• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.4
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• pp.15-27
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• 2015

The objectives of this study are to analyze the quantitative ecological principal components of Korean Peninsula using the multivariate spatial environmental datasets and to compare the ecological difference between South and North Korea. Ecological maps with GIS(Geographical Information System) are constructed by PCA(Principal Component Analysis) based on seventeen raster(cell based) variables at 1km resolution. Ecological differences between South and North Korea are extracted by Factor Analysis using ecosystem maps masked from Korean ones. Spatial data include SRTM(Shuttle Radar Topography Mission), Temperature, Precipitation, SWC(Soil Water Content), fPAR(Fraction of Photosynthetically Active Radiation) representing for a productivity, and SR(Solar Radiation), which all cover Korean peninsula. When it performed PCA, the first three scores were assigned to red, green, and blue color. This color triplet indicates the relative mixture of the seventeen environmental conditions inside each ecological region. The first red one represents for 'physiographic conditions' worked by high elevation and solar radiation and low temperature. The second green one stands for 'seasonality' caused by seasonal variations of temperature, precipitation, and productivity. The third blue one means 'wetness condition' worked by high value such as precipitation and soil water contents. FA extraction shows that South Korea has relatively warm and humid ecosystem affected by high temperature, precipitation, and soil water contents whereas North Korea has relatively cold and dry ecosystem due to the high elevation, low temperature and precipitation. Results would be useful at environmental planning on inaccessible land of North Korea.

• The Korean Journal of Ecology
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• v.12 no.2
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• pp.83-108
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• 1989

To discern general tendency in relatively pure even-aged pine stands, to group the stands and to perceive neighbourhood effects a total of 39 sites of pine stand was surveyed from nearby Seoul (12 sites), Chunsung, Kangwon (13 sites) and Sosan, Chungnam (14 sites), for herb and shrub species 32, 19; 37, 19 and 41, 14 in the respective areas from September 1987 to July 1988. In terms of detrended correspondence analysis (DECORANA), the stands were subjected to ordinate with 16 physical variables and the vegetational variables. The resource ratio (N:P, N:K, P:K) as physical variables also was tried out in the DECORANA as well as independent variable (N.P.K). The outcome did not show any meaningful difference. It is suggested that there seems to be no apparent interaction among the elements in the study. Three vertical vegetation componeent, that is, tree layer, shrub layer, herb layer were subjccted to DECORANA independently, pairwisely and as a whole (a total 7 combinations). Of those analysis herb layer trial alone seems to indicate relatively clearer differences among the physical variables. In the stands nearby Seoul first axis indicated soil field capacity and exchangeable cations (K, Ca and Na) and second axis did not show any tendency. For the Chunsung stands first axis also revealed soil field capcity and amount of arganic matter and second axis showed amount of exchangeable cation (K, Ca and Na), In the Seosan 1st axis indicated pH and exchangeable cations (K, Ca and Na). For the 39 sites 4 clusters in terms of herb layer might be defined: Peucedanum terebinthaceum-Cymbopogon tortilis-Polygala japonica-Festuca ovina (1); Atractylodes japonica-Patrina scabiosaefolia (2); Potentilla fragarioides-Atractylodes (3); and Cymbopogon tortilis (4). In the neighbourhood effects study in terms of the basal area distribution, Thiessen polygon area and Gini coefficient for the Pinus thunbergii stands of Seosan the Thiessen polygon area approach seems to indicate earlier (30 years old) neighbourhood effect than the others (45 years).

• Journal of Korean Society of Forest Science
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• v.94 no.2 s.159
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• pp.121-126
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• 2005

This study was carried out to construct a single diameter and a single height model that could localize Chamaecyparis obtusa stand grown in 3 Southern regions of Korea. Dummy variables, which convert qualitative information such as geographical regions into quantitative information by means of a coding scheme (0 or 1), were used to localize growth models. In results, modified form of Gompertz equation, $Y_2={\exp}({\ln}(Y_1){\exp}(-{\beta}(T_2-T_1)+{\gamma}({T_2}^2-{T_1}^2))+({\alpha}+{\alpha}_1Al+{\beta}_1k_1+{\beta}_2k_2)(1-{\exp}(-{\beta}(T_2-T_1)+{\gamma}({T_2}^2-{T_1}^2))$, for diameter and height was successfully disaggregated to provide different projection equation for each of the 3 regions individually. The use of dummy variables on a single equation, therefore, provides potential capabilities for testing the justification of having different models for different sub-populations, where a number of site variables such as altitude, annual rainfall and soil type can be considered as possible variables to explain growth variation across regions.

