• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.63-71
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• 2023

Urinary incontinence (UI), affecting 3%-11% of males and 25%-45% of females globally, is expected to rise with an aging population. It significantly impacts mental health, causing depression, stress, and reduced quality of life. UI can exacerbate psychiatric conditions, affecting treatment compliance and effectiveness. It is categorized into transient and chronic types. Transient UI, often reversible, is caused by factors summarized in the acronym DIAPPERS: Delirium, Infection, Atrophic urethritis/vaginitis, Psychological disorders, Pharmaceuticals, Excess urine output, Restricted mobility, Stool impaction. Chronic UI includes stress, urge, mixed, overflow, functional, and persistent incontinence. Drug-induced UI, a transient form, is frequently seen in psychiatric treatment. Antipsychotics, antidepressants, and other psychiatric medications can cause UI through various mechanisms like affecting bladder muscle tone, altering nerve reflexes, and inducing other conditions like diabetes or epilepsy. Specific drugs like lithium and valproic acid have also been linked to UI, though mechanisms are not always clear. Managing UI in psychiatric patients requires careful monitoring of urinary symptoms and judicious medication management. If a drug is identified as the cause, options include discontinuing, reducing, or adjusting the dosage. In cases where medication continuation is necessary, additional treatments like desmopressin, oxybutynin, trihexyphenidyl, or amitriptyline may be considered.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.258-266
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• 2023

This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.457-463
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• 2023

To investigate residual pesticide levels in agricultural products contained in Meal-kits, 27 Meal-kit products were collected from marts, Meal-kit shops, and online stores in Incheon City, South Korea. Seventy-six vegetable and thirty-seven mushroom products were analyzed for residual levels of 339 pesticides. Residual pesticides were detected in 23 out of 76 vegetables and were not present in the 37 mushroom products. The residual pesticide detection rate was 20.4% (23/113 cases). The pesticides famoxadone 0.034 mg/kg (standard: 0.01 mg/kg or less, PLS) and fenpyroximate 0.302 mg/kg (standard: 0.01 mg/kg or less, PLS) exceeded their maximum residue levels (MRL). This survey revealed that various types of pesticides remain in agricultural products in Meal-kits. Due to the nature of Meal-kit products, there is no separate standard for residual pesticides in agricultural products. Therefore, continuous monitoring of residual pesticides is necessary.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.483-488
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• 2023

The status of residual pesticides was investigated in four pepper seed oil samples and 36 pepper-flavored oil samples oil distributed on the market from August to December 2022. A total of 179 pesticides were monitored in 40 samples, and 14 pesticides were detected in 39 of the samples, with a detection range of 0.01-2.16 mg/kg. In chili seed oil, 10 pesticides were detected 27 times with a range of 0.11-2.16 mg/kg, and in pepper-flavored oil, 9 pesticides were detected 94 times with a range of 0.01-0.80 mg/kg. The most frequently detected pesticides were tebuconazole, ethion, and difenoconazole, with ethion being detected in large concentrations in products using Chinese raw materials. Ethion, an unregistered pesticide in the Republic of Korea, has not been detected in the Gyeonggi-do area in the past 10 years. It is thought that the detection of ethion can be utilized as an indicator of products made in China. Peppers are a representative agricultural product for which many pesticides are used, and if the pesticides transferred to pepper seeds are not removed, the probability of detecting various types of pesticides in pepper seed oil is very high. Therefore, continuous research is needed to ensure the safety of pepper seed oil.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.489-495
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• 2023

