• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.30-30
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• 2020

본 연구에서는 여름철에 농업용수(벼농사용)로서 집중적으로 지하수를 사용하는 지역에서 시기별 지하수 사용에 따른 지하수 수질변화를 평가하기 위해 수행되었다. 연구지역은 충남 홍성군 양곡리와 신곡리 일부를 포함하는 면적 2.83 ㎢(283.3 ha)에 해당하는 지역이다. 연구지역 지하수 수질의 공간적 분포 및 시간적 변화 특성 평가를 위하여 2019년 2회(7월, 10월)에 걸쳐 지하수 관정(21개소)에 대하여 조사 및 분석을 수행하였다. 지하수 샘플은 현장에서 온도(T), pH, 용존산소(DO) 및 전기전도도(EC), 산화환원전위(Eh) 등을 측정하였고, 실험실에서 주요 양이온 및 미량원소(Ca, Mg, Na, K, Si, Sr), 음이온(F, Cl, Br, NO₂, NO₃, PO₄, SO₄), 알칼리도, 용존 유기탄소(DOC)와 용존 유기물(DOM) 등을 분석하였다. 지하수 수질조사 결과, 전체의 14~15개소(67~71%)가 Ca-HCO₃ 유형으로 분류되었으며, 다음으로는 Ca-Cl 유형이 4~5개소(19~24%)가 관찰되었다. 얕은 심도의 관정에서 상대적으로 심도가 깊은 관정보다 대부분 성분(TDS, Ca, Mg, Na, K, Cl, SO₄, HCO₃, DOC)에서 높은 농도를 나타내었다. 지하수의 수질자료를 이용하여 다변량통계분석법인 주성분분석(PCA: Principal Components Analysis)과 계층적 군집분석(HCA: Hierachical Cluster Anlaysis)를 수행한 결과, 초기 3개 주요 고유성분(eigenvalue)는 PC1 54.0%, PC2 14.2%, PC3 12.3%로 전체 분산의 88.3%를 설명할 수 있었다. PC1은 Ca, Mg, Na, K, Cl, SO₄, DOC가 주요한 영향 인자였으며 PC2는 HCO₃, NO₃, DO에 영향 받음을 확인하였다. 계층적 군집분석 결과, 연구지역 지하수는 Na-Cl 유형의 C-3 관정을 제외하고는 크게 두 그룹으로 구분되어 졌다. 다변량통계분석의 결과에서도 수리지화학, 동위원소, 용존유기물 등의 특성에서 나타나는 것과 유사한 연구지역의 수질특성을 확인할 수 있었다. 연구지역은 차시기 동안 수질변화는 일부 관정을 제외하고는 유의할 만한 수준으로 관찰되지는 않았고, 지하수 사용에 따른 지하수위 회복도 빠르게 진행되고 있는 것으로 나타났다.

• Korean Journal of Ecology and Environment
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• v.56 no.2
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• pp.140-150
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• 2023

The Hangang River^* is necessary to manage the water environment of severe pollution due to the high density of residential areas, parks, and agriculture and the large population concentrated there. Benthic macroinvertebrates, such as chironomids larvae, are bioindicator species that reflect environmental changes and are crucial for water quality monitoring. In this study, we investigated morphological characteristics and molecular analysis of the chironomids larvae inhabiting the Hangang River area for water environment surveys. For this research, 20 rivers, lakes, and urban area in the Hangang River basin were selected. Chironomids larvae were collected from July to September 2022, and their appearance and characteristics were identified through morphological identification. In addition, phylogenetic analysis was performed based on the mtCOI gene sequences of the collected chironomids larvae, and identification at the genus level was confirmed. As a result, 32 species and 18 genera of 3 subfamilies of Chironomidae larvae were identified, and Stictochironomus sp. dominated most sites(6 sites). The morphological characteristics of the identified chironomids larvae, such as the mentum, ventromental plate, and antenna, were organized into table and pictorial keys, and a Bayesian inference molecular phylogeny was presented. These results provide basic morphological information for genus-level identification and can be used as fundamental information for water quality management.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.451-458
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• 2023

As forest fires continue to increase in scale worldwide, the importance of forest roads in relation to forest fire prevention and suppression has become increasingly evident. To ensure effective functioning during a forest fire disaster, it is crucial to apply appropriate road planning and ensure roads' structural integrity. However, previous studies have predominantly focused on the impact of forest fires on firebreak efficacy and road placement, meaning that insufficient attention has been paid to ensuring the safety of these facilities. Therefore, this study sought to compare the strength of concrete facilities within areas damaged by forest fires over the past three years by using the rebound hammer test to identify signs of thermal degradation. The results revealed that concrete facilities damaged by forest fires exhibited significantly lower strength (15.6 MPa) when compared with undamaged facilities (18.0 MPa) (p<0.001), and this trend was consistent across all the target facilities. Consequently, it is recommended that safety assessment criteria for concrete forest road facilities be established to prevent secondary disasters following forest fire damage. Moreover, continuous monitoring and research involving indoor experiments are imperative in terms of enhancing the stability of forest road structures. It is expected that such research will lead to the development of more effective strategies for forest fire prevention and suppression.