• Journal of Microbiology and Biotechnology
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• v.21 no.4
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• pp.341-346
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• 2011

To study the effects of salinity-sodicity on bacterial population and enzyme activities, soil samples were collected from the Bay of Yellow Sea, Incheon, South Korea. In the soils nearest to the coastline, pH, electrical conductivity ($EC_e$), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP) were greater than the criteria of saline-sodic soil, and soils collected from sites 1.5-2 km away from the coastline were not substantially affected by the intrusion and spray of seawater. Halotolerant bacteria showed similar trends, whereas non-tolerant bacteria and enzymatic activities had opposite trends. Significant positive correlations were found between EC, exchangeable $Na^+$, and pH with SAR and ESP. In contrast, $EC_e$, SAR, ESP, and exchangeable $Na^+$ exhibited significant negative correlations with bacterial populations and enzyme activities. The results of this study indicate that the soil chemical variables related with salinity-sodicity are significantly related with the sampling distance from the coastline and are the key stress factors, which greatly affect microbial and biochemical properties.

• Journal of Korean Society for Atmospheric Environment
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• v.6 no.1
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• pp.11-21
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• 1990

To quantitatively estimate the effect of yellow sand(loess) fromt he Northern China, various soil sources having similar chemical compositions to yellow sands should be separated and identified. After that, mass contribution for yellow sand can be calculated. The study showed that it was impossible to solve this problem by the traditional bulk analyses. However, particle-by-particle analysis by a CCSEM (computer controlled scanning electron microscope) gave enormous potentials to solve it. To perform this study, seven soil source data analyzed by CCSEM were obtained from Texas, U.S.A. Initially, each soil date was classified into two groups, coarse and fine particle groups since the particle number distribution showed a minimum occurring at 5.2$\mu$m of aerodynamic diameter. Particles in each group were then classified into one of the 283 homogeneous particle classes by the universal classification rule which had been built by an expert system in the early study. Further, mass fractions and their uncertainties for each class in each source were calculated by the Jackknife method, and then source profile matrix for the 7 soil sources was created. To use the profile matrix in the study of source contribution, it is necessary to test the degree of collinearity among sources. The profiles were tested by the singular value decomposition method. As a result, each soil source characterized by artificially created variables was totally independent each other and is ready to use in source contribution studies as a receptor model.

• Journal of Biosystems Engineering
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• v.28 no.4
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• pp.369-376
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• 2003

This study was conducted to develop a precision measurement method of water content in soil (find sand and silty sand) using dual RF impedance changes. The electrically stable perpendicular plate capacitive sensor was fabricated and utilized to sense the water content in soil. Crystal oscillators of 5 and 20 MHz and related circuits were designed to detect the capacitance changes of a perpendicular plate capacitive sensor with soil samples at various volumetric water contents. A multiple regression model for volumetric water content having dual oscillation frequency changes at 5 and 20 MHz as independent variables resulted in coefficient of determination of 0.963 and standard error calibration of 0.030 cm$^3$/cm$^3$ for calibration and coefficient of determination of 0.966, standard error of prediction of 0.027 cm$^3$/cm$^3$ and bias of 0.001 cm$^3$/cm$^3$ for prediction.

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.75-87
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• 2005

Remote sensing(RS)- and geographic information system(GIS)-based information management to measure and assess agri-environment schemes, and to quantify and map environment indicators for nature and land use, climate change, air, water and energy balance, waste and material flow is in high demand because it is very helpful in assisting decision making activities of farmers, government, researchers, and consumers. The versatility and ability of RS and GIS containing huge soil database to assess agricultural environment spatially and temporally at various spatial scales were investigated. Spectral and microwave observations were carried out to characterize crop variables and soil properties. Multiple sources RS data from ground sensors, airborne sensors, and also satellite sensors were collected and analyzed to extract features and land cover/use for soils, crops, and vegetation for support precision agriculture, soil/land suitability, soil property estimation, crop growth estimation, runoff potential estimation, irrigated and the estimation of flooded areas in paddy rice fields. RS and GIS play essential roles in a management and monitoring information system. Biosphere-atmosphere interection should also be further studied to improve synergistic modeling for environment and sustainability in agri-environment schemes.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.2
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• pp.7-14
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• 2009

The purpose of this study is to identify how much the MODIS NDVI (Normalized Difference Vegetation Index) can explain the forest soil moisture simulated from SWAT (Soil and Water Assessment Tool) model. For ChungjuDam watershed ($6,661.3\;km^2$) which covers 82.2% of forest, the SWAT model was calibrated for four years (2003-2006) at two locations of the watershed using daily streamflow data and was verified for three years (2000-2002) with average Nash and Sutcliffe model efficiencies of 0.69 and 0.75 respectively. For the period from March to June, the average spatial correlation between 16 days composite MODIS NDVI and the corresponding SWAT forest soil moisture was 0.90. The two variables averaged for each data set during that period showed an inverse relation with the average coefficient of determination of 0.55.