• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.725-733
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• 2020

The objectives of this study were (1) to develop a temporal mask map using MCD12Q1 data, and (2) to extract the annual variations in paddy, (3) to investigate the correlation analysis between MYD13Q1 NDVI and rice yield, and (4) to review its applicability. For these purposes, the temporal mask map was created using annual MCD12Q1 PFT data from 2002 to 2019, and compared with the fixed mask map. As a result, it found that the temporal mask map well reflected the variations of the paddy area. In addition, the correlation coefficient between NDVI and rice yield was also high significant as compared to the fixed mask map. Therefore, the temporal mask map will be useful for NDVI extraction, crop monitoring, and estimation of rice yield.

• The Korean Journal of Pesticide Science
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• v.21 no.1
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• pp.75-83
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• 2017

The single residue analytical method was developed for determining fungicide pencycuron residues in various agricultural commodities with high-performance liquid chromatography (HPLC). Pencycuron residue was extracted with acetone from representative crops such as Korean cabbage, apple, brown rice and green pepper. After ethyl acetate/n-hexane partition and subsequent clean-up with silica gel chromatography, pencycuron residue was quantified by reversed phase HPLC with UV detection at 240 nm. The suspected residue of pencycuron was confirmed using selected-ion monitoring (SIM) LC/mass spectrometry (MS). Instrumental limit of quantitation (ILOQ) and method LOQ (MLOQ) were set at 2 ng and 0.02 mg/kg, respectively. Overall recoveries of pencycuron from different crop samples fortified at three levels (MLOQ, 10MLOQ, 100MLOQ) were 72~108%. This proposed method could be useful as official analytical method for quantification of pencycuron residues in agricultural commodities.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.2
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• pp.115-124
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• 2016

The objectives of this study were to evaluate the soil fertility about plastic film house, upland, and orchard in Gyeongbuk Province, Korea. The surface chemical properties of soil samples were investigated every 4 year from 2000 year at upland, 2001 year at orchard, and 2002 year at plastic film house. During 12 year's monitoring, mean soil pH was increased by 0.7 and 0.8 pH unit from pH 5.7 in upland and orchard, respectively, 0.5 pH unit from pH 6.5 in plastic film house. About 50% of all the field samples occupied within the recommended pH range (pH 6-7). Although soil organic matter (SOM) was gradually increased by about $10g\;kg^{-1}$ for 12 years, 40% of orchard, 49% of plastic film house, and 77% of upland soil samples were still below the 3% SOM. The mean concentration of available phosphate for 12 years in upland, orchard, and plastic film house were 530, 600, and $760mg\;kg^{-1}$, respectively. The relative frequencies exceeding the recommended available phosphate range ($300-550mg\;kg^{-1}$) were 43%, 53%, and 66% at upland, orchard, and plastic film house soils, respectively. $NH_4OAc$ exchangeable $K^+$ of upland, orchard, and plastic film house in the last soil test were 0.8, 0.9, and $1.6cmol_c\;kg^{-1}$, respectively. The relative frequencies above the recommended K level were 56% and 70% of orchard and plastic film house soil samples, respectively. The levels of crop nutrients except exchangeable Ca and Mg in upland soil were tended to increase gradually in the three fields. Exchangeable Mg, EC, available phosphate, organic matter and soil pH could be used as principle components to differentiate the chemical properties of three land fields. This analysis revealed that the soil fertility was affected by cropping method and field management, although additional research is needed to assess the importance of management on soil chemical properties and many fields indicate an opportunity for improvement in fertilizer management.

• Weed & Turfgrass Science
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• v.4 no.1
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• pp.1-9
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• 2015

Many irrigated and drainage canals, reservoirs, lakes, and rivers are choked by the explosive growth of aquatic weeds, resulting in enormous direct loss in Korea. Distribution of aquatic weeds and exotic invasive plant species in irrigation and drainage waterways, and riparian wetlands was reviewed to provide basic information for management of aquatic weeds and invasive plant species in wetlands. Dominant emergent weeds in canals, reservoirs and lakes were Phragmites communis, Leersia japonica, Zizania latifolia, Conyza canadensis, C. annuus, Rumex crispus, Panicum dichotomiflorum, Bidens frondosa, and Oenothera odorata. Dominant emergent weeds in wetlands of rivers include Digitaria sanguinalis, P. communis, R. crispus, Artemisia princeps, Humulus japonicus, Echinochloa crusgalli, B. frondosa, and Persicaria thunbergii. In irrigation and drainage canals and lakes, dominant submersed weeds were Hydrilla verticillata, Najas minor, Potamogeton malaianus, P. crispus, Ceratophyllum demersum, Myriophyllum verticillatum, Salvinia natans, and Trapa natans. Dominant exotic weeds in wetlands include Trifolium repens, O. odorata, C. annuus, B. frondosa, Avena fatua, Ambrosia artemisiifolia, X. strumarium, and P. dichotomiflorum. Approaches to aquatic weed control were mechanical, chemical and biological control techniques. Periodic monitoring of aquatic weeds and exotic weeds, and their integrated management studies are needed in waterways and riparian wetlands in Korea.

