• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.495-502
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• 2016

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. The objective of this study was to evaluate the use of unmanned aerial vehicle (UAV) for the monitoring IRG growth. Unmanned aerial vehicle imagery obtained from middle March to late May in Nonsan, Chungcheongnam-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between $NDVI_{UAV}$ of IRG and biophysical measurements such as plant height, fresh weight, and dry weight over an entire IRG growth period. The similar trend between $NDVI_{UAV}$ and growth parameters was shown. Correlation analysis between $NDVI_{UAV}$ and IRG growth parameters revealed that $NDVI_{UAV}$ was highly correlated with fresh weight (r=0.988), plant height (r=0.925), and dry weight (r=0.853). According to the relationship among growth parameters and $NDVI_{UAV}$, the temporal variation of $NDVI_{UAV}$ was significant to interpret IRG growth. Four different regression models, such as (1) Linear regression function, (2) Linear regression through the origin, (3) Power function, and (4) Logistic function were developed to evaluate the relationship between temporal $NDVI_{UAV}$ and measured IRG growth parameters. The power function provided higher accurate results to predict growth parameters than linear or logistic functions using coefficient of determination. The spatial distribution map of IRG growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when $NDVI_{UAV}$ was applied to power function. From these results, $NDVI_{UAV}$ can be used as a new tool for monitoring IRG growth.

• Korean Journal of Environmental Agriculture
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• v.34 no.3
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• pp.192-195
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• 2015

BACKGROUND: The growing season (GS) has been understood as a useful indicator for climate change due to high relationship with increasing temperature. Hear this study was conducted to examine changes in the thermal GS over South Korea from 1970 to 2013 based on daily mean air temperature for assessing the temporal and spatial variability in GS. METHODS AND RESULTS: Three GS parameters (starting date, ending date, and length) were determined at 19 stations throughout South Korea. The results show that the GS has been extended by 4.2 days/decade between 1970 and 2013 on average. The growing season start (GSS) has been advanced by 2.7 days/decade and the growing season end (GSE) has been delayed by 1.4 day/decade. Spatial variation in the GS parameters in Korea are shown. The GS parameters, especially GSS, of southeastern part of Korea have been changed more than that of northwestern part of Korea. The extension of GS may be more influenced on earlier onset in spring rather than later GSE. CONCLUSION: Under climate change scenarios, the GS will be more extended due to delayed GSE as well as advanced GSS. And These are more notable in the northeastern part of Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2569-2575
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• 2015

Network RTK generally uses a linear interpolation method by using the corrections from reference stations. This minimizes the spatial decorrelation error caused by the increase of distance between the reference station's baseline and user's baseline. However, tropospheric delay, a function of the meteorological data can cause a spatial decorrelation characteristic among reference stations within a network by local meteorological difference. A non-linear characteristic of tropospheric delay can deteriorate Network RTK performance. In this paper, the modeling of tropospheric delay irregularity is made from the data when the typhoon is occurred. By using this modeling, analyzing the effect of meteorological difference between reference stations on correction is performed. Finally, we analyze an effect of non-linear characteristics of tropospheric delay among reference stations to Network RTK user.

• Journal of Radiation Industry
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• v.7 no.2_3
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• pp.115-119
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• 2013

The density profile measurement technology by gamma transmission has been widely used to diagnose processes in the field of refinery and petrochemical industry. This technology can reveal a clue and position of abnormal phenomenon of industrial processes during their operation. In this paper, the feasibility of the gamma transmission technology for detecting changes in the amount of fluid in a distillation column was evaluated by using Monte Carlo simulations. The simulations assumed that $^{60}Co$ (1.17, 1.33 MeV) sources and NaI (Tl) detectors (${\Phi}5{\times}5cm$) are located in opposite sides of a column and it concurrently moves in vertical direction. To determine the dependency of a spatial resolution on aperture size of a collimator, the simulation model for a tray in a column were simulated with the aperture sizes of 1 and 2 cm. The thickness of the high density area including a tray and fluid was 7.6 cm in the simulation. The spatial resolution of the tray was 8.2 and 8.5 cm, respectively. As a result, it was revealed that the conventional density profile measurement technique is not able to show the deviation of liquid level on a tray in a column.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.577-584
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• 2018

