many studies have been attempted to identify marine phytoplankton size classes using the remote sensing techniques. One of successful approach was the three-component model which estimates the chlorophyll concentrations of three phytoplankton size classes (micro-phytoplankton; >20 ㎛, nano-; 2-20 ㎛ and pico-; <2 ㎛) as a function of total chlorophyll. Here, we examined the applicability of Geostationary Ocean Colour Imager (GOCI) to the mapping of the phytoplankton size class distribution in the East Sea. A fit of the three-component model to a biomarker pigment dataset collected in the study area for some years including a large harmful algal bloom period has been carried out to derive size-fractioned chlorophyll concentration (CHL). The tuned three-component model was applied to the hourly GOCI images to identify the fractions of each phytoplankton size class for the entire CHL. Then, we investigated the distribution of phytoplankton community in terms of the size structure in the East Sea during the harmful Cochlodinium polykrikoides blooms in the summer of 2013.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.553-566
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• 2021

River confluence is often characterized by shear layer and the associated strong mixing. In natural rivers, the main channel and its tributary can be separated by the shear layer using contrasting colors. The shear layer can be easily observed using aerial images from satellite or unmanned aerial vehicles. This study proposes a low-cost identification method extracting geographic features of the shear layer using RGB aerial image. The method consists of three stages. At first, in order to identify the shear layer, it performs image segmentation using a Gaussian mixture model and extracts the water bodies of the main channel and tributary. Next, the self-organizing map simplifies the flow line of the water bodies into the 1-dimensional curve grid. After that, the curvilinear coordinate transformation is performed using the water body pixels and the curve grid. As a result, the shear layer identification method was successfully applied to the confluence between Nakdong River and Nam River to extract geometric shear layer features (confluence angle, upstream- and downstream- channel widths, shear layer length, maximum shear layer thickness).

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1777-1788
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• 2022

The reservoir is a major water supply source in the domestic agricultural environment, and the monitoring of water storage of reservoirs is important for the utilization and management of agricultural water resource. Remote sensing via satellite imagery can be an effective method for regular monitoring of widely distributed objects such as reservoirs, and in this study, image classification and image segmentation algorithms are applied to Sentinel-1 Synthetic Aperture Radar (SAR) imagery for water body detection in 53 reservoirs in South Korea. Six algorithms are used: Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), and Chan-Vese (CV), and the results of water body detection are evaluated with in-situ images taken by drones. The correlations between the in-situ water surface area and detected water surface area from each algorithm are NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, and CV 0.9736, and the larger the scale of reservoir, the higher the linear correlation was. WS showed low recall due to the undetected water bodies, and NN, SVM, and RF showed low precision due to over-detection. For water body detection through SAR imagery, we found that aquatic plants and artificial structures can be the error factors causing undetection of water body.

• Journal of Environmental Impact Assessment
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• v.31 no.1
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• pp.63-74
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• 2022

In orderto understand the urban thermal conditions, many studies have been conducted to estimate the thermal fluxes. Currently sensible heat fluxes are estimated through various methods, but studies about comparing the differences between each method are very insufficient. Therefore, this study try to estimate the sensible heat flux of the same area by two representative estimation methods and compare their results to confirm the significance and limitation between methods. As a result of the study, the heat balance methods has a great advantage in terms of resolution but it can not consider the anthropogenic heat flux, so sensible heat flux can be underestimated in urban areas. When estimating based on physical equation, anthropogenic heat flux can be considered and the error is relatively small, it has a limitations in time and space resolutons. The two methods showed the largest difference in industiral areas where anthropogenic heat fluxes are high, with an average of 135 W/m² and a maximum of 400 W/m². On the other hand, the green and water have a very small difference with and average of 20 W/m². The results between two methods show significant differences in urban areas, it is necessary to select a suitable method for each research purpose.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1111-1119
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• 2022

Tourism projects through islands in the waters of Sinan-gun became active, and as a result, a total of 13 marine bridges connecting islands were completed. However, the marine bridge constructed in the fairway is dangerous for traffic. Particularly, in the case of the marine bridge connecting two islands, the width of the fairway is extremely narrow, therefore the risk is higher. In this study, we evaluated the risk of collision between marine bridge piers and ships using the IALA Waterway Risk Assessment Program (IWRAP), a risk assessment model for port waterways, based on a maritime traffic survey on the coastal bridge in Sinan-gun. The results, indicated that No.1 Sinan bridge had the highest probability of collision and most of the transit ships were coastal passenger ships. In addition, No.1 Sinan bridge was the place where the most collision accidents occurred among the marine bridge piers in the target sea, and the cause this study was analyzed. An analysis of the satellite images of the sea environment of No.1 Sinan bridge using an image processing method, confirmed that obstacles that could not be seen in the chart existed nearby the bridge. As a result, traffic was observed to be concentrated in one direction, unlike two-way traffic, which is a method of inducing traffic of bridges to avoid obstacles. The risk cause analysis method using the image processing technique of this study is expected to be used as a basic research method for analyzing the risk factors of island bridge in the future.