• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• Research in Plant Disease
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• v.18 no.4
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• pp.361-369
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• 2012

A population density model for bacterial wilt, which is caused by Ralstonia solanacearum, in hot pepper was developed to estimate the primary infection date after overwintering in the field. We developed the model mechansitically to predict reproduction of the pathogen and pathogensis on seedlings of the host. The model estimates the pathogen's populations both in the soil and in the host. In order to quantify environmental infection factors, various temperatures and initial population densities were determined for wilt symptoms on the seedlings of hot pepper in a chamber. Once, the pathogens living in soil multiply up to 400 cells/g of soil, they can infect successfully in the host. Primary infection in a host was supposed to be started when the population of the pathogen were over $10^9$ cells/g of root tissue. The estimated primary infection dates of bacterial wilt in 2011 in Korea were mostly mid-July or late-July which were 10-15 days earlier than those in 2010. Two kinds of meterological data, synoptic observation and field measurements from paddy field and orchard in Kyunggi, were operated the model for comparing the result dates. About 1-3 days were earlier from field data than from synoptic observation.

• The Korean Journal of Ecology
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• v.26 no.3
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• pp.143-150
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• 2003

Korea has plentiful precipitation but rainfall events concentrate on several months of rainy season in her weather condition. Korea, therefore, experiences drought for a given period every year. Moreover the soil has usually low water holding capacity, as it is composed coarse particles originated from the granite. Response of several oaks and the Korean red pine (Pinus densiflora) on water stress showed that water budget was significant factor determining vegetation distribution. In addition, dehydration level due to cold resistance mechanism of several evergreen plants during the winter season was closely related to their distribution in natural condition. Experimental result under water stress showed that the Korean red pine was very tolerant to desiccation but the seedlings showed high mortality during the dry season. The mortality tended to proportionate to soil moisture content of each site. A comparison between soil moisture content during June when it is severe dry season and moisture content of the culture soil when the pine seedlings reached the permanent wilting point due to water withheld proved that high mortality during the dry season was due to water deficit. Water potential of sample plants measured during the exposure experiment to the air pollutant showed a probability that water related factors would dominate the occurrence of visible damage and the tolerance level of sample plants. In both field survey and laboratory experiment, plants exposed to air pollution showed more rapid transpiration than those grown in the unpolluted condition. The result would due to injury of leaf surface by air pollutants. Aluminum (Al/sup 3+/) increased in the acid soil not only inhibits root growth but also leads to abnormal distribution of root system and thereby caused water stress. The water stresses due to air pollution and soil acidification showed a possibility that they play dominating roles in inducing forest decline additionally to the existing water deficit due to weather and soil conditions in Korea. Sludge, which can contribute to improve field capacity, as it is almost composed of organic matter, showed an effect ameliorating the retarded growth of plant in the acidified soil. The effect was not less than that of dolomite known in widely as such a soil ameliorator. Litter extract contributed also to mitigate the water stress due to toxic Al/sup 3+/. We prepared a model showing the potential interaction of multiple stresses, which can cause forest decline in Korea by synthesizing those results. Furthermore, we suggested restoration plans, which can mitigate such forest decline in terms of soil amelioration and vegetation restoration.

• The Plant Pathology Journal
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• v.17 no.2
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• pp.67-70
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• 2001

Medicago truncatula is a diploid legume plant related to the forage crop alfalfa. Recently, it has been chosen as a model species for genomic studies due to its small genome, self-fertility, short generation time, and high transformation efficiency. M. truncatula engages in symbiosis with nitrogen-fixing soil bacterium Rhizobium meliloti. M. truncatula mutants that are defective in nodulation and developmental processes have been generated. Some of these mutants exhibited altered phenotypes in symbiotic responses such as root hair deformation, expression of nodulin genes, and calcium spiking. Thus, the genes controlling these traits are likely to encode functions that are required for Nod-factor signal transduction pathways. To facilitate genome analysis and map-based cloning of symbiotic genes, a bacterial artificial chromosome library was constructed. An efficient polymerase chain reaction-based screening of the library was devised to fasten physical mapping of specific genomic regions. As a genomics approach, comparative mapping revealed high levels of macro- and microsynteny between M. truncatula and other legume genomes. Expressed sequence tags and microarray profiles reflecting the genetic and biochemical events associated with the development and environmental interactions of M. truncatula are assembled in the databases. Together, these genomics programs will help enrich our understanding of the legume biology.