GNSS based transport infrastructure cluster is to broadcast satellite navigation correction information and integrity information capable of precise positioning for land transport users. This makes it possible to do lane-level positioning reliably. However, in order to provide the lane-level positioning and correction information service nationwide, new station sites selection and to build GNSS stations have a heavy cost and a burden for a considerable period of time. In this paper, we propose the cluster design criteria and national-wide network-based precise positioning for land transportation (NETPPI-LT) cluster design for a cluster-based precise positioning. Furthermore, it is analyzed the precise positioning pre-performance of this cluster design based on the spatial error and verified its suitability as the precise positioning pre-performance of the cluster design.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.1
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• pp.27-34
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• 2019

The seasonal and spatial variation of pathogenic Vibrio species, such as V. parahaemolyticus, V. vulnificus, V. alginolyticus, and V. cholerae were investigated in seawater and in bivalves off the Gyeongnam coast of Korea, which is an important area for shellfish production, during the period 2013-2016. V. parahaemolyticus, V. vulnificus, V. alginolyticus, and V. cholerae were detected in 12.1%, 5.2%, 15.4%, and 0.9% of seawater samples, respectively. V. parahaemolyticus, V. vulnificus, V. alginolyticus, and V. cholera were detected in 21.9%, 7.1%, 12.2%, and 0.0% of shellfish samples, respectively. The Vibrio spp. in seawater and bivalve samples were detected at high levels during the summer to early autumn; however, the levels were low during the winter. Therefore, their occurrence was seasonally dependent and correlated with high water temperature, which is also the biggest factor contributing to foodborne outbreaks associated with Vibrio. Relatively high detection rates of the strains were also found in the sea area that was continually exposed to inland wastewater. Our findings show that continuous monitoring is needed to reveal the patterns of occurrence of these pathogens from marine samples collected off the Korean coast, to reduce seafood-borne outbreaks caused by Vibrio.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1861-1869
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• 2022

Recent climate change can increase the frequency and damage of wildfires worldwide. It can also lead to the deterioration of the forest ecosystem and increase casualties and economic loss. Satellite-based indices for forest damage can facilitate an objective and rapid examination of burned areas and help analyze inaccessible places like North Korea. In this letter, we conducted a detection of burned areas in North Korea using the traditional Normalized Burn Ratio (NBR), the Normalized Difference Vegetation Index (NDVI) to represent vegetation vitality, and the Fire Burn Index (FBI) and Forest Withering Index (FWI) that were recently developed. Also, we suggested a strategy for the satellite-based detection of burned areas in the Korean Peninsula as a result of comparing the four indices. Future work requires the examination of small-size wildfires and the applicability of deep learning technologies.

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2002.05a
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• pp.152-152
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• 2002

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.60-74
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• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• Journal of the Korean association of regional geographers
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• v.7 no.1
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• pp.35-50
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• 2001

Since remotely sensed images of coastal wetlands are very sensitive to spatial resolution, it is very important to select an optimum resolution for particular geographic phenomena needed to be represented. Scale is one of the most important factors in spatial analysis techniques, which is defined as a spatial and temporal interval for a measurement or observation and is determined by the spatial extent of study area or the measurement unit. In order to acquire the optimum scale for a particular subject (i.e., coastal wetlands), measuring and representing the characteristics of attribute information extracted from the remotely sensed images are required. This study aims to explore and analyze the scale effects of attribute information extracted from remotely sensed coastal wetlands images. Specifically, it is focused on identifying the effects of scale in response to spatial resolution changes and suggesting a methodology for exploring the optimum spatial resolution. The LANDSAT TM image of Sunchon Bay was classified by a supervised classification method, Six land cover types were classified and the Kappa index for this classification was 84.6%. In order to explore the effects of scale in the classification procedure, a set of images that have different spatial resolutions were created by a aggregation method. Coarser images were created with the original image by averaging the DN values of neighboring pixels. Sixteen images whose resolution range from 30 m to 480 m were generated and classified to obtain land cover information using the same training set applied to the initial classification. The values of Kappa index show a distinctive pattern according to the spatial resolution change. Up to 120m, the values of Kappa index changed little, but Kappa index decreased dramatically at the 150m. However, at the resolution of 240 m and 270m, the classification accuracy was increased. From this observation, the optimum resolution for the study area would be either at 240m or 270m with respect to the classification accuracy and the best quality of attribute information can be obtained from these resolutions. Procedures and methodologies developed from this study would be applied to similar kinds and be used as a methodology of identifying and defining an optimum spatial resolution for a given problem.