• Journal of Environmental Impact Assessment
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• v.23 no.4
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• pp.285-295
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• 2014

In this study, we analyzed probability distribution of EMCs (Event Mean Concentration) of COD, TOC, T-N, T-P and SS from rice paddy fields and compared the mean values of observed EMCs and the median values of estimated EMCs ($EMC_{50}$) through probability distribution. The field monitoring was conducted during a period of four crop-years (from May 1, 2008, to September 30. 2011) in a rice cultivation area located in Emda-myun, Hampyeong gun, Jeollanam-do, Korea. Four probability distributions such as Normal, Log-normal, Gamma, and Weibull distribution were used to fit values of EMCs from rice paddy fields. Our results showed that the applicable probability distributions were Normal, Log-normal, and Gamma distribution for COD, and Normal, Log- Normal, Gamma and Weibull distribution for T-N, and Log-normal, Gamma and Weibull distribution for T-P and TOC, and Log-normal and Gamma distribution for SS. Log-normal and Gamma distributions were acceptable for EMCs of all water quality constituents(COD, TOC, T-N, T-P and SS). Meanwhile, mean value of observed COD was similar to median value estimated by the gamma distribution, and TOC, T-N, T-P, and SS were similar to median value estimated by log-normal distribution, respectively.

• Journal of Food Hygiene and Safety
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• v.35 no.1
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• pp.13-22
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• 2020

A total of 244 cereal samples (oat, sorghum, adlay, and proso millet) were collected from fields to examine the contamination of Fusarium mycotoxins in cereals during harvest season in 2017 and 2018. The contamination levels of deoxynivalenol (DON), nivalenol (NIV), and zearalenone (ZEA) were analyzed individually by using the immunoaffinity column clean-up method with ultra performance liquid chromatography, and fumonisins (FUM) were analyzed by using the QuEChERS method with liquid chromatography-mass spectrometry. Highest level of NIV contamination (120.0-3277.0 mg/kg) was observed in oat samples among the analyzed cereals. In the adlay samples, DON contamination was the highest (maximum level 730.0 ㎍/kg). The proso millet samples had a high frequency of detection of NIV and ZEA (61.5% and 57.9%, respectively), but the levels were low (average detection level of NIV, 75.6 ㎍/kg, for ZEA, 21.5 ㎍/kg). Among the cereal samples, sorghum had the highest contamination frequency of DON, ZEA, and FUM, and the co-occurrence of Fusarium mycotoxin was 70.0%, which was higher than the average of 29.9%. In order to safely manage Fusarium mycotoxin levels in cereals, continuous research on the development of contamination prevention technologies together with monitoring of mycotoxin contamination is needed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.4
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• pp.132-140
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• 2008

Two different sensitivity classes of black cherry (Prunus serotina) under the natural growing environmental conditions were assessed adjacent to Air Monitoring Station located at Horton research center in Giles County, Virginia, USA. Ambient ozone concentrations, leaf gas exchange, and visible foliar injury were measured on-site during the growing seasons of 2000, 2001, and 2002. Ambient ozone exposures were sufficient to induce typical foliar visible injury corresponding with the reduction in photosynthetic activities only in sensitive black cherry. There were positive correlations between increasing cumulative ozone concentration and percent reduction in maximum net photosynthetic rates ($Pn_{MAX}$) under saturating light conditions and in quantum yield for carbon reduction (${\Phi}CO_2$) of sensitive black cherry compared to tolerant black cherry. There was a negative correlation between chlorophyll content and percent leaf injury in sensitive black cherry. Furthermore, $Pn_{MAX}$ was inversely related to percent leaf injury.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.285-290
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• 2016

In rural area, our farmers confront decreasing benefits owing to imported crops and increased cost. Recently, the government encourage the 6th Industry that merges farming, rural resources, and information and communication technology. Therefor the government makes an investment in supplying 'smart greenhouse' in which a farmer monitor growing crops and environment information to control growing condition. The objective of this study is developing an Moving Monitor and Control System for crops in green House. This system includes a movable sensing unit, a controlling unit, and a server PC unit. The movable sensing unit contains high resolution IP camera, temperature and humidity sensor and WiFi repeater. It rolls on a rail hanging beneath the ceiling of a green house. The controlling unit contains embedded PC, PLC module, WiFi router, and BLDC motor to drive the movable sensing unit. And the server PC unit contains a integrated farm management software and home pages and databases in which the images of crops and environment informations. The movable sensing unit moves widely in a green house and gathers lots of information. The server saves these informations and provides them to customers with the direct commercing web page. This system will help farmers to control house environment and sales their crops in online market. Eventually It will be helpful for farmers to increase their benefits.