In this study, we detected the presence of residual pesticides in 341 agricultural products collected from local food outlets in western Gyeonggi Province. Residual pesticides were detected in 105 (30.8%) samples. Six samples exceeded the legal limits for residual pesticides, resulting in a non-compliance rate of 1.8%, which was slightly higher than the average non-compliance rate of 1.4% in the last three years. Among the tested agricultural products, only fruits and vegetables were found to have pesticide residues, with 24 of 34 fruits (a detection rate of 70.6%) and 81 of 277 vegetables (a detection rate of 29.2%) testing positive. In total, 59 types of pesticides, including acetamiprid, which was detected 208 times, were detected and had a detection range of 0.01-2.38 mg/kg. Among the 105 agricultural products containing pesticide residues, a single pesticide was detected in 62 samples (59%) and two or more pesticides were detected in 43 samples (41%). In particular, 14 pesticides were detected in the same sample of peaches; dinotefuran was detected 21 times. Upon examining the toxicity of the detected pesticides, Class III pesticides (moderate toxicity) were detected 44 times (21.2%) and Class IV pesticides (low toxicity) were detected 164 times (78.8%). Class I, II, and III pesticides with fish toxicity were detected 68 (32.7%), 14 (6.7%), and 126 times (60.6%), respectively. Upon examining the exposure to high-frequency pesticide components detected five or more times, the hazard index was found to be ≤2.8%. Accordingly, the hazard of residual pesticides based on dietary intake was deemed insignificant.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.631-639
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• 2023

Flash drought (FD), characterized by the rapid onset and intensification, can significantly impact ecosystems and induce immediate water stress. A more comprehensive understanding of the causes and characteristics of FD events is required to enhance drought monitoring. Therefore, we investigated the FD events took place over the Korean peninsula using Global Land Data Assimilation System (GLDAS) data from 2012 to 2022. We first detected FD events using the stress-based method (Standardized Evaporative Stress Ratio, SESR), and analyzed the frequency and duration of FDs. The FD events were classified into three cases based on the variations in Actual Evapotranspiration (AET) and potential Evapotranspiration (PET), and spatially analyzed. Results revealed that there are regional disparities in frequency and duration of FDs, with a mean frequency of 6.4 and duration of 31 days. When classified into Case 1 (normal condition), Case 2 (AET-driven), and Case 3 (PET-driven), we found that Case 2 FDs emerged approximately 1.5 times more frequently than those driven by PET (Case 3) across the Korean peninsula. Case 2 FDs were found to be induced under water-limited conditions, and led both AET and PET to be decreased. Conversely, Case 3 FDs occurred under energy-limited conditions, with increase in both. Case 2 FDs predominantly affected the northwestern and central-southern agricultural regions, while Case 3 occurred in the eastern region, characterized by forested land cover. These findings offers insights into our understanding of FDs over the Korean peninsula, considering climate factors, land cover, and water availability.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1321-1330
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• 2023

Normalized Difference Vegetation Index (NDVI) is the most widely used remote sensing data in the agricultural field and is currently provided by most optical satellites. In particular, as high-resolution optical satellite images become available, the selection of optimal optical satellite images according to agricultural applications has become a very important issue. In this study, we aim to define the most optimal optical satellite image when monitoring NDVI in rice fields in Korea and derive the resolution-related requirements necessary for this. For this purpose, we compared and analyzed the spatial distribution and time series patterns of the Dangjin rice paddy in Korea from 2019 to 2022 using NDVI images from MOD13, Landsat-8, Sentinel-2A/B, and PlanetScope satellites, which are widely used around the world. Each data is provided with a spatial resolution of 3 m to 250 m and various periods, and the area of the spectral band used to calculate NDVI also has slight differences. As a result of the analysis, Landsat-8 showed the lowest NDVI value and had very low spatial variation. In comparison, the MOD13 NDVI image showed similar spatial distribution and time series patterns as the PlanetScope data but was affected by the area surrounding the rice field due to low spatial resolution. Sentinel-2A/B showed relatively low NDVI values due to the wide near-infrared band area, and this feature was especially noticeable in the early stages of growth. PlanetScope's NDVI provides detailed spatial variation and stable time series patterns, but considering its high purchase price, it is considered to be more useful in small field areas than in spatially uniform rice paddy. Accordingly, for rice field areas, 250 m MOD13 NDVI or 10 m Sentinel-2A/B are considered to be the most efficient, but high-resolution satellite images can be used to estimate detailed physical quantities of individual crops.