• Korean Journal of Environmental Biology
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• v.41 no.3
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• pp.283-290
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• 2023

The Republic of Korea reclaimed land to increase its food self-sufficiency rate, but the yield was reduced due to abnormal climate. In this study, it was hypothesized that rapid and continuous monitoring technology could help improve yield. Using the vegetation index (VI) analysis, the drought stress index was calculated and the drought stress for corn grown in Hwaong, Saemangeum, and Yeongsan River reclaimed tidal land was predicted according to drying treatment. The vegetation index of corn did not decrease during the last 20 days of irrigation when soil moisture rapidly decreased, but decreased rapidly during the 20 days after irrigation. The reduction rate of the vegetation index according to the drying treatment was in the order of Saemangeum>Yeongsan River>Hwaong reclaimed tidal land, and normalized difference vegetation index(NDVI) decreased by approximately 50% in all reclaimed tidal lands, confirming that drought stress occurred due to the decrease in moisture content of the leaves. In addition, structure pigment chlorophyll index (SIPI) and photochemical reflectance index (PRI), which are calculated based on changes in light use efficiency and carotenoids, were reduced; drought stress caused a decrease in light use efficiency and an increase in carotenoid content. Therefore, vegetation index analysis was confirmed to be effective in evaluating and predicting drought stress in corn growing on reclaimed tidal land corn.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.328-335
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• 2023

For appropriate nutrient management and enhanced plant growth, soil sensors which reflect soil nutrient levels are required. Because there is no available sensor for nutrient monitoring, electrical conductivity (EC) sensor can be used to evaluate soil nutrient levels. Soil nutrient management using EC sensors would be possible by understanding the relationship between sensor EC values and soil temperature, moisture, and nutrient content. However, the relationship between soil sensor EC values and plant available nutrients was not investigated. Therefore, the objectives of the study were to evaluate effect of different amount of urea on soil EC monitored by sensors during pepper and broccoli cultivation and to predict the plant available nutrient contents in soil. During the cultivation period, soil was collected periodically for analyzing pH and EC, and the available nutrient contents. The sensor EC value increased as the moisture content increased, and low fertilizer treated soil showed the lowest EC value. Principal component analysis was performed to determine the relationship between sensor EC and available nutrients in soil. Sensor EC showed a strong positive correlation with nitrate nitrogen and available Ca. In addition, sum of available nutrients such as Ca, Mg, K, P, S and N was positively related to the sensor EC values. Therefore, EC sensors in open field can be used to predict plant available nutrient levels for proper management of the soil.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.456-465
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• 2023

Melon fruits exhibit a wide range of morphological variations in fruit shape, sugar content, net quality, diameter and weight, which are largely dependent on the variety. These characteristics significantly affect marketability. For netted varieties, the uniformity and pattern of the net serve as key factors in determining the external quality of the melon and act as indicators of its internal quality. In this study, we evaluated the effect of fruit morphology and growth on netting by analyzing the changes in melon fruit quality under LED light treatment and monitoring fruit growth. Computer vision analysis was used for quantitative evaluation of fruit net quality, and a three-variable logistic model was applied to simulate fruit growth. The results showed that melons grown under LED conditions exhibited more uniform fruit shape and improvements in both net quality and sugar content compared to the control group. The results of the logistic model showed minimal error values and consistent curve slopes across treatments, confirming its ability to accurately predict fruit growth patterns under varying light conditions. This study provides an understanding of the effects of fruit shape and growth on net quality.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.2
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• pp.109-117
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• 2013

The Global Framework on Climate Services (GFCS) will guide the development of climate services that link science-based climate information and predictions with climate-risk management and adaptation to climate change. GFCS structure is made up of 5 pillars; Observations/Monitoring (OBS), Research/ Modeling/ Prediction (RES), Climate Services Information System (CSIS) and User Interface Platform (UIP) which are all supplemented with Capacity Development (CD). Corresponding to each GFCS pillar, the Commission for Agricultural Meteorology (CAgM) has been proposing "Global Initiatives in AgroMeteorology" (GIAM) in order to facilitate GFCS implementation scheme from the perspective of AgroMeteorology - Global AgroMeteorological Outlook System (GAMOS) for OBS, Global AgroMeteorological Pilot Projects (GAMPP) for RES, Global Federation of AgroMeteorological Society (GFAMS) for UIP/RES, WAMIS next phase for CSIS/UIP, and Global Centers of Research and Excellence in AgroMeteorology (GCREAM) for CD, through which next generation experts will be brought up as virtuous cycle for human resource procurements. The World AgroMeteorological Information Service (WAMIS) is a dedicated web server in which agrometeorological bulletins and advisories from members are placed. CAgM is about to extend its service into a Grid portal to share computer resources, information and human resources with user communities as a part of GFCS. To facilitate ICT resources sharing, a specialized or dedicated Data Center or Production Center (DCPC) of WMO Information System for WAMIS is under implementation by Korea Meteorological Administration. CAgM will provide land surface information to support LDAS (Land Data Assimilation System) of next generation Earth System as an information provider. The International Society for Agricultural Meteorology (INSAM) is an Internet market place for agrometeorologists. In an effort to strengthen INSAM as UIP for research community in AgroMeteorology, it was proposed by CAgM to establish Global Federation of AgroMeteorological Society (GFAMS). CAgM will try to encourage the next generation agrometeorological experts through Global Center of Excellence in Research and Education in AgroMeteorology (GCREAM) including graduate programmes under the framework of GENRI as a governing hub of Global Initiatives in AgroMeteorology (GIAM of CAgM). It would be coordinated under the framework of GENRI as a governing hub for all global initiatives such as GFAMS, GAMPP, GAPON including WAMIS II, primarily targeting on GFCS implementations.