• Journal of Korean Society of Forest Science
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• v.98 no.6
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• pp.791-798
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• 2009

The role of forest and soil carbon under global climate change is getting important as a carbon sink and it is necessary to research on applicable forest models as well as in the field for a study of these dynamics. On this study, historical annual litter dataset as a major input data for the forest soil carbon model, Yasso was established using a dendrochronological reconstruction method, and the soil carbon dynamics of a Pinus densiflora forest in Gwangneung, Korea was simulated using Yasso. The amount of litter (needle, branch, stem and fine root) production, which was estimated using the dendrochronological method, has increased continuously from 1971 to 2006. Furthermore, there was no significant error between estimated and measured values of litter production (needle and branch) in 2006. The average of simulated soil carbon stock up to 30 cm depth was $46.30{\pm}4.28tCha^{-1}$, which accounted for 53% of carbon stock in trees of the forest, and had no significant difference and error with measured soil carbon stock. Under the climate change trend in Korea according to IPCC A1B scenario, it was estimated that the simulated soil carbon stock in the region would increase continuously from 1971 to 2041 and then decreased until 2100. Compared to the result of the scenario that there is no climate change, the soil carbon stock could be decreased up to 7.58% at 2100. It was inferred the dendrochronological reconstruction method and simulation of Yasso model are useful to estimate soil carbon dynamics of the natural P. densiflora forest. Follow-up researches, such as improvement of the dendrochronological method and Yasso model and their application and validation in various environment, are needed to produce more reliable results.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.133-144
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• 2016

In this study, Frequency Domain Reflectometry (FDR) and COSMIC-ray soil moisture (SM) stations were installed at Sungkyunkwan University in Suwon, South Korea. To provide reliable information about SM, soil property test, time series analysis of measured soil moisture, and comparison of measured SM with satellite-based SM product are conducted. In 2014, six FDR stations were set up for obtaining SM. Each of the stations had four FDR sensors with soil depth from 5 cm to 40 cm at 5~10 cm different intervals. The result showed that study region had heterogeneous soil layer properties such as sand and loamy sand. The measured SM data showed strong coupling with precipitation. Furthermore, they had a high correlation coefficient and a low root mean square deviation (RMSD) as compared to the satellite-based SM products. After verifying the accuracy of the data in 2014, four FDR stations and one COSMIC-ray station were additionally installed to establish the Soil Moisture site with FDR and COSMIC-ray, called SM-FC. COSMIC-ray-based SM had a high correlation coefficient of 0.95 compared with mean SM of FDR stations. From these results, the SM-FC will give a valuable insight for researchers into investigate satellite- and model-based SM validation study in South Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.83-96
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• 2018

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• Journal of the Korean Geosynthetics Society
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• v.16 no.3
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• pp.51-62
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• 2017

For the purpose of the study, of the 76 areas subject to preliminary concentrated management on sediment disaster in the downtown area, 9 areas were selected as research areas. They were classified into three stratified rock areas (Gyeongsan City, Goheung-gun and Daegu Metropolitan City), three igneous rock areas (Daejeon City, Sejong Special Self-Governing City and Wonju City) and three metamorphic rock areas (Namyangju City, Uiwang City and Inje District) according to the characteristics of the bedrock in the research areas. As for the 9 areas, analyses were conducted based on tests required to calculate soil characteristics, a predictive model for root adhesive power, loading of trees and on-the-spot research. As for a rainfall scenario (rainfall intensity), the probability of rainfall was applied as offered by APEC Climate Center (APCC) in Busan. As for the prediction of landslide risks in the 9 areas, TRIGRS and LSMAP were applied. As a result of TRIGRIS prediction, the risk rate was recorded 30.45% in stratified rock areas, 41.03% in igneous rock areas and 45.04% in metamorphic rock areas on average. As a result of LSMAP prediction based on root cohesion and the weight of trees according to crown density, it turned out to a 1.34% risk rate in the stratified rock areas, 2.76% in the igneous rock areas and 1.64% in the metamorphic rock areas. Analysis through LSMAP was considered to be relatively local predictive rather than analysis using TRIGRS.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.