• Korean Journal of Veterinary Research
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• v.23 no.2
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• pp.159-163
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• 1983

Infectious coryza is one of the important acute respiratory diseases causing a significant egg drop and retarded growth in chicken. An attempt for the isolation of etiologic agent was made by utilizing SPF sentinel birds housed in commercial farms and the results obtained are as follows. Fifteen isolates of Haemophilus gallinarum were tested for their biological and serological characteristics with reference strains, 221 and Modesto and subsequently classified into two serotypes. Of them, isolates immunolocally identical to the standard strains were also selected as vaccine strains for future studies.

• Korean Journal of Veterinary Research
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• v.64 no.2
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• pp.18.1-18.5
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• 2024

The surveillance and epidemiological investigation systems for companion animals in South Korea are significantly underdeveloped compared to those for humans and livestock. Recent outbreaks, such as idiopathic neuromuscular syndrome and highly pathogenic avian influenza among cats, have highlighted the need for reliable systems. This short review conducts situation analysis regarding disease surveillance and epidemiological investigation for companion animals in South Korea. The current challenges include an absence of administrative leadership, a lack of legal support, and unreliable medical data. The recommendations for future directions include clear leadership by the Animal and Plant Quarantine Agency, amending the Act on the Prevention of Contagious Animal Diseases to include companion animals, and enhancing the quality of medical data through standardized coding systems, such as Systematized Nomenclature of Medicine Clinical Terms. In addition, sentinel surveillance rather than universal systems should be established to provide adequate incentives for local practitioners to provide data and develop sustainable public-private networks. These recommendations could be important for developing a comprehensive and sustainable system for disease surveillance and epidemiological investigation in the companion animal field.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.36-41
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• 2016

Purpose Sentinel lymphoscintigraphy (SLS) was using only $^{99m}Tc-phytate$. If the supply is interrupted temporarily, there is no alternative radiopharmaceuticals. The aim of this study measure the particle size of radiopharmaceuticals and look for radiopharmaceuticals which can be substituted for $^{99m}Tc-phytate$. Materials and Methods The particle size of radiopharmaceuticals were analyzed by a nano-particle analyzer. This study were selected known radiopharmaceuticals to be useful particle size for SLS. We were divided into control and experimental groups using $^{99m}Tc-DPD$, $^{99m}Tc-MAG3$, $^{99m}Tc-DMSA$ with $^{99m}Tc-phytate$. For in-vivo experiment, radiopharmaceuticals were injected intradermally at both foot to perform lymphoscintigraphy. Imaging was acquired to dynamic and delayed static image and observe the inguinal lymph nodes with the naked eye. Results Particle size was measured respectively Phytate 105~255 nm (81.9%), MAG3 91~255 nm (98.7%), DPD 105~342 nm (77.3%), DMSA 164~ 342 nm (99.2%), MAA 1281~2305 nm (90.6%), DTPA 342~1106 nm (79.4%), and HDP 295~955 nm (94%). In-vivo delayed static image, inguinal lymph nodes of all experiment groups and two control groups are visible to naked eye. however, $^{99m}Tc-MAG3$ of control groups is not visible to naked eye. Conclusion We were analyzed to the particle size of the radiopharmaceuticals that are used in in-vivo. Consequently, $^{99m}Tc-DPD$, $^{99m}Tc-DMSA $are possible in an alternative radiopharmaceuticals of emergency.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.35-39
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• 2010

Purpose: The aim of this study was to investigate distribution of particle size in phytate kit and compare filtered method with non-filtered method using 200 nm filter for sentinel lymphoscintigraphy (SLS). Materials and Methods: Five phytate kit of having the same available period was measured by particle size analyzer. For in-vivo experiment, $^{99m}Tc$-phytate was injected intradermally at both foot to perform lymphoscintigraphy. Imaging was acquired at 1hour after injection. Region of interest (ROI) was drawn in inguinal and background area for analysis. RAW 264.7 cells (Murine macrophage cell) were prepared for measurement of celluar uptake as a representative of macrophages. Paired t-test was performed using SPSS (SPSS Inc, USA) for statistical analysis. Results: The size of most particle in Techne phytate kit was distributed in 130~650 nm(90.5 %). In-vivo study, the ROI analysis showed similar result between filtered and non-filtered sample, and the numerical value of count/pixel were $58.3{\pm}5.97$ and $60.2{\pm}4.88$. In-vitro study, cellular uptake study also showed no difference between filtered and non-filtered sample by gamma counting. Conclusion: The present study demonstrates that there was no meaning of 200 nm filtered method for SLS using $^{99m}Tc$-phytate.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.41-54
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• 2021

It is very essential to estimate the water body area using remote exploration for water resource management, analysis and prediction of water disaster damage. Hydrophysical detection using satellites has been mainly performed on large satellites equipped with optical and SAR sensors. However, due to the long repeat cycle, there is a limitation that timely utilization is impossible in the event of a disaster/disaster. With the recent active development of Micro satellites, it has served as an opportunity to overcome the limitations of time resolution centered on existing large satellites. The Micro satellites currently in active operation are ICEYE in Finland and Capella satellites in the United States, and are operated in the form of clusters for earth observation purposes. Due to clustering operation, it has a short revisit cycle and high resolution and has the advantage of being able to observe regardless of weather or day and night with the SAR sensor mounted. In this study, the operation status and characteristics of micro satellites were described, and the water area estimation technology optimized for micro SAR satellite images was applied to the Daecheong Dam basin on the Korean Peninsula. In addition, accuracy verification was performed based on the reference value of the water generated from the optical satellite Sentinel-2 satellite as a reference. In the case of the Capella satellite, the smallest difference in area was shown, and it was confirmed that all three images showed high correlation. Through the results of this study, it was confirmed that despite the low NESZ of Micro satellites, it is possible to estimate the water area, and it is believed that the limitations of water resource/water disaster monitoring using existing large SAR satellites can be overcome.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.637-654
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• 2023

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.439-449
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• 2023

This study was conducted to verify the role of forest roads in the extinction of large forest fires in Korea. The study area was the forest fire-damaged area of Gangneung City, Gangwon Special Self-Governing Province, in April 2023, which is one of the areas with the highest road density among the major forest fires that have occurred so far. The scope of the forest fire damage area was confirmed through on-site survey, and the intensity of the fire was carried out through Sentinel-2 satellite imagery analysis. After that, the relationship between the damage range and intensity and the forest road was examined. About 59.6 km of roads were built within 50 m from the boundary of the forest fire damage area, which can easily access the entire 149.1 ha of forest fire damaged area. The road density is as high as 168.9 m/ha. All forests that were fragmented by roads were fragmented into 83 places, and all of these forests could be judged to have spread by spotting fire. As a result of analyzing the distribution of damage intensity by distance from the road to see the extent of damage according to the ease of access of fire extinguishing vehicles, it was confirmed that the proportion of areas with low-intensity damage has increased sharply even from 75 m or more away from the road. The results of analyzing the distribution of damage intensity by altitude to see the extent of damage according to the ease of access of fire extinguishing showed that the proportion of areas with low-intensity damage increased as the altitude increased, while the proportion of areas with damage of more than strong intensity decreased as the altitude increased. It was confirmed that there is no data that roads inside or adjacent to forests in the forest fire area of Gangneung City are effective in extinguishing forest fires. These results are contrary to the logic that increasing the road density in forests is effective in extinguishing forest fires. In the case of this fire area in Gangneung City, the road density is 43 times higher than the current road density in Korea claimed by the Korea Forest Service of 3.9 m/ha. This study suggests that roads can be a hindrance to extinguishing forest fires.