• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Journal of the Korean Society of International Agriculture
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• v.23 no.5
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• pp.475-484
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• 2011

International price of cereal has been dramatically increasing for the past few years. This price hike amplified the importance of food self-sufficiency in numerous countries due to the fact that food security is directly proportional to food self-sufficiency. In this study, we conducted a survey to provide useful information of Cambodia's agricultural environment to possible Korean agricultural investors and as to highlight Cambodia as a strong candidate for the establishment of Korea's foreign base for cereal production. The survey conducted includes information regarding Cambodia's agricultural environment and investment circumstances including the political, economical and other contributing factors affecting agricultural investment in Cambodia. Seventy percent of the Cambodia's total population engage in agriculture and this comprises about 30% of the country's GDP. This statistics reflects the possibility of Cambodia's poverty alleviation which proves that agriculture in Cambodia is the driving force for the improvement of the country's economy. In addition, low labor cost, fertile land, abundant water resources, like the Tonle sap lake and the Mekong river, and unreclaimed lands are the strong points that could attract agricultural investors to Cambodia. Poor infrastructure, irrigation systems, law reforms, including social and cultural differences may be the biggest setbacks for the acceleration of Cambodia's agriculture development. However, the Cambodian government is open and willing to make adjustments for Cambodia to be both foreign and domestic agricultural investor-friendly, expecting that it will boost its country's agricultural development. Making the best out of this opportunity, the coordination of KOICA with Korean agricultural investors in building infrastructures and with the help of the KOPIA program for the transfer of agricultural technology will benefit both countries and will play an important role in Cambodia's agriculture.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.

• Journal of the East Asian Society of Dietary Life
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• v.19 no.2
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• pp.287-294
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• 2009

This study was conducted to evaluate the perception and satisfaction of undergraduates majoring in culinary arts and food service with working and internship abroad. The responses of the participants to 10 questions regarding perception and 13 questions regarding the importance and satisfaction with working and internship abroad were measured on a 5 point Likert scale. The primary results were as follows : 1) The subjects were composed of 50.9% male and 49.1% female students, of which 42.1% were employed and 57.9% experienced an internship abroad. 2) Most students went abroad to gain experience with respect to various foreign cultures in response to recommendations by the western cuisine department. 3) The items "I wish to conduct my affairs continuously"(M=4.21) and "I have good relationships with my colleagues at work"(M=4.11) received the highest points from male and female respondents, respectively. 4) Male students considered "cooperation among divisions"(M=4.11), "language skills"(M=4.38), and "kitchen environment"(M=4.34) to be very important. However, female students believed that "language skills"(M=4.36),"social relationships"(M=4.21), and "wage income"(M=4.18) were most important. Furthermore, male students were most satisfied with "company size" (M=4.28), "kitchen environment"(M=4.21), and "business hours"(M=4.10), while female students were most satisfied with "kitchen environment","incentive"(M=4.14) and "social relationships"(M=4.11).