• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.253-265
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• 2020

As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• 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.

• 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 Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.82-88
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• 2021

Sun-induced fluorescence (SIF) retrieval using remote sensing technique has been used in an effort to understand the photosynthetic efficiency and stress condition of vegetation. Although optical devices and SIF retrieval methodologies were established in order to retrieve SIF, the SIF measurements are domestically sparse. SIF data of paddy rice w as measured in Naju, South Korea from June 10, 2020 to October 5, 2020. The SIFs based red (O2A) and far-red (O2B) w ere retrieved using a spectral fitting method and an improved Fraunhofer line depth, and photosynthetically active radiation was also produced. In addition, the SIF data was filtered considering solar zenith angle, saturation conditions, the rapid and sudden change of solar irradiance, and sun glint. The provided SIF data can help to understand a SIF product and the filtering method of SIF data can contribute to producing high-quality SIF data.

• 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 Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.125-131
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• 2022

Recently, the prevalence of remotely managed patient care systems in medical institutions is increasing due to COVID-19. In particular, in the case of fluid monitoring, hospitals are considering introducing it as a system that can reduce patient safety and nurses' work. There are two products under development: a load cell method that measures weight and a method that detects drops of sap by infrared sensing. Although each product has differences in operation principle, sensor type, size, usage, and price, medical institutions are highly interested in the accuracy of the data obtained.In this study, two prototypes with different sensor methods were manufactured and the total amount of infusion per hour was measured to test the accuracy, which is the core of the infusion monitoring device. In addition, when there was an external movement, the change in the measured value of the sap was tested to evaluate the accuracy according to the measurement method. As a result of the experiment, there was a difference of less than 5% in the measurement value error of the two devices, and the load cell method showed a difference in the low-capacity measurement value and the infrared method in the high-capacity measurement value. As a result of this experiment, there was little difference in accuracy according to the sensor method of the infusion monitoring device, and it is considered that there is no problem in accuracy when used in a medical institution.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.194-208
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• 2021

The Hwanggang Dam in North Korea is located upstream of the Imjin River which is a shared river in the border area. It is known to have a reservoir capacity of 350 million cubic meters and releases a discharge primarily for generating hydroelectric power and partly for transferring to the Yesung River basin. Due to the supply of water from the Hwanggang Dam to another basin, the flow of the Imjin River has decreased, which has a negative impact on the water supply, river maintenance flow, water quality, and ecological environment in Korea. However, due to the special national security issue of the South and North Korea border region, the hydrological data is not shared, and the operation method of the Hwanggang Dam is unknown, so there is a risk of damage to the southern part of the downstream area. In this study, the monthly diversion as the long-term runoff concept was derived through the calibrated hydrological model based on optical remotely sensed Images and water balance analysis. As a result of the water balance analysis from January 2019 to September 2021, the average diversion of the Hwanggang Dam was 29.2m³/s, which is equivalent to 922 million tons per year and 45.6% of the annual inflow of 2.02 million tons into the Hwanggang Dam.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.88-99
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• 2022

The river network is one of the essential topographical characteristics in river management. The river network which as previously constructed by the ground surveying method has recently begun to be efficiently constructed using the remote sensing datasets. Since it is difficult to remove these obstacles such as bridges in the urban rivers, it is rare to construct the urban river networks with the various obstacles. In this study, the Sentinel-2 satellite imagery was used to develop the automatic method for detecting the urban river networks without the obstacles and with the preserved boundaries as follows. First, the normalized difference water index image was generated using the multispectral bands of the given Sentinel-2 satellite imagery, and the binary image that could classify the water body and other regions was generated. Next, the morphological operations were employed for detecting the complete river networks with the obstacles removed and the boundaries preserved. As a result of applying the proposed methodology to Han River in Seoul, the complete river networks with the obstacles removed and the boundaries preserved were well constructed.